From 69a0d51f3cca5cf89feb144bd8724496b0ba3cb6 Mon Sep 17 00:00:00 2001 From: Pawel Sarkowicz Date: Fri, 31 Jul 2026 08:33:52 -0400 Subject: [PATCH] Refactor research pipeline and add webapp --- .gitignore | 7 +- README.md | 158 +- docker-compose.webapp.proxy.yml | 22 + docker-compose.webapp.yml | 14 + frd/__init__.py | 31 + frd/research.py | 251 +++ .../01_data_overview_and_market_stats.ipynb | 249 ++- .../02_factor_analysis_and_diagnostics.ipynb | 306 ++- ...or_construction_and_composite_signal.ipynb | 216 +- notebooks/04_backtest_and_performance.ipynb | 1758 +++++++++++++---- notebooks/05_risk_decomposition_via_PCA.ipynb | 238 ++- .../06_synthetic_market_generation.ipynb | 821 ++++++++ pyproject.toml | 3 + requirements-dev.txt | 2 + requirements-webapp.txt | 9 + requirements.txt | 37 +- tests/test_research.py | 107 + webapp/Dockerfile | 17 + webapp/app.py | 325 +++ webapp/static/app.css | 520 +++++ webapp/static/app.js | 286 +++ webapp/static/favicon.svg | 4 + webapp/static/index.html | 167 ++ 23 files changed, 4841 insertions(+), 707 deletions(-) create mode 100644 docker-compose.webapp.proxy.yml create mode 100644 docker-compose.webapp.yml create mode 100644 frd/__init__.py create mode 100644 frd/research.py create mode 100644 notebooks/06_synthetic_market_generation.ipynb create mode 100644 pyproject.toml create mode 100644 requirements-dev.txt create mode 100644 requirements-webapp.txt create mode 100644 tests/test_research.py create mode 100644 webapp/Dockerfile create mode 100644 webapp/app.py create mode 100644 webapp/static/app.css create mode 100644 webapp/static/app.js create mode 100644 webapp/static/favicon.svg create mode 100644 webapp/static/index.html diff --git a/.gitignore b/.gitignore index 818df93..2cc4619 100644 --- a/.gitignore +++ b/.gitignore @@ -5,7 +5,12 @@ models/* *.npy *.pkl *.pth +*.py[cod] +*.bak +.pytest_cache/ .ipynb_checkpoints/ __pycache__/ .DS_Store -.venv/ \ No newline at end of file +.venv/ +Factor_Risk_Decomposition.txt +flashcards*.txt diff --git a/README.md b/README.md index b99fa59..e2ef568 100644 --- a/README.md +++ b/README.md @@ -2,11 +2,7 @@ *A quantitative pipeline, viewed through the lens of linear algebra.* -An end-to-end quant research pipeline — from raw price data to a backtested long-only momentum portfolio with a Fama–French alpha, explicit risk decomposition, and (planned) synthetic stress testing. The target reader is someone fluent in linear algebra who wants to see how those tools show up in finance. Every concept is introduced first via its linear-algebra structure (matrices, vectors, projections, eigendecompositions, subspaces) and then named in finance terms. - -> **Status.** Notebooks **01–05** are complete and reproducible from this repo. Notebook **06** (synthetic markets / stress testing) is planned — see the todo at the end of Part II. - ---- +An end-to-end quant research pipeline — from raw price data to a backtested long-only momentum portfolio with a Fama–French alpha, explicit risk decomposition, and synthetic stress testing. The target reader is someone fluent in linear algebra who wants to see how those tools show up in finance. Every concept is introduced first via its linear-algebra structure (matrices, vectors, projections, eigendecompositions, subspaces) and then named in finance terms. ## Contents @@ -14,11 +10,12 @@ An end-to-end quant research pipeline — from raw price data to a backtested lo 2. [Setup and Reproducibility](#setup-and-reproducibility) 3. [Part I — Data and Factor Analysis (Notebooks 01–03)](#part-i--data-and-factor-analysis-notebooks-0103) 4. [Part II — Backtest and Risk Decomposition (Notebooks 04–05)](#part-ii--backtest-and-risk-decomposition-notebooks-0405) -5. [Results Summary](#results-summary) -6. [Limitations](#limitations) -7. [Tech Stack](#tech-stack) +5. [Part III — Synthetic Markets and Stress Testing (Notebook 06)](#part-iii--synthetic-markets-and-stress-testing-notebook-06) +6. [Results Summary](#results-summary) +7. [Limitations](#limitations) +8. [Webapp](#webapp) +9. [Tech Stack](#tech-stack) ---- ## Finance $\leftrightarrow$ Linear Algebra Dictionary @@ -26,7 +23,7 @@ The single most useful thing to keep in mind: **a stock return panel is a matrix Each notebook opens with its own "Terms used in this notebook" table covering only what appears there. The project-wide reference, with the notebook(s) where each term appears, is below. -Notebook numbering: **01** Data & market stats · **02** Factor diagnostics · **03** Signal construction · **04** Backtest & performance · **05** Risk decomposition (PCA) · **06** Synthetic markets & stress testing (planned). +Notebook numbering: **01** Data & market stats · **02** Factor diagnostics · **03** Signal construction · **04** Backtest & performance · **05** Risk decomposition (PCA) · **06** Synthetic markets & stress testing. ### 1. The data object @@ -65,8 +62,8 @@ Notebook numbering: **01** Data & market stats · **02** Factor diagnostics · * | **Portfolio weights** $w$ | A vector; long-only: $w \ge 0,\ \sum w_i = 1$; long-short: $\sum w_i = 0$ | 04, 05 | | **Portfolio return** | Inner product $w^\top r_{t+1}$ | 04 | | **Portfolio variance** | Quadratic form $w^\top \Sigma w$ | 05 | -| **Turnover** | $\ell_1$ distance $\|w_t - w_{t-1}\|_1$ — how much the weight vector changes between rebalances | 04 | -| **Transaction cost** | $c \cdot \|w_t - w_{t-1}\|_1$ — proportional to turnover | 04 | +| **Turnover** | One-way turnover $\tfrac{1}{2}\|w_t - w_{t-1}\|_1$ — how much the weight vector changes between rebalances | 04 | +| **Transaction cost** | $c \cdot \tfrac{1}{2}\|w_t - w_{t-1}\|_1$ — proportional to one-way turnover | 04 | | **Backtest** | Replay history: form $w_t$ each month, accumulate $w_t^\top r_{t+1}$ net of costs | 04 | | **Sharpe / Sortino / Calmar** | Signal-to-noise ratios on portfolio returns (downside-only for Sortino; return/max-DD for Calmar) | 04 | | **Max drawdown** | Largest peak-to-trough drop of the equity curve | 04 | @@ -86,7 +83,7 @@ Notebook numbering: **01** Data & market stats · **02** Factor diagnostics · * | **Ledoit–Wolf shrinkage** | $\hat\Sigma = \delta F + (1-\delta)S$ — convex combination of sample $S$ and a structured target $F$ | 05 | | **Systematic / idiosyncratic risk** | Variance in the top-$k$ factor subspace vs. its orthogonal complement | 05 | -### 5. Synthetic markets & stress testing (notebook 06, planned) +### 5. Synthetic markets & stress testing (notebook 06) | Term | Linear-algebra meaning | Notebooks | |------|------------------------|:---------:| @@ -101,18 +98,18 @@ Notebook numbering: **01** Data & market stats · **02** Factor diagnostics · * This project constructs and backtests a **sector-neutralized momentum factor** about ~500 US large-cap stocks (2005–2025) using only free data. The pipeline: -> 1. **Assemble** a survivorship-aware universe and return panel (the matrix $\mathbf{R}$) from free price data +> 1. **Assemble** a current-constituent, survivorship-biased universe and return panel (the matrix $\mathbf{R}$) from free/cached price data > 2. **Diagnose** four candidate factors via information coefficient analysis and walk-forward subperiod stability > 3. **Select** momentum as the headline factor (the only one with positive IC) and sector-neutralize it via orthogonal projection > 4. **Backtest** a monthly rebalanced top-decile long-only portfolio with transaction costs and walk-forward validation > 5. **Decompose** portfolio risk into systematic vs. idiosyncratic components via PCA (eigendecomposition + random matrix theory) -> 6. **Stress test** *(planned, notebook 06)* by generating synthetic markets and re-running the backtest across alternative histories +> 6. **Stress test** (notebook 06) by generating synthetic markets and re-running the backtest across alternative histories -The project is structured in two parts (plus a planned third): +The project is structured in three parts, all complete: * **Part I — Data and Factor Analysis** (notebooks 01–03) — *complete* * **Part II — Backtest and Risk Decomposition** (notebooks 04–05) — *complete* -* **Part III — Synthetic Markets and Stress Testing** (notebook 06) — *planned* (todo, not yet written) +* **Part III — Synthetic Markets and Stress Testing** (notebook 06) — *complete* --- @@ -131,14 +128,15 @@ Factor-Risk-Decomposition/ │ ├── 02_factor_diagnostics/ │ ├── 03_factor_construction/ │ ├── 04_backtest/ -│ └── 05_risk_decomposition/ +│ ├── 05_risk_decomposition/ +│ └── 06_synthetic_markets/ └── notebooks/ ├── 01_data_overview_and_market_stats.ipynb ├── 02_factor_analysis_and_diagnostics.ipynb ├── 03_factor_construction_and_composite_signal.ipynb ├── 04_backtest_and_performance.ipynb ├── 05_risk_decomposition_via_PCA.ipynb - └── 06_synthetic_market_generation.ipynb (planned) + └── 06_synthetic_market_generation.ipynb ``` --- @@ -151,10 +149,16 @@ Factor-Risk-Decomposition/ pip install -r requirements.txt ``` +For the dashboard-only runtime, install the smaller pinned set: + +```bash +pip install -r requirements-webapp.txt +``` + ### Data Sources (all free) - **Prices:** adjusted close via `yfinance` (2005–2025) -- **Constituents:** current S&P 500 list from Wikipedia +- **Constituents:** cached S&P 500 snapshot in `data/raw/constituents.csv`; set `REFRESH_DATA=true` before notebook 01 to intentionally replace it from Wikipedia - **Benchmark factors** (notebook 04): Kenneth French Data Library (MKT, SMB, HML, MOM, RF) via `pandas-datareader` — the standard "Fama–French" factors used to decompose returns into market, size, value, and momentum components No paid data feed is required to run the pipeline end-to-end. @@ -164,10 +168,10 @@ No paid data feed is required to run the pipeline end-to-end. Run notebooks in order: ```text -01 -> 02 -> 03 -> 04 -> 05 (06 planned) +01 -> 02 -> 03 -> 04 -> 05 -> 06 ``` -Each notebook writes to `data/processed/` and `images/`, so later notebooks pick up where earlier ones left off. Random state is fixed at `RANDOM_STATE = 3` throughout. +Each notebook is self-contained and writes to `data/processed/` and `images/`, so later notebooks pick up where earlier ones left off. Random state is fixed at `RANDOM_STATE = 3` throughout. Generated CSVs are intentionally gitignored; rerun the notebooks to refresh them, and use `REFRESH_DATA=true` only when you want a new constituent snapshot. --- @@ -177,7 +181,7 @@ This part builds the data matrix $\mathbf{R}$, diagnoses individual factor vecto ### 1. Data Overview and Market Statistics -We pull the current S&P 500 constituents and download adjusted close prices. This introduces **survivorship bias** — names that went bankrupt or were delisted between 2005 and today won't appear. In linear-algebra terms: the columns of $\mathbf{R}$ are a non-random subset of all stocks that existed; the columns we *don't* see are exactly the ones that went to zero, biasing returns upward. Notebook 04 includes a sensitivity analysis for this. +We use a cached snapshot of S&P 500 constituents and download adjusted close prices. Because that snapshot is still based on a modern S&P 500 membership list, it introduces **survivorship bias** — names that went bankrupt or were delisted between 2005 and today won't appear. In linear-algebra terms: the columns of $\mathbf{R}$ are a non-random subset of all stocks that existed; the columns we *don't* see are exactly the ones that went to zero, biasing returns upward. Notebook 04 includes a sensitivity analysis for this. Key findings: * **Universe breadth** rises from ~385 to ~501 stocks over the sample — but the column set is fixed to *today's* constituents, so this counts how many of today's survivors had price data in month $t$. The matrix isn't truly "getting wider"; its survivor-only columns fill in over time. @@ -199,7 +203,7 @@ A **factor** is a vector $f_t \in \mathbb{R}^{N_t}$ — one score per stock at e The **information coefficient (IC)** is the Spearman rank correlation (cosine similarity of rank vectors) between $f_t$ and $r_{t+1}$. We also run **walk-forward subperiod IC analysis** over 5-year windows. Key findings (full-sample monthly IC): -* **Momentum wins** — the only factor with positive IC: mean +0.006, IR 0.11. Value (-0.022), quality (-0.003, ~zero), and low-vol (-0.026) are all negative or flat; the price-based proxies don't capture the real factors. +* **Momentum is the only usable signal in this setup** — mean IC +0.006, IR 0.11. That is weak in absolute terms; the point is not that this is an industry-grade factor, but that it is the only price-based proxy here worth carrying forward. Value (-0.022), quality (-0.003, ~zero), and low-vol (-0.026) are all negative or flat. * **Walk-forward:** momentum's IC is positive in **2 of 4** five-year windows (2011–16 and 2021–26); negative in 2006–11 and 2016–21. The signal is real but regime-dependent. * **IC decay:** momentum's edge fades beyond 1 month (negative at 3, 6, 12-month horizons). * **Turnover:** momentum rank autocorrelation ~0.89 (the vector rotates meaningfully each month). @@ -230,27 +234,29 @@ A portfolio is a weight vector $w$. We form a top-decile long-only portfolio at * **Signal at month-end $t$, traded at $t+1$** to avoid look-ahead bias * **Portfolio return** = $w^\top r_{t+1}$ -* **Transaction costs:** 5 bps round-trip, $c \cdot \|w_t - w_{t-1}\|_1$ +* **Transaction costs:** 5 bps per unit of one-way turnover, $c \cdot \tfrac{1}{2}\|w_t - w_{t-1}\|_1$ * **Benchmark:** the **equal-weight (EW) universe** — since the portfolio is equal-weighted within the decile, the fair comparison is an equal-weight portfolio of *all* stocks, isolating stock-picking from the size effect. -**Fama–French alpha:** an OLS projection of portfolio returns onto MKT/SMB/HML/MOM; the **alpha** is the orthogonal residual — returns *not explained* by exposure to known factors. +**Fama–French alpha:** a regression of portfolio excess returns onto MKT/SMB/HML/MOM; the **alpha** is the intercept — average return not explained by exposure to those known factors. Notebook 04 reports both ordinary OLS t-stats and HAC/Newey-West t-stats. | Portfolio | Ann. Return | Sharpe | Max DD | Sortino | |-----------|------------:|-------:|-------:|--------:| -| Long-Only (net) | 19.6% | 1.01 | -57% | 1.41 | +| Long-Only (net) | 19.5% | 1.00 | -57% | 1.40 | | EW Universe | 15.9% | 0.95 | -47% | 1.27 | | Long-Short (net) | -0.7% | -0.04 | -70% | — | -| Portfolio | FF 4-factor alpha (ann.) | t-stat | MKT $\beta$ | MOM $\beta$ | -|-----------|-------------------------:|-------:|------:|------:| -| **Long-Only** | **+5.95%** | **3.95** | 1.19 | 0.25 | -| Long-Short | -2.95% | -1.41 | 0.15 | 0.91 | +| Portfolio | FF 4-factor alpha (ann.) | OLS t-stat | HAC t-stat | MKT $\beta$ | MOM $\beta$ | +|-----------|-------------------------:|-----------:|-----------:|------:|------:| +| **Long-Only** | **+5.97%** | **3.98** | **4.17** | 1.19 | 0.25 | +| Long-Short | -2.95% | -1.41 | -1.37 | 0.14 | 0.91 | -The long-only portfolio beats the EW universe by **3.6% per year**, though that raw active edge is only marginal (IR 0.43, t = 1.92); the factor-adjusted alpha is the stronger result (+5.95%, t = 3.95). The long-short alpha is not significant — the short side adds noise, so momentum's predictive power is concentrated on the long side in this universe. +The long-only portfolio beats the EW universe by **3.5% per year**, though that raw active edge is only marginal (IR 0.42, t = 1.89). After beta matching, the raw outperformance versus the equal-weight universe falls to about **0.5% per year**, so the factor-adjusted alpha is the stronger result. The long-short alpha is not significant — the short side adds noise, so momentum's predictive power is concentrated on the long side in this universe. **Walk-forward (5-year windows):** long-only Sharpe is positive in **4 of 4** windows; the active return (vs EW universe) is positive in **3 of 4**. The exception is 2006–2011 (active -4.8%), which spans the 2008–09 momentum crash — a well-documented regime where momentum reverses. Per-window information ratios: -0.51, 0.71, 0.82, 1.08, improving over the sample. -**Survivorship-bias sensitivity:** re-running the Fama–French regression with a synthetic annual return drag, the alpha stays significant (t > 2) up to roughly **3%** annual drag from missing delisted stocks — well beyond the plausible bias for US large-caps. +**Survivorship-bias sensitivity:** re-running the Fama–French regression with synthetic return drag, the alpha survives **2%** annual drag under both flat and crash-concentrated assumptions. At **3%**, the flat-drag test is borderline, while the crash-concentrated version loses significance. + +**Pipeline robustness:** notebook 04 now checks decile cutoffs of 5%, 10%, 15%, and 20%, plus 1-, 2-, and 3-month rebalance intervals. Across that grid, alpha remains positive and HAC-significant. This helps with parameter fragility, but does not solve the larger universe-construction limitation. ### 5. Risk Decomposition via PCA @@ -263,9 +269,22 @@ Portfolio risk is the quadratic form $w^\top \Sigma w$. This notebook decomposes $$w^\top \Sigma w = \underbrace{w^\top \mathbf{B} \Sigma_f \mathbf{B}^\top w}_{\text{systematic}} + \underbrace{w^\top (\Sigma - \mathbf{B}\Sigma_f \mathbf{B}^\top) w}_{\text{idiosyncratic}}.$$ -The momentum long-only portfolio's risk is **~90.7% systematic** and **~9.3% idiosyncratic** — overwhelmingly driven by common factor exposures, consistent with a diversified ~50-stock top-decile portfolio. The Fama–French alpha of 5.95% (t = 3.95) from notebook 04 is precisely the component of return *orthogonal* to these systematic factors. +The momentum long-only portfolio's risk is **~90.7% systematic** and **~9.3% idiosyncratic** — overwhelmingly driven by common factor exposures, consistent with a diversified ~50-stock top-decile portfolio. One caveat: the Fama–French alpha is orthogonal to the Fama–French benchmark factors, not literally to the PCA basis. These are related decompositions, but they are not the same coordinate system. -> **Todo — notebook 06.** The natural next step is stress testing: build a synthetic market generator from the truncated-SVD factor structure ($\mathbf{R} \approx \mathbf{F}\mathbf{B}^\top + \mathbf{E}$) via block bootstrap and/or a conditional VAE, then re-run the momentum backtest across many alternative histories to ask whether the 5.95% alpha is genuine skill or luck. This notebook is planned but not yet written. +--- + +## Part III — Synthetic Markets and Stress Testing (Notebook 06) + +A single backtest is one draw from a distribution of possible histories. This part asks whether the alpha is unusually dependent on the specific historical ordering of months. We generate many synthetic markets from the notebook 05 factor model and re-run the momentum backtest on each. + +### 6. Synthetic Market Generation + +Reusing the truncated-SVD factor model $\mathbf{R} \approx \mathbf{F}\mathbf{B}^\top + \mathbf{E}$ (top-$k$ eigenvectors $\mathbf{B}$, factor scores $\mathbf{F}$, residuals $\mathbf{E}$, with $k$ estimated by Marchenko–Pastur), we generate alternative histories and re-derive the full momentum pipeline (signal $\rightarrow$ decile portfolio $\rightarrow$ Fama–French regression) on each. + +* **Block bootstrap — the trustworthy generator.** Resample time indices in blocks (length $\approx\sqrt{T}$) and reconstruct $\mathbf{R}_\text{synth}[t]=\mathbf{F}[\text{idx}_t]\mathbf{B}^\top+\mathbf{E}[\text{idx}_t]+\bar r$, using the **same** index for factors, residuals, *and* the Fama–French factors — so each synthetic timeline is a reshuffling of real joint return rows. Over 300 paths, the mean synthetic alpha $\approx$ 4.85%/yr and **~56% of paths beat the real 4.49%**. The real alpha sits near the median: it is **typical of the factor structure, not a lucky sequence**. +* **Conditional VAE — a cautionary result.** An autoregressive VAE on $\mathbf{F}$ ($f_{t-1}\to(\mu,\sigma)\to z\to\hat f_t$) can generate factor paths, but reconstructing markets from a *generated* $\hat{\mathbf{F}}$ stitched to independently-resampled residuals **fabricates** return rows with spurious cross-sectional persistence — inflating momentum alphas to 10–25%. The lesson: a generative model that splits $\mathbf{R}=\mathbf{F}\mathbf{B}^\top+\mathbf{E}$ and regenerates the parts separately can inject the very signal under test, so we do **not** rely on it. + +**Honest scope.** Both generators hold $\mathbf{B}$ fixed and preserve the factor structure that *produces* the edge, so this tests **path dependence**, not "does momentum work without a momentum factor." Combined with notebook 04's walk-forward checks, HAC alpha, survivorship sensitivity, and robustness grid, the evidence is stronger than a single backtest — with the residual caveat that the bootstrap cannot rule out an unmodeled structural explanation. --- @@ -273,34 +292,83 @@ The momentum long-only portfolio's risk is **~90.7% systematic** and **~9.3% idi | Metric | Long-Only (net) | EW Universe | Long-Short (net) | |--------|-----------------|-------------|------------------| -| Annualized return [mean of the inner product $w^\top r_{t+1}$] | 19.6% | 15.9% | -0.7% | +| Annualized return [mean of the inner product $w^\top r_{t+1}$] | 19.5% | 15.9% | -0.8% | | Sharpe ratio [$\bar r_p / \mathrm{std}(r_p)$ — a signal-to-noise ratio] | 1.01 | 0.95 | -0.04 | | Max drawdown [largest peak-to-trough drop of the compounded wealth curve] | -57% | -47% | -70% | -| FF 4-factor alpha (annualized) [orthogonal residual of the OLS projection onto the factor basis] | **+5.95% (t = 3.95)** | — | -2.95% (t = -1.41) | -| Active return vs EW universe [$w^\top r$ minus its projection onto $\mathbf{1}$] | +3.6% (IR 0.43, t = 1.92) | — | — | +| FF 4-factor alpha (annualized) [regression intercept after controlling for FF factors] | **+5.97% (OLS t = 3.98, HAC t = 4.17)** | — | -2.95% (OLS t = -1.41, HAC t = -1.37) | +| Active return vs EW universe [$w^\top r$ minus its projection onto $\mathbf{1}$] | +3.5% (IR 0.42, t = 1.89) | — | — | | Walk-forward: Sharpe positive [positive signal-to-noise in each sub-window] | 4 of 4 windows | — | — | | Walk-forward: active positive [positive projection residual in each sub-window] | 3 of 4 windows | — | — | -| Survivorship drag to lose alpha [bias from the non-random column set needed to cancel $\alpha$] | ~3% per year | — | — | +| Survivorship drag to lose alpha [bias from the non-random column set needed to cancel $\alpha$] | survives 2%; flat 3% borderline, concentrated 3% fails | — | — | | Systematic risk share [variance in the top-$k$ eigenspace, $w^\top B\Sigma_f B^\top w$, as a share of $w^\top \Sigma w$] | ~90.7% | — | — | +| Stress test (block bootstrap) [share of 300 synthetic markets whose alpha $\ge$ the real alpha] | ~56% beat real $\rightarrow$ typical, not path-dependent | — | — | -**Bottom line:** a sector-neutralized momentum signal, traded long-only, generates a Fama–French 4-factor alpha of **5.95% annualized (t = 3.95)**, with a positive Sharpe in all four walk-forward windows and robustness to plausible survivorship bias. The long-short variant does not work — the edge is on the long side. +**Bottom line:** a sector-neutralized momentum signal, traded long-only, generates a Fama–French 4-factor alpha of **5.97% annualized** (OLS t = 3.98, HAC t = 4.17). The evidence is meaningfully better than a single backtest because it includes walk-forward checks, beta diagnostics, survivorship-drag stress tests, a decile/rebalance robustness grid, and synthetic-market path tests. The long-short variant does not work — the edge is on the long side. --- ## Limitations -* **Survivorship bias.** The universe is reconstructed from the current S&P 500, so delisted/bankrupt names are missing. The sensitivity analysis (notebook 04) shows the alpha survives up to ~3% annual return drag — far more than the plausible bias for large-cap US equities. A survivorship-free database (CRSP) would eliminate this concern entirely. +* **Survivorship bias.** The universe is reconstructed from a cached modern S&P 500 snapshot, so delisted/bankrupt names are missing. The sensitivity analysis (notebook 04) shows the alpha survives 2% annual return drag under flat and crash-concentrated assumptions; at 3%, the conclusion depends on the drag model. A survivorship-free database (CRSP) would eliminate this concern entirely. * **Price-based factor proxies.** Value and quality are proxied by price-based measures rather than fundamentals, and have negative/near-zero IC. A real implementation with Compustat/Sharadar fundamentals might produce a working multi-factor composite. -* **Marginal raw active return.** The long-only portfolio beats the EW universe by only 3.6%/yr (t = 1.92); the statistically strong result is the *factor-adjusted* alpha (5.95%, t = 3.95), not the raw active return. -* **No intraday execution modeling.** Transaction costs are a flat 5 bps. Real slippage depends on order size, liquidity, and volatility. -* **Monthly rebalance only.** Daily/weekly rebalancing might capture different signals but would dramatically increase turnover. +* **Marginal raw active return.** The long-only portfolio beats the EW universe by only 3.5%/yr (t = 1.89); the statistically strong result is the *factor-adjusted* alpha (5.97%, t = 3.98), not the raw active return. +* **No intraday execution modeling.** Transaction costs are a flat 5 bps per unit of one-way turnover. Real slippage depends on order size, liquidity, and volatility. +* **Limited rebalance grid.** Notebook 04 now checks 1-, 2-, and 3-month rebalance intervals, but does not model daily/weekly trading or alternate calendar-day execution. * **Momentum crash risk.** The 2006–2011 walk-forward window shows negative active return, driven by the 2008–09 momentum crash. A crash-protection overlay (e.g. volatility scaling) would improve robustness. +* **Stress-test scope.** The synthetic-market bootstrap preserves the factor structure (the momentum PC lives in $\mathbf{F}$), so it tests **path dependence**, not "momentum without a momentum factor"; the conditional-VAE generator was found to inflate alphas (it fabricates cross-sectional persistence) and is not relied upon. + +--- + +## Webapp + +An interactive dashboard in `webapp/` showcases the pipeline: a **FastAPI** backend + a single-page **Plotly.js** frontend themed to match the rest of the site. It consumes the precomputed CSVs in `data/processed/` and recomputes the light ML **once at startup** (PCA + Marchenko–Pastur cutoff, the Fama–French alpha, and the NB06 factor model for the live button), so the numbers always match the notebooks. + +Sections: **Strategy** (the trading rule, realized alpha, and short FF/t-stat explanation), **Generate** (a live block-bootstrap alpha generator), **Performance** (equity curves and drawdowns), **Factors** (IC bars, correlation heatmap, walk-forward), **Risk** (scree + MP cutoff, systematic/idiosyncratic split), **Ticker explorer** (PC1 vs PC2 loadings), and **Process** (the notebook-by-notebook research pipeline). + +### Run locally + +```bash +pip install -r requirements-webapp.txt +uvicorn webapp.app:app --host 127.0.0.1 --port 8055 +# open http://127.0.0.1:8055 +``` + +Or with Docker (binds `127.0.0.1:8055`): + +```bash +docker compose -f docker-compose.webapp.yml up --build +``` + +### Deploy behind Caddy + +The proxy compose only `expose`s its port on your existing `caddy` Docker network — no host port, so it coexists with other sites + +```bash +docker compose -f docker-compose.webapp.proxy.yml up -d --build +``` + +Then add a Caddy site block reverse-proxying to the container: + +```caddy +frd.example.com { + reverse_proxy factor-risk-decomposition-webapp:8055 +} +``` + +The data stays **mounted read-only** (`./data:/app/data`), mirroring the `.gitignore`. The app validates the required CSV artifacts at startup and tells you to run notebooks `01 -> 06` if anything is missing or malformed. --- ## Tech Stack -Python, pandas, numpy, scipy, scikit-learn, statsmodels, matplotlib, seaborn, yfinance, pandas-datareader, joblib, torch. See `requirements.txt`. +Python, pandas, numpy, scipy, scikit-learn, statsmodels, matplotlib, seaborn, yfinance, pandas-datareader, joblib, torch, FastAPI, Uvicorn. See `requirements.txt`, `requirements-webapp.txt`, and `requirements-dev.txt`. + +### Tests + +```bash +pip install -r requirements-dev.txt +pytest -q +``` --- diff --git a/docker-compose.webapp.proxy.yml b/docker-compose.webapp.proxy.yml new file mode 100644 index 0000000..9e273dc --- /dev/null +++ b/docker-compose.webapp.proxy.yml @@ -0,0 +1,22 @@ +# Production run behind Caddy. The container only `expose`s its port on the +# shared `caddy` network (no host port), so it coexists with other sites. +# Add a Caddy site block: reverse_proxy factor-risk-decomposition-webapp:8055 +services: + factor-risk-webapp: + build: + context: . + dockerfile: webapp/Dockerfile + container_name: factor-risk-decomposition-webapp + restart: unless-stopped + expose: + - "8055" + volumes: + - ./data:/app/data:ro + - ./webapp:/app/webapp:ro + networks: + - proxy + +networks: + proxy: + external: true + name: caddy diff --git a/docker-compose.webapp.yml b/docker-compose.webapp.yml new file mode 100644 index 0000000..8787cde --- /dev/null +++ b/docker-compose.webapp.yml @@ -0,0 +1,14 @@ +services: + factor-risk-webapp: + build: + context: . + dockerfile: webapp/Dockerfile + container_name: factor-risk-webapp + restart: unless-stopped + ports: + - "127.0.0.1:8055:8055" + volumes: + # Data is mounted read-only (not baked in), matching the .gitignore and + # the ClimbingBoardGPT pattern. Re-run the notebooks to refresh these. + - ./data:/app/data:ro + - ./webapp:/app/webapp:ro diff --git a/frd/__init__.py b/frd/__init__.py new file mode 100644 index 0000000..66e8eaa --- /dev/null +++ b/frd/__init__.py @@ -0,0 +1,31 @@ +"""Shared research utilities for Factor Risk Decomposition.""" + +from .research import ( + FF_FACTOR_COLUMNS, + ArtifactError, + ArtifactSpec, + block_indices, + decile_long_returns, + fama_french_alpha, + fama_french_regression, + form_decile_portfolios, + marchenko_pastur, + momentum_signal, + series_metrics, + validate_artifacts, +) + +__all__ = [ + "FF_FACTOR_COLUMNS", + "ArtifactError", + "ArtifactSpec", + "block_indices", + "decile_long_returns", + "fama_french_alpha", + "fama_french_regression", + "form_decile_portfolios", + "marchenko_pastur", + "momentum_signal", + "series_metrics", + "validate_artifacts", +] diff --git a/frd/research.py b/frd/research.py new file mode 100644 index 0000000..2eb33a0 --- /dev/null +++ b/frd/research.py @@ -0,0 +1,251 @@ +"""Reusable finance and validation helpers for the project. + +The notebooks remain the narrative surface, but core arithmetic lives here so +the research pipeline, dashboard, and tests do not drift apart. +""" +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +from typing import Mapping + +import numpy as np +import pandas as pd +import statsmodels.api as sm + +FF_FACTOR_COLUMNS = ["Mkt-RF", "SMB", "HML", "Mom"] +FF_COLUMNS = FF_FACTOR_COLUMNS + ["RF"] + + +class ArtifactError(RuntimeError): + """Raised when notebook-generated CSV artifacts are missing or malformed.""" + + +@dataclass(frozen=True) +class ArtifactSpec: + path: Path + columns: tuple[str, ...] = () + + +def momentum_signal(ret_df: pd.DataFrame) -> pd.DataFrame: + """12-1 momentum: trailing 11 monthly returns, shifted one month.""" + return ret_df.rolling(11).sum().shift(1) + + +def block_indices(T: int, L: int, rng: np.random.RandomState) -> np.ndarray: + """Stationary block bootstrap: T time indices in variable-length blocks.""" + if T <= 0: + raise ValueError("T must be positive") + if L <= 0: + raise ValueError("L must be positive") + idx: list[int] = [] + while len(idx) < T: + start = rng.randint(T) + blen = rng.geometric(1.0 / L) + idx.extend(((start + np.arange(blen)) % T).tolist()) + return np.array(idx[:T], dtype=int) + + +def equal_weights(tickers: pd.Index | list[str]) -> pd.Series: + """Equal-weight vector for a selected set of tickers.""" + tickers = pd.Index(tickers) + if len(tickers) == 0: + return pd.Series(dtype=float) + return pd.Series(1.0 / len(tickers), index=tickers, dtype=float) + + +def turnover_from_weights(prev: pd.Series | None, curr: pd.Series) -> float: + """One-way turnover from prior weights to current target weights. + + The first rebalance buys the whole portfolio, so turnover is 1.0 instead of + NaN. This keeps the backtest net-of-cost from quietly skipping startup cost. + """ + if curr.empty: + return 0.0 + if prev is None or prev.empty: + return float(curr.abs().sum()) + names = prev.index.union(curr.index) + return float((curr.reindex(names, fill_value=0.0) - prev.reindex(names, fill_value=0.0)).abs().sum() / 2.0) + + +def portfolio_return(next_rets: pd.Series, weights: pd.Series) -> float: + """Portfolio return with missing selected names skipped and reweighted.""" + aligned = next_rets.reindex(weights.index).dropna() + if aligned.empty: + return float("nan") + live_weights = equal_weights(aligned.index) + return float(aligned.dot(live_weights)) + + +def form_decile_portfolios( + signal_df: pd.DataFrame, + return_df: pd.DataFrame, + decile: float = 0.1, + min_names: int = 50, +) -> pd.DataFrame: + """Form top/bottom-decile equal-weight portfolios with weight turnover.""" + if not 0 < decile <= 0.5: + raise ValueError("decile must be in (0, 0.5]") + common_dates = signal_df.index.intersection(return_df.index) + common_tickers = signal_df.columns.intersection(return_df.columns) + signal_df = signal_df.loc[common_dates, common_tickers] + return_df = return_df.loc[common_dates, common_tickers] + + rows: list[dict[str, object]] = [] + rebalance_dates: list[pd.Timestamp] = [] + prev_long: pd.Series | None = None + prev_short: pd.Series | None = None + + for i in range(len(common_dates) - 1): + date = common_dates[i] + next_date = common_dates[i + 1] + scores = signal_df.loc[date].dropna() + if len(scores) < min_names: + continue + + n_side = max(int(len(scores) * decile), 1) + ranked = scores.sort_values(ascending=False) + long_weights = equal_weights(ranked.head(n_side).index) + short_weights = equal_weights(ranked.tail(n_side).index) + next_rets = return_df.loc[next_date] + + long_ret = portfolio_return(next_rets, long_weights) + short_ret = portfolio_return(next_rets, short_weights) + rows.append( + { + "long": long_ret, + "short": short_ret, + "ls": long_ret - short_ret, + "long_holdings": list(long_weights.index), + "short_holdings": list(short_weights.index), + "long_turnover": turnover_from_weights(prev_long, long_weights), + "short_turnover": turnover_from_weights(prev_short, short_weights), + } + ) + rebalance_dates.append(next_date) + prev_long = long_weights + prev_short = short_weights + + out = pd.DataFrame(rows, index=pd.Index(rebalance_dates)) + if not out.empty: + out["ls_turnover"] = out["long_turnover"] + out["short_turnover"] + return out + + +def decile_long_returns(signal_df: pd.DataFrame, ret_df: pd.DataFrame, decile: float = 0.1) -> pd.Series: + """Top-decile equal-weight long-only monthly returns.""" + port = form_decile_portfolios(signal_df, ret_df, decile=decile) + return port["long"] if "long" in port else pd.Series(dtype=float) + + +def _is_datetime_like(index: pd.Index) -> bool: + return isinstance(index, pd.PeriodIndex) or pd.api.types.is_datetime64_any_dtype(index) + + +def _period_index(index: pd.Index) -> pd.PeriodIndex: + if isinstance(index, pd.PeriodIndex): + return index.asfreq("M") + return pd.DatetimeIndex(index).to_period("M") + + +def align_ff_frame(returns: pd.Series, ff_df: pd.DataFrame) -> pd.DataFrame: + """Align returns and FF factors by month when dated, otherwise by index.""" + missing = [c for c in FF_COLUMNS if c not in ff_df.columns] + if missing: + raise ValueError(f"FF factor frame missing columns: {missing}") + + ret = returns.rename("r").dropna() + ff = ff_df[FF_COLUMNS].copy() + if _is_datetime_like(ret.index) and _is_datetime_like(ff.index): + ret_pm = ret.copy() + ret_pm.index = _period_index(ret_pm.index) + ff.index = _period_index(ff.index) + return ret_pm.to_frame().join(ff, how="inner").dropna() + return pd.concat([ret, ff], axis=1).dropna() + + +def fama_french_alpha(long_ret: pd.Series, ff_df: pd.DataFrame, min_obs: int = 20) -> tuple[float, float, float]: + """Annualized FF 4-factor alpha, alpha t-stat, and regression R^2.""" + model = fama_french_regression(long_ret, ff_df, min_obs=min_obs) + return float(model.params[0] * 12.0), float(model.tvalues[0]), float(model.rsquared) + + +def fama_french_regression(long_ret: pd.Series, ff_df: pd.DataFrame, min_obs: int = 20): + """Fit monthly return on FF 4 factors after subtracting RF.""" + reg = align_ff_frame(long_ret, ff_df) + if len(reg) < min_obs: + raise ValueError(f"Need at least {min_obs} aligned observations, got {len(reg)}") + y = reg["r"] - reg["RF"] + X = sm.add_constant(reg[FF_FACTOR_COLUMNS], has_constant="add") + return sm.OLS(y.values, X.values).fit() + + +def series_metrics(r: pd.Series, freq: int = 12) -> dict[str, float]: + """Common annualized performance metrics for a monthly return series.""" + r = r.dropna() + if r.empty: + return { + "ann_return": float("nan"), + "ann_vol": float("nan"), + "sharpe": float("nan"), + "sortino": float("nan"), + "max_drawdown": float("nan"), + } + ann_return = float(r.mean() * freq) + ann_vol = float(r.std() * np.sqrt(freq)) + sharpe = ann_return / ann_vol if ann_vol > 0 else float("nan") + downside = r[r < 0] + dvol = float(downside.std() * np.sqrt(freq)) if len(downside) > 1 else float("nan") + sortino = ann_return / dvol if dvol and dvol > 0 else float("nan") + wealth = (1 + r).cumprod() + dd = (wealth - wealth.cummax()) / wealth.cummax() + return { + "ann_return": ann_return, + "ann_vol": ann_vol, + "sharpe": sharpe, + "sortino": sortino, + "max_drawdown": float(dd.min()), + } + + +def marchenko_pastur(eigvals: np.ndarray, n_obs: int, n_assets: int) -> dict[str, float | int]: + """Marchenko-Pastur bounds and signal eigenvalue count.""" + if n_obs <= 0 or n_assets <= 0: + raise ValueError("n_obs and n_assets must be positive") + q = n_obs / n_assets + sigma2 = float(np.sum(eigvals) / n_assets) + lam_plus = sigma2 * (1 + 1 / q + 2 * np.sqrt(1 / q)) + lam_minus = sigma2 * (1 + 1 / q - 2 * np.sqrt(1 / q)) + return { + "q": float(q), + "sigma2": sigma2, + "lam_minus": float(lam_minus), + "lam_plus": float(lam_plus), + "signal_count": int((eigvals > lam_plus).sum()), + } + + +def validate_artifacts(repo_root: Path, required: Mapping[str, ArtifactSpec]) -> None: + """Check that required notebook outputs exist and have expected columns.""" + missing = [str(spec.path.relative_to(repo_root)) for spec in required.values() if not spec.path.exists()] + if missing: + joined = "\n - ".join(missing) + raise ArtifactError( + "Missing notebook-generated data artifacts. Run notebooks 01 -> 02 -> 03 -> 04 -> 05 -> 06 first:\n" + f" - {joined}" + ) + + bad: list[str] = [] + for name, spec in required.items(): + if not spec.columns: + continue + try: + cols = pd.read_csv(spec.path, nrows=0).columns + except Exception as exc: # pragma: no cover - surfaced in message + bad.append(f"{name}: could not read CSV header ({exc})") + continue + missing_cols = [c for c in spec.columns if c not in cols] + if missing_cols: + bad.append(f"{name}: missing columns {missing_cols}") + if bad: + raise ArtifactError("Malformed notebook-generated data artifacts:\n - " + "\n - ".join(bad)) diff --git a/notebooks/01_data_overview_and_market_stats.ipynb b/notebooks/01_data_overview_and_market_stats.ipynb index 61cd0c4..cf90566 100644 --- a/notebooks/01_data_overview_and_market_stats.ipynb +++ b/notebooks/01_data_overview_and_market_stats.ipynb @@ -7,38 +7,47 @@ "source": [ "# Data Overview and Market Statistics\n", "\n", - "This notebook builds the foundational data object for the entire project: the **return matrix** $R \\in M_{T \\times N}(\\mathbb{R})$, where each row is a month and each column is a stock. Everything that follows in the sequals is essentially a linear algebra operation on this matrix or its covariance matrix $\\Sigma = \\frac{1}{T-1}X_c^TX_c$.\n", + "This notebook builds the data object used by the rest of the project: the **monthly return matrix**\n", "\n", - "The main goals of this notebook are as follows.\n", - "1. To assemble a clean survivorship-aware univerise of US large-cap equities (the columns of our return matrix $R$).\n", - "2. To build the return panel (fill in the matrix) from freely available price data.\n", - "3. To identify broad trends in cross-sectional dispesion, sector composition, and turnover. \n", - "4. To create a clean descriptive baseline fo the later factor and backtest notebooks. \n", + "$$R \\in \\mathbb{R}^{T \\times N},$$\n", + "\n", + "where rows are months and columns are stocks. Most of the later notebooks are different ways of asking questions about this matrix: which column-wise signals predict the next row, how a portfolio weight vector interacts with a row of returns, and how the covariance matrix\n", + "\n", + "$$\\Sigma = \\frac{1}{T-1}X_c^\\top X_c$$\n", + "\n", + "splits into common and stock-specific risk.\n", + "\n", + "The goals are:\n", + "1. Build a current-constituent S&P 500 universe. This is convenient, but survivorship-biased.\n", + "2. Assemble monthly adjusted-price returns from cached/free data.\n", + "3. Describe breadth, cross-sectional dispersion, sector composition, and equal-weight market behavior.\n", + "4. Save clean CSV artifacts for the later notebooks.\n", "\n", "### Finance terms used in this notebook\n", "\n", "| Term | Meaning |\n", "|------|---------|\n", - "| **Return** | Fractional price change: $r_{t} = p_t / p_{t-1} - 1$ |\n", - "| **Return panel** $\\mathbf{R}$ | The $T \\times N$ matrix of monthly returns — months (rows) $\\times$ stocks (columns); the project's central object |\n", - "| **Cross-section** | A row of $\\mathbf{R}$ — all stocks at one date |\n", - "| **One stock's history** | A column of $\\mathbf{R}$ |\n", - "| **Universe** | The set of stocks (columns) we're allowed to hold — here the S&P 500 |\n", - "| **Sector** | A categorical partition of stocks (Technology, Financials, …); a 0,1 matrix $\\mathbf{D}$ |\n", - "| **Dispersion** | Cross-sectional standard deviation — how spread out returns are across a row |\n", - "| **Equal-weight index** | Mean of a row $\\bar{r}_t = \\tfrac{1}{N_t}\\mathbf{1}^\\top r_t$ (every stock at weight $w_i = 1/N_t$) |\n", - "| **Sharpe ratio** | Mean return / volatility — a signal-to-noise ratio (computed here for the equal-weight index) |\n", - "| **Survivorship bias** | Only stocks that *survived* until today appear; delisted names are missing, biasing returns upward |\n", + "| **Return** | Fractional price change: $r_t = p_t / p_{t-1} - 1$ |\n", + "| **Return panel** $R$ | The $T \\times N$ matrix of monthly returns: months $\\times$ stocks |\n", + "| **Cross-section** | One row of $R$: all stock returns at one date |\n", + "| **One stock's history** | One column of $R$ |\n", + "| **Universe** | The stocks we allow ourselves to analyze or hold |\n", + "| **Sector** | A categorical grouping such as Technology, Financials, or Healthcare |\n", + "| **Dispersion** | Cross-sectional standard deviation: how spread out stock returns are in one month |\n", + "| **Equal-weight index** | Mean row return $\\bar r_t = \\tfrac{1}{N_t}\\mathbf{1}^\\top r_t$ |\n", + "| **Sharpe ratio** | Mean return divided by volatility; a rough signal-to-noise ratio |\n", + "| **Survivorship bias** | Bias from omitting firms that disappeared, merged, were delisted, or left the index |\n", "\n", - "Throughout, I treat each stock-month as a separate observation unless explicitly noted otherwise. That matters because the universe changes over time — names enter and leave the index — so the matrix $\\mathbf{R}$ is *sparse* (has NaN entries) wherever a stock didn't trade yet or was delisted.\n", + "A practical note: because constituents enter, leave, and have missing history, $R$ is sparse. Missing entries are not zeros; they mean the stock was not available in our panel at that date.\n", "\n", "## Outputs\n", "\n", - "We produce a cleaned monthly return panel ($R$), a sector mapping table, and exploratory plots that motivate later notebooks. Topics include:\n", - "- single-factor diagnostics (vectors $f_t$ that predict rows of $R$),\n", - "- composite signal construction (linear combinations of orthogonalized factor vectors),\n", - "- backtesting (weight vectors $w$ and portfolio returns $w^Tr)$,\n", - "- risk decomposition (eigendecomposition of the covariance matrix $\\Sigma$). \n", + "This notebook writes:\n", + "- `data/processed/returns_monthly.csv`\n", + "- `data/processed/prices_monthly.csv`\n", + "- `data/processed/sector_mapping.csv`\n", + "\n", + "Those files are the handoff to the factor, backtest, PCA, and synthetic-market notebooks.\n", "\n", "## Notebook Structure\n", "1. [Setup and Imports](#setup-and-imports)\n", @@ -65,7 +74,14 @@ "cell_type": "code", "execution_count": 1, "id": "eba6fb3d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:28.595596Z", + "iopub.status.busy": "2026-07-31T11:08:28.594495Z", + "iopub.status.idle": "2026-07-31T11:08:29.655509Z", + "shell.execute_reply": "2026-07-31T11:08:29.654776Z" + } + }, "outputs": [], "source": [ "\"\"\"\n", @@ -77,7 +93,6 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", - "import yfinance as yf\n", "import os\n", "from datetime import datetime\n", "\n", @@ -102,19 +117,29 @@ "source": [ "## Universe Construction\n", "\n", - "We use the S&P 500 current constituents as a starting list and pull price history for each. This introduces **survivorship bias** (i.e., names that went bankrupt or were delisted between 2005 and today won't appear). In linear-algebraic terms, the columns of our return matrix $R$ are a non-random subset of all stocks that existed; the columns we don't see are exactly the ones that went to zero, which biases our return estimates upward. This can be properly handled with different data, but we work with this as is for the sake of simplicity. " + "We start from the current S&P 500 constituent list and pull historical prices for those tickers. This is simple and reproducible, but it is **not** a fully historical S&P 500 membership file.\n", + "\n", + "The main limitation is survivorship bias. Companies that were removed from the index, acquired, delisted, or bankrupt between 2005 and the present may not appear as columns. That can push historical returns upward because the missing names are not a random sample of the market. We cannot fully solve that without a survivorship-free database such as CRSP, so this notebook keeps the caveat visible and later notebooks test how much return drag would be needed to erase the alpha." ] }, { "cell_type": "code", "execution_count": 2, "id": "f99f0d78", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:29.657559Z", + "iopub.status.busy": "2026-07-31T11:08:29.657294Z", + "iopub.status.idle": "2026-07-31T11:08:29.758892Z", + "shell.execute_reply": "2026-07-31T11:08:29.758270Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Loading cached constituent snapshot from ../data/raw/constituents.csv\n", "Total constituents: 503\n", "First 10: ['MMM', 'AOS', 'ABT', 'ABBV', 'ACN', 'ADBE', 'AMD', 'AES', 'AFL', 'A']\n" ] @@ -248,25 +273,32 @@ "import requests\n", "from io import StringIO\n", "\n", - "url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'\n", + "constituents_path = '../data/raw/constituents.csv'\n", + "refresh_constituents = os.environ.get('REFRESH_DATA', '').lower() in {'1', 'true', 'yes'}\n", "\n", - "# Wikipedia requires a descriptive User-Agent per their API policy\n", - "headers = {\n", - " 'User-Agent': 'EquityFactorResearch/1.0 (quant research; contact@example.com)'\n", - "}\n", + "if os.path.exists(constituents_path) and not refresh_constituents:\n", + " print(f\"Loading cached constituent snapshot from {constituents_path}\")\n", + " df_constituents = pd.read_csv(constituents_path, index_col=0)\n", + "else:\n", + " url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'\n", "\n", - "resp = requests.get(url, headers=headers)\n", - "resp.raise_for_status()\n", + " # Wikipedia requires a descriptive User-Agent per their API policy\n", + " headers = {\n", + " 'User-Agent': 'EquityFactorResearch/1.0 (quant research; contact@example.com)'\n", + " }\n", "\n", - "# match='Symbol' targets the constituents table directly\n", - "tables = pd.read_html(StringIO(resp.text), match='Symbol')\n", - "df_constituents = tables[0]\n", + " resp = requests.get(url, headers=headers, timeout=30)\n", + " resp.raise_for_status()\n", + "\n", + " # match='Symbol' targets the constituents table directly\n", + " tables = pd.read_html(StringIO(resp.text), match='Symbol')\n", + " df_constituents = tables[0]\n", + " df_constituents.to_csv(constituents_path)\n", + " print(f\"Saved constituent snapshot to {constituents_path}\")\n", "\n", "df_constituents['Symbol'] = df_constituents['Symbol'].str.replace('.', '-', regex=False)\n", "tickers = df_constituents['Symbol'].tolist()\n", "\n", - "df_constituents.to_csv('../data/raw/constituents.csv')\n", - "\n", "print(f\"Total constituents: {len(tickers)}\")\n", "print(f\"First 10: {tickers[:10]}\")\n", "df_constituents.head()\n" @@ -284,32 +316,26 @@ "cell_type": "code", "execution_count": 3, "id": "12116b17", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:29.760587Z", + "iopub.status.busy": "2026-07-31T11:08:29.760419Z", + "iopub.status.idle": "2026-07-31T11:08:30.245820Z", + "shell.execute_reply": "2026-07-31T11:08:30.245253Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Downloading price data for 503 tickers ...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[****************** 37% ] 188 of 503 completed$HONA: possibly delisted; no price data found (1d 2005-01-01 -> 2025-12-31) (Yahoo error = \"Data doesn't exist for startDate = 1104555600, endDate = 1767157200\")\n", - "[******************* 39% ] 196 of 503 completed$FDXF: possibly delisted; no price data found (1d 2005-01-01 -> 2025-12-31) (Yahoo error = \"Data doesn't exist for startDate = 1104555600, endDate = 1767157200\")\n", - "[*********************100%***********************] 503 of 503 completed\n", - "\n", - "2 Failed downloads:\n", - "['HONA', 'FDXF']: possibly delisted; no price data found (1d 2005-01-01 -> 2025-12-31) (Yahoo error = \"Data doesn't exist for startDate = 1104555600, endDate = 1767157200\")\n" + "Loading cached prices from ../data/raw/prices_monthly.csv\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Saved to ../data/raw/prices_monthly.csv\n", "\n", "Price panel shape: (5282, 503)\n", "Date range: 2005-01-03 00:00:00 to 2025-12-30 00:00:00\n" @@ -333,6 +359,8 @@ " print(f\"Loading cached prices from {raw_path}\")\n", " df_prices = pd.read_csv(raw_path, index_col=0, parse_dates=True)\n", "else:\n", + " import yfinance as yf\n", + "\n", " print(f\"Downloading price data for {len(tickers)} tickers ...\")\n", " df_raw = yf.download(\n", " tickers,\n", @@ -359,20 +387,29 @@ "source": [ "## Return Panel Assembly\n", "\n", - "We resample daily prices to month-end and compute simple monthly return \n", - "$$ r_{t,i} = p_{t,i}/p_{t-1,i} - 1. $$\n", - "Here $t$ is the time index, $i$ is the stock index, and $p_{t,i}$ is the closing date. The ratio $p_{t,i}/p_{t-1,i}$ tells us what \\$1 invested ends up as, so we subtract 1 in order to get the simple net return. \n", + "We resample adjusted daily prices to month-end and compute simple monthly returns:\n", "\n", - "The result is the return matrix $R \\in M_{T \\times N}(\\mathbb{R})$ with months as rows and stocks as columns. \n", + "$$r_{t,i} = \\frac{p_{t,i}}{p_{t-1,i}} - 1.$$\n", "\n", - "We are working with month-end for both simplicity, and the fact that a daily rebalance would contribute to large $\\ell^1$ distances between consecutive weights." + "Here $t$ indexes months, $i$ indexes stocks, and $p_{t,i}$ is the adjusted close price at month-end. The ratio $p_{t,i}/p_{t-1,i}$ tells us what $1 invested at the previous month-end became by this month-end; subtracting 1 converts that gross return into a net return.\n", + "\n", + "The result is the return matrix $R \\in \\mathbb{R}^{T \\times N}$ with months as rows and stocks as columns.\n", + "\n", + "We use monthly returns because the rest of the project is about medium-horizon momentum, not high-frequency trading. Monthly rebalancing also keeps turnover and transaction-cost assumptions easier to reason about." ] }, { "cell_type": "code", "execution_count": 4, "id": "249ffb9c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:30.247588Z", + "iopub.status.busy": "2026-07-31T11:08:30.247411Z", + "iopub.status.idle": "2026-07-31T11:08:30.293016Z", + "shell.execute_reply": "2026-07-31T11:08:30.292555Z" + } + }, "outputs": [ { "name": "stdout", @@ -383,6 +420,14 @@ "Unique tickers: 501\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_144016/1360406685.py:8: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Either fill in any non-leading NA values prior to calling pct_change or specify 'fill_method=None' to not fill NA values.\n", + " df_returns = df_monthly_prices.pct_change() # fractional change between current and prior\n" + ] + }, { "data": { "text/html": [ @@ -403,7 +448,7 @@ "\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -465,7 +510,7 @@ "" ], "text/plain": [ - "Ticker A AAPL ABBV ABNB ABT\n", + " A AAPL ABBV ABNB ABT\n", "Date \n", "2005-02-28 0.085482 0.166711 NaN NaN 0.021546\n", "2005-03-31 -0.075000 -0.071111 NaN NaN 0.013699\n", @@ -506,7 +551,14 @@ "cell_type": "code", "execution_count": 5, "id": "9691c46b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:30.294692Z", + "iopub.status.busy": "2026-07-31T11:08:30.294517Z", + "iopub.status.idle": "2026-07-31T11:08:30.617121Z", + "shell.execute_reply": "2026-07-31T11:08:30.616498Z" + } + }, "outputs": [ { "name": "stdout", @@ -539,22 +591,36 @@ "source": [ "## Market Statistics\n", "\n", - "Let's get a feel for the market itself.\n", - "- How many columns are available each month? We'll call this `breadth`, but this really counts how many of today's survivors had valid prices at month $t$. As we have ~501 stocks at 2025-12, this means that ~116 stocks from 2005 have been replaced with different ones. \n", - "- What does cross-sectional dispersion look like (the standard deviation across each row of $R$)?\n", - "$$ \\sigma_t^{XS} = \\sqrt{\\frac{1}{N_t - 1} \\sum_{i=1}^{N_t}(r_{t,i} - \\bar{r}_t)^2} $$\n", - "- How does the equal-weight market index behave? The equal-weight market index return at month $t$ is simply the mean of the row: $\\bar{r}_t = \\frac{1}{N_t} \\mathbf{1}^\\top r_t$, where $\\mathbf{1}$ is the all ones vector and $N_t$ is the number of stocks alive at date $t$. This is the return of a portfolio that holds every stock at equal weight $w_i = \\frac{1}{N_t}$. \n" + "Before building factors, we want a baseline feel for the panel.\n", + "\n", + "- **Breadth:** how many stocks have valid returns each month? This is not true historical S&P 500 membership; it is the number of current-constituent tickers with usable data at that date.\n", + "- **Cross-sectional dispersion:** how spread out stock returns are in a given month:\n", + "\n", + "$$\\sigma_t^{XS} = \\sqrt{\\frac{1}{N_t - 1} \\sum_{i=1}^{N_t}(r_{t,i} - \\bar{r}_t)^2}.$$\n", + "\n", + "- **Equal-weight market return:** the return from holding every available stock at equal weight:\n", + "\n", + "$$\\bar{r}_t = \\frac{1}{N_t}\\mathbf{1}^\\top r_t.$$\n", + "\n", + "This equal-weight series is useful later because the traded portfolio is also equal-weighted within its selected names. Comparing equal-weight to equal-weight keeps the benchmark cleaner than comparing an equal-weight strategy to a cap-weighted index." ] }, { "cell_type": "code", "execution_count": 6, "id": "1b1798b7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:30.619064Z", + "iopub.status.busy": "2026-07-31T11:08:30.618871Z", + "iopub.status.idle": "2026-07-31T11:08:30.957964Z", + "shell.execute_reply": "2026-07-31T11:08:30.957393Z" + } + }, "outputs": [ { "data": { - "image/png": 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" ] @@ -601,11 +667,18 @@ "cell_type": "code", "execution_count": 7, "id": "0766c03b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:30.959816Z", + "iopub.status.busy": "2026-07-31T11:08:30.959610Z", + "iopub.status.idle": "2026-07-31T11:08:31.308702Z", + "shell.execute_reply": "2026-07-31T11:08:31.308108Z" + } + }, "outputs": [ { "data": { - "image/png": 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9evUCAOzbt8+jt1qWZezfvx+9e/dWl3Xo0AELFizAggUL4HQ68dRTT+GBBx7AxRdfjOuuuy6ode6++26/7SgpKQkYAI4cORKff/45jh07ht69e6s3CO655x4MHjw44Hvr378/AOCXX37BueeeG3C95lQab87nFawtW7YAAK688kqP5YFmIAjVlVdeiSuvvBKPPfYY5s6di/79+6uf5cUXX4zf//73jb6+sc+pU6dOKCkp8Vl+4MABHVquD4fDgbfffhu5ubkYN25co+t26dIF999/P+6//37Y7XY8+uijePTRRzFjxgxcfvnl6nr79+/3ee2+ffsAzbHSr18/SJKEP/3pT0HNTBFum7R69eoFp9OJgwcPetxYczgcOHTokK5ZL0RE5MIx+0REFJa//e1vyMnJwdy5c9V0b621a9d69BIHMmvWLMTFxeGpp57yWP7mm2/ixIkTahC/b98+tVcb7p5cZWyvMjY5mHUCWbVqFa677jqfsdR2ux3/+9//EBcXpwb2c+fORUZGBh566CGf3mRJkrB3714AwDXXXIO0tDQ8/PDDPr3WR44cUR/n5uaioqKiyc8Kzfi8mkPJdFCCRLjH27/yyivN3lZT/va3vwEA/vznPwPuMebjxo3D008/7fczUHrC0cTnNHLkSGzdutXjWNy4cWOzs08ixW6344477sCBAwfw9NNPe0zv6G3v3r0ex5WSkQI/x/G6detw8OBB9efKykosW7YMkyZNQufOnQEAN9xwA5KTk/HnP//ZJ0tDGSbRlOa0Seuqq66CwWDAk08+6bH85ZdfRllZWUjHKxERNY49+0REFJZu3brhm2++wdy5c9G3b1/MmTMHAwYMQF1dHb7++musWbMGt912W5Pbyc/Px3/+8x/Mnz8f5eXlmDJlCg4ePIj//Oc/uO666zB//nwAwMqVK7Fs2TJMmzYNvXv3htlsxquvvoqBAwdizpw5Qa8TSPfu3fHll1+iT58+mDNnDvr164fS0lK8//77OHjwIJYsWYIOHToA7uEIK1euxJVXXomhQ4di5syZyM7OxuHDh/HFF19gzpw5+Mtf/oKcnBx8+OGHmD17NkaOHIkrrrgCiYmJ2LBhAzp16qSmuM+YMQNvvPEG/va3v6Fjx47o1q0bpkyZEtbn1RzXXHMNnnzyScycORN33nkn6uvr8fHHH+Ouu+7Cb37zm2ZvrzF9+vTBDTfcgOXLl+Onn37C8OHD8c4772DGjBno168f5s2bh+7du+PUqVP4/vvvERcXhzVr1gDuz2nFihW46667MHjwYJhMJtx4440AgAULFuDVV1/F5MmTMW/ePJw+fRqHDh3CVVddhffff1/X99CU06dPq7/bmpoaHDhwACtXroQkSXj77bdxzTXXNPr6t956C++//z6mTp2KM844AxUVFVi+fDmGDRuGK664wmPd+fPn46abbsKECROQlJSE1157DQDw0ksvqevk5eXhww8/xJw5czBy5EhceumlyMjIwKFDh7B69WrcdtttAYezhNImrX79+uH555/H7373OxQVFWHSpEnYs2cPli9fjttuu63Jz4KIiJpPkEMdgEdERKQhyzI2bNiADRs2oLCwEGlpaejevTumTJnikTK8bNky7Nu3D0888YTf7Rw6dAjvv/8+CgoKkJ6ejosuugiTJk3yWOfYsWP45JNPcODAAaSmpmL48OG47LLLYDQam7VOIA6HA1999RW2b9+OEydOID4+Hv369cOMGTP8FiKzWCz48MMPsXPnTkiShF69euGSSy7BGWec4bFeWVkZ3nvvPezevRtpaWkYP3682isK95jqFStWYMeOHbBarRg0aJA63OCuu+7COeecg7lz5zb787rllltw8cUX+wRjTz75JBwOBx588EF1WXV1NV555RXs378fnTp1UntcH330Udx5553qkIFA7fH2wAMPICUlBYsWLfJ57vjx43jkkUcwbtw4NViXJAmff/45Nm7cCLPZjPz8fIwfP95n+MNnn32G9evXo6qqCkajEUuWLPHY7quvvoqioiIMGzYM119/Pd577z1s3rwZ//znPxt9/8rv4be//S1mzpzpM1vC4sWLkZSUhAULFjT6vu+55x5YLBYAgCiKSE5ORpcuXTBixAhMmDABBoPB5zW33347pkyZ4vF7Onz4MD799FMcOnQIaWlpGDVqFKZPn66+vrq6GqmpqXj00Ufxu9/9DsuXL8ehQ4dwxhln4KabbvI7U0ZVVRU++OADNduhd+/emDFjBvLz85t8/8G0KdB7gTtr5IMPPsDJkyeRmZmJadOmeQxl0OOzJyIiFwb7RERERK2UNtj3d0OFiIjaL47ZJyIiIiIiImpjGOwTERERERERtTEM9omIiIhaKZPJhPnz52PYsGHRbgoREcUYjtknIiIiIiIiamPYs09ERERERETUxjDYJyIiIiIiImpjmp5smAKSJAmnTp1CamoqBEGIdnOIiIiIiIiojZNlGRaLBV26dIEoBu6/Z7AfhlOnTqFbt27RbgYRERERERG1M8ePH0fXrl0DPs9gPwypqamA+0NOS0uLdnOoHZAkCSUlJcjNzW30Lh5RJPE4pFjBY5FiAY9DigU8DtsXs9mMbt26qfFoIAz2w6Ck7qelpTHYpxYhSRKsVivS0tL4RU5Rw+OQYgWPRYoFPA4pFvA4bJ+aGkrOI4GIiIiIiIiojWGwT0RERERERNTGMNgnIiIiIiIiamM4Zp+IiIiIiKidkCQJNpst2s2gRphMJhgMhrC3w2CfiIiIiIioHbDZbDhy5AgkSYp2U6gJGRkZ6NSpU5NF+BrDYJ+IiIiIiKiNk2UZp0+fhsFgQLdu3Vi1P0bJsoza2loUFxcDADp37hzythjsExERERERtXEOhwO1tbXo0qULkpKSot0cakRiYiIAoLi4GB06dAg5pZ+3c4iIiIiIiNo4p9MJAIiLi4t2UygIyg0Zu90e8jYY7BMREREREbUT4YwBp5ajx++JwT4RERERERFRG8Mx+0RERERERBSzHA4HPvnkE3z77beYPn06zj//fJ91duzYga+++gpmsxlDhgzB5ZdfHpEihJ9//jm+/PJLDB8+HPPmzfN4bvv27XjttdfQuXNnLFy40Oc9PPDAA+jSpQsWLFige7v8Yc8+ERERERERxaTvv/8evXr1wuuvv45XXnkFW7Zs8Vnn9ttvx0033YRTp05BEATcd999GDduHKxWa0Tas2TJEtxzzz2or6/3eO7JJ5/EkiVLsGLFCp/Xff7553jxxRdx33334cSJE7q3yx/27BMREREREVFM6ty5M3744Qd06dIFXbt29bvOHXfcgaVLl6o/33nnnejRowdWrFiBW2+9FQDw1ltvQZIkDBgwAOvWrUNVVRVmzpyJ4cOHY+vWrfjss88QFxeHq6++Gr169Wq0TX369IEkSVi5ciWuvvpqAEBFRQVWrlyJSy+9FHv27PF5zcsvv4xbbrkFP/zwA1555RX8+c9/DvOTaRp79oko6k6UVaOwojbazSAiIiKiGNOzZ0906dKl0XWGDRvm8XPHjh3RoUMHnDx5Ul325Zdf4r777sONN96I2tpaHDhwAKNHj8ZvfvMb3HrrrRBFEdu2bcOIESNQVFTUZLtuvvlmLF++XP15xYoVGD16tN8bBadPn8b//vc/3HrrrbjtttuwfPlyyLIc5CcQOvbsE1FU2Z0Sdh4rh0EU0CmTc74SERERtQhZBuz1QawYAaZ4IIKzAqxbtw4FBQU477zzPJZLkoTvv/8eqampAICzzjoLn376Kfbt24ekpCTIsow+ffrgnXfewR/+8IdG9zFv3jwsWrQIBQUFyM/Px7Jly3Dfffdh165dPuu++uqrGDFiBAYPHoyePXvij3/8I9auXYsLLrhA53fuicE+EUWV0ym5/pVkyLLM6WCIiIiIWoK9Hvj7nOjs+//eBuISIrLp48eP49prr8V1112HyZMnezw3duxYNdAHgH79+sFkMqlz2guCgD59+uD48eNN7qdDhw64+OKL8eqrr2Lq1Kk4duwYZs2a5RPsy7KM5cuX48EHHwQApKSkYO7cuXj55ZcZ7BNR2ybJno8NjPWJiIiIKASnTp3C5MmTMXz4cCxbtszneSWoVxgMBr/LHA5HUPu7+eab8fvf/x7Hjx/HnDlzkJiY6LPON998g4MHD+KHH37Azp071XZ++eWXKC8vR1ZWVjPfZfAY7BNRVHmOV5IBMNonIiIiijhTvKuHPVr71tnp06cxadIk9OnTBx9++CHi4uJ034e3adOmwWazYfny5fjxxx/9rvPyyy/jwgsvxKBBg9RlPXr0wMGDB7FixYomhwuEg8E+EUWVNtSXZMAQxbYQERERtRuCELFU+pZWWFiISZMmoXfv3vjoo48QH6//zQR/DAYDXnnlFRw5cgQjR470eb6yshIfffQRVq5ciYsuusjjOavVimXLljHYJ6K2S9L07LdEVVIiIiIiaj2Ki4vx97//HQBQVVWFzz77DIWFhRgwYABuv/12AMDs2bNx+PBhTJo0CQsXLlRfO2bMGHVqvEjxDuK13nzzTcTFxWHSpEk+z82cORMPPvggNm/ejLPPPjsibWOwT0RRJXuN2SciIiIiUphMJvTo0QMA8Oijj6rLO3bsqD6+5ZZbMHv2bJ/XZmdnq4/nzp3r07F0ww03wGQyeSy77bbbkJ6eHrA9l1xyCfr37x/w+RkzZuDMM88EAHTr1g3Lly/32QfcxQGXLFkS0c4uQWZXWsjMZjPS09NRVVWFtLS0aDeH2gFJklBcXIwOHTpAFMVoN0cXlTX1+H6fay7TyWd2QUIc70HGurZ4HFLrxGORYgGPQ4oFwRyHVqsVR44cQc+ePZGQ0DbS99uyxn5fwcah/EYioqjSpvGzZ5+IiIiISB8M9okoqrS5RTIY7RMRERER6YHBPhFFlUewz1ifiIiIiEgXDPaJKKo80/gZ7RMRERER6YHBPhFFlewx9V5Um0JERERE1GYw2CeiqPJM42e0T0RERESkBwb7RBRVEnv2iYiIiIh0x2CfiKJK9njMaJ+IiIiISA8M9okoqjhmn4iIiIhag19++QU7d+5sdJ09e/Zg+/btLdamxhij3QAiat8kWfuY0T4RERERNThw4AAOHTqEbt26YdCgQR7PlZaWYuvWrUhNTcXYsWN13e/u3bths9kwdOhQddnTTz8Nh8OBN954I+DrXnjhBZw4cQIrV67UtT2hYM8+EUUVe/aJiIiIKJDXXnsN06ZNw6xZs3yee+KJJzBt2jTcfvvtuu/3n//8JxYvXqz7dlsSg30iiipW4yciIiKixvTp0welpaXYuHGjusxut+P111/HmDFj/L5m9+7dWLduHY4fP+7znJKO73A48Ouvv+LHH39EXV2d+vyBAwdw/PhxFBUVYfXq1Vi9ejXKysrU5wO9ztvx48exbt06v8u//vrrZn0GoWAaPxFFFavxExEREVFj4uLicN1112H58uVquv6nn36KtLQ0nHfeefjss8/UdcvLy3H55Zfj119/Rd++fbFz505ce+21WLp0KQRBANzp+Hv27EFdXR2SkpJQXFwMSZLw9ddf44wzzsAPP/yAX375BTabDc899xwA4PHHHwfcNwKGDx/u93XeLBYLJk+ejD179qB///7q8nvvvRc2mw2TJ0+O6OfGnn0iiiqPnv1oNoSIiIionXFKUlT+C8X8+fPx3nvvobq6GgDw8ssv4+abb/ZZ709/+hMqKipw8OBB/PDDD9i8eTPeeustrFixwmO9n3/+GcuXL8emTZuwf/9+dO3aFU888QQAYN68ebj44osxbtw4tWdfGbvf2Ou8DRw4EGPGjMHy5cvVZeXl5fjvf/+L+fPnh/Q5NAd79okoqrSp+yzQR0RERNQynJKEL34+EZV9TxnaFQaxef3OgwcPxoABA/Duu+9iypQpWLt2LZYtW4Z//OMfHuu98cYb+Oc//4nMzEwAwKBBg3DNNdfg9ddfx7x589T1Jk2ahJEjRwIAjEYjzj///KBS65v7ultvvRX/93//h7///e8wGo148803kZWVhWnTpjXr/Yci6sF+WVkZ3nvvPRQVFWHw4MGYOXMmxCZ+8Rs3bsT3338Po9GIcePGYdSoUc1eZ+XKldi0aZPHsk6dOuHuu+/W6Z0RUTC04T3H7BMRERFRIPPnz8fy5ctx6tQpTJkyBZ07d/Z4vqSkBNXV1ejTp4/H8n79+vmMnc/JyfH4OTExsdHx96G+7uqrr8bdd9+Nzz//HJdeeimWL1+OefPmwWAwNLmvcEU12D9y5AjGjh2Lvn37YtSoUfjjH/+I5cuX49NPP/Ub8EuShLFjx8JoNGLMmDGora3FX/7yF9x666145plngl4HAFavXo2NGzfi2muvVZelpqa20DsnIgXH7BMRERG1PIMoYsrQrlHbdyjmzJmDBQsWYN++fXj55Zd9nk9PT4coiqiqqvJYXlVVhaysrJDbG46kpCTMmTMHy5cvR35+Pn7++We8++67LbLvqAb7CxcuRI8ePbB27VoYDAbceeed6NevH959913MmTPHZ31BEPDcc89h9OjR6rKLL74Yl1xyCebPn49BgwYFtY6iX79+eOCBB1rgnRJRIKzGT0RERBQdoQbd0ZKWloaFCxdi+/btmD59us/zcXFxGDZsGD777DNccsklgPv68tNPP8X48eObta+kpCTYbDZd2n3LLbdgzJgxiI+PVzu7W0LUgn2Hw4FPP/0UTz/9tJrCcMYZZ2DChAn46KOPAgb72iAeAIYPHw64py9Qgv2m1lEcPXoUf/3rX5Geno5x48ZhxIgREXmvRBSY55j9qDaFiIiIiGLcX/7yl0aff+KJJzB16lQkJyfj7LPPxjvvvIOTJ0/iT3/6U7P2M3ToULz++ut46623kJWV5XfoeLBGjhyJM888E++9955Hsb5Ii1qwX1BQAKvVit69e3ss7927N3744Yegt/PGG28gPj6+0UA90Drx8fGwWq04dOgQ7r//fvz+97/HU089FXA79fX1qK+vV382m82Ae+iAFGJVSaLmkCQJsiy3qePNqfn74d9S69AWj0NqnXgsUizgcUixIJjjUFlH+a+16NOnD8aOHRuwzX379vV4ftKkSdiwYQOWLVuGN954A3369MGzzz6LTp06qeuceeaZ6ueh6NmzJ8aMGaMumzNnDkpLS7Fy5UqYzWY89thjQb1uwIAByM3N9Wnv7NmzcfDgQcyePTuoz1/5Pfm7Pg72+0aQo/Sb/uWXX3DWWWfhhx9+wDnnnKMuX7hwIT788EMcPHiwyW189913uOCCC/DYY4/hnnvuadY6Bw8e9LjR8MUXX2Dq1KlYt24dJk6c6HdbDz/8MB555BGf5fv37+d4f2oRkiShqqpKHY/UFuwvrMbJCldRk14dkpGfnRTtJlET2uJxSK0Tj0WKBTwOKRYEcxza7XZUVVWhe/fuSEhIaPE2tnfTpk1Djx49sGTJkqDWt1qtOHbsGNLT02EymTyes1gs6Nu3L6qqqpCWlhZwG1Hr2U9JSQHcxRK0Kisr1ecas2XLFkyfPh133XVXwEC/sXW8MwqmTJmCvLw8bNiwIWCw/+CDD2LBggXqz2azGd26dUNubm6jHzKRXiRJgiAIyM3NbTMXFEVWI8wO11dRZmYGOnTg31Ksa4vHIbVOPBYpFvA4pFgQzHFotVphsVhgNBphNEZ9UrZ2Y9OmTdiwYQO+/fZbPP/880F/9kajEaIoIjs72+fmTLA3a6L2W87Pz0dycjL27duHKVOmqMv37duHAQMGNPrabdu24aKLLsL8+fPxxBNPhLyON6fTCavVGvD5+Ph4xMfH+ywXRZFf7tRiBEFoY8ecoL4X5b1R7Gt7xyG1VjwWKRbwOKRY0NRxKIoiBEFQ/6OW8d5776GgoAAfffQR+vfvH/TrlN+Tv99psN81UftGMhgMmDlzJl5//XW1yuHu3bvx3Xff4corr1TX++ijj/Dcc8+pP//000+48MILMX/+fDz99NN+t93UOg6HA5s2bfJY9uGHH6KwsBDnn3++ju+SiJqiHUnUekaPERERERE17dlnn8WHH36ozg7QkqKav/H4449j/PjxGDNmDEaMGIFPPvkEs2fPxsyZM9V1Pv/8c2zatAl33303ampqcOGFF8JkMsFoNHpMmzdr1iyMGjUqqHUEQcADDzwAURQxaNAgFBQU4IsvvsCf/vQnBvtELUwb4EutqFgMEREREVEsi2qwn5eXh507d+KTTz5BUVERrrrqKlxwwQUe68yaNQtjxowB3OkK9913n99tKen1waxjMBiwfv16fP/999i+fTvGjBmDZ599FmeccYbO75CImqIN8BnrExERERHpI+qVGVJSUjB37tyAz0+bNk19nJiY6NFT708w6yjOPfdcnHvuuc1oLRHpTRvgt6ZpYIiIiIhaI15vtQ56/J5YRYSIokr7RcY0fiIiIqLIMBgMAKDWS6PYVltbCwA+0+41R9R79omofWN8T0RERBR5RqMRSUlJKCkpgclk4uwRMUqWZdTW1qK4uBgZGRnqTZpQMNgnoqiSPHr2o9oUIiIiojZLEAR07twZR44cwbFjx6LdHGpCRkYGOnXqFNY2GOwTUVR59Oyzm5+IiIgoYuLi4tCnTx+m8sc4k8kUVo++gsE+EUWVzJ59IiIiohYjiiISEhKi3QxqARyoQURR5Tn1HqN9IiIiIiI9MNgnoqjSxvfs2SciIiIi0geDfSKKKo+efTDaJyIiIiLSA4N9Iooq1ucjIiIiItIfg30iiiqZY/aJiIiIiHTHYJ+Iokob3zPWJyIiIiLSB4N9IooqyWPqPUb7RERERER6YLBPRFHFnn0iIiIiIv0x2CeiqNJW4Gc1fiIiIiIifTDYJ6KoYs8+EREREZH+GOwTUVRJrMZPRERERKQ7BvtEFFXs2SciIiIi0h+DfSKKGu+efFbjJyIiIiLSB4N9IooaySu2Z6xPRERERKQPBvtEFDXePfusxk9EREREpA8G+0QUNd6hPXv2iYiIiIj0wWCfiKLGp2ef0T4RERERkS4Y7BNR1HiP2ff+mYiIiIiIQsNgn4iihmP2iYiIiIgig8E+EUWN91R7zOInIiIiItIHg30iihrv4J5j9omIiIiI9MFgn4iixrtnn2P2iYiIiIj0wWCfiKLHHdwLQrQbQkRERETUtjDYJ6KoUXr2RU20793bT0REREREzcdgn4iiRonrtcE+Y30iIiIiovAx2CeiqFF68Q2iNthntE9EREREFC4G+0QUdR7BflRbQkRERETUNjDYJ6KoUcfss2efiIiIiEhXDPaJKGr8jdnn9HtEREREROGLerD/+uuvY8SIEejatSumTZuGnTt3Nrr+L7/8gptuugn9+vXDoEGDcPvtt+PUqVMhbbe5+yYifSk9+4IAKJ377NknIiIiIgpfVIP99957D7fccgt+//vfY+3atejatSsmTZqEoqIiv+s7nU5cf/31mDhxIj799FO8+eabOHDgAM4//3zU1dU1a7vN3TcR6U+J6wUIENy9+4z1iYiIiIjCJ8hR7EYbMmQIzjnnHCxduhRwB/NdunTBHXfcgUceecTva2RZVoMCADh06BB69+6Nr7/+GpMmTQp6u6Hs25vZbEZ6ejqqqqqQlpYW5qdB1DRJklBcXIwOHTpAFKOemBO2k+U12HG0DFkp8TDX2eBwypgwsDOSE0zRbho1oq0dh9R68VikWMDjkGIBj8P2Jdg4NGpHQlVVFXbu3IkLLrhAXWYwGDB58mR8++23AV+nDfQBqD36CQkJQW831H0Tkb6Ue42iIECAu2c/ym0iIiIiImoLjNHasTLOvmPHjh7LO3bsiJ9//jmobciyjIULF6J///4YOXJk0NsNdd/19fWor69XfzabzYD7TpokSUG1mSgckiRBluU2c7w1/O3IAFzvy+l0QpIM0W4aNaKtHYfUevFYpFjA45BiAY/D9iXY33PUgn2lR89g8LyoNxqNQTd+wYIF2LhxIzZs2ACTyRT0dkPd92OPPeY3xb+kpARWqzWoNhOFQ5IkVFVVQZblNpGiVVpRB4ulGvGwwWK1o94uobjEiLqEqH01URDa2nFIrRePRYoFPA4pFvA4bF8sFktQ60Xtijo3NxdwB8paJSUl6nONeeCBB7B8+XKsWbMGZ511VrO2G+q+H3zwQSxYsED92Ww2o1u3bsjNzeWYfWoRkiRBEATk5ua2iS/yOsGC1BoBWZlJMNTaUFvvQHZ2NjKS46PdNGpEWzsOqfXisUixgMchxQIeh+2LMoS9KVEN9nv27InvvvsOl19+ubp8w4YNuOKKKxp97f/93/9hyZIl+PLLL3H22Wc3e7uh7js+Ph7x8b5BiCiK/KOiFiMIQhs65lzvxSCKEAXXexKEtvLe2ra2dRxSa8ZjkWIBj0OKBTwO249gf8dRPRLuuusuvPzyy/jxxx/hcDjwxBNP4NSpU7j99tvVde655x6MHz9e/fnPf/4zXnjhBXz55ZcYPXp0yNsNZh0iiiy1GJ8AKLU3Jc69R0REREQUtqgOjP3DH/6A4uJiXHDBBaivr0deXh4++ugj9O3bV13HYrGgoqICAFBWVobFixcjPj4el1xyice2nnvuOVx33XVBbzeYdYgosrTV+EWvmTaIiIiIiCh0gixHvxtNkiTU1NQgNTXV57nq6mo4HA5kZGRAlmWUlZX53UZqaqpPin1j223OOoEEO78hkV7a2hyqB05X4cDpKnTLSUFlTT0sdXaM6p2L3LTEaDeNGtHWjkNqvXgsUizgcUixgMdh+xJsHBoTJa9FUQwYbKekpKiPBUFATk6OLtttzjpEFBkNPftQe/ajf/uRiIiIiKj1420fIooaSRPYc8w+EREREZF+GOwTUdRox+wLcEf7jPWJiIiIiMLGYJ+IokaJ6wVBYM8+EREREZGOGOwTUdT4HbMf5TYREREREbUFDPaJKGqUMfvanv0YmCCEiIiIiKjVY7BPRFGj7dkX3NG+xFifiIiIiChsDPaJKGoaxuer5fnYs09EREREpAMG+0QUPe64Xtuzz1ifiIiIiCh8DPaJKGq0Y/ZFjtknIiIiItINg30iihp/Y/YZ6hMRERERhY/BPhFFjTJmn9X4iYiIiIj0xWCfiKJGVtP4WY2fiIiIiEhPDPaJKGo8evaj3RgiIiIiojYkpGD/9ttvx7fffst0WyLShahJ45f4vUJEREREFLaQgv0DBw5g4sSJ6NmzJ/70pz9hz549+reMiNq8hp59V8APjtknIiIiItJFSMH+119/jYKCAvzud7/D559/joEDB2LEiBF49tlnUVhYqH8riahNUuJ6V8++4LGMiIiIiIhCF/KY/by8PNx7773Yvn07fv31V0ydOhX/+Mc/0LVrV31bSERtlrZnn2n8RERERET6CbtAnyRJKCoqQlFRESorK5GYmKhPy4io7VOq8UPQpPFHt0lERERERG1ByMH+zz//jPvuuw/5+fm46KKLcPr0abz44osoKirSt4VE1GZpe/YVjPWJiIiIiMJnDOVFgwYNwu7duzF69Gg88MADuPrqq5Gbm6t/64ioTdOO2RcFZRnDfSIiIiKicIUU7F999dW49tpr0atXL/1bRETthueYfcFjGRERERERhS6kYP8vf/mL/i0honZH6cV39ewrXfvRbRMRERERUVsQ8pj9devW4frrr8fYsWPVZS+88AKqqqr0ahsRtXGSEtgLfpYREREREVHIQgr233//fcyYMQOpqan4/vvv1eW1tbV48skn9WwfEbVhMrQ9++5lTOMnIiIiIgpbSMH+4sWL8e677+LFF1/0WD5z5ky8/vrrerWNiNo4Ja4XhIaS/Az1iYiIiIjCF1Kwv3//fkyePBlAQ1EtAOjYsSMKCwv1ax0RtWmeY/Y9lxERERERUehCCvZzcnJw6NAhwCvYX79+Pbp3765f64ioTZPUnn1B/S5hrE9EREREFL6Qgv0bb7wRt99+O3bt2gVBEFBWVoY333wTt9xyC+bPn69/K4moTROFhhp9nHqPiIiIiCh8IU2999BDD6G4uBhDhgyBJEnIycmBKIq49dZbcd999+nfSiJqc7yDepE9+0REREREugkp2DcajVi6dCkeeeQRbN++HZIkYejQocjLy9O/hUTUJmnH5ouCoNTn45h9IiIiIiIdhBTsKzp16oRp06bp1xoiajckTUzvMWY/ek0iIiIiImozgg72Fy1aFPRGFy9eHGp7iKid8OzZV2fe45h9IiIiIiIdBB3sb9q0SX1ss9nw7bffIiUlBf379wcA7N27F9XV1Rg/fnxkWkpEbYrs1bMvqnn8UWsSEREREVGbEXSw/9VXX6mPFyxYgPz8fCxZsgSpqakAAIvFgjvvvBMdO3ZsdiO2b9+OoqIiDBo0CN26dQvqNXv27MG+ffswbtw45OTkeDz32WefweFw+LymR48eGDp0KABgx44dOHLkiMfzaWlpmDx5crPbT0TNp/Tsa2bvBNizT0RERESki5DG7H/wwQfYunWrGugDQGpqKp555hmMGjUKTz/9dFDbsVgsmD59Ovbs2YN+/fph27ZtuP/++/Hwww8HfM0333yDhx56CEePHsWxY8ewbt06TJw40WOdN998E3V1derPdXV1+PLLL/HQQw+pwf6SJUvw6aefYtSoUep63bt3Z7BP1EKUMftKjz6r8RMRERER6SekYL+0tBQVFRXo0KGDx/Ly8nKUlpYGvZ1Fixbh5MmT2LdvHzIzM/H111/j/PPPx8SJE30CeEVJSQkeeugh9OnTJ2AWwNtvv+3x8yuvvII1a9bgxhtv9Fg+ZswYfPDBB0G3l4j0492zzzH7RERERET6EUN50fTp0zFnzhxs2LABtbW1qK2txYYNGzBnzhzMmDEjqG3Isow33ngD8+fPR2ZmJgBg8uTJGDFiBFasWBHwdbNnz8akSZOa1d5ly5bhwgsvRI8ePTyWm81mfPnll/jxxx9RXV3drG0SUXiUnn0Brihf8M7nJyIiIiKikIXUs7906VLceeedmDhxoqZ3TsDVV1+Nf//730Ft48SJEygvL8eQIUM8lg8dOhQ7duwIpVl+7du3Dxs3bsT777/v89yPP/6Ixx9/HCdPnkRJSQmef/55zJ07N+C26uvrUV9fr/5sNpsBAJIkQZIk3dpMFIgkSZBluU0cb5LkdL0Pg+D6V3b9HTkloU28v7asLR2H1LrxWKRYwOOQYgGPw/Yl2N9zSMF+ZmYm3nnnHTz99NPYu3cvAKB///7Iy8sLehtVVVXqtrSys7NRWVkZSrP8WrZsGXJzc3HZZZd5LJ81axaeeuoppKSkAAAef/xx3HTTTRg6dCgGDhzod1uPPfYYHnnkEZ/lJSUlsFqturWZKBBJklBVVQVZliGKISXmxAyL1QGLxQKbSURxsQk19a6frQbXzxS72tJxSK0bj0WKBTwOKRbwOGxfLBZLUOuFFOwr8vLymhXga8XFxQEAamtrPZZXV1cjPj4+nGapHA4HXn/9ddx4440wmTyDhwsvvNDj54ULF+LJJ5/EqlWrAgb7Dz74IBYsWKD+bDab0a1bN+Tm5iItLU2XNhM1RpIkCIKA3NzcVv9FHldTj9RSB5LijejQoQNqrHakljhgMog+9UAotrSl45BaNx6LFAt4HFIs4HHYviQkJAS1XljBfjjy8/NhMBhw/Phxj+XHjx9Hz549ddnHZ599hqKiItxyyy1NrisIAtLT01FcXBxwnfj4eL83IkRR5B8VtRhBENrGMad5H6IowmAwuN6TeznFtjZzHFKrx2ORYgGPQ4oFPA7bj2B/x1E7EhISEjBp0iR8+OGH6rKKigqsXbsW06ZNU5f9/PPP+Prrr0Pax7JlyzBhwgT07dvXY7kkSSgvL/dYtmPHDhw9ehTDhw8PaV9E1DyyWqDP/S+r8RMRERER6SZqPftwj4EfP348br31VowZMwZLly5Fr169cPPNN6vrPP/889i0aRN27doFACgoKMBPP/2EsrIyAMB3332HyspK9O/fH/3791dfd+rUKfzvf//Da6+95rNfp9OJc889F1dccQUGDRqEgoICPPfcc7jgggtw5ZVXtsh7J2rvlKBeFDyr8TPWJyIiIiIKX1RzPEaOHImtW7ciMTERX3zxBWbMmIHvvvvOYwzCsGHDcP7556s/Hzx4EK+++io+/fRTXHbZZdi6dSteffVV/Pzzzx7b3rlzJy6//HLMmjXLZ78mkwmbN29GTk4OVq9ejdOnT+P555/HF198AaMxqvc/iNoNtWff3aMvCtrnGPETEREREYVDkIO8ql60aFHQG128eHE4bWo1zGYz0tPTUVVVxQJ91CIkSUJxcTE6dOjQ6sdjFVXWYtvhUmQkxeHc/p1gd0pYs+MEAGDqsG5qjz/FnrZ0HFLrxmORYgGPQ4oFPA7bl2Dj0KC7sTdt2qQ+ttls+Pbbb5GSkqKmzu/duxfV1dUYP358uG0nonagoWffncbv8Zzc0OVPRERERETNFnSw/9VXX6mPFyxYgPz8fCxZsgSpqamAe66/O++8Ex07doxMS4moTVHG7CsxvaAJ7pnFT0REREQUnpByPD744AM888wzaqAPAKmpqXjmmWfwwQcf6Nk+ImqjlHi+oUBfw3OsyE9EREREFJ6Qgv3S0lJUVFT4LC8vL0dpaake7SKiNk726tkX2bNPRERERKSbkIL96dOnY86cOdiwYQNqa2tRW1uLDRs2YM6cOZgxY4b+rSSiNkfyGrMPTeDPWJ+IiIiIKDwhBftLly5F3759MXHiRCQnJyM5ORkTJ05E//79sXTpUv1bSURtjnfPPjRF+jj1HhERERFReEKaVF4QBLzzzjt4+umnsXfvXgBA//79kZeXh8rKSr3bSERtkBLPix49+wIgyxyzT0REREQUppCC/czMTMiyjLy8POTl5fl9joioMWo1fs0yNe7nVwgRERERUVhCSuMPxGq1IiEhQc9NElEb1ZDGr+nZd4f+EoN9IiIiIqKwNKtn//HHH/f7GAAkScLWrVtx5pln6tc6ImqzGtL4G5Ypj5kdREREREQUnmYF+x988IHfxwBgMpnQo0cPLF++XL/WEVGbpYTzgveYfWbxExERERGFrVnB/tatWwEAU6dOxerVqyPVJiJqByR/1fjZs09EREREpIuQxuz/73//w6lTp9SfT548iaeeegoff/yxnm0jojZMCeh9qvFrbgQQEREREVFoQqrG/9JLL2HHjh148cUXYbPZMGHCBDidTpSWluJvf/sb7rrrLv1bSkRtihLPexbo83yOiIiIiIhCE1LP/nPPPYcFCxYAANavXw+DwYCDBw9i1apVePHFF/VuIxG1Qf7S+JVefgb7REREREThCSnYP3r0KLp27QoAWLduHS677DIYDAacffbZKCgo0LuNRNQGNVTj16bxu59jiT4iIiIiorCEFOz37NkTn3zyCWpqavDuu+/iwgsvBAAcPnwYPXv21LuNRNQGKWP2NR37DdX4GesTEREREYUlpGB/0aJFmDt3LjIyMtCpUydMnjwZALBs2TLMnz9f7zYSURsk+RuzLyjPMdonIiIiIgpHSAX65s6di3HjxuHEiRMYOXIkDAYDAGDSpElqLz8RUWNkjtknIiIiIoqYkIJ9AMjPz0d+fr7HsunTp+vRJiJqB5R4XjtmX32O0T4RERERUVhCSuMnIgoXq/ETEREREUUOg30iigq5kTH7rMZPRERERBQeBvtEFBVKqr6o6dlXAn+JsT4RERERUVgY7BNRVDSk8fvp2WcePxERERFRWIIu0Ld48eKgN7po0aJQ20NE7YQSz2sL9AngmH0iImr7JFmGzSEhwWSIdlOIqA0LOtj/7LPPgt4og30iaoo6Zl+zTGTPPhERtQO7CspxoqwGY/t3QnpSXLSbQ0RtVNDB/qZNmyLbEiJqV2Q/1fg5Zp+IiNqDUrMVAFBjtTPYJ6KI4Zh9IooKf2P2G4r1MdonIqK2ye6UYLU7Ac25kIgoEoLu2fenuLgYBQUFcDgcHsvPOeeccNtFRG2cvzH7YM8+ERG1cZY6m/qY5zsiiqSQgv2TJ09i7ty52LBhg9/nOd6WiJoi+Unj55h9IiJq6yx1dvWxxGifiCIopDT+u+++G507d8bx48cBACUlJVi1ahV69eqF559/Xu82ElEbpFzeaIN9Ba99iIioraq2aoJ93twmoggKqWf/m2++wU8//YSuXbsCADIzM3HxxRcjOzsbN9xwA37729/q3U4iamOU3nvRY8y+8rjpix+nJKOwshY5qQmI59RFRETUSmh79pnJRkSRFFLPfklJiRroZ2Vlobi4GAAwePBgHDlyRN8WElGbpE69pwn2m1ONv7CiFjuOlmH/6aqItZGIiEhvHmn8jPWJKILCrsY/dOhQLFmyBNXV1Vi6dCny8/P1aRkRtWnhjtlXKhnb3P8SERHFOqvdCbtTUn9mGj8RRVJIafyXXXaZ+njx4sW45JJL8OijjyIhIQErVqxo1rZ27tyJpUuXoqioCIMHD8Yf/vAHZGRkNPqa/fv346WXXsLevXvx2GOPYfDgwR7Pv/jii/j88889lnXv3h0vvPBC2PsmIn34q8av9OwHc+2jXCDxQomIiFoLbSV+sEAfEUVYSD37K1euVB+PGTMGBQUF2LJlC44fP47Zs2cHvZ3Nmzdj9OjRkCQJM2bMwBdffIFx48ahrq4u4Gsee+wxzJgxAwaDAatWrUJZWZnPOjt37kRlZSXuuOMO9b+rr7467H0TkX5k97h8bX0+dcR+EAG8032B5OSFEhERtRLaFH7whjURRVhIPfveUlJSMHLkyGa/7sEHH8RFF12EJUuWAO6Mga5du2LZsmX43e9+5/c1N9xwAx544AGcPHkSTzzxRMBtd+rUCdOnT9d130SkH/9j9t3PBfF6pyR5bIeIiCjWKcG+KLjG6/N+NRFFUsjB/qpVq7Bx40aUl5f7PPfvf/+7yddbrVZ88803WLZsmbosIyMD559/PlavXh0w4O7SpUtQ7duxYweuuuoqpKenY/z48bjuuusgimJY+yYi/UhqNf6GZQ0F+pq++lFSH9krQkRErYUy7V5aYhwqa21M4yeiiAop2F+0aBGeeOIJjB8/HpmZmSHtuKCgAE6nU63qr+jWrRvWrVsX0jYVJpMJkydPxoQJE3Dy5Encf//9eOedd7Bq1SoIghDyvuvr61FfX6/+bDabAQCSJEGSpICvI9KLJEmQZblNHG9Op9Izr3k/7sdOZ9N/U3b3Og6ns018Hq1JWzoOqXXjsUixINjjUJZlmGvrIUkyUhKNKK+2wslrSNIJvw/bl2B/zyEF+y+//DK++OILTJo0KZSXAwBsNleBkqSkJI/lSUlJ6nOh+tvf/oa0tDT156lTp2Lo0KH4+OOPccUVV4S878ceewyPPPKIz/KSkhJYrdaw2kwUDEmSUFVVBVmW1UyV1kiWZVgsFgBAaWkJTAbXe6moqIPFUo1EwYbi4sa/xMrLzbBY6uGsN6C42NAi7SaXtnIcUuvHY5FiQbDHYZ3NicoqM0RBgC1JhsVSjXLYUJzEWWUofPw+bF+U6+imhBTsOxwOjB49OpSXqpSq997DAMrKysKuiK8N9AFg8ODB6N69O7Zt24Yrrrgi5H0/+OCDWLBggfqz2WxGt27dkJub67NPokiQJAmCICA3N7dVf5E7JRmpJ11ZMh06dFCDfatYjdQaAWnpiejQIbfRbaSaBVhRh8Q4Izp06NAi7SaXtnIcUuvHY5FiQbDHYVFlLVJT7UhPikNuTgoKa4M73xEFg9+H7UtCQkJQ64UU7I8fPx5r1qzxmIKvufLy8pCdnY0dO3bgkksuUZf//PPPGDZsWMjb9UeWZVRWVsJkMoW17/j4eMTHx/ssF0WRf1TUYgRBaPXHnCRLavuNhob3YlDfl9Dk+5Pdf3vQ/Estpy0ch9Q28FikWBDMcVhd74QoikhLiofRYAj6fBeKypp6OJwyctKCCwiobeD3YfsR7O84pGC/V69emDt3Lm688Ub07t3bo5o2ANx9991NbkMQBFx//fV4+eWXcfvttyM7Oxtr1qzBTz/9hGeffVZd71//+hf27t2LF154Iai22e12vP7667j55pvVdv39739HVVWVenMi2H0TUWRoi+r5q8Yf1DbUAn36to2IiCgSlEr8KQkmiGLwBWlDseVgCRyShAvO6qpmzxFR+xNSsL969Wr07NkT33zzDb755huf54MJ9gHg0UcfxY4dO9CvXz/07dsX27dvx1//+lecd9556jo7duzApk2b1J/Xrl2LZ599Vh0j/+CDDyI7Oxtz587F3LlzYTAYsHXrVjz00EPo06cPjh8/DovFghUrVnj02gezbyKKPNEj2A/+4sfJavxERNSKKJX4UxNN6rkrEucwWZZhdxfBdTglBvtE7VhIwf6uXbt02XlKSgq+/vpr7Ny5E0VFRRg0aJDP1Hp33XUX5s2bp/7cr18/3HHHHYDXTYW+ffsC7pSGJUuWYPHixdi1axcyMzPRt29fn3ENweybiCIjUG+8EvgHc+2jBPtOdu0TEVGMk2RZM+2eCWZ3L38kpt7T3kDg1H5E7VtIwb7ezjrrrKCf69q1q8+Uef5kZ2djwoQJYe2biHzJsow9JyuRnhSHvKzkkLcBAKJX2r6gPt/0NrQXM7Is+wwnIiIiihU2R8MMMwlxRlRbHUCQ57vm0sb3jPWJ2reQg/3y8nK89NJL2LNnD2RZxsCBA3HbbbchKytL3xYSUUwx19lxtNgCo0EIOdhXAnXvAF35UW5GGr+yPQODfSIiilHKec3gvsut1NZyRiDa1/bmB3M+JaK2K6RBPFu2bEGvXr3wwgsvoKqqChaLBS+88AJ69+6NLVu26N9KIooZ9XbXfMAOp4zaekdI21CuPUSfYL85Y/YlzWNezBARUexSAnDltKec/yKRZq8N8FnXhqh9C6lnf8GCBbjuuuvw7LPPwmh0bcLhcOCee+7BH//4R2zYsEHvdhJRjFCK/gCAudaGpPjmf4009Ox7Lld79oPahv/HREREsUbpwRfcA9YaatTofwJzMtgnIreQgv0tW7Zg5cqVaqAPAEajEQ899FBQ4+mJqPWya8Ydmuts6JSZ1PyNuK89vBPvgy3Qp+3VB9MUiYgoximnqYY0fiWTTf99aU+RPD0StW8hpfGnpKTg9OnTPstPnz6NlJQUPdpFRDHKo2ffXU24ucIds++dts9qw0REFMtkr4w25d9ITb2nYM8+UfsWUrA/a9YsXHPNNfjqq69gNpthNpuxZs0aXHPNNZg1a5b+rSSimGFzONXH5lpbSNsIOGYfwfXsewf3kShwREREpBflJrVy3jM0o0ZNc3HqPSJShBTsP/300xg+fDguuugipKenIz09HVOmTMGIESPwzDPP6N9KIooZ2jR+q93pEfwHq6kx+01d/LBnn4iIWhPvm9xCkMPWQuE5Na3+2yei1iOkMfspKSl4/fXX8fjjj2PPnj0QBAH9+/dHly5d9G8hEcUU7VzBAGCutSMnzdCsbQTq2Q+2YJF3Tz7TFImIKJZ53+RWxuzDfQPbIOo3fax2zD4z34jat5CCfUWXLl0Y4BO1M8qYfYMowCnJMNfZkJOW0KxtyPDfs48gq/F79+TzWoaIiGKZEuwrQb42tnfd4NYx2Pfo2ecJkqg9CzrYX7x4MQBg0aJF6uNAFi1aFH7LiCgmKWn8WSnxKDFbYa5rGLfvlGQcL61GdmoCUhNNHq+zOZywOSSkJJjU4Ny7QF/w1fjlRn8mIiKKJd4ZbdrMNr2z0zzG7PP0SNSuBR3sf/bZZ4A7kFceB8Jgn6jtsjldY/SzUxNcwX5tQ0X+A6ercLjIDIMoYEiPbHTKcE3LV15txbZDpbA7JYzu06HJMftyE337PmP22XNBREQxTPIq0Cd4BPuR2RfYs0/U7gUd7G/atEl9vHr1amRkZPhdr7KyUp+WEVHMkWQZDqfrwiEn1ZW6X221wylJkGSgoNQCuIPxnw6Xon9eBuJNBuw8Vqb2auwqKEfvzumAjtX4WaCPiIhimdPPTW5lOJze5zBW4yciRUhj9jMzMwPeKWzsOSJq3RzOhqo/KYkmmAwi7E4Jljo7yqvr4XDKSI43Ijs1AQWl1dh7suHmX8f0RJRX16Om3oGjxa6bAt49+2Ko1fj5nUNERDFM9hqzj2bMQNNc2lMkY32i9i2kqfcCsVqtSEhoXqEuImo9lEr8RoMAURCQnhQHAKiqtakBfM+OaTgzPwsDu2aqrzujYyqGn5GD/nkZ6vrQ9OSrgh2zL8tAVSnw9ZvAsd28mCEiopjmbxYa5bHuwT7T+InIrVk9+48//rjfxwAgSRK2bt2KM888U7/WEVFMUYrzxRlcU+2lJZlQarHiUKEZVrsT8UYReVnJAIAeHVKRkRwHh1NWq/V3zU7G8dJqVCrBfoCefbgvULwL+CkkSQaO7gJqqoBd30Ia2AvI6ROR90xERBQup9eYfe3jiKbxM9gnateaFex/8MEHfh8DgMlkQo8ePbB8+XL9WkdEMcXmcBXnMxldSUGpia6efavdtbxHh1SPuYIzkuM9Xi8IAgZ2y8T3+4oAf2P2NT83NhGR0+EECo+6fpCckL5+G+jzJ0A0hP8miYiIdKam8WtObJHq2ZdZjZ+I3JoV7G/duhUAMHXqVKxevTpSbSKiGGV3j9k3GVzBvpLGD3ehoW45KU1uIyM5Ht2yk3G8rEa9aaDwmXc4QM++s+goYKsFDK79S0UFwOb/AedMD+2NERERRVDDLDSann0xuKFrzd9Xw2Om8RO1byGN2WegT9Q+KWP249xBenK8Ue3J75aTgjhjcD3rA7tlYmDXTPTulO71THBTEUlHdrsedOwODDwHEgRg7RtARVEz3xEREVHkKec0bYE+5aF30dlwabfHNH6i9i2kYH/Hjh249957fZbfe++92LFjhx7tIqIYpPbsu4N9QRDQJSsZiXEGnNEhNejtGEQRPTqkIineM7nIp2c/AOcxd7DfqSeQPxBS516AvR747N/NfEdERESR15DG7ztmX+/ed6bxE5EipGD/rrvuwmWXXeaz/NJLL8Xdd9+tR7uIKAapBfo0PfiD87Mw6cw8JMSFNJOnB48x+4EuUEpOwFlVBggGoEM+IIqQxs1yjdc/9DNQejLsdhAREelJ8jdmX1TG7EdmX2AaP1G7F1Kwv3nzZgwbNsxn+bBhw7B582Y92kVEMUhJ41fG7EeCYCkHdm2EvO0rVxE+yem5wt4fXWn7OXkwJrgKAEqp2UDPs1zP7/4+Ym0jIiIKhVJxX3tTW3kYyan39K70T0StS0hX7B07dsSWLVt8lm/evBk5OTl6tIuIYpB3NX7dSU6I274EjuyAvPpl4N/3AI9fB3z1BiC5bjRg32Y4IQKdesAoutohyzIw6FzX878y2Cciotiijtlvkan3/D8movYnpCv2efPm4aabbsInn3yCyspKVFRU4L///S9uuukmzJs3T/9WElFM8K7Gr7tfvgUspYAxDlL3M4G4RMBmBb77EHj/SaC8EDixH04IQMee6k0HpywD/c8GBBEoOspUfiIiiiktOfUe0/iJSBHSINs///nPOHnyJGbOnAnJ3dsmiiJuvPFG/OUvf9G7jUQUI+xe1fj13bgN+PotiMiAs89wyJdeAsSJwK7vgP8+D+zZBBzdBQBwZncFEpPVmw6SBCApDTjjLNe4/d3fA+ddqX8biYiIQuBUgn2xBabeYzV+InIL6YrdZDJh2bJlKCgowGeffYZVq1ahoKAAy5Ytg8lk0r+VRBQTvKvx62rraqCqBEhIBnoOdl38iAbgrAnAvEeAhBSgrhoAIOX1dbVDCfaVi5mBTOUnIqLYo5ymBD9p/HpPvSfJDPaJyCWs8tl5eXnIy8vTrzVEFLOckqxekGir8evCWgNseB8AIA44BzCYPFMPuw8EbnkcePNRwFwOZ9d+AACj2rPvXrf/aNf0e0VHgbJTQHYXfdtJREQUAuU85ZnG7/pX71R7zzR+XTdNRK1MyN1z69atw/XXX4+xY8eqy1544QVUVVXp1TYiiiFKrz4iMWZ/48euXvucrhB6DAT89Ubk5AG//Rdwz0uQkjJc7TB69ewnu1P5wd59IiKKHQ1T7/mm8etfjd93v0TUPoV0xf7+++9jxowZSE1NxfffN1xQ19bW4sknn9SzfUQUI9RK/HoH+vV1wA+fuh5fcB0EsZHtG01ASgackmehQI+LGSWVn1PwERFRjFBOU9ox+4JaoE/vfXHqPSJyCemqffHixXj33Xfx4osveiyfOXMmXn/9db3aRkQxJGLF+UpPAA4bkJwB9Dtbc/ET+AJFKXRk8k7jhzuVXxCBwiNA2Wl920pERBQCvz37EarG72QaPxG5hXTVvn//fkyePBnwKjTSsWNHFBYW6tc6IooZSrCve3E+ZZq83K6AIKgXP41doCi1A4wGP70iyWlAz8Gux+zdJyKiGKDclBb8jNnXu/dde/5kGj9R+xbSVXtOTg4OHToEeAX769evR/fu3fVrHRHFDJvTM3VeN6UnXP/muIp9Kt8oga5PZFlWn/Mp0KcYxKr8REQUOxrr2de9QJ/Enn0icgnpqv3GG2/E7bffjl27dkEQBJSVleHNN9/ELbfcgvnz5+vfSiKKOrt7zL7ulfhLT7n+zXYH+0pPR4ArFO1yv2P2AaD/Oe5U/sNM5ScioqhTx+z7KdDnjGA1fvbsE7VvIU2999BDD6G4uBhDhgyBJEnIycmBKIq49dZbcd999+nfSiKKOqUav/5p/J49+031dGjnIw4Y7Cup/Id3uFL5x8/St81ERETNoPbsi37G7EsBXxbWvrwfE1H7E1KwbzQasXTpUjzyyCPYvn07JEnC0KFDkZeXF1IjHA4HqqurkZGREfRrbDYbysvLkZWVhbi4OL/rWK1WGAwGmEwmn+fMZjNqa2s9lplMJmRnZ4fwDojaPrVAn55p/E4nUO7uec/p6vpXmXc40Es0cxU3Om3RoHNdwf6vDPaJiCi6lPOUvzH7uqfxs0AfEbmFdNUuyzJOnTqFTp06Ydq0aRg6dCjefvttfPzxx83ezsKFC5Geno7OnTsjPz8fn3zySaOvOXbsGB544AHk5+ejc+fOHlP/Kd59912MGjUKOTk5SEtLw9ixY7Ft2zaPde6//3706NEDQ4cOVf+bNYsBAVEgtkgU6KssBpwOwBgHpOcAHj37/l+iBvuioOkV8bOyNpW/nKn8REQUPX7H7Dd2wzoMLNBHRIqQrtpfeuklLF68GHD3sE+YMAEvvPAC5s2bh3/+859Bb+fZZ5/FSy+9hG+++QbV1dW4++67MXv2bOzbty/ga9577z2kp6fj888/9/u80+nExx9/jH//+9+oqqpCeXk5+vfvj2nTpqGiosJj3enTp6OwsFD9b/369UG3nai9UdL4dR2zr6TwZ3cBRNfXUZNj9t2BvUEQNBdKflbUVuVnoT4iIooif2P2g5lqNhTa4W4M9onat5CC/eeeew4LFiwA3BX4DQYDDh48iFWrVuHFF18Mejv/+te/cMstt2DkyJEwGAxYsGABunbtiqVLlwZ8zX333YcHH3wQHTp08Pu8wWDAO++8gxEjRsBgMCAxMRF//etfUVJSgs2bN/usX1VVBYfDEXSbidortWdfzzT+MndxvpyGIUBCE3n8ykWMQRShGfro/4JmoLsqP6fgIyKiKPI3Zt8QgTH73udCxvpE7VtIV+1Hjx5F166u8bXr1q3DZZddBoPBgLPPPhsFBQVBbaOkpARHjx7FuHHjPJaPHz/eb1AejiNHjgAAOnbs6LF85cqVyMvLQ3JyMs477zz8/PPPuu6XqC1pqMavY7DvVZwPQfTsNwT7Agyaiya/qfwDRrtS+U8fBsoL9Ws3ERFRkLRTxmpvUgsRGLPvb1vs3Sdqv0Iq0NezZ0988sknuOSSS/Duu++qPfGHDx9Gz549g9pGSUkJACAnJ8djeW5uLn744YdQmuVXXV0d7rrrLkyYMAFDhw5Vlw8fPhw//PADRo4ciYqKCvz2t7/FhRdeiF27dvncFFDU19ejvr5e/dlsNgMAJEmCpHcpVSI/JEmCLMtROd7q7U5IsgyDCN32L5SchABAyu6i6dqQ1b8pf/txOJ3u5bLHZ+FwOj0uogAAiakQegyCcOQXSL9uBMbO1KXd7V00j0MiLR6LFAuaOg6dUsNznuu5Hjt0vI60O5w+23I6nepQOWq7+H3YvgT7ew4p2F+0aBHmzp0LQRAwatQoTJ48GQCwbNkyzJ8/P6htKOOUvFPoHQ4HDAZ9xgTb7XZcddVVqKqqwqpVqzyeu+2229THWVlZWLZsGXJzc/HBBx/gt7/9rd/tPfbYY3jkkUd8lpeUlMBqterSZqLGSJKEqqoqyLIMsQVP3A5JRpX75lZleRksPlF1aDqUHIcAoFxMgqO4GABQVWmGxVKP0jIZ8VKtz2uKzfWwWCwwOE0oLRFRXW2BLANFxSWI95N1kNhtMNKP/ALnz+tR1mesLu1u76J1HBJ547FIsaCp49DhlGCxWAAAZSUlaip/RY0NFosFznoDiov1OX5tjoZ9KQqLivUdgkcxid+H7Yv333kgIQX7c+fOxbhx43DixAl1vD0ATJo0CRdeeGFQ2+jSpQsAoKioyGN5UVGR+lw4lED/119/xTfffIPOnTs3un5SUhK6dOmipvz78+CDD6q1CuDu2e/WrRtyc3ORlpYWdpuJmiJJEgRBQG5ubot+kdfZHEhNrYcoCOjcyX/mS7PVWiBaqwEAWb0HAXEJAIDMGhFW1CIzMxMdOqT6vMxmqEGqWUZmWgI6dOiA9FP1cEoysrNzkBTv5yst5SLIG9+DqewEOkg1QKfgso8osGgdh0TeeCxSLGjqOLQ5nEg9bQMAdOzYQe3wMlXXI7VCQlK8MWAtquaqszmQWmiDQRTUYW85ObmIN+lYXJdiEr8P25eEhISg1gsp2AeA/Px85OfnAwBWr16NqVOnYvr06UG/Pj09HUOGDMGaNWtw1VVXAe5e/bVr13r0rJvNZtjtdmRnZwe9bYfDgWuuuQY7duzA+vXr0a1bN591ZFlWv2zhvslw7NixRochxMfHIz4+3me5KIr8o6IWIwhCix9zDsl1nMcbddyvMh1eWg7EhCR1scHg2ocMwe++ZPffnMlggCiKMBhEyJABwf/6SEkH+o0Cdv8Acec3QJde+rS/nYvGcUjkD49FigWNHodCQ0+rNnvVaDS4lwc4f4XWEve5UQAEd62AQOdHanP4fdh+BPs71uVImDZtWkivW7RoEV577TUsW7YMu3fvxm233QZJknDnnXeq6yxYsAATJkxQf66rq0NhYaE65r+8vByFhYWornb1EEqShLlz5+K7777DO++8g7i4OHVqvbq6OsA99n78+PH45JNPcOjQIaxbtw6XXnopunTpguuuuy7MT4Oo7bE7IjjtnqY4HwAY3V9eTU69506DFNVqxo0UIBrqGmqEnd8ADnv4bSciIgqS93lL0VRB2pD25d6UAKHh/MgCfTGhxmrHocIqOJwcU08tJ6q3fWbPno1XXnkFS5cuxbRp01BcXIz169d7pDKlp6d7FPH75JNPMHToUEybNg0dO3bEb37zGwwdOhT//ve/AQCVlZXYsGEDBEHApZdeiqFDh6r/ffzxx4C7h/65557DG2+8galTp2LhwoUYN24ctm3bhvT09Ch8EkSxze4+MZl0rcR/0vVvTlePxUaDu55HgJOhM1Cw39jFTK9hQHIGUGsGDv6kS/OJiIiCoVTIF7zK3RgiEIwr2zKIDcE+Y/3YcOB0FfadqsLpCt96RESREnIav16uvfZaXHvttQGff/rppz1+vvrqq3H11VcHXD8rKwuFhU1PsTVy5Ei89957zWwtUftkc0+7p2uBHzXY96zRYXD37DsC9NR7z1Ws/NvoxZLBAAyZAHz/X+DndUD/0bq8BSIioqY4lWAfntG+cv7SMxhXbiyIggBJcD1mz35sUDpOlGsqopagy5X7kiVL9NgMEUVRTb0d3+8tRGGl7x1nm5rGr2OwX+a/Z1/psXc2MoWRdj21Z6SprLghk1z/7t8K1FSF1XQiIqJgKbG2bxq/cr7TLxhXtiUIQQ5zoxaj/G4CdWYQRUJIV+6yLOPUqVPqzzNmzMBTTz2lpskTUetTUmVFZa0NJ8tqfJ7TPY3fYQfK3Rk4PmP23Rc/Tv8nw4Zg39WWoMc8duwOdOkNSE7gl2/DfQdERERB0fa2a2ljf1mn3ndt9hvT+GOL8rvhmH1qSSFdub/00ktYvHgxAMBms2HChAl44YUXMG/ePPzzn//Uu41E1AKUgN7hp4tcKdCnWxp/RSEgS67p9lKzPJ4yGJQ0/sZ79pWLpKDS+BVD3b37P68Lp/VERERB0/a2a2mDf71S7dUsAkGISAFACp2SYeEI0JlBFAkhXbk/99xz6nzz69evh8FgwMGDB7Fq1Sq8+OKLereRiFqAEuz761FXAm+jXsG+Ml4/O8/n6sfQRM9+Q/EhV1ualaZ45jjAYAQKDwOnj4T1FoiIiIIhBerZF7XBvk77Um8ssBp/rGlI42fPPrWckK7cjx49iq5dXeNs161bh8suuwwGgwFnn302CgoK9G4jEbUAh9qz73tRoATeRr3mbd23xfVvpx4+Tyn7CDSGUe3ZV8bsN6dnPykN6He26/FPa0JrOxERUTMopydR9E7j1wT7OkX7TjWNPzIFACl0ynVKoM4MokgI6cq9Z8+e+OSTT1BTU4N3330XF154IQDg8OHD6Nmzp95tJKIWoKbx+xlLptyFNhgEn+earbqyYcz88At8nlan3guYxu9ui/fUe8FeKI1wfV9h5zeArb757SciImoGKcDUe2hO3ZkgaesDMI0/tkjs2acoCCnYX7RoEebOnYuMjAx06tQJkydPBgAsW7YM8+fP17uNRNQClCDfX4+6chfau5JwSLZ9CTjtrmJ5Xfv5PK30RAQa06acI5Uq/IKaphjk/nueBWR0AOprgd3fh/YeiIiIgiRJ/tP4tcv0Csi1+2KBvtiiXKewZ59aUkjB/ty5c3H48GF888036ph9AJg0aRJ++9vf6t1GImoBSnDtb8o75QZA2Gn8DjuwZbXr8Tkz/HZzKPsIdOGjtE+5KaA0KegLJVEEhrkzCn76qtlvgYiIqDnUNP7Ggn2dOnuVgNJjzD6neosJyvWLndX4qQWFPPWe0WjEueeei7i4OJw8eRJPPfUU7HY74uPj9W8lEUWccvKRZN/AWbc0/t3fA9UVQEomMHCM31WUNH6nJPudiqhh6j33mP1QLmaGTQYEESjYDZScCOWdEBERBcXZSBq/qHOqvaQZs880/tii9uzz5gu1IE69R0SA11h97xORLj37sgxs+sz1+OxpgNHkdzWDZh9+iwV6BfvNmnpPkZYN9BnheszefSIiiiB1HL2foXANRfT0TeM3eKTxM7iMNu11FcfsU0vi1HtEBHgH+5rHsiw3BPvh9Oyf2AecOggYTMCIiwKupq0L4O/ut+R10RTyeEelUN+Oda7hBURERBGgnJ4MLTFmX80iEJpf04YiRvv7lWX27lPL4dR7RARJlj0uBrQ96toTUlgF+pRe/bPOA5LTG11VTeX3M67Nu2dfuZhp9omz93AgNQuoNQN7NzfvtUREREFSzk9CC47ZF0Wh+TVtKGK8hxr6q49EFAmceo+IfKbb0/7s8Aj2Q0zjN5cDeza5Ho+e3uTqSu+HvzR+5aJFWccQ6jzCBgMw1DWTCH7Z0MwXExERBUfNSPM3Zj+UoWhB7Ytp/LHE+/fLIn3UUjj1HhH5nHScHj37rufCSuH/aQ0gOYH8gUCnHk2ubjCIHvtWSLLcUNVYTeNveK7Z+p/t+vfoLlf7iIiIdNbYmH29i+g1TL2nHSKgy6YpDD61kDj9HrUQYygvmjt3LsaNG4cTJ05g5MiRHlPvKb38RNR6eM9p7zl+37MnvdmcDmDbl67Ho6YG9RKj+4LIu11OP1kGYU0t1PkMID4JqK8FTh8G8vo0fxtERESN0E6H503v6fFkTc8+q/HHDu/fL4v0UUsJubR2fn6+OvWeYvr06Zx6j6gV8knj91M1Vultb7Z9WwBLuWuc/oBzgnqJEsh73wmX/NQPUHpKnKFczIgGoMeZrsdHfmn+64mIiJqgTa33pneBPqcmi4Bp/LHDZ0pj9uxTCwmpZx8AysvL8dJLL2HPnj2QZRkDBw7EbbfdhqysLH1bSEQR11gav3JCMoZanG/z565/h18YcLo9b2qBPsl/u7SFAoVwe0V6Dgb2bQYO7wTGXRHaNoiIiAKQgxqzr8++lNOmqKnGz8rv0ef9O/DuZCGKlJC66rZs2YJevXrhhRdeQFVVFSwWC1544QX07t0bW7Zs0b+VRBRR3icdbRX8hgA7hK+LkuOu8fCCCIycEvTLlH153/n21ztiEEIs0KfoOdj1b8EeTsFHRES6k4Kqxq93Gn8YBWxJdz7BPm/AUAsJKdhfsGABrrvuOhw5cgQrV67Exx9/jCNHjuDaa6/FH//4R/1bSUQR5TNmX68CfVu+cP3bbxSQnhP0y5rTsx92JeMO+a4hBg4bcGJfaNsgIiIKQJ0Oz2+w7/pXr1R7SZPGr+yNafzR532N4m9qYaJICCmNf8uWLVi5ciWMxoaXG41GPPTQQ+jataue7SOiFuCbxq+Zes8ZYs++uRz4ea3rcZCF+RRKMO9TvdbdLtEjjd/1b8i9IoLg6t3f9Z1r3L4yhp+IiEgHSrBt8JPHH6mp9wRNGj87kaPPt0AffynUMkLq2U9JScHp06d9lp8+fRopKSl6tIuIWpBPgT6nDj37X7wC2KxAXl+g51nNeqmaxu8T7PvODKD0lIRUoE+htO/wztC3QURE5IdTDcB9n9N7ejzlXr1BENSbC43dSNh+pBQ/7Cti73+Eccw+RUtIwf6sWbNwzTXX4KuvvoLZbIbZbMaaNWtwzTXXYNasWfq3kogiSjnpNKTPa6vxB+6RCOjQz8Cv37nG6k+/HWhmVoBSDNA7zU3y0xb1YiacKyVl3P7JA0B9XejbISIi8qLE0f7G7Os9PZ5HGn8T25ZkGacralFRU486m1OX/ZN/PtX42bNPLSSkYP/pp5/G8OHDcdFFFyE9PR3p6emYMmUKRowYgWeeeUb/VhJRRNndPfkJJtfQHO38r80u0Ge3Aatecj0++2LXXPbNZDQ03rOvTeMXwy3QBwCZHYH0XEByAgW7w9gQERGRJ8lPVppClxvW2n1psgiEJs6P2t5lzvseWd6/X47Zb3ntNXsl5DT+119/HSdOnMBXX32FtWvX4sSJE3j99deRnJysfyuJKKLsTtcd/QSTAQDg1KbxK73+wfbsf78SKD8NpGQCk+aE1B4lmPeZJSASBfrgvio6w53Kf+SX0LdDRETkRWokjb9hXL1Owb5yU1wQ1OJ/gW4kaOv1MK28efadqsSvx8uDXt/79+tdK4kia9uhEqzbdapdfu4hFehLTExEXV0dunTpgi5duujfKiJqUcoY/Xh3sK+9w6/0riu97Y0qLwS+/dD1eMpNQEJSSO0xBijQp01PVCgXT2HPI9zzLGD7WuDgduCiG8PbFhERkZu/c5dC7zH7SkxpEAXNtgME+w7fYrzUNEmWcajQDADo0zkdcUZDk69RrlFMBhF2pxT+NQs1S4m5DpIMlFms6JQR2rVpaxVSz35qaipKS0v1bw0RRYVypzMhzrdnX7nb7+8ixcePn7mmsOs5GDhzXMjtUW4s+Fbj9zNmX69ekd7DXDUGiguAslPhbYuIiMhNOT01NvWe/mn8QpPD3Bx+zvXUNI+6RkHeJFF+L3FG9zBFft4txuGU1JtplTX10W5Oiwsp2L/pppvwyCOPwG63698iImpxyklHTePXnMiUx02m8dttwM5vXI/Pvdx/vmKQDE2l8Qs6p/EDQFJqQ6G+PT+Gty0iIiI3JZBvrBq/XuOJndo0frHxbTONPzT+rpGCfY2aQclMihZT72goPlle3f6C/ZDS+L/77jt8//33eOedd9CnTx/ExcV5PL9+/Xq92kdELcA72PdXoK/JNP69PwJ11UBaDtBrSFjtMQRI449YgT7FgHOAwzuAPT8A42bqsEEiImrv1DR+fz37os5p/FD2pRnmFkywz7TyoDk9rpGCu0mi3PBRUv5ZELHlaIerVNXa4JTk5s0w1cqFFOxPmjQJkyZN0r81RNTiJFlWLzIS4lxfCZ4paq4vySa/GLevdf07bDIgNj1+rTGB0viVCybtzADawD/sL/D+Z7tmEjh5AKgqBdJzQt8WERGR1zh6bxEr0CcGk8bPnv1QhNSz75XGzzH7LcemCfZlGTDX2pCZEh/VNrWkkIL9xYsX698SIooK7Qk+XpPGL8syBEEIrme/osjVIw4BGHp+2G1S0/ilIKrxa66dXKmKYQT7qVlAt37A8b2uTIXRl4S+LSIiIq9x9N7UujO6jdl3/SsKgtqzH0w1/vZYpTxU2s8z2Js0yuWMv+ssiiybJo0f7lT+9hTsN2vMvtPpxKZNmwI+v2nTJjidzoDPE1HssWt67k0Gz15yaALuRnvMlV79M84CMjuE3SaD2NAboT2RaqcUUmgf69IzMnCM6989gb/riIiIgtX41Hue6+ixH2W7rMYfGaH07Cu/A5Om44RDJ1pGvd3zRlZ7K9LXrGD/7bffxr///e+Azy9ZsgTvvPOOHu0iohainOCNouCRHq+chJos0Cc5gZ+/dj0efoEubdJmEWgzD/z17AtB9F40S/9zXP8e2w3UVIW/PSIiatfUIWiNjtnXIdjXnAMNHmn8/rftkcbPMeRBCynY10y9p9ZSYDZFi7C7O6Izklw15tpbkb5mBfv//ve/cfvttwd8/o477mj0ZgARxR7lZK8E2EZ3777TPQ+sOtYwUBr/oZ8BcxmQmAL0H61Lm7Tph54nVf/TACoXNIGKEDVLZgeg8xmALAF7N4e/PSIiateUU5O/lG1RxzR+z559QVMPwP/6rMYfmnCq8RtEAUZ3x4qd2RQtQhmzn5OWAFFwHfc11vYzo1yzgv09e/ZgwIABAZ8fMGAA9uzZo0e7iKiFKCd7JbVM6XlwSLJHldmAafw/uVP4z5oAGE26tUs5Gfo7qXq3RdeK/HBX5QdT+YmIKHz+ZpJR6Hn+0sadrqn3lOVNj9l3MvAMmvbaKNibNOqMDKKgmXGIN1haQr3d1bOfYDIgPck1Vr899e43q0BfbW0tjMbALzEYDKitrW1WAywWC1auXImioiIMHjwYU6ZMafI1drsd//3vf7F3715cf/316N69e0jbDWXfRLFEj+Iu3j37BoMIOCQ4JQlOybVMFPxPGQRzGbDP3fs9/MKw2uHNIAqwOz17Gxqq8XsH++7n9Rr/NmAM8PVbwOGdgLUGSEjWZ7tERNSuaFPo/d0zVwJyPTLTZK9zZNNp/A3LWaAveP4yDpuirTlkMoiw2p2sk9BClNoUcSYDMlPiUFFTj8qaenTLSYl201pEs3r2+/bti/Xr1wd8fv369ejXr1/Q2ztx4gQGDx6Mf/3rXzh06BBuuukmXHnllQG/lOCuG9CrVy+89NJL+POf/4wjR46EtN1Q9k0USwpKq/HljhMos1jD2o46Zl9J41cq4Ttl9TntWH4P2750jdnPHwh09L3pFo6GO99B9OzrOOYRAJDbFcjsCEgO4NQhfbZJRETtjvYedGNp/HpcfyoBpbKbJtP4NVXKmcbv4nBKTf4uwknjF0XAYPA/4xBFhpLGH2cUkZnc/nr2mxXsz507F/fccw/279/v89z+/fuxYMECzJ07N+jtLVy4ELm5udi4cSOWLFmCdevW4eOPP8aHH34Y8DX5+fn48ccfsXz58rC2G8q+iWJJSVUdnJKM4qq6sLbTkMbvOvlo57h3NlaJ32EHtn7penz2xWG1wR9tOxROzZ1xLXXMvp6VbXO6uv6tKNRvm0RE1K5IHj37kR2zr2QHKNv0nZrWk7YaPCvDA1a7E2t/OYmfDpc2ul441fgNoqgOU2zJGyzB3MRoq2zuAn1xRoM65V5NvcNnSr62qlnB/t13340uXbpg0KBBmDlzJh544AEsXLgQM2fOxKBBg9C1a1f84Q9/CGpbTqcTK1euxLx582Ayucb59uvXD+PHj8cHH3wQ8HVjx45F586dw9puqPsmiiXKGKTqMIuMKCcbk9Gdxq/27EsNlfj9FefbswmoqQRSMoEB+hTm09K2w6etXu1panqhkGR2cv1bzmCfiIhC03Qaf+O9783bl7IfzzR++Dk/SrLsE7S212BQUV1nh1OSm+z1DatnXxAaevZbKI3fanNg7S8nsf1IWYvsL5Y4pYYs1TijiDijAcnxriHplTW2KLeuZTRrzH58fDy+/PJLPPfcc3jrrbewevVqCIKAPn364G9/+xvuvvtuxMXFBbWtgoIC1NbWom/fvh7L+/btix9//LF576KZ2w113/X19aivb/gCMJvNAABJkiAxFYdagCS57sxKkoQ6mwOSJMFcawvr+LM5nJAkCaIAj3/tDidsBsFjmZaw+XMIAKQRFwGCCOj8N6BthyS5bjxYbQ4AQJxR8GqP6zNxOnX8W8zsCBGAXH4aMv++PWiPQ6Jo4rFIsaCx49DhPi+JggBZ9g2oZdn1vNPPeba5HO7zJSD6tMnhlKC912BT1/Vc5n0zvT2xO1zXVfWSBLvDEXAIo8Pp1Hyuvp9j4NfIACQYvK5v9BLoOKysqYfd4URFtbXFvyvLLFYUlFZjYNdMxJsMLbpvuDvGJEmCIAjq556eZIKlzoYycx1yUuNbvE16CfZ32axgH+6Af+HChVi4cGEo7VJVV1cDANLT0z2WZ2RkqM9Faruh7vuxxx7DI4884rO8pKQEVmt446aJgiFJEqqqqiDLMkrKKyHLMiwAThUa1LH2/jickv/eeQClZWZYLPUwV8ooFuthMVtgsVhRUiYhwSjCYrHAKNWjuLhh+8bS48g5vheyKKI0fxik4mLd36vF7GpXSamMeKkWdTYnLBYLREFAZbnn3WmLxQxLrR0lJQJQr88Xd7yYgEwAjuITKIvA+2vNtMehGKieA1EL4LFIsaCx41A5dxlEAcV+ziX1dtfzguD/+eaoqLHBYrFAshlRXFzsukawWAAARUXFiDM2tK3W3S6jQYBTcmUgnC4sQkIUArJYUVRlVT+v46eKkBTn/7MoK3NdJwFAmWBDcWLT6eBVZjNkGSgvi4PFXAuLpQ7FpU6kivrFD4GOw8JK1/uyGkQUFzc79AvLjuNVKK+2QbDVoEtmYovuGwCqrQ5YLBbEGUWUlJQAABx1dbBYqnESNmSaWm/vvnKsNqVlf+Maycmu6tZK77iiqqpKfS5S2w113w8++CAWLFig/mw2m9GtWzfk5uYiLS0t5DYTBUu5O5mRmY2UlIYvqKTUDGQk+w9yjxSZsed0JUb1zkVumu8X7VEzkCpb0bFDNjpkJaPUZkK104K09DQkxhmRagGyMxLRoUOu+hrhR/dwlwFjkNOzTyTeKoqsRtTJ1UjLSEeHDukor65HaqodSfFGdOjQwWPdrCpAMliRmZWFDlk6Vc4XXcVGjZZSdMjNbah4ROpxmJubywCLoorHIsWCxo7DGqsdqcV2mAyiz7kL7mA/tcg1HM/f880hmOuQWiEhLSlO3VbaKRtkWUZObq5HIF9Z4zqnJhoFOCUZNgnIzMpGamJwGbptUZ1gQao7fkpNz0R2aoLf9U7XGmBx1gAA0tI8r4/8kWQZKSdcQX3HDrmohQVVdjPS0lPRoUOmbu0PdBzWwILUatcQgnCPseZKKHMiVbAjNT0DHTq0fKwkmq1ITXUgNdGkvnc5rhaFtQLiNX8nrVFCgv/j01vUgv38/HwkJibi4MGDuOiii9TlBw4caFZF/1C2G+q+4+PjER/vG1CJosiLDGoxgiDA5vS8a1trcyIr1f8xWFZtgyiKqKy1o2OGbyAsSa5jOM5ogCiKMLn/lWTXGEJRFGEyGBr2V2sBfvnW1ZazL4YQoWO/oR0CRFFEvUOCKIpIijf5/L0ZDK6/QRmCfn+LWZ0BCBBsVgh1FiAlQ5/ttiJ2pxQwpVMQBH73UUzgsUixINBxKLuXK+cpb0YjGpYLgv9pboNvBUR3AThlm0aD6B4v7nl+dMqAKMuI++ZtOOttcEy4Vj3ftlfKNQ8An+ssj/XQsJ72ccDtOiV1HZPRoF5vafenF3/HoUNqeC8yBP9FlyPE5nDtO5jPKRLs7vcebzKq+09KiIMoirA5pFZ9vAfb9qi9Q6PRiBkzZmDFihVwOFzjcA8dOoQNGzbgiiuuUNdbtWoVXnrpJV23G+y+iWKVUpxP0ViRvlr3OHerzX+amVKN33vqPVc1fnf1WG3A9/PXgMMGdOwB5A8I960E1DD1nuTR/gQ/aXURKdBnNAHpOa7H7bBI3/HSaqzZcQIny2ui3RQiolbLu2ieN23cFW5FfuXl2in+lIfetQIcThk4uR+mikIYayuB4gL1eqC90s5IoNQI8kdqZoE+7xkZlOutlpp6T5l6DpprqpYgybJ6TEVrake7Ou1ew7Vjovs6st4h6TuLU4yKWs8+APy///f/MHbsWEyaNAmjRo3CBx98gKlTp+Kqq65S1/n444+xadMm3HbbbQCAnTt34pNPPlFT8FesWIHvvvsO5513Hs4777ygtxvMOkSxynu6kGqr/5OSLMuoc5+w6gKcuLwr3CsFaRzOhqn31HoAkhPY8j/X47Mvjmhqu/amAwBY7a72J5h8v7aUasa6FxLO6gRUlQDlp4H8/jpvPLYp1Ygra+qRp9fQCCKidkYKMGWsorGK+c3elzL1nub+vADBVcTWa9N2ux04+BNMcF9PFB6Gw6n/zDqtiTYgtdoDj8NvbjV+5RgQBNeNGGXqPWcLVePXXjPanZJH4BvR/Wo+w5aaecCnDQ5l2r2GP4o4o8FVBFp2XVsmx5ui0raWEtVgv0ePHti1axfef/99FBUV4Z///CcuvfRSj7SE6dOnY8iQIerPTqcTVqsVcXFx+NOf/gQAsFqtag99sNsNZh2iWKX07McbXent1XX+e/br7U41APbOBlA4vKbXMxoaetQdTs/p+HBwO1BRBCQkA4PP0/ldedLedIC2Z99P8SDlYkn3O7SZnYAjvwAV7a9nXzlemvpMJVnGkSIzctMTkdaOx3oSEfmjBOCB7o0LHsF+mPvyc2NBvVfvdSPBvn87UFMFo0GA7HQCRcfgsNkAtN+bu9qAVM9g3+n+7JVrqYap91qmt9uu7dlvwaDb6hHsR6dn36b27HvGdwlxRtTWO2C1ORnsR1pmZqbaa+/P5Zdf7vHzsGHDMGzYsLC3G+w6RLHI6r5TmZ2WgFPltai1OeCUJJ9pYmo1vfn+TlzaeXaVIN+zZ1/2WIbN7l79YecDcZGdrkR700Hbfr9p/GIE0vihjNtvn2n8SrDfVFpncVUd9p2qQpmlHmf3ab2FboiIIkHtbW8kE84guorkhZ/G7yfYVzPfNNuWJNi3rQUAmIZOgLxnK1Bnh6NgL9BpTFhtaM20afX1AYY+wivAD+Z35vS6CWNS0/hbqme/4X211NABeF13RmuISL3as+957ZhgMqjBflvHbmyiVqje7vrSTEuMU08aNX5S+WvrG5Y5Jdnny1Z7p9Vo8OzFd0qSelIwGgSg7DRw8CcAAjByakTel5Zvz77rvSTG+UnjD9BzEbasTq5/22Gwr5ykm+oFUG4KeA8tISIizZj9RoqiBRpXr8e+BH81bfZtgaOiBDDGwXTmOBh7uOrvOA79Etb+Wztn0D372jHwwafxK8G+cp3Vcj370Umnr4+Bnn17gJ595Voy0BDXtiTqPftE1HxqGr/JgJQEEypq6mGx2pGW5JlGXed1x9Jqc8KU2PCFpwT/BrGhArDSo+6QZPXEZxTFhrH6fYYD2Z0j+fbUNsF9UpVkGfXuL+zG0vjD7RXxoQT77SyNvzlFdRzqem2/yA0RUXMF07Pvek5W071D5d2DDACGmirg0G7IW3cCBhtgjANKT8KBOKDHmTAlJ8PZcxCwZw8cR/cATidgaJkx3bFG2+tttTshy7LHMAuFR89+EL+zhloKynVWCxfoczbv5oRePIL9KBXCs/kp0AdNkb7Gbuq0FQz2iVqhhjH7BqQkuoJ9fxX5tT37cBe5S01sGJvkUIP5hpNZQ+GYhp59g2RzVeEHgFHTIvGWfGhvOijvVxB8784ikmn8me5gv9YMWGtctQragfpmpN7Z3cdQe6/iTETkj7Y4WyBKcB7uKazhxoKr9x4bV0IoqAWEeDjlEwAaZlexm84AzhgCo0GAKa8XYEqAo74KKNgN9BwcXkPCVGdz4FiJBd1zU/1m80WKdyZbvUPy28HgPWY/0E0B7/WVTgyj2rMf+QDY7pQ8jquWPFfHQs++0oY4k++YfbBnn4hilTIGKd5kQGqCK3j3V6TPJ9j36ul3eE27B23hGE3PvmHfZlewm9kR6N10zQw9GDQ3HbTF+fydUBum3tO5EfGJQHIGUFPpSuXv0kvnHcQm7Qm6qV4AtWe/BccBEhG1Fk1NvQcds9OUYQCiwwa89yTgtENEDyCnG+ShFwCZya6pc+022O0dgbhEGA0iHEYj0KkHHAUlwJ4fox7sHy224EixBQDQPy+zxfbrHQhbbY4mg324b7IYGvn9Kr9XZR3tdMb+6i3pyXuInbMFg25rM64lIkHWZCkG7NlvB2P2GewTtTKSJMPukCCKIuJNIlKUYN9Pz75yxzI1wQSL1e6TrmT3mnYP2iBbkl0BnCTB+NMXridHTfOc0yeC1Kn3ZLmhOJ+fafcQyTR+uFP521mw35yiOkqwL8uuY8bQyLhUIqL2xumVwu2PXtlpas9+4WHAaQfScyFM/A0gx0PqmQNkJqnr2n89BdQ7YDKIcBpkoNMZcBRsA/ZsAqbe3GLnen+UG8619S0biGkLFjucst8Ub39Bq+vcF3i7yktEr559uHv3G3ttuLSV+NHC6fTeHQeSLDd600tv2usXn2r87uvJOnvb79lngT6iVkYZe+VKaXel8QNATb3DJ7VMOVFlpboq53ufuPz17GtPQjaHBBQfg6H8FBCfBAy/MKLvTUu58+1wyupNi0Q/lfihSY3TPY0fmlT+djRu3/sE3VjRKO3JNFppekREsUpuYuo97XPhnsOUawDhxD7Xgr4jIKak+d22Q3Oz32AQgNyucJgSAEsZcOpgWO0IlzLO2trCKdZKhlqKeyo2f72+/n5HTfVaKwX9lEBXEISGIn0RzoqzeQX7zhbMwvOe8rklrhG01yvKezcaBJ+bDMr1pMMpt/lrFwb7RDHGNZVZZcAAS/nyinenJCWYDOr49pr6ht59JUA2iAJS3fOfe5841TH7Hj37DV+ITkkGDm6HERIwaiqQkISWom2HMhzB37R70F4oRapnHwDKT+u/7Rjlc1Ookc9VO+aQqfxERJ68U7j9MeiWxu/6n+HEXteC3sM19QA8t61m9hlFV3afwQh7pz6uJ/dtCasd4VJSz1tyPLXrxrbrcbI7Y7Kpnv2GKYIb/71JXmP2oeO4fXOtrdFg1adnv4WK6cqawsotte+Dp6uw+ufjqKypBzTHUZyfgpNGg6j+/ryLWbc1DPaJYszuExU4VGhGidnq93l/VemVVH7t9HtKgJwUZ1TXDZzG7zlNj3pCKjsFVBS6bgaMvkSvtxgUbYaBMkShyTT+SPTst8Pp95pzN97h0bPPivzUvjklGTuOluF0RW20m0IxQokDGyvgJuhUd0aSZaCmEqKlDDAYgZ6D/W7blVLteuwKetzD93LzXQuP7w2vIWFSOjXqHVKLjfXW9ninJLiuNax+UryV9QyiEPRNGu80fnhkL4Z+k7zMYsV3ewuxq6A84DreY/ZbqhdbG+ibdHivwSiqqoMsAyfKXIUobQGm3VMkmgL/ntsSBvsUFTaHEyXmurDnlG2LlN535c6kN7VnXxPsp/oZt6+mvscb1R7xYAr0QXv3+dDPrp/POg9IzQr3rTWL9qZDjRrs++/Zj1g1fgDIck8z2K6DfabxEwWjzGLFyfIaHCysinZTKEZ4VMgPQBkeH/aYfUkGigogAED3QUBcgt+aNtrvaqMoqDfX7Rnum9snD7im4IsS7XmlpQIx7VTESseCvzR+bWV9bY2jxvibElGtSxTGzQyLuzBzebX/60Vohn42DBtometutQq+UYTJqO9Ug4FiByW7tcRcBwCwqZX4/V87KtfG7NknioDdxyuw5WBJwN7r9srulNQ7wIG+vP0F+0rKWVWtTV2m9OwnxhnUINnulDzuXgcK9o2iCJjLgaKjAAQYxl6m11tsFuXEqGYzBEjjb7iYiUAjlDH7ljLAHviE2pYEqu3gj/bkzen3qL1T/gZ444sUcjAF+nRK45dkACUFECGpM+cou9UGSNrivIIgNPTsJ2VAjktyneuKj4XVllA5Jc/e/Jaqlu7UTEWc0Mgc7JJHsB9kGr/sJ41fh95updfeanf69OA3rOOZDdpiPfvKFNEmg65TDZaarfhyxwkcL632WG5zONXt19mcqLbam+zZV6bfa+naEC2NwT5FRa37D8t7arj2TtujWlVr83uXv95PsJ+TmgC4e5WUYF75jJPjTYgzGtSTjGelddf2Td49+04bsO9H1+POPYCcPB3fZfCMBs+Lo4QA8+0q58+I9OwnpbqKEwJARbH+249BynGodEIEuhsvy7LHydt7jmKi9ka5kI7GNFMUm9QU7iCm3gs321Gy2YCyUxAhA72HAwGGCDTc6HdXh1euAUQRjry+rscn9ofVllB5F5RrqWBfOc8ZDKIaGHtnucGjZ1/UBPuNB9Bqz76/YD+M7wrtZ2XxM/0yNDcEkuJd108tFew3zKJkUN9rYx0CJ8tq8O2e0x61p/wpqqqFU5JRWOk5VMo7nig1W9Vpqr2n3VMkmtizTxQxSlDAnkBP3lXQq/18edvVAn0Nf75pSXFIjDPAKclqtkRdvWcFe+XmgPbE6X3Cx4n9wMp/wfjJv4DCI67n+o3Q/40Gyeg19U98gLuzEU3jF4R2VaRPkmX1AiIpTrk48P+5el+k8O+Z2jsG++RNUqvxBzP1Xnj7kouOApITYkoGkNvVtW0/N8O9p901iIJ6c9epBPvH94XXmBB5B/stVaTP4adn3ynJPuc1bRq/2MyefX9p/OEE39prxkDBvnLNmKicz1s4jT/eZAhqzP6J8mpY6uwoqqxrdLvK+9TWqPL3c3FVnfreA47Zj+OYfaKIUe6gMtXRk3fKWIWfcfv+evYBoGOGq/dZudup3KlMdN/N9VekzyONf9dG4OWFwM9fu3r2U7OAYRfAkNNF1/fYHNqUtwSTIeDFkl4pkAEp4/bbwfR7Ns3xofQEBArivf9++fdM7Z1dE+yzJg1Bm8bf2Jh9nYrMOk8ecm2vxyA1NUsJSD3S+B2ac7+bcnPd3tldkf9EtIJ9z+ugOj+965GgvR4yiKIaoHpnFjhDSeP3V43fENx4/8Z49uzb/K6j/K6V87kzGmn8mqmUA7HZXe1q6uaOEuzX2jynm1YyAjKTXVNNl1db1evgwGn87NknihgHe/b98k4Z8xfsK8VWvIP9ThmJgPtups3hVD/bZCXY91OkT9mWSXYCa15zLex3NgzTbgYmXA107etRFb+lGTRp/IHG60NzAo1Izz4A5HZz/Xvkl8hsP4ZoU++Ui51AFwc+U/qwN5PaObt2WAv/HkgT6DWexu+5bmMKK2tRGqDekXT6sGt7PQaqy/yl8Xv37EOT4efoeIZrQflpoMbcZHv05pvG30I9+14BeUMHief+ParxN7tAX8My9fMOp2dfc2PE3EQaf7R69rXTQzdWoE+59mhseK/V7vSIG7Qp/8rrOqQnIsFkgCQ3XEMHSuNvuC5mzz6R7pQ/eO9gob1TvhyV6vqV1Z53amVZhs3h+qL2DvYzk+NhMohwOGV12pE4o6iejBK8phixOZzql2Py7m+AqhJXb/6sBTB2zFd7BQxi9L4mtGn8gabdg/ZiJlInsUFjXf8e+AmorozMPmKE37vxAT5X9uwTefKYipLBPgU59V6wQ9HMdTb8dLgU2w6XeGaOlJ8GvvsIsqUCEESI+QMatu2nHoBynJqM2mDf/X0fl9hQpycKvftKdplyI6Klel2dXjdA/A19hNf4++YX6Gv4vJXHehTog3s2Jn/ZREqnjtqz30JZR/579v2/V0luGC7R2O/bO3tBm7pfo1zPJhiRm5bgsV7Ann33daUk+2aUtCWBr56JIkSSZSjfM+zZ96R8OXbMSISl0I5amwP1dqd60rE7JfVL2jvYFwQBnTIScbysBkeLLYBmzDX8pPEr1f5TDDISNr7vWmnC1UBcPIyGGvV13kXyWpJHGn8jPfuiTnMUB5TbFeja11XTYOc3wLnRmZ2gJXieoBvvebB7feAM9qm90/4NOJ0SEGDKJ2o//FVi9+av992HLKPg6Cmg6ASctVWwF61DXHUZcPowUHrC9Xq4CuqKCUnqy/yP2XePT/eTxu9wykDXfkDpSVew329UaG88REpwmp4Uh1KLNegx+5Is48DpKqQnxaFTRlIQr/Ck9uy7P5NAFfn9jdlv6iaN5K9nP8yp8JySV4FcSfbpFXddM7oeJ2quoZySHPFrO22WYE0T1fi12RyN/b696xJop5tWevmT4o3ITXNdCysC9ewbRAFxRhE2h4Q6mzPgeq0dg31qcdqLIQb7npQvx+QEE1ISTKi22lFRU6+euBrmLTX4TQnsmJGE42U16naUO7nQnLjq3XdNyyyuNMDso5uBWrNrXPqwyUCAu8/RYDBoe/YbCfZ1mqO4UUMnu4L9n78GxlzaUKq+jbF6TJfT+N147+X8e6b2zs6effLSUKAv8DqNjtk/fRj46g04Th7CyfqOalKuXT6MOLiDHdEAdB8IKX0U0Kmvx76am8Zvd0quYP/nr6NSkV8J/NKSTCi1WNUied6zBnk7UmTGoUIzTAYxtGBfGbPvk8YfKNgXm92zr63Gbwhz6j2lJ1oQgLTEOFTV2mCus8PgZx1XUNvwjEOSEem4VhliEEzPvndxam0nl5YS7BsNAhxOWQ32tdPuJccbkRhnhCBAvdHh07NfVwPs3gj0Ho7EOCNsDhvqbA6kJ8Xp8t5jDYN9anHaO3tM4/ekHeOUmRyHaqsdlZpgvyEQ83/Sy05NgEEU1BNPYnzgnv0ySz1QX4fsXV+6Vph8LWBwre9ZRCZ6Qa22XkBigGn3AMCgU3GjRg0aB6xeDhQXuC6+uvSK3L6iqN7vdDnBpfFzjDK1d9pzWlPTcVH7oPTqNprG737KJ7265Djw+sNAnQWnkAGnYARS0oGUDNg79QVyMoHMTkDPs4DEZEi7TgI2p8c53F89AIefYN+jRku3fq6FJw8AktN1M6GFaMeYmwwi7E4JdTYHTImBAzGr3YmDha76AnanhBqrHcnu4ZDBUm7OKec99ZrJ5j1m31+BvuCm3tN2nigBqL/p/YKhZuEZDUhLNKGq1gZLnR0Zmksl5ftI+d0qQbIjwllHNodTE2g3PfWe92dQZ3P4DfaV4L5jehJOlteoPyvp/Akmg+smjHtoa3l1PUTBM4MFpSeBt/8OlJ0CEpKRcPatqMro6TvFoyy3mU4dBvvU4rQFOhor1tEeae+EZiTH43hZDSo04/br7cq0e4FTkjqkJ+J0hasivzZATlCnGHHCane6viT3b0GWvQLofAYwcIy6rjbIbiz1MNKMQfbsm4wG9S5unc3R6I2BkCUmA/1HA7u+dfV4tNFgX72hZBQ10woFSON3n7gTTAafwjlE7ZHHmP1GKk9T+6EEPYZGg30/dWcqS4AVjwB1FqBLbxwbNBeISwMMriDW3jsXSEv02I6/Gwv+xuw3VOMPMO97x65AXCJgq3Pd4O7UM4xPoHkapkszIDHOAHudBKvN6f1WPew/Velxs7mipr7Zwb7Tu2fffR3hHYh6FOgLsl6QvzT+1ERX+yxWOyRZbrSAoz82zbRyqYlxAGpgsdqQkeJ/HbiHajiczogPubNq6i4YRKHhRlKAz8n7M66tdyDDXVVfIcsNPfmdM13Bfo27ToEyfEGbzZqbloDy6nrP1PxDO4D3nwSsNYAgAtYaJGx4C8g/F1bnmcCBAuDEAeDkfmDmH4Ceg/X6SKKKBfqoxXlfDOlRKKSq1oYdR8tabD7WSHBKknpxGG8yIDPF9UVXVVuv9ljbNDcDAumY3nBGTNZ88WnnqD9VVg3s2YTUI9sQBydw/vUNufBe6fPRTOPXnvyaqsavFjWs8T/9jC6GuoY54JcNgMN/5dvWTu3ZjzM2eTdeDfbdvxuO2af2Tpu6z0wXQoAUbm8NY7/dC2qqgBUPA+YyIKcrKmcuhCUxG6LRpAaJ3lXrta/Xnjv9pfH769n3SOMXDUCeewq+4y1bpE8boCo37hu7tquqtalFibPc100VIVwHKOczg1fPvnfBOHUaPSH4An1OP8dAUpwRBlGALHuOPQ+W9npQvXHgNaZde+MEms6bSH83aWv/aPcb6FrCZ7pFP79vZao9UQBy0hIgCq5jus7mVMfra2/wdMpMgtEgICvVfdNg2xrgjb+6Av2u/YC7lwLjZyERDuD4HtStWg6seR3Y84Pr7y4KQ1gihcE+tTjv3g49egOPFltwsrxG/cJvjZRee1FwnYBTEkwwGURIMmCutbnXaTrY75CeqN491gb7giC4Tl6ShJNfrQQO/oRs1AIXzAN6D/XYhrZnP5pT72l7HRp7zwDUu8CVfqYr1M0Zg4HUbKCuGti3JXL7iaJ6v2P2A6Xxu4eLKFP6sCeTouhosUUtThoNkix7XEQzjZ8Qypj94/uAVxa50ozTc4HrH8KxGtfznTOT1d5Lf8MgG24saLft+tejZ99fNX5Rk8YPNKTyt3DQo5yDTEYxqHnQdx+vAAB0yUxCj9xUAEBldfOvA5S/3Yae/YbCyE4/N/EMzanG7zWtH9zXZGnuoQneQXowtDWclGC/tt7hcdNdCaKVnn1TmHUCmts2ZchpU9P4etdFqPXz+1Y+o5QEE0RBQHK86z1XW+1qJX5tUerkeBPOH5yHId2zgYoiYNVLgCwBQyYCNz4KpOcA51+HxCt+B6RkwZqY6crevGCe6/mzL9bp04g+pvFTi/P+UrQ7pbArYCpfLDUh3B2NFf4C+YzkOJSYrSizWJGRHO9RPC0Qo0HEqN4dYHNIahqaIsEAWLevheXUAQACss+/Ahg3xe82/D1uacrFR7xRbDLFLSM5DgWlrrv8ESMaXCeK7z4EfvrKNfShjYzpgvtisN59ARlvMqgXh00V6FOq/HJYDkWL3Slh9wnXRX9Warx6Ed2SfKei5M0vLbtTwvbDpeicmYRuOSk+z8uy3Oi49pZw4HQVjhZbcG7/jmowES7ZT2+7N1EAYK+HtOlrYN8nAGQgOQO4/iHYkjNx+vBJAED33BQcL60GAnSUqMG+No3fT8V45bX+zvVqjZau7mB/3xag6BjQsXuoH0HQZM0UbK40fvfwwwA9+6fKa1BRUw+DKKB/XoZ6PrZY7UEV9dNyeH0mrkLIrt7jervTY+o6eFXjb7JnX/L9vcCdyl9RU++eUi456LZCkwERbxIRZzQgwWRAbb2EmvqGQNnmdVPHoM53H9p3k83hhNHQ9PWYT89+E/tV3ktqggkWqx119b6/byXYT3V/tye716222humkU7wvOZVM1PXvwNIDuCMIcDld3lctyX2Ggg4slFnMgCD84L9KFoV9uy3Y05Jwp4TFZENjvyIxAWRcvfSe9qR1sRfsJ/rHqR24HQVyixWTUGWxv90s1MT0DnTqxptrRkJ37wJnDrgGqs0/EJknXuh39d7FPeJYs++coLSFhoMJD1JGfZgi3BV/kkABODQduB/y1zFi9qIek1PUZxRbOgFaHLMvuv3I8tMXabosGl6hpRgqKV5B1/s2fdUZrai1GLF0RLf7Iviqjp8ueMETpVHNzuvuKoOdqeEihB6hhVHiy3YsPu0GqBKAQI9HNwOrPgr8J/7Iby5GFizAtK+ba5Af8gk4DfPATl5OFlWA0l2BYYZyfEwGRt6nL35u7Eg+Clg21g1fvUa7YyzXPV8rNXAa38BCo+E/JkES/uePNP4fc+zTknC3pOVrqZ2TENCnBEJJoOafl+lSeWvrKnH17+cxMlGjq+GAn2+GYXe1eKhVuNvfCy6ItBQjjR3j7y5NoSefUdDzz40NQCqNdfBNq80/qZm2GlMnc2Btb+cxE+HS5tcVzvtHrQ9+5Ls9/pM+Xwz3MMwav3c3GkI9l3vM8Ud2NdY7Q1p/P5u0BUXADu+cT0+/1qfDhptPauIXjtGEYP9duxkeS2OFFuw81hZi+7Xp2dfh4r8yhdaTSsO9v312ufnpqBjeiIkGdh6qATV7oqjTaW0+yg9Cbz8AOKLDgHGOODsi5HeZ2DAu96GGEnjz05NQK9OaRiQl9nkuikJRnUmglDGvwUtJw+YNt/1ePMq4INnAHvL3jCLFO3NJFEQNFMDNZ7Gr62nwHH7FA3aG1WnymujctPJ+++EU+95Um7K+/uOKHNPsXbKXVw2WpTrEX/j4YN1yl0lvMTsmt42YBr/F6+4bhqfPACxshBw2iClZQM3/BWYeReQnA4AKHDfvMp3Z0Mo523vayftMa8NKhsK9CnrNcy9bvLTs6/e3DWagOsfBjr3ck3P+9pfgFOHQv5cgmHTFA4UBUHNGvM3hvtwkQVWuxMJJgPO6JiqLs9Ux+033LDZf6oKVruz0ZtJDQX6tBXzfW+shFKNXzkGvIs0prmnejPXNf8awmb3LL6n9Hhre/a9q/GrPewhdLJV1dggyw3TNjdGGZaqdARoM0j8/f2rwb7786izOXzqeVncn1FKgsnj3/LqevX9JMX7uTb++i3XDbQB5zTUodBwVfB3fS7+MgraAgb77Zhy19lS13BXrCV4343WY8y+cofT7pR8Cn20Fsp70FadFwUBQ3vmICslHk5JDqpAn4/j+4CXHwDKTyMxNQ0YewXQIR/ZqQkBXxIrafwGUUC/LhnqybsxgiCoc6RGtEgfAIy+BJi1ABCNwO7vXUVf6usiu88W4J1dYtL0cPg7QWuLPCknSwb7FA3a7327U0JxVcv/PfpmrfFvQUsJ5PwFGsp1QFUka64EweZ0HUfhBPtK6rRyA18N9LQ3zqsrXdPqAcCV90KcdTdw3lWQpt3mUQG8zGJFTb0DBlFAXpYrzVvJePO+1tEGR9pdKfGlkmGgvUmgPb+b/N3cTUoF5j3iCpLq3D38337omqc8AtQx5gbXOUg7ZbD2/VltDhwuck211z8vw6OQcKZX/Z5qqx2l7gC1sQ4h5eacQXPeUz4T7WetBPZiM8bsq2n82l9MyXGk/vwFcPowbNb6hnHrkhM4ugv47mPX1IcB1HtdD/rv2fccs6/WZQgh60hpn1OSAw6rUNvmdS0hCoJ6TPr7+1f+3tKTXddwsuybTaH87tISPYN9Zbky7Z6HE/uBvT+6slknzw3YXmWIRmvuMGwMx+y3Y9o/pMKKWvTqlN4i+/X+kgk32Lc7G+5Sw53KH24NgGjQFlvRMogCRvTKxY/7i9STV9DBfn0d8MHTrjS8rv2QMPUPQLHrxk52auAAOlam3muuDPe8qlU19YCfMaGhqqypx96Tleifl9EwHczg8a6el3ceB479Cvz3X8CV97XqMfze2SXaE6dDkuH9Z6Ud42gyiHBKTvZmUlR4B2fHS6t9hzJFmPe5rKnpuNobJQj2NyxI+ezqHZLaW9vSJFlWAxF7GJ0GSjCtBETK9YlHPYJjv7r+7dgDGDQWybU2wF6IijqHxxzxx0pcvfp5WckNY8kDzJIieQT7vj37yvPeKdaKgDdsE5OB6x8C3lwMHN8LrH0D+PYDYMQUYOzlQEpGiJ+UL++p4uJNDdPq1tudasr1XvdUe5nJ8eiS5TnWPSO54aa/LMs4phk2ovQYe9eGkDXFNdVsB0lCXOVpoKoeti4N18dOSQbq62BY/zYMkhPoMglSI7MFybLcMP2i3QpsXuOa0afoKAwAkoWeqBGTYNmXjoS0RFeNhFpzwwb6jwYmXwt06Ob5WdkDpPFbGwJWtRq/sx744N8wmroCXc4O6TxttTds12K1+9SE0vIu0Af3dYLNIfn8/TslWTMk0DXdYp3NiVqbQ92HUo/LaBDUZd7j85P8Dfdc+4br3yETgdxuvs9rXmups7fqocCNYc9+O6atfnm6BVPnfKrxh5nG7313u7X+sTZWad/kLrqXkRyPnNT44IvOrHsbqCpRK/omZLjS4QWhYYoafwwx0rPfXNqTfDDKLFZsOVjcZFraibIalFfX+473O+Ms4Lq/uHv4fwA2fhx642NAvZ+LwMaq92rHfRpbqMovkT/Kha9yE7PUYm3xqVi9z2Xt5cZXmcWKdbtONplNoZyrXbU9AmdBRKJ3v7zaiqLKxq9ztL8/WxjfY3avnn0liPSIL4/ucv3bYxDgTufOTXNl2+0/XQW4v4+LqlxtztfcvFZ69r2PN+3hJvgJ9pWAM9C1RqM1WhKSXRXKL7/LFTTZrMAP/wX+9Vtg02eAU5+MSiXYV96jOouQZtx+ZU09TpW7PpeB3XyH+KUlxcEgCrA7JVTV2jzO27Lsf/y/9m/VcHQX8MmLwDO3wLTqRWDjR7C9/6zrHO90wnloJ7DubRi2r4G4Yy3w3UdwmisCvif1JowkQXzvCeCr14Gio66Cv2cMQWpyIiA5YD60F9i+1hXoJ6YAPc9y1Qfa+yPw4h+A1cvVX6JHMV33Z5USb4R4ZAdM+zej2lLt/jzdMxv8+Amw6zsYt68BNn4MZ2XT4+69WTWfW421iZ59P1mogeoFKG0U3DNRqXUaNMMR1PH6CQ2FVw2iqA7zgNe0e6gsAd5+DDjyC2AwAhOvbrS9ylj/lsxybkns2W/HtMG+2Z3Kr1f12cZ4/6GH27OvjFtStNY0HH+Blla8yYBz+3VEcXGQPccn9gM/rnI9nn4HEJ+IjDgZuWkJyEiO90130jC04p59uO86OyUp4HuUZBkHT1fhYKHr7rkoCo0Oa1CCBqu/6X/y+7vG8K9aCqx9E+h0hs9UhrGszuZAvMkAURD8XgS6Lpp8/26VacZEUYDRIKjZIHoMy4kFTknCgdNmdMpIbMjmoJilXPhmJMdDll3jOE+U1aBP55bJWEM7TuMvrKxFnc2JwspadEhPDLieNji1O2Vo7yNrOwGqam3omKFfVoYsy9h6qAQOp4zJg/MCnmO12SGhdkJov/+U84UMP+O1j7p79nucqS7q1yUDJeZCnK6oxRkdbSg110GWXeOYlbHd0Ab7ATJJvM/ZymlQCToDXWtoa7T4nRnBYHQVqB3y/9k77zg5zvr+v2dmZ/teb9Lp1LstWe7dwg33AgQcWgwYCDEBAj9CHEJCEkISQgohCSXU0EIwzYCNDQYb9y4XuahYXafrd9vL7Mz8/pid2dnZckV30kl63q+XX9btzs7OzD7zzPNtn+9rYMczVjDh0Ktw99csI/WaP7Seh4eBVqzOcAz5fWQLOtlCESUj8fzeMQAWtUec0r2K8y21tBtP53lp/zhF3STs9yFJ1vowky9WRYHten1p13MoW7/uvO5XF4Kpoo0Owg/+CYIR9PwCQEHpXISciUNqDP3+/4PICDR3Ykgyo5pC69IV+HxKOcV/3yvIe18ANQBXvNPq5hNuounQBAOv7CIZ3wWhFKw6DZacBIpiicvd97/w8mPw2M+hbQGcdVWlkGGppbL8y6/Q+eJWhqUIe+68nY1vucVyWqUm8D9zDwA+1QfxIbQ7vgiZyy0LOzECmaRVnti1uO5v43aSNNJFKhR155zdY8xXRy+grBWkIEkS4YCPsVS+QqQvmasU57OJBlXnuCIBH+hFy/l0//dBy1sOlSveCS1ddY8XV1bAsRosnAxh7J/A2A+ioKqQ0/Qjlspve1ADPpl80TjsBVH+BIjsT5uiZnmmTQM2brYeHqWH4JkrG0969nYBn0xBN5x0umMBW4k3p+nEMwXaotUGfLZQZMvukYrov9dh5MW+V9ylLxWccYUlXLTlXqts4r2fhbaewz2dOWcineeRbYPEgipnruysKRKpKjI5Ta8WH3P97TsOI/t7h1PsGkyQyBQ4a9Xk94zg6OLomfgU+tqjJWM/xcqepiPWzs2t5l3UzROmM8Wk82MJtzGt6wa45hm38TLbHYLS+aIzX2XyWgNj39WybKbGvutz9nxalcbv1OtLsGS9s31T2M/C1jD94xm2HZxwooyLOytL0iZL4/cO93pp/NWR/fIHdcOsUKWvQJJg9emWU/uZe+He71iR6m/+JbzzU9A3c4Pfm8aPSwB2x6G4E8xRFZnVC+uXD7RGA4yn80yUxtKSzigjJf2DTF4DKtcGRcOEfS/je/FB64WNr4GNm/FHl8CeIQoHn4Ptd0AujeELwNqzUa5+LXIuAd/5Lvr4APzk3wE4SDMvSD0s7e1k/R/8MYbks/QOXn7ESqe+5K3WmqFEUzgAzR0kuhfA+gWVJ9K1GG76M3jkDvjVN+Ger8Oi1eRbFzvXQTZN+MUX4Zl7WU6YIaIc3LGDpU8/gCkthZcfxW/kYeWpKOe/A37+Y/SxV+Gu/678roM7rbVLnSCJO42/kbFvG99+n1wRcPHVGbfe37zcgcFl7JfE+WoZ+8OJHCTHCT/xO3jlt5C2OjSweD1c+4cNHRg2Ebtmf5KMhWOVY2cVL5hVDFcv0yWlB8mRSuUv9+a2bq7Djux7HsrHorHv7W9+2DzyUxjaC+EmuOJdM9rFWau6OWdV9zGnfzCZSN+WXZah71MklnZZCr5eh5GXbOkhl6u3mJUkuPo9lohRLgW//OrhncQRwl5UJ3MaD78yQKL0d0XqXR0jvqLXsCS5tjs+DJyB0nw42dgQzA/c6b89rSF8ikS2oB/R1rL2syykVvbjPt6xF+WTG/uVIopu3PPLbAus2inA1GnhVuuYZlqzX7EP3UA3jGpxNjuq373Eeka7WLWwGUmyy1B0VEWu0p6wI/umWXndnPZuHmtfmmIaf4VGy1TWZbJiGa0f+C9YdbrVx/wH/wzp+OSfrYO3VRwuRXfb0F/QGuaCdT0NdR3skj5Kz6je9khDEbbi1kfguftRMOC8G6xuCCs34Q8EwB+icPJm+JP/htf/Cfolb4Xlp6CoPpTmdjj3Bli+CaOjD9p7ScUWgKyQOrAHvvspjGwGXngApZiH3tVw9tUV393k1Npr9eeMc6+HNWdakevbP0shbekQ+I08/PQ/LKeLJNN64y1EV6zFBF65+2ew9yWUgV1WVslr34na0gHn3UDx5Ius9crqM+HMK8EfgoFd8NIj7B1OsnswUfH1pmlWrH/SjYz90vUNe2r6nfZ7nrHldT55o+ymaTprE6+xH4n3W46Q+/+XyNO/sAz9SAtc/36r7GQKhj4uYz+rFY/L9nsisn+CYk/2kgR9HVG29cdnLZXfMM3qfrIunPQevw8yhcOv2S+dSyykHvHOArOFu21U4HAj6Qd3wv0/sP595S0QaZrsEzXxTqrHCi0RP4PxbM0Fo6Ybjqf/vDVW5H3PULJhBwdNNxwDNl/Ua6c3Aqh+uOED8IUPwq7nLHHEQP2U1vmAe+HrHoM1U++8NbYeMSPV26P5GCZbKDrj5Hg4nxMBd2RfkWWaw35Gk3lSOe2IlWHYYyWgKiRz2gkzdrIziOxXZwpVGsm1Uq1nStLV1qyRinhhFmr2vesZ9xzrZNc79fon4yUSUFnUHmV/qd1eb3ukqhxNkWVkyarR13TDcbTaafzVxr71/8kE+ijN55puTE9vItIEv/f/4L//FEYPWu1o3/5XljNgmnjV48HSF9o1aBnF6/taa2bseWl13fMLW8P4fYqzts3kNNj5LPTvsBwT6TjFl54DFqIu3wCX/4Fz0fzukolQBP3ki+BZq4uCIkuWA0dR4KTz0E95E7Iik901Aq/uJPfET2Hvi+hf+3NIRK3f5fo/qrouQb/Pue6pnFazNAFJsvQSvvQRGB+kcMeXQWslMLQNjD2W2vwbPgzrz6M7uo9XxycYGTsEz9+Pig5nvBa6+lASWZAViusvgPVvLO8/0gL3f5/ib/6XF8/vBVlhYVvEMcALxUoh7HzRQNONmvpRdvp9yHP/2l0ONM+9X/AY+952iyPJHPmigU8pd1wiNQG/+Q7RLY+C1AfIhFdthNMus7JOlOnNHVYpo3VPZQvFI1LSfCQRkf0TFHeNjN+n0FGqVx44zOj+4zsGuX9rf0PjqRzZVyr+nil25M2e3AuzUBpwpHH3Nz+slNNcGn74z5aHfd05lmL8CUaLp+2OG9s7HPIrRIOq8yBvlHLrTiUzzUnSOzsXQWu35X3f/cLhnsqcY6flLe9uqhBsDNaM7NdenPuc/r0NxJ2OMdxZTseLBsGximmabO+fmFRE0+7r7C+pP4edVNAjl5lhG3r2s+1EiOzrhlGhpO/tjV3ernKOdT+jdcN0xOXs3202MzLckf262VmeaL43aj5VvPOFO9PQMcIbGPsAqxY0O3X3i+t0lSm3hJs8sq9MMY2firrqaZ57IAQ3fcyqR9/9PNz/f9P7fAmtRhp/V3OIyzb2cv7anikZ+pTOrTnsR5JgSSmDL+yXof9VMnd+Hb7zN1b/9cfvhK0PWc6N3tUoZ7y2og7C2+bQLSwpl7LabOzxndOK0L6Q3PlvhFAUI26J4SlrzrCyOWpgB1cSjcZ9KApv/CjIPvL7d8DgHvxGwdIJesvH4eQLAGiJBmm74CpQg6VrqcBr3gxukTzvc/rc6yHcRGZ8FPZtA49YXc61RrUDUvWi+/aYd5x1ug7JsXJk3/PdeY9OQ7hkaGcLOoZpsm+k3JFCyWfgvu9bwpBbfkMzWcKLV9L1unehvPUvYN3Z0zb0KWW/2N97LGYHT4aI7J+gOPX6pUVJT2uYkWSOQ4dRt18o6owmLQNr33CKlXWEkeyHiG1QHI7qLa6HQyRQ9o5m8sUKQZujjWGaPPzyAIosce6a7iqDflbq9U0TfvZfMD4ILd1w/R8f023gZor9u+c0vaqFkx3tbw5bhq3fV27roxV1lBqtZLKeiT+v6fV/J0mClafBk7+Enc/A2rNm89RmHXseiIVUVi1oZsehCUyTipY69YT3bOPfXhzW7NE818ev6RSKOk2h2b3XB1yq3XXFqgRHhJFkjp0DCQYmslzkrWctYZomhaKOLMsEfHZ0qLruc67RXJF9jhPH12R4RUs13ahZ+lU1fxjuKH/53+2xAJnRIvFMftZaJ041jd/ryC0UjWl3o/EayW7DQZIkKyI5cqCqXt9NUFU4Z3U3Rd1weol78fsUK7rqOmbbzyLLU0vjD9ZoF2cZg/rMnJxdi+G6W+HH/wYP3A7P/w4Mw9IOcv8/0gx/8NfQ3FG1C6/h5z7f6XLmyk403bCitDueJnL3d2HMTwbTMoTXnQNNbRBuoqh2grQQn/d7PU4Vw9VZwTb0FVkqObMMQHHuiWJTJ/offArj/z4Hvl7kdfXXA01hP2OpPInsJE6uRavh+lspPPQ7aF2P/+QNcMq6qs1WLl/Ek6dfDs/dj3rKWU6Gp1JHJI9ACC56I5m7/w92PAmLVpHJF2kr+ZrsjJig34ciS+RT9bOmsoUimCah8YPw3I9g64OQjuNbdy0s3Vw1tmxHrb1WC/jKmSvxdIHBiSwUsvS9+BA88wsolLp+LFyJctUtbF60Zlaez5GAj1ROI50r0jmzhNh5izD2T1C8nt2elhBb91mq/NlC0VkoTQf3A3XvcJLlPU010/nt9DD7O2Yrsu9XFSIBHxOZAul5Zuwns5qjJhrPFKomyEae9inz5N1WaxjZB2/8f1Zv3BMQVZGJBlVSOY14Ok/QpepsR4uaw+UFlF+xhCLzRYNgjSHjXRzmNJ2Gz4FVJWN/xzPW6mqaD6FCUeeZXSP0tkXoqxPVmS3c6ZyKLLG2t7qNUb2afW8av69ej+Y55MmdQySzGpecvLBhz9/pkC0Uq0pAioZZIV4lOHLYxlKmQXmWOy3UjsSFjoK6steRfSJE9r2R8pym1zTMvNl+bmOjnCUk0RwJsH80PWt1+0XdqFD1buT8qRYO06ddSuB1GLijo7LkrdeP1d1PzVRuF7UU+R1tgCqBPuv/hmlimKZzjLXS+H1OXfUMx+7Gi6B/p6UcPzFU9XaCAPtzGtr996OddBGmCSt6mpxuOLUE+maK36fgT47Cj78G254gBKBuQF+2gdw1txFsLj/vikNJODDuGMM2qmssWxoM1R0Pysa+WdJfKo/1XGsvxrv+AbYPovjqp4bbdfv2OrpQ1ElkNFoi/mqH06aLybedAiMpAl21g2rtsSBty1Yy1tmH3+U0qxddB0toOPPQ/ZBOw56tZBa1l8/DtVYI+hXGUnlStcTsihqZbc/CtucIJ54Fyo5z38uPwL4hipuvhL4253VvsEuSJIJ+H5l8ke39E3BgOy0v3ktT3so4oGsJbH6T5ayRZWbryexoBRzhlq1HgqNu7P/oRz/i85//PIODg2zYsIG/+7u/Y82aNYf1mZNOOol0Ol31ube97W383d/9HQB/8Rd/wXe/+92K99esWcM999wza+c2n/HWbPl9Ck0hlURWI54uzMjYd6cf5YsGA+MZFrZVGpymabpq9q3vnq3We36fTDhoGfuNFoZHA/e1GYpnq4z9ydruTUr/q5ZKK8Dlb7eEV05gWiJ+UjmNsVS+ooVTIlMZ2afkJMoXjbr1pm4FWiZJAwVg6QZQVIgPw/AB6Oqb1rH3j2UYS+XJFfQ5N/YbRXhsHGPfY7hYf0tVafxHMu3dVgRO5rRZM/btUqbWSIBEtoBumGjF2rWJgrnHNs4Mk6pMHRtHnE+RHQezt+7zSGAbsPZYtPrJm8dU+9Lp4r2+eU2HGlIl3lp2r5AdpahyS8nITWQLs5JR41UNr9k+tYTXUJ/JXFYvsi9JpQj7nlJ517IN0963G3s+cpce1EvjtyP9plmp11RrTvMptTO5psWV74LTLrcisJJsqbuX/v/iI88w/txjsHsv9OWc426PBStKPfyHK1ScmoDHfwGP/hyKBZBk5HOuJdS5mSw+MkqoQo/fNn6910SRJceYd7eUc7dRtO9vwzBLpSzlz+c0Hd20+h96My7c2Nlp8UyBZ3aNMBTPYJhWid3a3uquA3adeyOnyPq+Vl7cN05fe3kd4RyrWUNfy6eSOfVKeOhe2PE06aVLYKHVRjjryga269kr7q18Fp64C/OxO8lmOgGJkCLB2gvglM2g+PD95FuWPsIvvw77l8PKU2H5Rlc2R/lcwn4fmYkJRp+4Cwb3sNg8ZGWOXPxmWHNW3Y4Bh4Mj4NhAfPBY5aga+3fccQe///u/z7/9279x7rnn8q//+q9cdNFFvPjii3R0VKf3TPUzv/zlLzFcXqunnnqKN77xjZx77rnOa6Ojo6xfv54vfOELzmt+//yJBM81tYzLprCfRFYjkS3QM4P0uUTJI2mn0u8ZTlYZ++5IR6hiQVS/J/pkuIWZIvO05sadmjUUz1a1jLF/jxk94DIJ+L/PWHXia86Ec647/AM+xmmPBTkwmmbEVedbKOqOx7bJFdkP+BSSaHV1JryR/clEqPAHrFrMV7dYqfzTNPZtrYFMoUihWDtKNhtU9sKt/yioF7G3/lZcAn1HNo2/UNSr0lJng4EJK0VwQWuYTN5SRxZ1+1NnLJXjwGiatb0tszJ2M/nyb5vNF2sa+/bv410sUlpsTyYaO1toenXE1Hq2HVsdTaaDd36s18bUa0i75xN7zlAVmWhIRZas1zL5IpE6aexTxXbwtoT9TGQKjkJ+rfWG9xkwk/Z77jGQ03Tn+lTX65807X27qRXZr7eOcDtMbOeM3dPcy6y1UK3z3EstzMFzj7N8Yhv+ZnglDuPpPEW9Umtpxs7VsQFLnf3Z31pGPiVthKvfA12LCe8YJJvMV6So43Jm13LMqYqMbuhoLkPePX7s31Y3zCoByJymO46BRnNQJKgiSdY+3GVk9Vrc5V3r3no0hfycu6a74jV3lkCxRslNpu9kaN0K4wNkfvU96JBh+UYn6BHUc0QVpfLYtj0Jd30F4sPkUTBCy2DZRkLXXlKRveJ7ay/c9QuK+7fACw9Y/wGFlvNgxWkE1paONZcm/NLv4IUXQC/gkyUWXHgtXPi6GdXjT5X5aj/MBkfV2P/bv/1b3v72t/PHf/zHAHzzm99kwYIFfOlLX+ITn/jEjD+zeHFlq4V//Md/ZNGiRVx55ZUVr4fDYZYuXTpHZze/sW9cd9q45VlMO0b7dLEfqmt6W3hx/xgT6QIT6XxFFNsdHXQvzLSigeKf/uTublnn98nOAq9Wa5WjSSJTvqaJrFYVoZrKxF0TQ4cf/ZsVRW7tgRs/dELW6XuxBSeTWc2psbdT+MN+X8UDzu+rrMnzYtfsx4IqyZw2eWQfYNWplrG/4xmrjc80cKevTqQLdDXPjaK/fR6qIjeMPE7Wes9+v55q/1zh/r1my9jPFYqMl5wtPS0h9o2kyB+Dgp9Hk1cHEgwncrREAnXFxaaDO3KcKRRppbpGtFAyFt3Ps4Ba1uPIa/qMstWmiz32/a6a0+M9lb+WcVOL6jT+6pp9Xykzoynst9YPmcJhG/t2+VxbLEAqr1HUTbIFnWiwer1hZx8EfHJVPfxUsY3vWEglp+lOlqEkAclxGDlo1esvrl2vP1VqCfTZ90rIk6nlnt6z+cZZhPUyuWaDom6g+YLQ2s2Kse2oo1vZF95EplBkNJlz7tEZp/Dv2Qrf/lvQS+ut3lVwwRss7ZzSuigcUBktGfveY8NjDNv4fXJJH8aoqNO3sf9dyzGcK+hO5lyjeJYiS/S1RxmKZ+luCeH3Kew4FK8bhCh4BEmniixJDeemTMGAs6+Fp+4mM7IPvvspuOo95PanYO9ughPPE5UL0HI2mfaFGFv3IL/8qPXh5k4y5/4+KMsIhfzInjIVXygCp15KcfUGyL8Mu55H799FMT4Kz/yawL5fwOpT4ZlfE8r6QeqElm56L74SZWO1LsFsEwmW0/iPN52eo5aXmEwmeeaZZ7jiiiuc11RV5dJLL+X++++ftc9kMhn+93//l1tuuQVFqZzcHnzwQdavX8+5557Lxz72MeLxmfcGPdYoC2KUF0B2tLOhGmgddMMkVXqodTYFHWGdPcPJiu3ctXmSJJUfWDNtc+P6nF9VyjfrPDL2TdN0Ivv2+Q7HsxXbzDiN/77vw6vPWgq4N/3ZCVun7yWgKo66rR3dj9vifJHKDB6/01qmXmTfGkt2394pGZYrT7P+v/clK71tirizD6jTUWC2sNNZvQtDL/Ui9uWafa9A35ExjN2/w5QcMFPAjuq3RPwE/b5ySuthtgc9kbCvVWGWfhP3XF5vXtdq9OaWJMnpd38knge6UY76+RTZifwdScHKo4HbaUiDebQ6Rb58XQqudQGuevXZUOS3a6BjQb+z3qlX2mEfo+1gaNRVqB72WIyV0rJte0qWJNhbqtfvWdqwXn8q+GtE9u0sGG9/c7fRYj9fAnXmfXU20vjrYH+3b+EyVAx45Qk6mizH/Egy51z/aQc9AEYPlTIcNVi8Dm7+FLz7M5Y6u+v8I3W0PBzB2RqOb3tesbLhrGOsNPbtOnizqkwkpxVdWgqNjceTF7dxyYZeTuprc7rj1Hv2zDhA5Dpe729smiZZrWi1ET7rGrSelda9+Ysvknv+YYgPE0IjaORQxvth5zOkX37aKtE470Z4/+fJrjobFKWmc9VxJLUthMveDu/9LPkPfQXWnIXsU1GH98DDP4FsinBrO5z+Wjj/dSxetXLa5zgTgi4H8WytKeYLRy2yf+DAAQB6enoqXu/p6eH555+ftc/cfvvtpFIp3vWud1W83t7ezic+8Qk2b97MwYMH+Yu/+Avuuusunn76aQKB2j158/k8+Xx58Z1IJAAwDKOibOBYIJvXMAwDv09yjj0S8GEYBpm8Qa6gVSycDNNE8jw03CQyBXTdQC215VjcHuHASIr+0TSrFzSXlfe1IoZhIPsUDMNAkS3HQ0HTMQLTv4a5grU/VZHBNAmqsnMOWrE449KA2SSd19CKOrIksag9zKsDCYbiGXrbyqUS5fOQGo4lw7BaGxmGAdueRH7wh9br1/2RVc90jI3DuaQt6ieezjMcz7CgJcR4OodhGDQFfRXXWJWta27/Bm50w3QWhs1hlb3DhnPvNKS1B6m1B2l8gOKrzyKvPXtKxzyWzFXseyyVm7O5JVM6j4BPbvgdkmSNt0KxfH0Mw4p6mZhWlMAwkEqvF4oc1jEbpslze0YJ+hXW1RAMtMkWyr9DNl/9282EgfE0hmHQ3RzCMAx8pbGR12Zn/ycC+aI+a9esqFv7sUnnClX7tMacjmkq+JTKsRdUZVI5g3ROozUyt2V6ec06b6kUObPvC61YxDCO3zR+ex6JRf2MJHJka8yjgDMegn5Lrdw9n2il95TS/dYUUjEMg4k6818yW3B6k09GPJ3HMAwiAYWATyZhGGRyGka0cp1nd3QwTZOQXymNYX3KY9h+NueL1nowGlAqPishY+5+AQkwl5yEeZj3hlJjbrLvD3sd5D0+9zZ+pfa8r5Zen9Jzbpqkc9Y+Q30rYCuYe7bSrursMQwGJzI0h63f3TfJOqiKbArpe3+HlE1hLlyF+da/tAIgpgmeVpCh0rVJZivnEq00b9n3rRv7ePKFIpTGhuTazn5GakXduW6yJGGYVlp/uPSZWvuuh0+m7hjUS9/lPjabijViHex1t+ZZd2fyRXTdOna/30futMtJHwD1xd+QbV+O0b0C/+YzMKQi4cdeIDFwkFSwl8hrrrEcWEA6F687BhVnTiyfU84Xwlh5GoGVGzDHH4P9L2OedhnNq89FemWIjqYgEc+9NJeEVIVUTiOZKTgtBuczUx5Pc34kdbAP0OerPARVVdH12h6VmXzma1/7GldccUVVav+nPvUp5JIhuHHjRk455RSWLl3K97//fW6++eaa+/qHf/gH/uZv/qbq9eHhYXK5xj2AjxbpfJE9I1b9z0m9loa4bpiMTVhZDIlxPxl3DU8+Q7ags2v/IdpKi6NC0eDJ3eNEAz5OWVxb+fPQRI5kMklrWGVoyFJglYs54lmNF1816GuzUpHH0wWSySS6X2FoaIhMKkkyV2RgUKKYqe1kacRERiOZTBIu7Q8gm0lR1E32HRxwvLhHk+FEnmQySSzoQ9Fkkskk2UyKBWHd8fQOj05gmCaJCRUtU39haBiGlYGSS9P1M0tvIr3hYpJda2CoWvn2REbKW2NtVy5NT0hn/8Co9YDLKgwNle/XVMIau0NmnqFQ5TySLegkk0mr1UzaRzKZJJ+VGRqa/CEQWLCOl8f9jD/8IifHFk+p08Lu4TTJZIZowEcqXySbSbEkNjfpZIdK39XkKzI0VD/6mMha95iWUxgass7BMAziiSQFyU9iQmLIzJLXrGslSZJzL86EiYzG9n0TADQrhZqRFoCBsSzJpNV/VyrmGIrOgrE/Mka2oKNlFIaGsqQSCZLJPIPDBgEjM4U9HL+MpQvsGkqzoivS0HAeHZtA0w0GpQLt/sMTOkrliiST5eywAT1Hj+ceNQyDsYkEyaKPdNBgaKjsHMhlkiSTOfoHDfx6tWjvbJLOW8eqKjJDQ0OkU0nS+SKDQzKFOXY0HE0GR8bRDZP2gE4ymWFIzzEUrl6PDQ1b95ISVklmNORiznlkDY1Yc1HKV2RoSCdf+t1zmTRDLZX3fzJX5Knd43RE/Wzoa9wmOK/pjE3EkSTIJALk0qnSeNCr7mdNN5wATj5kkEymGZIKdAamNobtZ/PomE6+aJJJKmTSKSeqW/Qr6K8+hw+YaF1E/jCf14nSukLRcwxFre8YHBlH0w3SCR9DhVTF9ulUCsM0GTDyJNMFMiGDoaHqc0uX9ivrOYYis2tgHSzN2cFYgGJrD77xAdTtj5Eyl5FMmviNHMlklpBUmPpyRtdpves/CIz2o0dbGb3sFozx+lm6aWdsVT7HR8fiJNMF4hMwZFZm46WTKZLJLAPDOmG/QjKZxG/mnWNMlp4TIyNmaU2apymkksxa41zP+Ukm00RkjaGhqV3TfNEoPU9hcNBXsQbIlZ61siQxPjpS8Tl7HJqm6dg4VdcgmSRVmpu0THlustfnYb+C5JNJZjT2rX8tyTOvZWLHKACJvEZKlij2LCUR6mFfZwRJDjvrz4OD1pybCxoMDdVeT2VkiaEhK3tmOGmNNymkMrjhcthwubVxIs4pPX6QjMNaT0yXQjZFMlXgwCETIzc3JZSzifv52IijZgnZYnqjo6MVr4+MjNDZ2Tkrn9mxYwcPPvggP/7xj6ve894ECxcuZMmSJbz00kt1j/nP//zP+chHPuL8nUgk6Ovro7Ozk6am+dmUMZ3TeHn4EIos0dHZiSxJpPMasVgeRZZYuKAyS6IvLXNoPEMg3ERXl3VO+0dSBEIFNKC9o6NmtHykME4sBn3dMbq6rGjcUs3PrsEE4Wj5NTOeJTZu0BIJ0NXVRUcCSORobm2jq236KejGRIbYmE5b1NofQM+YQTxTIBxroatldvr0Hg4TxTixmElfR5QVfa3siVvpYGq4mfZYkEJRJxK1jM9FC3sa1k/bkaPux3+InE1gdvQSuu4PCTVo6XKi0t5hsDdhqfv6Qk34gzn8QVjet6CyLi+Q5WAKQmG/M4ZsRpM5YjGNaFCld0EX20aKSFL5XqpHJl/kydZTyUr9ltANIfq6qhV1vexJDBGLKaxf1Mq2/gl0wyTS3Fa31/LhMJAdJZZX6OlqpqtO+x6AcE4jNqqj+mTn+hiGQWDPBAE1RHdXJ13NIYq6QWzQWjzWmyfcZAtF8ppe1Zkivn+Y2Nh+CIQIR1fQEg3W/Py4NkEsY/0GQb+v6rebCcFDBXwBg96eLiJBleG8SsZMEmtuomsKv9/xzMDeUVANDqQkli5qr+u8Ch3MEzRNwrHg4f8m8SyxmHXPmaaJP1D9OxuGQeBgkhh+erra6Oosp0cn9AApPU4wEqGrq73GF8weE+k8seEi4dIxto2byOk8LW3tc6a7cbTRdINwxHp2LV3UyWh+mFBIrfm770lAzMyxoD2CPpom4tpupDBOLK/Q2W7dZy1FnVdGLKdNe0dnxTOxMJoiFitiKPKk42soniUW04iFVHp6ukkRJKnHCUWrx0M6pxGLFVAVmQXdrQxlZSLRqY9h+9kcyhbwGyY93V10pWVHxCxqFvBNDGIi0bzhXAgdnp6FHMyxL2ESLl1HTTcIhnMEgb7enqqsh+ZDBUuQLeAjJgdY0NVOV3v1mssXzrMvYRKsca8dLqOFcWIZiQXdMZT158HDP6Z98BUWbzzD6kCDQizmozNs0v30z2DnM5ivfaclPFwL00S688tIB7dh+oNIb/0EHd2NdbjaDcMZWy1t7U4Ga2TcRJPzdHd2VK0bE3qAuBYn2hSlKaQSS0F7a5iuLssmac8o5EnT3NpKTsoQM/0s6YqxZyhJwO+jpTVCLCvT1halq6utxlFVY5gmsQGrjKW1vaMi03YinScW0wjVeO7Z47Czs7Ousd8+YSKl8rS0tVWca34kRSxm0NkUIqDK6KNpQtFmmppDxGIFAqpCT48lopc0gqSNCfzhyntp14RJzFBZ1NNetaYvFHViJQeTvYbKySlicZOu5hBdXbVtvyNJb0GlICUJuOyW+UwwWHt95OWoGfvd3d0sXryYRx55hOuvv955/eGHH+aaa66Zlc987Wtfo7u7m+uum1ydPJfLcejQIdra6t+IgUCgZoq/LMt1b6qjTSwcIKD60HSDVK5ISyRAoWh5/EJ+X9VxN4cDDMZzJHNF572RZN75d7Zg0BSuHjbJrLV9SzjobGvvv6CXPYyGaV0vNRtHHi7g90WQZRndqHbATAWttO+AWj6XaMhPMlckqxnz4ndJ5jRkWaY5HEBRFLpbwhwcSzOSzNPZHKZo6MiyjE+RKnq61iPQvx352d8CIF13K5J/+hkRJwKyLNMWCzKazLNrKIUsy0QCPvwe5flgaZxqerUnPF+0xlA4oFbcL0XDShGuRSJb4MkdQ+SbF6LIMno2xYGHfs3q33sDSgPVe0vbwRor7U0hWuI5xtN5ElmNpvDs/8b2PBAOqA3vE79PQZZl59610U3wSRJ+1XpflSTnfd2UUCe5957eNUoqp3HhugWWvoJWgKfvYez+R5HzVvQjlXiRtte/C4LVTjvNKP9eBd1a4BxOBoRuGM45BkvXJKDWPvcTEfv+0HSTrfvHOXNltSGguX6HYo37abrkSnO4raSe0wyQpCpHm2aYSIpEyF85liNBP7IsO/uZS4qlZ5haul9Un3zcj5183nruq4pMtHSt7XnFiz0erN8kW/HMt/9tzyVBv4xPsa5d0TArnov2/nXTGh/hBtl76dLxNYUDpfnfOkZ7Xq84Ptcx2OuJojH9MayXPhP0+wgFfGRKNdzKkFWGKvUsQ4ocfnAoUHoe2dfV/VsEajxnlNJYtM89FKhe/1ESsKt3jQ4X+z6MBPxI686Gh3+MtHMLXee9i4mMRiGVQn71WYJ7H0TSDwEg3fGfcOu/Q1ONtfmjP4Nnfg1ISG/4CNKC5ZMeg732zRcNcppB0G850u371K9WXxf3tTZMqbReU5ztVMW6102zPD5bo0H2jaQp6AYm9membivIgOpTSu0IK+cQe6zazycvUulZXO+71DrPdHvNHA2pBHwKspwlqxnOGt69BoqFrXspU9Ar9uH8xqX5oPJ7y2sEw7Rq+O3nSrCGPXI0cJ4ZhflhP0zGdMbTUePWW2/lq1/9Klu3bsU0Tb7whS+wZ88e3vOe9zjb3HbbbRWCfFP5DECxWORb3/oW73znO6vS/guFAh/+8Ied1JBUKsUf/uEfYpomN91005yf95HGFhazVb4b9dZucnrcWt433TAq2pfVagPiFqCLuVqa2cJnbiEtR6Dvhd/BF/8E9bffhpF+q/5IK1iCZo/cAbu3TuncbEEXd6uZcB0BFkoe0W39E45oz5HA/i5bANGO8gwnchSKOrsGrdTBKQmtaHmafvdd699nXAlLDk/R93jHVuW3x7BXnA+P+I6XrEvETpIkR3vCq0Bd3r7IY9sHyRcNYtEwF513CkGKaNu3MPCNv4f4SM3PUVqYFnWrBj4WUqvu29mmrNzc2OdrZ0HYLTJtHEGj0vuSJDkCW/ok4k45TXfmkoGJjNW65/O3ot/9DSbyBgSjgExy+wvw5Y/Age1V+3DPK9Yi6/BSTu3Pu/tP+5xe1nNfL+gWf5qPuMWchhM59gxVpw+6xRkP9/fALY4ZDTiK4rX6pDsCfR4HXCig1P3MbGOfuzN2bEN2DoTO5ss4ybl6bwfUsuhXLZVvezzYz2etjhq/jT0vewVR86552t3SthZlcT7VOU5qtAukon2jMmmHlnq4RQd9ilwhgCwP77P+cZgt92y8An32eqee4Kp9/9gl7PXardoZO9acOrv3jX2M4YAPFq6EaCsUsnQ8/F144Ha4939g93P49Rz0rYXupZBLwc+/UFV7z7Yn4Z5vWv++4h31o/81CNdosWaPwVqZleU1goFhVrfok+32tIbpdLpqKa2lTbN8nzTK2qxFvXFoi8dNpTSwFvXEQ53fx+9zRCrTec1Z7wRdawU729BtExim6RxbqIYTTpElZxzac0T+MM9ltpmPIt+zwVEtaP7TP/1TDh48yBlnnEEgECAYDPK9732Pk04qT4YjIyMcPHhwWp8BuOuuuxgYGOCWW26p+l5VVVm5ciVnnHEG2WyWRCLBGWecwX333XdctuJriQQYTlhRwqXEnJuxlvK7beynchq6YTCazFc8uGu1tMsULLVRSaIi3ThY09g3wTBQMAAJ9cDLcHAQbasM6Z3llikAm98Em29q2K+k4CiSuqI5AR+YJsmJCbL6EEp8iIlskV3BPsZMK0KayBRqRqZmm0JRd663rc5rq8+mchr3v9jvTLg9rZOXHEgP3I4vMYwZa0O67G1zeuzHAx1NIbb1l+v3WmpEyO1FqmFaD3z3gjPrecgFSr2T6ynyD4xnKOomsaDK2au78K+/icWBHrbf/xv29I/R+6WPWAuTjZvB03vbVt5vDgeQJamU3p6cM0X+RvOAG/cCpaib2JenrMZfvl4+Waao65MqOTsdPwyDoYd+zaoXLQfWWHQR5vLXwOI1MDFMcsvPYXwrfP3jcOMHYeNFzj6qjIBSi8WZ4iw6XE43+9zmQpnajaYb3P9iP2G/jwvWLZjT75op9oKzpyXMwESGVw6O0xYLlFq2WridIpMZCnlNZ/dQkr6OiNPf2Iut3h32+wj5faTzRbKFYlU0t6AbhDxq/LhUybPa3LdS8hr7issAmE2e2zNK/3ia89f0OM/ro4XbYagqsqNkXShWtzq07yFbR0c3TOc30WoY+3afeq8qtvvvZFajp0F1TdnRbl0n29iv5ay1x6vfJ9fsYT8V3L3aZUmqCKg4xv6yDdPaZz3scWaUnLC2QRmucy95x36gTmaaIltdkjTd2qf3njocnPs54LPWdWvOhKd/RfPL96FKK9FQoLUH9cI3wKYzYHg/fPmjsONp2PIbOO0ya0cDu+GH/wqYlmL7OZNn77oJB3yMpyvb79ltM2u13nM/Bxqp8ee0otN9Iej34ffJFIqG8z2TqfF78fsUS0PGMw41Z6zO7Ldx2uR69mu3iQwHfM6aJ5Mv1lwrhAM+JMnuQFAk6Pc584EiSw1bOxaKBlppzs4f5rnMNvazJZ3Xjqv2e0fV2Jdlmc9//vN85jOfYWJigu7u7qqUhM985jMVCvhT+QzARRddxN69e+nr66t6T5Ik3v/+9/P+97+f0dFRmpqaUNXjt+a5tVQTaxsNjTxpQVVxJqhkVmOw1CJOkSWrvV6NyH4yU/aeuycztzfURjcMkGV8F98E196Ieu+dsH03WmIc0CDSjNaxGHnvVpTf/QCG9sPrPgj+2nUpdgtBZ6KIjxB+8tfw3E7Gc2nu834g2gZdfSRXnAxHwNhPlK5N2KUcrCoybdEAY6m8ZRiGVNb1tjpOgLrset7KegDMq9+LFBRt9iajKaQ6CxdcLZ3cKLLsjO98Ua9p7NvRkqCqEG/Q6m28FIVf2BZ2xmTf2Reww2wj/vSvmJgYpOWn/wGP/txqPbPyVKctkB3BtyP69v8TWcvxNpudJdzRt1oZPm7siH1RNynqBgFVKbUZq2HsKzJo+qS9xeOZAmh52PIb4oN7yKEQPOsKRk9+HYxkiAZVUnIPyYvfAa/+CF5+FH7y72AacMprwDWPuVvlHE5yrJMl5HIcznTRP10ypayOufitZwvbkF+1oBndMBhO5Ng3nOLkxeX0Wvd1so2QeudyYDTFrsEEmm6wYXHt8rmsKxIYDljGfjpfpN3VtUw3TMdh6u3PHfC0UrIN0Of2jCJLsGFJ4zp+e4xPZcGneTJdFKXcjmu22Duc5OCYJTQ4lsofdWPfieKVMp8CvrIz1G3s60Z5vnE7aoqGiapIVY4SXOuTKqdeYWqRfcMstwO227Da0Wyj5JDwV5QHlI/Bft1KoTanHJH1nodj8GTTSIkRQILFs5ON53M5V7SiQbp0rvUj+65ItNTYuAr6FbSscdhzqhsrc8msPMZzr4eBPUgtnXS0nMKhyCIIRvCv6LQm9q7FcMmb4dffgru/Dgd3WFleQ/usZ8GyDXD1eypa600FJ/uz4I7sN2q9V24rqRvVkX3735lc0dneNngLrt9musZ+vXaWebtFYR2HzWTUc0Ta1yMU8Dn3b6FokMxVjy1ZkgiXHLDxjNUdw3ZqNAog+GSZAoZzr9j387TbTs8RIb/lxDBMa+4JTpL5eKwwL84iFAoRCtUWsGlvr/0wbvQZgJaWFlpaJhdUqrf/4wk7dTlb0Cs85fWiYE0hPyPJHIlMgaEJS7F2UXuEvcOpmsa+/cD1GlLetD5F9njw2xegXnIT9B2kmB+CDSvINXXz4MsDRHpe5rwn/9ta5I8cgNMuJ7F4E0+OSqzoaWZpl7XaKxR1ME38g6/Cb34J25+i2TRppY+kFEAPxjDDTfiMIn3j21mU3M2DqTFyu19Aiz+NevGbatYDzxb2tfEuylYtaGbnQJyFrREWtUcmX0xODMHt/4xkGmRXn0NgGilrJzKSJNEeC1qp4q5SCi9+n0y2oFPQDNx6ceU0/nJknxoLUBvboeYWnQuoCgv7FnIw+Hr2DK5g0wu3w+Ae+O6noLUHmtoh1spEoQd8UVpyMSh0EFq0moDPqp2MZwq01RGqmwl2FEh19QNvhDdibxs2liPAbexPrUfzxMGD8OAdkI6DrDD0mvex+MLLGHtlAIAlnVFe3D+OJqvkb/wwgVDUqs38yefBNDFPeY3TmzsatFSPD7cvrpMlpNaI7M9xGr97PGULOtHg/DL2TdN0pTnLdDeHGE7kqq659zrlNYNwoPa52PdWvZIY9zZBf7lvs7dHuh3lkiWpSpRMkiRCqo9MwcoICPl9JDIFx2Be0dPcsOb7ub2jDMWzXLB2QcPtcI15+x6wjYbZSrmPZwq8dGDc+bter/jDZXv/BIPxLGev6po02uZkPpXumXqZT+4SGb+v7IAp6lbb3HJJUPk5WG+urYjsZ+qX46VyGqZp7dMeO4osOXNqtlDH2PfJFVkKWlFH8Sz4DdMKfOi6SUvE7zy/7fOwnYSOw2OsHxkTFiyD0Ow56dVSlLSgG869Um+cupcY9VL4y+8rszKnurENwYDP9czp6IX3fAaAztEUh/aOlbZxjbtzr4dXnoD9r8DTvyq/3rcW3vQxUKZvxjjR25Jx7r5HlRqRfXc6fS1j3zbi0/nq+4Gs5oyt2UrjL2iHFw1XHUdkZSaWPX4jAR9KSf9B0w3GkvnSeVVe646mIOnhFIfGM3S3hCucs/UoZxVYrS7jdglwaH4EXGXXMyOdLwpjX3DsoCoysaBKMqcxkc6Xa/brGfthlZFkjgOjafJFq/ft4o4Ye4dTpHPVqS3xjH2zVhq0tdL67InSXgipPgWCYbSONdDRzdBwEk03mOheQ/LNnyT2k89aqVz3fJ299JCPLmV/zwKWnroCFq+nsG87vPQ0gbEnAcugU5aezLlnXgmrzwA1UI7OZJOw63mCDz1HbnA/qcd/RevW38Fpl1o9WWUfRJrh5POtv2cBO125yTORtceCtMemaLwV8vD9z0A2iblgOfGL3szc5yQcP3Q0WcZ+LKTWNWwDpXS5vMeDnvPUtTcy9rOFcrpbi0cbYElnlINjaQ71nsa6C19D4LGfwhN3wvgAjA+gI5GQVluffelVoAiRFlpOfhODneuYSM+ysa9N7oF3443YOzW2nsWLz6kFbGDgvPQoibt/DrpBZ0hh+IzXM7igj4W6wUTpfulqDrF7MEmmUCSZ1wlc+z5rtfr0r+Cn/4FW0DB9a6HknExmtboOmKlSlSXkWhQ1PJ9ZwL2ozuSLc9J94XBwO29Un1xX56LguU6Fol534Wf/XvV+N003nO+10/hxRfud/bgyMmo5TYN+xTL28zpELb0Bm0SmUPf4Mvki/WPWM+XgWJpVCxq3eaubxq8ffmS/qBts2TWCaZaz7GbTEHOzfyRFvmgwksixcJIOOeWafesa2sZJ3muc2L+RS8/A/n1DrvGlKuV7r25k3zXmMoViVemVTblev3IutsTZCmQLxYoAhTc12jGkiwb2LnYNJjg4miaV15wS8k1L253rpNWL7I8ctIz9pSc3vJ7TxT5GrWg490U9DRZZrnak1KNWCeZkpEvXpN7c5TjO69xvHU3l4J1bgwlZgTd8GO79NsTaoW8NLFpTW7BvikQcXSdrjLjv0VqRfbdApH1N3GsJ+163f3/7fvAaivI0jX31CNbs13LGRAI+JjIF13lVfl9vmxUEHJjIoOmGI0bZ2NgvP1OH4llM08oKjsyjZ14kWDb23VlkxzLC2D9BaIn4HWPffkA3iuwDzsK7oylIJFhObckWKhdwXgE6m1ppfV5RLycSWJrMhlwLscFoH7Fb/x1eeBBj21MM7MtBepzkq+NoO+9AxSAvrQQUVAXY9Fo4+1ro6qs6DgDCTXDyBUQDq8nt3EbylZ/RGt8JD/6o8gJsfRDe/HGYhXZ2CU/N4LQxTUucZmAXhJsw3/QxyM9uDejxzqL2CNlCkc4GZRL24sL9UM1ruqv+rpzGT500fjsNv6mGU6ElEnAUxfdnJFZe8Q44/0YYPgCpceIjY3AwS6CQJqQErGyW5Bgtj/+QwdhqxtedDJEMJMes9MU1Z8GKU2YU1cATMZ0KPs9ixjFsPGnT5Qd5jTGaS8NDPyb30M/ISyugvZdVN/0+w/uzjCZzjhFmG3axkGoZ+9mCVeJyzR+CJMNTd5O/62uw9ibUtWc6c9FsRfbdqeC+I1SzX6iI7M8/YSD7vvApVi2yLYRX0Cqvi9cp0kjgzDba6hkU9nVQFUvFulbqLe7frc7zLBzwMZbKO58bSZZ7aE9kCnW1Ug6MlvuUD4xnWNXTBGMDEG2BQHVWoeMAk4Bn7kUxW4DOWUnj37p/jEyhSFBVWNnTxNb943MyTgzTdAz1eKYwZWM/5JkfqyP71Ya0phvopXnCrpdWa0T23fd1oag7Rra9j2RWozVa7Zy3jRdbcMsm6PdBplAl2lhwZa7Yx2rXFlOKgr5ycKLqe9zXyUqLlpx50ZlfR/vnxtj3yZC31k8V9fA1cKeQT2rs+6c+pxZ1g+2H4uwZSqLIEpds6K3KsMEj/lbzO1WFtb0tFHWj2gnd0gW/9/8mPZapYl+jfNFKJy+66vBrOQwVWXKcbPazU66Rxu8+F2o402dSs0+N589U9XbqUatmP1tD8yFcMvZtvOuFlkiASKm8amA8MyXR3wpjv/TMn4pe1ZGkuyVMNKg6wp7HA8LYP0FoiQTYP5pmPFVwFln1Fvpew7S7OYQsSUQCKqmcRiqnOZNlLQE6N36fTE7TnUWfbpQXjVQJn5iMupT/B+NZVi7ogXOvY/zky9Fe2m8ZR6P9TCShY2Q7mhKEpScRuOb/QevUenRGQyoj3UtIrvsYDD1p1X/pOhhFeOlRePVZK134DX9SJaI2HXTDcMoeZmzsP/BDeOEBy8h540ehuRNKXSQEU0OWJNYsbFzSYws81jK6gqriPKRrLUBtxksp/LUWngCLO6NM7B3jwGiaFT1NSNEWy3AAJgYT0DxBS3MIVnRCUYOnf0XLfXdYzoAn7wPz1fLOnv6V5bw66Tw470Zo7Z7WNZksu8eLzxPhrijHySThvu/BSD9qwgc5BS2swZIe6F0JwQhsfRi2PQHFAnGisPwUYqdeREtnJ6HBg2QLOtv7rYV0e8y6frGQymA8Wy4dkmW45r0QjFB46Jew7Qn8hSSBK26sOKeZUksoyF60W/XnU6/dnel3U0cp/GjjqN2XIq9OZF/3pPHXiOzXw3YU5ItGTSGkjCcl1P6/N7LvjRp7caf/64bBeKqsAZTI1K75Nk2TA6Np5+/k0ADpx75MZN9zoAat7K/TLodFq538aOeeeOLnsOXH+GiBtlMonnYuLGmbdl1x+fx0J8Pg1GUdzm7mosOA+x6K17k2NqZpktWmlvnkTpHHU+5j6X9Qer1SoM+7L3veVRWZloif4USORLZQc86tZ3iUFfm95SCVUXlVkWBimMITz8G69SSbFznvX7Cuh8GJLC8dGPcoulvGvj0W/T4FOZfGSE9Yz5BZqte3sY81ndeq6+E9VBr7jcuEnOfcJA6lQ+MZXto/5jiIdMMkkSnUzFrMuurB67G8e7YUAhrj9ymE/FY233Ai50T6a0X1bVRFRjf0ChE6G+9zwev8spl2ZL9ezX6DjlpTwcnAc6Xx21kOdgcToCraXmu90NsWYfuhOAfH0hg1dDmqvrt07+c0neGE5XjtaZlfxv7ijujRPoRZRxj7Jwj2w3Aik3e1Xqk9UVj1OpLz8OgspVdFgj5SOY10ToNS+7haAnRu7Jol+yFdFkEpPVBdAlhjqRy6YToe+3im4NRZHhrPWKn1C1fAwhVMLLiWpqgErwyDoqK2dEz5WjgtQzQDzrq68s0NF8H3/h5efAjCUbj6vTNepNkZD6oiz8wD+/BPLSMK4Mp3WWI086Tl0vGGnaZXy+hyP1DLas41Ivup6np9Nwtaw7y4f5xMochYKl+xIBp3av1LTiGfCmdfQ/NJF8HP7iYXHyHXs4xgcytkU/DSI1a9+5N3W/WM7/knq/Z/itRqpdMIb8TeuY8x4Qf/BHu2lv7uBKkNPT0Gw8/CU54ddSwifsobILKc5tL5dzWHrBKh0qK5rfS67TysaJMpSXDZ28gprfDAQwR2PU3wFweg7XRyCxbCsvaa3TtGkzn2DCc5qa+t7r1oG5/uhbB78afpBsphOP8akdfcEZYjG9kvFHWyBb2meKV7G1zztR39LOqVAmbemv2pRPYpXVtv/WnOo5dhL6DzRaNC+M+pX60X2XepSo+l8hhmWdSxyqA1TdizlZGROLkRA5+q0jT8KmPbX2LAHGQFgJazlMG3/MYy9l//YWjrse6JgztQt/wa7HtmfAD93u/Ajp9ZkcnY9FOP7bHhUyRaowHnWZrT9FlXi3aPvXim0HD/lpPG+rdtHPprzKO4nUWl993OQ2+7OptajgO3uHAsZBv7tev2a7aiMwxC2QkYPEB2KAVDqrWu8KkUDhSgYOJnP7w0iP+pFyCepWAOwoPfILH+SujbTFNHCyG/r+x8cl0zy+Ejo+aS8OzzkBgh+MowGUBq65nVen1c1zNeWodV1MN7mG7NPpNE9ofiWbbstlrJhv0+ZFlygkG1jP3MJGUGR5oFrWF2DSY5NJ5hSadl3NWq17exA1eNBPps7OvrzaCYac2+e16tENedaRq/0yK3Oo3f3RnFnYVRT9+nt90y9sdSeef86mVv4HJg9I9lME3L3pgv9frHM/PjrhPMOZGAz1HUprRwqP9QkIgFVSYyBVoifmfCigZVBsmScnmybSOlXuTanowKjrFfSpfyRPZ1w2RwwvbyhUjlioyn8wxOZFnSGWWwJLDW2RRkOJFjIp2nu6XFMvQVeVrpUXb9fKrWImHlqfD6D1ltXZ68G0YOQqTFMr4WrbFav0xRKdtehDRaRNflsV/Ar//H+vfFb4Gzr5n+PgRTJlCjNq5WZMgez5pu9du1x51umI7QTGsdY1+RZRa2htk/mmb/aMpZEGULRYZKXS+8iyRfNEbzaRcSzxQY6mtlcWepgOzKW2D3C3D316yU/+/9Pbzz72qmF9diSmmAz/8O7voKNHXgi62D4AKKoXXQFSun8T93r2Xo+4Nw5S34ClFIgqbmIL8H+ndAchxWnQanXAwLlhN/dRgSOee+6G4Os3e4nDLd7hj71n2arKETUlh/IaTDBLb8hMD+F+BAkvzzOtx/yGrDdP7rQC3fd7sGEwwncrRF0iyrEz3yphpTmgtt52NRN2COFIMLxeqMkiPFs7tHGUnmOH9tT925ytupwO3YdQuYVUf2axv7RU8v9pxW3eKrnJZcNiTtZ5hbxND+joCvTmQ/UHbQjZTSRhe0hjk0btWZ2g5l4iNWydTOLRxgIUgxes0JYuQZk7o5tOgMVrzhDZAYscQiX3zEUgb/8v+DGz9AcTwEz96HDx0ueD3K2kvht/dT3Ps07H0JvvEJuPlvoXnqjmkq2ssqznnazorZVot2OzF1wyTdQD8iVyPzafI0/srxU9SNmkr8uI19V+aHOyOpKaSCrpMcHIBAEnx+6zmdS8OeF8k9vw/GhgmZ+0EpWNlxuTShoh+kXnJmDtjrfJ9WKgn0m3uAPH56QGmj0LECBp8m+dLTsOMATRs2gLyaYCnS75SVGDpS/6tIe3ahDj0DpmUIB+kjI4WRF62c6c9SF9v5ZmeoNIqau9dIkxmJjrHfIHvEXpP1tIQ5ZWkbOw7FSeW0SuesC/s6RSYRujxSLGiNsGswyXAiy4JSGnmjyL41P5XPrVbrPRun7M8TeZ9xGr9rHrV/k0Zr+Mmo1VI2XaPMwh2hr5dFEPL7nO5STnZJg9/YPmZ7PMy3FP7jlflx1wnmHEmSaAkHGCmlyU/m2W1vCjKRKbCwteyJjpY8fmmXIv9wyUipVw/tVys9/U4vU9lO5ytPVofGSwZ9c4hIsGTsxzM0hVXyRUu1d2VPc8nYLziROG+7pcmwU5PyRaOq/Q4AJ19gRU/v/LJlUNk8+1srovq6D04pQjNWutbTTuF/4i7LiAO46I2w+Y3T+7xg2vhrLFK9bfegUkna3V4qXsqY8fvkhilsfR1R9o+mGZzIopWUqHcOJDBNaIsGamYF9LSEiGcKDExky8a+osDKTfDWT8BXPmZpOvz4c3DTx6ZUepKbrGZ/7BD8/EtWFDOXxjeUBamV4kt3wumnoq27Bv/ATtRtj1vbv/5PYO3Z+AYTcHACvTUMy15Tc9e2toFtWLbFAo4RFwn4nIVmOOBDLvXxzRSKFRGHfFGHhSsILP4jAgNPw/Zx8hODGLk08v3fh+fugyveZfVxliTnfL313m4co9GzEPYpEpo+t4r8FeMuf2SN/WTJSRXPFOoa+5pHo0GSJKdFq1vAzL5G4YDVhkmrk8ZfJbqm6eDxU9USHAv5fSSzWoWI4WR9mp00fq3sVOtuDpPMWoZJPJ0n9OID1pybz1BQAgy0rAZdZ5GiEQx3sHXRVSQ6+8iE2wi3dsGS9XDJW+GH/2KphP/fZ9DUdWAYqMtOgkvegpIqwMkXoK/dBA9/zrqnbIO/deoSq95WuW4tnGxhdo19bwlJPFNoYOxXzyH1tBy8ziLFSeM3y1oHSqUhFECHXAoKBfJ70gQLKfJ7D8GhBIH8EE3pXZBoIomOaW7H/WkTyEprAAib45bgaYmgokKkjWwkCtEOKBagqFFId1n19iEFQkHUBWdBZDnaog4wBkj+5CcwkSD2zC/gmQRhJAicQhHQ7tqFT88RLHYiSVF8ZtEKDnT0EjS7QW5B3rBx2r/HZNhGm+MYazAWKiL7k6R/2+97ndpu7FruhW1hFFl2hBBrdWwyTXNK9dxHkuawn7DfEmLrH7dKdmoJPdp415kVavyej9Wr2Z9uZF91tfyzKYvrzvw6KnI5SGHjLZvCo3fRyEHU2xZhrJTZqCpyzSxfG9Vzny+YZyn8xyvz464THBFao2Vjf7KarZU9TXQ2BSsUwO0b357MC0XdmfA7m2tHFO1ohJ2KaGcW2A97WSoLn2i6gSRBRyxIPqjzysEJxlJ59o9YE3FXc4jmiB9Zsh5CdlbBdBVJ7bT6nKaTymm0RWt8/swroXup1SKtqEEmbkXbdz0HX/wwXP9+WLzWUvBXfBVRREpCR0OJcqbClDAM+O334KGSYOB5N8LFb57WuQlmRqCBB927OKnVS3o81Tiqb9MSCVg95HMah8bSdDaHHCGwemrfPS1htvXHGU3lqp1Trd3w5j+Hb/6VVRN/53/D5psmVSrONlowGLqlWaHlYMlJcN4N+Hbshb2DFIcn4Kl7KD63laDehIphjdG1Z8MUBO2yhaJzn9tp+rIk0dkU4tB4piKzQZYkIqW2esmsVmns2yn3XQsJbFgLHfvBMCgo+wj+9lswPgjf/wc46QJ4w58459uoHj5fQ6CP0nyRRZ9TkT532nO+aMypPoAbtyBbpoGToeBJw6Z0HxSKRlU6PqXoXSZfrBvZ96Z519JbyNQwDsIuY7/62Go/04KqgqRrmAdfJe1Toa2H9lgvw8M6yd0vEH/oMXrGrDIUelfRf9F7MNMBYiGV5nULAGjbPshYKs/ARKZcV9zcAe/4FPzmu/DITykWixBuQb3hVpAVx6Gth1usrJv/+StrXH7zE3Dz30DbgrrX202tax/y+yra6M4WtiFhE88U6K0j0lfWNCn/PgGPc9/Gew7u1l8V+h8AWh4e+wXSQz8mUFhIHoW8uYcgeXJ0gdRKwBwjzDCyFEFX/GTUdiJ6xnpWKwr53vWYwQ3QsZDAhgWW+W8YEAgSirTDiwPkAePUPmTJaglsPnfAOsZNi0CWUQcS0D9hGVpL15G4KAx7X6EpuxWGX8U3dgi1kEJDIWvmaaJAXg1j9p2C/4KzYflyAGIDceiPE2jQKnqmeMd8w8j+NNT46zm1bXTDcCL4LSUHYdTOxKoR2c9rZWHFmdaZzwU9rWF2DSYcJ2AjY98rRlupxl87su/3KcglYWsOp/We69kzXb2dWjhlNKXgm2GWu3u4x5A7m6qRU7GnNcyL+8cwzPqaEd7vpjSfz1jPSjAthLF/AuFuBzaZV1CR5apWX7aHv1CKiA/HLcdBLKTWnXi8dXdl1d3yDW8LnwC0R4P4SurLtlFk90Re0BpGliSaw4FSir+VCTCTXqOxkGoZ+1mtbkuzgabFBNuXlaOtG19jRXIG91iGhJuuJXDqpbBxM0SaGB0apXhwF4H0GM3RxbB8Y2N1/6IGP/0PqxMAWAbba26asV6AYHqUW0ZVtnWijsBTTtMtZ0BpHTyRblyv72ZRe4RXDk5wYDRNIqs5Uf16rRgjJVXYZE5jKJ5lUbtHPKZvLdz4AfjRv1rCfU//Gpasg5POh02XWCn2LqzUWdM5lyoe/qkVrfSHrCyWli58LWtg4QTF/Nnwwv+h9yeRJANl2Xor+6REQzV+IF6K6seCasXCZ21vC36fzIqeSodHLFQ29ntcGovuaKckSSXnHeRWn01w/Znw4A/hkZ/Biw9RjHVQ7LoYGkTNNb1cf+ydT+ZakV83zKrrldMqMxnmCrcgZTpfv2e5HaGvmLdrlL7YzrJIUGU4kasy+mzynsiv929cxqQ70hSqUSedn6RmXzq4g+DvfkA2bTnVWswc/sfSNKclDhhtJMy0lQK++U1w3o3s3z4EaPS57rOelnC1sQ+Wo/e1N2MuWU/xwRdg9en4Ilb2je3QLhqGpSb+zk9bBv9ofznC39Fb85jdlNtsla99yK8wnp79kg/bwWl3DrHv10bH5V7c205Ta0yXW+J5NR/c95TTm14qZc/99nuQGLX2J+nk1Qj5YA9EFPL+FeDvILioFXlpL7ExlbjhJ7G8k4grHTibysP2QavEoLvyGvtN0zHAcqXOQva4tVTXK3UptKJV6lE0JaQl64ic8lqQJchnCT+znXjeINPXTLQ5wOgrY0jBGL6OsmDq0q4YkaDasBvMTPFGUBtFzaejxo/LqZ0rVBv7ttZFwCc7RmA0WC7lyWt6xXdkXFly001ln0sWlIx9RyBy0jT+MvVq9r217cFSz3ZmlMZv7cc0ce6nSbPypoD3OW0/FxVZqlrLh/0+ElmtoXNBVWR6WsL0j2cq1PwbfTdAT+vsO8AEtRHG/gmEO0VzJv05fa6IeDpXdJQ0O5vq37BuI8owTWdS9U6O9sLB/UDsbgmRGtCc7W1jqDXqZzydd2rip5vGT8lxMZzIkayRckYpe+GZXSMossR5a3qs+uHORfDuz8BvvgNP3WOl/9kM7YV7vg6//ha09TA4UgSpmS5zAunFQcvgWnkq9Cwrf8YwoJCFfBb6d8KhXVYK9nW3wqmXTPucBDPH3XrPNE0KxXLkwtu6qZYi/4SjxD+5l7q3LcK2/glrMV1KoZ6sh3dPa5jkoTgDEzWMfYANF1qOocd+AQe2WTXCe1+CR38GN/xxRcsn+7gVWapOtxvYDfd93/r3Ve+2jBT34qBrCbz7MxTu/x25ff2oF19e4ZCq1dLHjZ0J5E0XD/l9nNRXnY1gpYZmqlJDvanb9ryU03SIhOGyt1v32g//hdxjd8FpvbBodd00ftvoteogKxdkag2RpNnENoQkyVpYpfNWT/gjYey7x/DUIvvl8VKrntQd2adBzX6j3umUrom9EHUbk47YXqE6sl9Vs68XrW4mD9xOyOwlG+oEf4iOxHZIjNBMEJpWMbHsQrjyNRCKEM8USGY1JMlKT7bpaQnx0oFxJtIFcoViVZSruPJ0SFsGntNW1o7s22G9pnZ4x9/Btz4Jw/tLBv/fQNfimtfIfS3wGBv1FOUPF3s8dLeEmMgUSGTri/Rla2Q++RTZydTLa3rZqPeMH5+rz7emG2AYVheDXb+0dtTcCZe8hUB4HSTz5Ba3QUeU/LZBSOcJLOuA1jBNjBIfTZPMFpy6ayr0HqqXuJZz0DLAsoUi4YCv7IxQ3OO7fOz2syAacDkpAyFC3b3EJzJkoy3QFkVjAr/nt1Jkec7Uxr3R5qmk8dec92vgOLUbtJl1O7cVWXbS4lM5rWKNmc3XzpI72rhT+TmMNH73v71GcUBVysb+NCP7iiw7jqlCsWTse8p6ZoLbqaEbRsMykJZIgERWm1R7auWCZvJF3RE7nMp3zzcV/uOZ+XXnCeYUv09xemLONAUoErTSB5M5zemL3dVc32PtFuxxGwC+GhEiSqn6Nt3NIV4dSDj/tidU6wGTdLabyaRnZynUE5OxX9cNk2d3j3De2h7r+1W/pYx/5busdGddh3zGatm35Tdw6FXMkQMMSish2kp3TzccKkBq3NrmpUfrH1QgDG/6mNU/XXBEcT/INd2gv5RN0hL2VxldXqXiTL5Ivmilpk9FjDGgKnQ1hRiMZyeN6tt0t4TYcSjOSCJbETGr4OQLrP8mhi1ticd+Xkob/ks48yrLAA6EypEB932TjltRtcfutFpQrj0bNl3svO0W1EKWKfatJy914AtWHrfqSQ/0kqhj7NcjVic1tOBZ8NRs+XXyBTC4h9yDv4Tn7odIC3prV02dDsd5oCiWkbhnK7z8GOx7BX/HabD0/LoOjMPFyVLwKYRL83MjbYHZZPrGviuKq1Znw5SNfet3q+cg8baSKngMCtuQ9HvUxWu13/NGjZkYtjQbtvwWJgatzy1bx9iaS8EfpH1JEyQOEFODMKiiSRI5JUAQ2DNkPVcWtIQ9xrWPloifibSlnbG0K1ZxvPZ5ylJ54e+ui3UM5lirlfr/rb+2MsS++Zfwtr+yuszUwVuzjyszb9bT+Evjrj0WRJETDUX6yh09PMaNzzJu8kWdCHY2oMthMbgXX956vagbaAUNttyL7+CzlrP74jdbIpuqn+DeUSDvXIO8J8vBKgVKV3VVyE1SHx70W8doXz9va0A8mSv2vOVVDbfFI+37taib+GvoD8wVVZH9QP21kOJpHzsZ1hgr1Cyxsa+3O1uU0vXJFIoks5WK/I2cL0cbO5WfSX4377Wua+x77oegX4FSF8+ZZDX4SxkWhaKVhTIbafzu4y3qJqNJK1hRqwxk3aIW+jqikz6zo0GVs1dN3gI46OquMpVMSMHsMP/uPMGcsqQzxq7BBB0zTCmLBlVGk3n6x9JouoFPkRresHbEVDdM54EqS5WTnj2JhgO+ir6ezWG/E7FzK3Z6v28mkX37oV1LTAaPCGEyp/HKwfHqyKOsoCPx4qhO56rNLDjrKhjcS3xggHy6GSUcoX3jIqte8NAu2P4kJMdKH5YsV7s/ZCmoB8KWmFhbz7TPRXD4yC7V9bxmOKUjve3V9apexWlbO6I55J+yOm5fR5TBUp3gZFF9gKZQOQIxnMhVRLGqaOmE826wOkf86n8s5fAnf2mViJx6KbmVmwGFkJ61jODtT8Erj1tGLqWo2rXvq4jY24uDRLbAQy8fcha/XqfDpGn8trE/xYe83TnD7iOtyBKmq868obGP1ckit38A9o7AU3fD6a8lW+ipMvYLmgHZNP5XnoZf3GMJg9nnNJSBfQfRLr4KFmya0nFPB7cxZy+EJutvPVt41ddzml5zEWkb8bUi+7ah7i5ziLhSemsJfNnnbDufvUZrtoYyNC7jzTYedKOs6h/Y/xI88QvYuaUk0QaErPapoY6NcCiOIku0tjVDewsKEEscIpnTrM4zkuQIdXmNeUpRqIl0geFEtbFf9NadewwH3TDLf0earRT+b/8NHHoVvvpncP6NVjmMWn1f1MpcaNQCdKa49RvCAR9NISuDLp6uLdJXT3DNjmS6SzPsmmP/C/fDL7+IT2mFBReinXQKxe0PQX8/qizBG/8U1p1dsS9c48XWFLBfbwrXdgZmnKyD+u2Fx1L5kjhlpPH41nUni9DWGbFxxB8LVks2eyxOJXI+G3gj+40i55KnY8Jk2A4Vr44Drky25nDleI2GVAbj2aqMyfnWds/NApex3+j5PdU0fu/1df893cg+pd84p5U1Y6bUSWcSJJdW1u6hBLsGLSdndw3tLUWWZ9ZRqg7NYT+blrZX3UuCuWX+3XmCOWVpV6zmQmaq2BEbW3mzIxZq6K1UXWl9tgHtnVBth0CXZ6KRJInTlncQzxQq0n2CqkLIr7iiPzOP7BfqKPI7Pb9LLUX2DqfoiAXp9qQdDU5kOTCa5sBomqDaTWv3EgaKzaAn6HKyESToXWn9J5i3+H2WsT+atHo3SxI1jWr3AtQ0TaedV3Nk6g+vjqYgvW0RVJ88aVTfxo5AHBrPNDb2bYIRuP5WOOk8+MWXrCj/I3eQe+RhiCwjmN4PDJS3X7gSTn+tFRH3tPCzFxZF3SSR1TBME1WpXgQ4qa96ZS90SgZ7WZxvainqQX+5ZWg6r9EU8lcYlfb31e0LLctkL34b/PB/rOyah39MdvfdNF90lVWiIEmg6+Qf+R28tIOAHgdSEG6ysht6V6L+9h5Ixyn+4r9h9Cy48I2z2i/bbeyH/ZWRwrnGe70y+dq1mY3SnG1j1DZ4ZalyIVooGlX7tL+3OewnnS9WOWmcGl9PpMmOpBZ1K01cLxbh4E6adj6N/3ePlTdctsHSUFl7DvgDNDsq/JXPq6awn2ROI5EpkMgUME0rWlnLgW1HMWtlg1WJzHmec0XDpOIRE45ZKfw/+bwlrPngj2DrQ3DN+6wuGy5qRfZDc+AUsh0HsmQ9U5sjlrE/kclXOT0LRd1xDHgNaq+B7uhhjA3gf+SrAKh6AQ5uo3jgeYpkQWnHd9lbKwx9774sx5H1uj2ebIPBjnzaz/FanRzctMeCTleUtb2tTmaGqrg7r5Qdl7Zz03Yu2DjGfr5YMS8dKWO/IvtEbVwPb781VSPRyR7xOJQKRd1Ze3mfeeWMycpMi1r6G/MFdyp/45r9yt/Ufa0r2hp6NX5c13smoquqUjnPljNqDu9a+krrctvQX97dRF9H4xT82WJhHdFPwdwx/+48wbzG6+GfiuiM3yeTLeiOAe19EC7viqHIEit7qvtft0RqtyNriQTIFmyBvuk/WH2K7DgMrJQzj7Ffckws6YzRHPazeyjJC/vG6PQsFt0e7C27R7hgXY8jHFjLSyqYvwRUhXS+yO4hy8vf1RSq6UiyF0FWlHvAGQNTNdopLQ5OWdo+rePraQmV+sVnqwzphqzYBB/4L9jxDDz5S3I7+yGTIIgOC1ZYZSPrz2uYStwU9rNxSRta0SBcao+Xjo9VLd78PsXJkEjlihXOgESmFB0LqtNKZ4wFLaMjmbGMfdtQVBXZ2U/dyD6Qk/xwzvWw/QnYv53Mwb3wv39fsU2BdpA68Hf1wuXvsq5JqYWh2rUJfnUnhf1PwyN3wDP3wvmvg7OvqRI/nAl5V+S2HNmf3fTsut9dZewXaaux3msY+Swdv5MKrchIrkyZQrE6W8DeNhZSYby6Zr9e1FiRZScb4OlHn2b18z9GHoUgRUw1gHTqZXDONVVK913NIc5Z3VUVTWoO+zk4li5Fea3xuayr+jlEA8MSl6PDXyPV14r4GoBnLglGrE4aLz8Ov/yK5Yz7zt/AtX8EZ7zW2axWa0HbwM4X67dGmy7ell7NpfP1psi7XwsHfFVzpLe8o1DUIZtCeeqXKIYG687Fd+q18MBjFPtfRpP8cMaV+JadXPU97vvaHqvu/uKqIjttHuOZgqMfNFmbt87mEJJkOfVTOc1VZlApHGxjO5+848ee/zKFYtlh4JNrahzMBW7jdDJD2v6dGin2u3GyRzxzhF2vHwn4qtZy9TImM3UydeYLa3pb2DkQrwrmuHGPDVmi4jeWJMmpra9Vs1/+3MzS+CmVChk1stpmiqLIUNrX4o4oa3tbJv2M4Nhlft55gnlL1CNWVq/lnpuAT6kw9hVPXVQkqLJ+Ueu0jqM1EuDQuGVUz3TSiwZVsgWr/Z7XULOPNRLw0dUcYt9IikLRIJWzDA6bhGshlNN0ntw5TDpfRJKmdm0E8wcnKlQytGql8OMab3aPcUWWWN7dNOdiMy2RgFPWMhzPVZS2TIqsWGUia84ku+UVGBgmeNIqWDz1shG3MKBhGGSTtRcusZDqpMi6jX072jPdlMCWUoRxLJWjtz3i9PCuqGFukNacKxQhFEE97VK01WeSPfQM7H/A6oBhGmCaFFpWwdILCZy8Dnor5yJfJAKnXoq2fBW89AMY3o/xm++QfPQemtedCstPgWUnW9kAM6Aysl8yHhrUz88mtoFnG6W1vtfdqUD1VUc+baPOm8puZ8rUqtu3z9luu1TUzYp2g2XjoHpuP7XV4PE7f8lE/262oGOqEYwlqzBv+EukaP2SmFpdV+yxaGeqBVWF7jqtUt0tW70OYrtspaqsxTb2XWUt6ZxGKOArL/zXnW11a7nnG1bJzS++ZGnDnPKa8rU3TQITh2D3c/Dqs/izKeTeqzC6lzmK8odL1qPy3ezKZPA6FMoCbX7IJOGRn8L2p2HJOvwrLwfKtcVaNg9P3o2aT1pda278AD5DgdOiFE+5kGJQAc1XlZKOJ2PHrW3hpjnsJ5MvknAZ++U2YrXXBqoi0x4NMpLMMTiRrSlA6XZY4fr93djOBN0wHYfAkYrqe49xshT5JZ1RFFliUZ3nmpd62VKNOs/YmZ9F3XSELOu1dZtPLGgNT5ot5x6ftRztiixj6Ebtmn1nm5kY++WOFm5tkypB0mkSVBUy+SK9bRFO6pve+ltw7DE/7zzBvCXo9zkLw6YGLffc2ItyO1rum2pEsgFuYZiZRPYpGSXDiVy1+FexXB8VCVrn2xz2M5ay6hfdxr792fWLWnnl4LgT8WiPBo/oQ19w+HijOt6yEhs7sq0bJku7YizpjM6olGQmLGyLsGswwYGx9PSMfRe5QDN0hwk2T64VMBPKxn7lfVWv7nUy2mNBdg8lHUFQr0gXdTok2NgOgLZogEE9SmbTa+H33laxTX7XCExk8NdoSeqIDvasgM3/Bi88yO7f3MW2pMzap59i+dO/skp12hdCx0Jo77VKItafC1OY69yRW3sxnNP0WYvYNsJ9bYYTOcfJ6caOelptycrH41Xj1zwiZ36flSlTS5HfPudoUHX6eReK5RZf6VzJ2Wpnkpkm7H4BHr+Tpu1PcZap8ri0BG3pKZirzsRQS6nx08Sblr2kM9bwmtstW70CZLXS+HFFz2zBypFEjid2DrGwNcymZR3lDQMhuO6PrFZ+T/7SasPq85OPdMFLT6Mc2omSfqpi38GDGpm+jWQXX084MPXFumma7BlKMprKs3FJm/M7egX3IoHys97r5J5I50Er0PLi4/C/P7VEarG60gSeehx6LyDfGoWHtlM42A/ZJvx+FX7/oxAIodqCdr4AxUAAtHzN56UT2S/qzlj1Ovebw34OjWecTh95TXdq5xu1GO5qDjGSzDEUz1b0RXej+srGvnesULonAj6ZvEvE70g/9+1jnEpkv6Jt5CR4tWls6onzUboeduZNMqcR9PucLAtvec+xhiKXS1JrGe2RoI94plCd/VGa02Y6LtzlUu56/cPNHjl5cRvxdIEFbeEjlokiOHoIY18wbaJB1UqZm2Lk2q7Jt6M1s6FU2xT2Ewn4kKfYRqYWdkmCN+XMXmgGVcXx4NrGfsJVi6bp5cm3tz2CLEts3WcJ8NWLDgnmL+5F5MK2cN1FvyJLbD5pQSl178g+JBe1W8b+UDxbV0xtMuwIVGSOoiyxOveVHdmfar2+TXssgCRRavmpOYZiwFOviqMVULkYs+/RtmiAwXjWiWC6qZXGa2PPL5puWBkSp7yG0cBq2LWTfeP7WTb+ONLIfhg9aP3Hk9YHV2yC1/+JJcjWAHemQsAnO8bvbEVsG2FfS9vYrxnZd6Xnu7GdLbYIX0Gv3M4dkXJTKOpOpoDfpzj9vPOaZey7o6QRqQhP3g1P3GW1qyvRvGI9Z134Vh6f8GMUdVS5ttDqZCiyTDSoksppKLJEX0fjqGcs5K/ZsrUc/a2cD+w0azvyP5qyHFb94xmWduUro6OSZLW71PJWZ4zbP0ueEEiLCaCBosKS9VaJSWqC4KPPktn/Mrn/eRauehOsOQsUz3yQmigLbwK5QIxn9004mQz9YxlHw6dsSPhKh1N2co+n8pXG/t7d8NSvacm+COSgeymceSW8/DjBV7fDwe3kD+SBPRRoArUD/wXXOAK0tlPEMKlIz/diz8mmWS6Z8855dnaGbWzbxmXAJzeMpHaX2imOp/PEdGtO8mYX+BWZkhuj4vzdhAI+8sVC2dg/zIjrdLGPsZ4Y4UyxHSC6YbVHtO9rO6ujXoZWLGTpcCSzGp1NIfrHMs7rxzqqIqMbek2hvbNWdqHp1fokQb+PU5a2zzgo5ThVdWNWxPlsokG1pvCm4PhEGPuCabO0M8be4SSLpyjm4U0Ha9TLdKrIksRF6xdgwoy9kvbDxxuBTOer+6vb6abxdNnYt42XoGrVKS/uiJLOaYyn81MTUBPMK9wP495JBGSmXC8/y0SDqtMC7OBomhU1dC4akXNFveYqpTJao12ephuOkT1dY1+RZVojllDmSDLnGI9u54zfpzg1k3mtbCTrhuEYYq1Ry7Cq1Zu8lgiajeoSHbRJFAzoWUqmZyljq95AO1kY2mcZ+8MHrDacrz4LX/oI/N7/s4y0OjjfbRSQDh0idGgPmYkxsvsThH1Fy9LxB+HMq6G1a1rXrhFF3XCMUCvFPV7T2K+V4ozH+C8UjerIvqvcpdb+rNpryUmNd7eyJJvCt/tZgvfcWY4aq0HY9Bo462ro7KMFOCuVZ8ehCVoOI7OmNeInldPobYtMmqFTbgVZWcdeS40f1zxh33NuB9i2/onqVlWybIlqFjXY+iAFJQhdywksXw2bz6nQiAg1PwUP/Ipc8lX4wT9BrB1Ovxx6V8Grz6Jte5oD41kMrOejicTuppVo577OEeAcT+dZSsnYr6Fg39kUZCyVZyieZUlnDAydzH0/RntoCxIGsdZWuPSt5SyWM64gsHs73P8YKQwmVl1GIboIClH8nWWnl6+GM66W097dJcU2pr1p0vazOVvQK8TjJpvfQn4fsZBKMqs5jgTvMbgN93rzVtjvYyJdIJ49OpH9pV0xDo6l62aizRRFlh1x1FxBRw3JFSKrTXWNfZWBCWusF3XDaWd5OMLQ8wV/SRm/lhPJp8h117aTrScaURboc2W3zLJjR3D8I4x9wbTpbY/UrWeuhXeR2EjxdDpIksTh7Cka9CFJtiFSdFJIU3YKqau/eosdPcgWnPRaR3DMtQhYN03tAcH8wf79IwHfvO7/uqg9ykR6jAOjqWkb+5mSIyvkb6zcfDi4hczsiFAqW47KzaTkoSNmGR0jiZyj+VEtDGZpg7iNfXtxpMiSc1xWG9BKgbVafeRt3O0ETdN0tBpsDoymaV/abvVQX3GK9eIZV8Dtn4WRg2jf/CRq32qINFl1/f6QpRUAoOXJ79MglSCQfR7QCNFHRgqTNQ8BifKBbPktvOlPLaX5WcB2Miiy5KQo24J67uvgNeJtvCJ82hQj+97aa0cDQ9OhqJF+6Ofw+BaixSSQscT2zroKNl1iidq5aI0GOGNFJ0NDQzO+DqsWthAJqlNyXjfVcRDn6xisdrRaL6Xxp1yfG01a47mqDa6sWBkhZ19DXmqDoSz+llCVGGRw8UrY3EL24JOw825IjsL933fef5VOdknd5VISw4TkBM3P38OiG2/mxYNxxksRflxOMHfUsKs5zLaDE4weGkAf34Ky9QHie/eDtJCmpatQfv8tVZ07mpauou38ZsZSeZ5UZMvJFs9WjB936y/bEVLPUAqqCppuOOnj3pp9VZEdNfVERisrv09BDK6nJUwyG3f+9q5V3PdBo8g+rrnmSEf2F7ZF5kzdPKj6SOkaOa1ILKSWo/ohf93nh5MxmdXYO5xySgwWHgcBEGs8aDOqvZ8pap00foFgOghjXzDneKNlyjypZVdkmeaQn4lMgbFknt72Ur2oHdl3RQbCgXILsFRWc1o2cZykpwmsKNb6Ra3TUtU/GixoDfPygXHS+SIT6fy0HBN2icpcpodXCpkVaIsGnfKX6Ub1bTqagmw/FGc0lXMW3e6afUoL02xBr6jbdy+O3FHkTL7oLOQN03QMVe8+8RhwRcN0DD07k2BgIsNJemulsdK9BN7zWSbu+CqPvHyQBfsmOJWXqvatI6FLq63vpgiRZsLRXkaDC8i2b4JYKb1725NWT/Zv/TVcdQuceVW5l9YMqbw2slN77L42VJQ4VC8wAyVDrKAZZUX60nZ+pbxIdePNovDLQHKM/Cu74LkfkhzJgdRBpKsHLn87rDh1StoHMyWoTr2WOVJyENtihuGAz2q/mbTS8+3sERs7sl/UTQyzXJ7Q3RxiMJ5lW/8EHU01RDJlGfrWUOifALJVBi5ASFVA9ZM75TK4/vfg5cfgqXtgYghz6Qb6QxugdRFdHc2oiow5eoimu/6DpYe2YbzSy4uxM52MCvu+wDQJjR2Al7fB2CFiYwOEDhbJ5gsMmwfpIcWEr7rTDA4AADFBSURBVBc2XELzqWdUGfqUDPkzVnTyxI4hJjIFhkptD73jxxYvdP6usy4IqArJnOa6R6uvRVPYT6ZgKfI7XQWmYOx3NYfYccht7Ffu2zb+JcmlH+HBK4ynHqWsr7kgqCqkcppzz9rifI3azDrZLzmNTKmzzcqepuOiLtweD0fS2Hfa2RYN53cQxr5gughjXzDneBcqsxXZnw1aowHL2E+XewlnvOJQpQVMU6gk0pcpWMZ+yYBpmqEBI5hfSJJ0TKQaqopMd4tVC7l/ND0tY79crz+3Y9YWMktlNdqiQcdAnqljrDnsd5xt46UFp3fRX6v9XtYjOhby+8hpVqpvSykY5jZGa6XgKnK5rZJWNJz7vrMpZC1o80UGJjIV3QqsAwpx8Jy3QvsuDmUStEeyLJaSUCylgEsyOVOBTDtKrBXfuesgFCV4KA6H4mTbI7Ck1J7x/BvhZ1+AFx6Au0pt2q5454yupU3OY3RHgir5VJ50vlgxpmq13bNxR++rIvt1BL4cY3/wVfjNPxIczoDZSs6cAAZJB1fCukuInnU2LJgbEcmZIksS0YBKMqeRzBYIByxRLrsrR7WxX47sZ/JFTNN67aTFbYy82E88U2BgPFNXbLNRmy3bmM0WiuBTYcOF1n/AeCpHbvsQPkXi1GUd1nEsbQd+D37+BeT7v0fs4sUko91MJDJ0auMUXnwSDu4gmHoKKP9mXXSxV+lgqPNkelb3MdF0KsiRmgJtNj5F5oyVnTy+Y8i596sy/BTZOT9q6B3Y+D0OuFrXojlspY7HMwUni6JWJ4fqz/nLjo4aUXl7LEcDal0Dz5tBMNPa7PmII3xasPRS7Pr7Rs+ccKDsENMNk5BfOW76qquOsX/kfmN3GZm3PaZAMFXEiBHMOd6H82zU7M8WrdEAu4eSjJUiM6Zp1qzZxyXSF88UWGSah23ACAQzpa89Sv9YhkPjadYvapny4sNpaTbHwm/RoFohZFa+V2bmZJAkifZYkMGJrCPu5nUi2rXG9v2LK7XWXhyF/Arj6crWdm5xvnrRJ7VkmGi64ZxTU9hPS8TPtv44B0bT1cY+WBHf1i5o7eJlWaJ9XU+Fo6WQysP2Qfx+BULW52u231MDVmp391K499vw6M8s9f8zrpjmlSyT87RaCwd8jKXyVXX7hToCfbgioe6afZ9TZlGOSLnJazrs3IL/pV8Cg/hpAZ9KoakP1p5Dqvs1oMtE5qkTNRayjX2N7hacLhEdTcGq1OZyGr/p1OtHSt08lnXF2DmQYNdQoq6xb7faqmVANmo3abel7WoOVRqpp10Ge1+C5++n5aHvkfR1MpHdR8wcA2k5Mqb1m644HTr7oK2HnlAXe1NRBv1+jI29JJ47AIY5qZPR71M4e1UXj24bJJ0vVomBuceTJNU3oKoEz+pE9imJ9NnnO5XIPqVrtG8khSRVj3Fbf6QtVv9cve395tP65nCxx1g8W2D/qJWS3xRS6WkgQOx2iAGs6G464kK2c4U93x3ZyH5ZKNERjxY1+4JpIox9wZzjTY2dT5H9tlIkxmoRZYmXGaa1+PCm57lVfzOFIrphltL7xG0kOLK0RQOE/FaN+sBEdsoCQHb7y7k29mMekb5k7vDS+CnV7Q9OZJ2//XVUud11yE5/Z5dBi0ekz1HDb6AlYEchiy6hsFhJLHFbf5yxVJ50Xqsw5LOFotPKriVslQs9t2eUc1Z3O4vffA2xwbpGnCTBBa+z6v1/8x0rwt/Z11D8rxHeKFFNJ8MkegYNI/uOI8B1Hlqe/H0/gF27CaLBaZcTOPUGGCqSjwRgbQ/pZ/cD5rxVio6F/DCecYwZO029q6naAHLS+A3Tuffs81rUHmXnQKIUja7dzqteb3lc6eNWBwrD+S7TNBkoGfsLWz3zgiTBtX8I/TtpGRlnvxZg3AzQqYShvY/gktVw2QUV6fmtponv+QNousH+kRS6YeJTpCl18/D7FC5Y10Myq1U5B9xGcaN2vN5zrx3Zt+79TKGIfRmnOsd1t1jGfq1r3NMS5vy1PQ3HYlUa/3EU2bcdK/a8G/b7OHNl16TO5WjJIRZUFXprOEGPVdqiARRZoj165DR9VMXVoaWBkKxA0AhhpQjmHHd/UuZRzT6lxYjdF3YslXcWHWG/r8obbdepJbKFsjhfUD1uvNaCYwdJkljUHmXHoTi7BhP0tISnFG2w0/jn3tgvC5ll8kWKuuUYOxwDzitkFvAsqu0U6kRWc4wfr6BRRepzCdvgbrRIV30y5C3DN5UrZykE/T46YkFGkjkOjqZZvbDF+Ywd8W2J+Nm0tIMHXz7ERLrAroEEK0vp6bWMOcchoRUxTbM62+CC18PAHnjxIUuF/b3/DM0dTJd8yckRdKXx48mMwGWs17o+trGftyP7hoH62B3wwj34CwaYSyiYBvzkFUeUsMAikGL4N78eXnM1wUwBhget+nGXE3Wux+hMcTuycpruCMfVakVrO7Z13SBVcprY90A44HMEDpPZQs1IuT02vanslIwA+7maK+hEgtY2Y6k8+aIljFkl/geW0N87PkXrtudhzE8i2kJm5ULYP0EwGqiqw5clic6mEIfGM7w6YNVgN4f9U67BVmS55rm5jf1GCvZuw0aRpZqRc79PcdLxbRmAqbai62wKsba3pe7cVK/FnI0sWVogmXz9DJhjlcqOJzJnruqckqG5oCXMUDzLmt6WIxoFn2vaY0Fee8qiI64/oCpyRbmZqNkXTJfjZ1YSzGvcD4j59jBsKwmyjZeic1BbjCcSUPEpEoYJ/WNpECn8gqNIX0cUVZFJZjVeOTg+6faFou60WptKVO5wiAbLUUdbvCwaODzHWCSgOoucWin3Ib/Ped9WjS7X7NePXhemEC2x56x4Jo9Rqru2jdFFJa2PA6NpDLMsODacsKJhnU0hwgEfJ/VZnTp2DMQdA7pWy7+gquBTJEyz7DCoQJLghvdbKf3pOHz7b+Dur8P9/weP3wljh6Z0PcuR/cqsB29kX6vTeg9P9F4rFGDLvaiP/RTScfxaCnQN09DRypeFXLAZzrmWwGmXgCSV1fiLuhMtr+VsnS/YGi2pnMbgRMZ5rdYC3Onk4ErjdxuVdt37RLpQ9VlcWRX1xqb9nVlXFkh/Karf0xKqfw2jLUROuxBfVy96IMxQ0sqG8Uapbey2brbzrDl8+JFNd42+r069Pp5zb3SPukXjLEfI1NcZy7ubDqt1ndsxNd/WN4eDPVYVWeLMlV1T1nrpaQ1zxaa+w2o5N185GkKD7qyqeg4vgaAR89N1LjjuCPgUZxE53zy9bZEA+0dSjKfyzmK9njFki/QNlRbyh5OWLBAcDkFV4ZSl7Tz16jB7h1O0R4N1a39xGXEB3/QWwjNBkcvtsMqOscO/VzqbguwfTddNuW+NBjg0nmE8lac9FqyqS7fraysj+/WNWRt7AT9WMoqiQdVZ9HW3hFGVcXKazuBElgWtYQzTZLTk5OgsRVcXtUfZPZgkmdMYTuTobYvUFGCzszb2DCXZO5ysMEJM02TvcIrmsJ/W3/9z+MqfwsgB6z+bX0qw6jQ4+xpYfkpdJftaNfuUDMyibjgLSk2vkca/eys8dQ9+XzsoiyhoXWhP3Adjh1Bl4JpbUZaejPLKGLoJhTWdqCE/SDKFnROgu9T4na4IZaN3vqbwU3Ic2WKRu0s9xOsZinLpWVfUyxkhkQpjP8BwIsdEJg9UioMWdcPJhqs33kN+KyvNdtwYrhT+Bd4Ufg+SJNFa+v6RklOpXi1wpydDoJE431RxZ/g1Ml6CHkdYPZpD/nK6+RHOCnE7SY6nNP5oUOXMlZ2EA745F3UV1Mf9bBJRfcFMEMa+4IjgXszON6+kLb4TzxacxVm9NjstEcvYtwN4wtgXHE26mkMs746xazDJ8/tGaQr76y507frxemN7tomFVDIFqzyGWcqC6W4Js380Xfe+a42UjP10Ht0ot9SzF0j2otwo1T8GVQWtWL8u2sZewMdrtBBUZKuLw45DcXYPJljQGiaeLlDUTVRFrkgD7my2FPyH45bOQj0BtiWdlrE/nMhVaAHsHU7x0oFxVEXmkg29KO/5J9j6EOTS1n9jA7D7edjxtPVf+0KrTd+mSyBYdgSZplnWC5AMiI+gRpqdtPJMvuiInjlp/IoM+Sz8+lvw1N3WZwmD1EcGHVDA50e96eOw8hTrvJoNsgUdLdwMkYD1vfqE9Vm1LHZlG8+2g+RIjdGZEgv6GU+XxQzrGft2Gn86X3QMd7fGiz024jUi+/bv0yiSZ6eq25H90WQOTbdS+BuJytnYzgb72EJ1DAm/T6EtGnDu5el0AKmHOoM0/kaR/SbXfVYvQ2GuOF4j+5QykwRHF/eYEuJ8gpkgjH3BEcEt0jefBPpwpf/mNN1ZzNSL7Fvpi0nn7yaRxi84yqxe2MJYMs9EpsATO4dY0BqmNRKgJeKviMYeKSV+m1hIZTCerfj7cOlqDnHu6u66oph2xHE8nSdXKGcS2dfBrq/NaTrZfJGgqpQj+1NI47edfN77fnFHlFcH4kxkCoyn8k7pQnssWJH22d0cYtdggqFEFsM0a6bxUypZ6GwKMpzIsXc4xfpFrRSKutMTXNMN+sfS9HV0w4VvqDzYkYPw5N3w7G9htB/u/hr89ruwcTMsXgedfRSinZiD+6F/B4F77oV8GpCIhNYzEWwn/SI0dTajxzowBhTIZfDvH4Zdz0J82PqeTZegymHYnSAfH4FwDPnMK1BKhj4lIzFb0J3zdNeduh0cAZ9CUS86fbyj81z0tCmsOi0gvQ4dN7aRbt97kUBleYI9XtP5omOk29jCkY0yTuzylHReYziRZechq6Z+QWt4SmUQ3gh9IwX77uYQY6k8QVWZleiiex3QKADgc2kTNIzsVxj7R9Ygsr9PkkSKtWD2qZgrRWRfMAPm9xNVcNxQUXM0Dx+GbdGAU+tIg8hSU7j8ut8ni4lXcNSRJYlTl3fw0MsDZPJFR0QL4NRlHSwopfbbehTevtBzhTcVu2kSoaup4u1l7v0ORbaixLbB7b1HwwEfOU0nUyjSSqBhezMb7wLe67gIqAq9bRH2j6bZNZRwjFuvQFpLxO9Ez8dT+XJ0vUZWwZLOGMOJHAdGU6xZ2MyOQ3E03XCUmfcMJenrqKF03dELV90Cl7wFnrsfnrjLSvN/6h7rPyBPAKSl+NGRTcvQB5NwdoyJXJHM+DC8OkYBH0grkACfua10El1w/fth+UYCmg4vHARDB0lG9Vzrslq/ZbjmXVkCbmM0oCqk80VHXG0+p/FTEma16WoO1a3j9Zasec/L71OcrhrxdKFivDQaGza28ds/lnF6oANTrpX2RugbRQ172yMMJ3IN265Nhwo1/gY1+5TGRyZfbPi8Dahlkb4jHdm3v0+d5DwEgpmg+tylLMJsE0wfMWoER4RKgb7590BsdRn7iizVjSBEAqqzWBcp/IL5Qsjv48L1CxiKZxlP5RlN5shpOvtGko6xnzniafyVgllHotZQliSaw1apjd1r3Pu91sI8T/9Ymp6W0JSMKm9qbq17f2lXE/tH0xXtAb21zpIk0dkcpH8sw1A821CArbMp6OgebOuPs3c4BcCmpR08v3eUZE5jNJmjPVZDcR0sVfWzroIzr4TdL8CLD8Pwfhg+QC5rgD9EsG8pXHAz9K2DbJLIqwfgwAhJKQW+EQqj45CI4g8FYfGp0NwJ6891FNud+mRZKf3tNfZt8b2Sse/pjuAcqjezYb4b+66x3UjYzWvs1zqvlkiAbCFDPJOvNPZtJ9QUU9cDPpmu5hDdLeGGDjE3qiITDaqOnkCje9TvUzhrVdeU9jvV767171qE/T4y+eKkRnxXs9VGb6rnP1u0RgN0t4QwQ8YUthYIpoeo2RccLsLYFxwR3BPUXIuDzYS2aHmRNZlSeVNYZTSZFyn8gnlFUFVY3BFlcUeUdE7jdy8dYiyVd9KDj3QafyToc6LQR9Ix1lqqLbZLcrwGwsK2MAfH0gwncjzyyqDLqJpcoI+SUVWr53wspDpt+CjNI7WMk+7mMP1jGQ6NZ8oCbDW+W5IklnRGefngBHtKQnDdzSEWtIYZTebYN5Jiz1CyvrFf3hEs32j9VyJ3YBAGsgRawrC0ZMBFW2hfEWSH3sSwImNs7KWQzMHOYfxBFdYvqNq1LEmO89N7naiI7JfS+O3UdM/5VtRl++R5X/ccC6mOIV+zvV0Jb0ZIrfKE5rCfQ+OZKkV+xxHUIOOkOeznvDXdyJI048yZloifVE6rKHc5EijK1NL4Adb2tjAUz06qmH/y4jbW9LYc8fEjSxKnL+9kaMicwtYCwfQQNfuCw2V+P1EFxw32om+ydL2jRTTocybUyVJIl3TGiAR8x2VbGcHxQSSoEgn4ME0YSeQo6oZjPBwpY1+WJKIlYbkjauxPkprc2RTi7FVd+H0yyZzmpI5PRaCPSYQGl3WXFdVr9V2nZBxKUrmNmSJLdR2gizqijlEpSZbRA7C0y/qewXjWKc+YDnklCLJcVaPdGglUlBnYbfcaKYy7o07e+d2bxm+fs/dau/+e71F9SsbpOau7OXd1d0PD0qtPU+vZUm6/l694vZ6eQ/XnA4dVImOn8h/piKE6RTV+ShkMKxc0T6mTz3x3FAkE00VE9gWHi5gVBUeEprCftmiAxbVqTOcBkiQ5qX+TLTZ7WsJsPmnhrNUgCwRzgR0FG5zIOFF9VTmyUVNbEXzS6PMsUiU6VmNx1B4LcsHaHlpK9/BkvYvd7zVyXHQ2hZz3u+sY+6oiVzgkJisfWNRuORWXdcWcuSkaVJ0SATu9341pmmzrn2B7/0TN/drihd5rI0kS3aWa7IGJjGOkNzb2XSVanu1UJ41fr/i/14B1ZzbM93p9m+awf9JngFefptazxRaWyxcN53fBVbM/19H2npYQLRE/SzpjU9h69nDfU35hoAsEdfFX1OwLY18wfUQav+CIIEsS56zuPtqH0ZA1C1vw+2SWdM5Ph4RAMB26W0LsLrVvsw3/yUpUZpu1vS0sao/WVSufC/w+hUjA57QarFfnG/T7OHt1N3uGEoQn6SGtTtHYBzhzZRfpnNbQwdFVUjZnCpHbtb2tdDeHafe0UlvaZQn47R9JsbKnqWJBuK0/zq7BhPNdXiG2XIOocVdziAMl7YG+Duv9RganOyW/Xhp/KqdRKOp1o9Xuv48VY38quCPRQVWp6WhTZJlYUCWZ05jIFOgpjVe75GGuRWD9PoXz1vTM6XfUwjeNyL5AcCLjjuw30vAQCOoxL4z93bt3Mzg4yJo1a2htbT3sz2zfvp3+/v6K1yKRCGeeeeasfLfg+CQWUtm4pP1oH4ZAMCu0uFKyD46lAQgf4ZZmily/Ldlc0hYNOMZ+o0iIIkus6GmedH/uiPVkWh1TaU3W3RzilYNW1L2RVoB9jLXqwjubQo642mPbhzhzZSchv48DoynH0Ac4OJaua+zXOs7OpiCKLJHTdKfvfaNOBW5HgNcp0BIJoMgS2YLOI68MYovWV6Xxq+40/nmxLJkVZElClsAwG59XS8RvGfvpAj0tlqBmObJ/fBrCakXN/vws7xMI5gPhgI/ulhBhv29KLTUFAi9H9SmSzWa54YYb2LBhA+95z3tYuHAh//Zv/3bYn/nXf/1X3vjGN/LXf/3Xzn9f+MIXDvu7BQKB4FhBliQnoj+csEXjjp+oaSPcxu1sCBqpihV9DfkVorOgPxAJqk4LxMNJyzx1WQdBVSGV03h02yD7RlK8sG8MgI5SZkH/WFkI0CZXKBn7Na6NIsvOZ+3sg0alH24BOe92QVXh3NXdhPwKmULRccBUCfT5js/IPi5B2kbn1Vwar/FMuW6/XsnD8YJbp0LU2QsE9ZFKApDrFomApGBmHFUX+l//9V+zZcsWXn31Vbq7u/n5z3/O9ddfz7nnnss555xzWJ/ZvHkzP/zhD2f1uwUCgeBYoqs55ET1KbWwOhFoK+lv+JTZUxg/f52V6jxbkZXe9gg7DsUPq6tHLKRy7ppuntgxRDpfZGvJ0O9pCbNpWTv3b+0np+kMxbNOC0bdMB31/HqOhq7mEIPxcgvBqUb2axltTWE/56/t4dndo06nglpp/LbGwZHukT7XKLKEpjc29m3tiHimgGmaGCYUdbtTw/Fp7AMs6YySzhWPmGioQCAQnIgcVXfqN7/5Td797nfT3W3Vcl933XVs3LiRb3zjG4f9mWw2yxNPPMG2bdsoFosz3o9AIBAcq3SWlN9tjqcU6UZEgiqnLG3n1GUds7ZPKyV79lIoV/Y0cf7aHkeAb6aE/D7OXdPtGIzNYT+nLG1DliR6S/s+MFoW8bPr5mWpfi2+LdJn08hh4i5xqCfk5/cpnLmykzULW1jWFavp4Dh9RSenr+ic5GyPPWyHSiOnTrTUyq+om0ykC05UX5KO76j3SX1tnLWqC0mkJgsEAsGccdRWfgcPHmRoaIjTTz+94vXTTz+dLVu2HPZn7r33Xg4dOsTAwACSJPGlL32J6667bsbfDZDP58nny2l2iYRVF2kYBoZhTPncBYKZYhiGFfkR400wBWQJWiN+Rkpp/EFVnpWxcyyMwwUlg3U+H2Ms6MM0TUzz8Ppz+2SJM1d2MJLI0RYLIpXOe2FLiB39EwzFs2TyGkFVYSiewTAMggFf3WvjkyVawqqTxu+T619HVZac92Sp8fVe1mWJn87mbzLfx+JJfa0kswWaw2rDY2yPBhiYyPD0riFOWtSKYRiE/PV/I8H8Yr6PQ8GJgRiHJxZT/Z2PmrE/Pj4OQFtbW8XrHR0dznsz/czVV1/Npz/9adrb2zEMg0984hPcdNNNPPfcc6xatWpG3w3wD//wD/zN3/xN1evDw8PkcrkpnLVAcHgYhkE8Hsc0TeQ6vbkFAje+YpZkMoVPkZgYG52VfYpxOD+RgPHRylZ8cjFHPKuxdadOJODjhQMJTNOkPRBmaGio7r58epZk0ioBiY+rFNK1o/upXJFkMlnazkcxc2SXFcfCWPQBQ0Pphtt0hwwODmYYTuo8OB63dBaCvoa/kWD+cCyMQ8HxjxiHJxb2s3cyjpqx7/dbKW3ZbLbi9Uwm47w3089cf/31zr9lWeZTn/oUX/ziF/n5z3/ORz7ykRl9N8Cf//mf85GPfMT5O5FI0NfXR2dnJ01NTVM6b4HgcDAMA0mS6OzsFBO5YEq0tOmkjCE6moJ0dc2OwI8Yh8cOJ8lhXtg3RkJXGE0YRKNRetsibFzS1jB9OtKsMZQ9BMDCBd1108mbNJ3YiFUqt6Cn+4j3gT6exmJrWwePbR90uiV0NYfo6jr+ShuOR46ncSg4dhHj8MQiGKzf4tfNUTP2Fy1ahCzLHDx4sOL1gwcPsmTJkln7DICiKLS1tTnt+Ga6n0AgQCAQqHpdlmVxUwmOGJIkiTEnmDJBv8xrTu6d9f2KcXhssLA9yiv9cQpFE5DobglxyrKOSfUHYqEAJy+2WpEG1PpLhaBfIhxQMU2T4FFqDXW8jMVoyM+Zq7p5bPsgulG6nsf4OZ1IHC/jUHBsI8bhicNUf+OjNhLC4TDnn38+P/vZz5zXUqkU9957L5dffrnz2rZt23jyySen/BnTNEmnK9Pltm3bxp49e9iwYcO0vlsgEAgEgmMZVZEdwb2WsJ/Tlk9u6Nss7YqxtCvWcBtZkrhgXQ8Xrl8gekDPAs1hP6cv76Ql4qe37fDEGwUCgUAgkMzDVQY6DB588EEuvfRSPvShD3Huuefyn//5n+zfv58tW7YQjVpCPu9+97t57LHH2Lp165Q+UygU2LRpE+94xzs46aST2LdvH//4j//IwoUL+d3vfuek6U/luycjkUjQ3NxMPB4XafyCI4JhGAwNDdHV1SW8toKjhhiHxxaFos7AeIYFbZHjTt1djEXBfECMQ8F8QIzDE4up2qFHdSRceOGFPPDAAwwODvLlL3+Z0047jYcffrjC2F6zZg1nnnnmlD/j9/u57777SKVSfOlLX+LRRx/lE5/4BA8++GBFPf5UvlsgEAgEgmMdv09hcWfsuDP0BQKBQCAQNOaoRvaPdURkX3CkEV5bwXxAjEPBfEGMRcF8QIxDwXxAjMMTi2Misi8QCAQCgUAgEAgEAoFg9hHGvkAgEAgEAoFAIBAIBMcZwtgXCAQCgUAgEAgEAoHgOEMY+wKBQCAQCAQCgUAgEBxnCGNfIBAIBAKBQCAQCASC4wxh7AsEAoFAIBAIBAKBQHCcIYx9gUAgEAgEAoFAIBAIjjOEsS8QCAQCgUAgEAgEAsFxhjD2BQKBQCAQCAQCgUAgOM4Qxr5AIBAIBAKBQCAQCATHGcLYFwgEAoFAIBAIBAKB4DhDGPsCgUAgEAgEAoFAIBAcZwhjXyAQCAQCgUAgEAgEguMM39E+gGMZ0zQBSCQSR/tQBCcIhmGQTCYJBoPIsvDVCY4OYhwK5gtiLArmA2IcCuYDYhyeWNj2p22P1kMY+4dBMpkEoK+v72gfikAgEAgEAoFAIBAITiCSySTNzc1135fMydwBgroYhkF/fz+xWAxJko724QhOABKJBH19fezfv5+mpqajfTiCExQxDgXzBTEWBfMBMQ4F8wExDk8sTNMkmUyycOHChpkcIrJ/GMiyzKJFi472YQhOQJqamsRELjjqiHEomC+IsSiYD4hxKJgPiHF44tAoom8jCjoEAoFAIBAIBAKBQCA4zhDGvkAgEAgEAoFAIBAIBMcZwtgXCI4hAoEAn/zkJwkEAkf7UAQnMGIcCuYLYiwK5gNiHArmA2IcCmohBPoEAoFAIBAIBAKBQCA4zhCRfYFAIBAIBAKBQCAQCI4zhLEvEAgEAoFAIBAIBALBcYYw9gUCgUAgEAgEAoFAIDjOEMa+QHAEyeVyfPGLX+QNb3gDV111FX/+53/O0NBQ1Xbbt2/nlltuYfPmzdx88808//zzM9rm5z//OW9+85u5+OKLuemmm7j99tvn7NwExxb33HMP73rXu7j00ku55ZZbeOqpp6q2SafTfPKTn+SSSy7h+uuv5wc/+EHVNhMTE3z+859n8+bNfPCDH5zxdwlOTLZv385HP/pRLr/8cm666Sa++93vUktK6Dvf+Q7XXnstl156KX/3d39HNputeF/Xde644w6uv/56zjnnHAqFQs3vO3jwIB/96Ee59NJLufnmm3nxxRfn7NwExw7xeJzPfvazXH/99Vx33XV8+tOfJplMVm33xBNP8Na3vpXNmzfzvve9j717987JfgQnJoZh8IMf/IC3ve1tXHbZZbz//e9n+/btVdsNDw/z4Q9/mNe85jW88Y1v5N57753RfmwOHTrE5s2bufLKK+fkvARHF2HsCwRHkKuvvpqtW7fylre8hQ9+8IM89dRTnH766QwPDzvb7N27l3PPPZdCocBtt91GIBDg/PPP55VXXpnWNt/61rd4/etfz+mnn84nP/lJzj//fN7+9rfz+c9//oift2B+8fd///d87nOf48ILL+TjH/84HR0dnHPOOdxzzz3ONqZpcu2113LHHXfwwQ9+kNe+9rX8wR/8AV/60pecbUZHRznppJPYsWMHqqry0ksvzei7BCcmzzzzDDfeeCMLFy7kYx/7GJdccgkf+tCH+PCHP1yx3Wc/+1ne9773cd1113Hrrbfyne98hze+8Y0V21x99dV87WtfY9myZTz++OMYhlH1fS+99BKnnHIK+/fv58/+7M+47rrreOc731nXMSA4cTjnnHMYGRnhPe95D7fccgs//vGPueiiiyqcSk899RQXXXQR3d3d3HbbbQwNDXHuuedWPL9naz+CE5Nbb72Vn/zkJ1x11VXcdtttZLNZTj311IpgTjab5aKLLuK5557jox/9KBs2bODKK6/krrvumtZ+bAzD4K1vfStjY2PCEX+8YgoEgiNGMpms+DuTyZjhcNj8r//6L+e1W2+91Vy3bp1pGIbz2hlnnGG+7W1vm9Y2r3vd68xrr7224vve+ta3mhdffPGsn5fg2MI7Dk3TNK+77jrzqquucv6+6667TMDcsWOH89onP/lJs6Ojw9Q0zTRN09Q0zUyn06ZpmubNN99sXnrppTP6LsGJSSaTMXVdr3jty1/+sqmqqpnL5ZxtotGo+W//9m/ONk8++aQJmA899JDz2sTEhGmapnn77bebgJnNZqu+7/zzzzcvv/zyitdyuVzFPCo4MfHOU3v37jUB884773Reu/rqq80rrrjC+btQKJgLFiwwP/GJT8z6fgQnJrWelxs3bjT/6I/+yPn7C1/4ghkKhcx4PO68dvPNN5ubNm2a1n5s/vZv/9a8/vrrzc9+9rNme3v7LJ2JYD4hIvsCwREkGo1W/O33+/H7/RWRpd/85jdcc801SJLkvHb99ddXpGlNZZszzjiDF198kXg8DkAqleLZZ5/lrLPOmrPzExwbeMeh/Zp3HK5du5aVK1c6r91www2MjIzw3HPPAeDz+QiHw4f9XYITk1AohCxXLkOi0Si6rlMsFgF4/PHHSaVSXHfddc42Z5xxBgsXLqyY75qbmxt+1+7du3n44Yf5oz/6o4rXA4FAxTwqODHxzlORSATAmacMw+C+++6rGIeqqnLVVVdVjMPZ2o/gxKTW8zISiVQ9mzdv3kxTU5Pz2g033MCzzz7LyMjIlPcD8PDDD/PlL3+Zr371q7N8JoL5hDD2BYKjyBe+8AXS6TRXX32189revXtZuHBhxXYLFy5kYGDAmainss1tt93G2972NpYsWcKpp55KX18fr33ta/n0pz99RM5NcOzw8ssv85Of/IQbb7zRea3eGLPfm83vEggoGUT//M//zOWXX+4YSfZYqzUWpzMO7RKTjo4Obr75Zi655BLe+9731iw9EQj+/u//npaWFjZv3gylGulsNjvtcThb+xGcmPz2t7/lsccem/Kzed++fVPez/j4OG9961v56le/Smdn55ydg+DoI4x9geAoce+99/LRj36Uf/mXf2H16tXO65qmEQgEKrYNhULOe1Pd5s477+Tf//3fue222/iXf/kX/uqv/oqvf/3r/PCHP5zzcxMcOwwNDXH99ddz8cUXc+uttzqvT2WMzdZ3CQSmafKud72LQ4cO8ZWvfMV53R5rfr+/YvtQKDStcZjL5QB4xzvewfnnn8/HP/5xDMPg9NNPZ+vWrbN2HoJjn29961v8+7//O9/4xjdobW0F1zisNSfWG4eztR/Bicm2bdu46aabeM973sO1117rvD7dZ3O9/dxyyy3ccMMNQpTvBMB3tA9AIDgR+d3vfscNN9zAX/3VX/GBD3yg4r22tjbGxsYqXhsdHSUYDDop01PZ5mMf+xhvf/vbue222wC45JJLGB0d5cMf/jA33XTTHJ+h4FhgeHiYSy+9lMWLF/OjH/2oIqW6ra2tKuo5OjoKQHt7+6x+l+DExjRN3vve9/LrX/+a++67j76+Pue9trY2KEWhOjo6nNdHR0c57bTTpvwd9n4++MEP8t73vheASy+9lEceeYT//u//FsKlAgC+//3v8+53v5uvf/3rFVHQ1tZWJEmq+dytNR/O1n4EJyY7d+7k0ksv5YorruCLX/xixXv11n/UeDbX2088HucnP/kJmzZt4pxzzoGSIn88Huecc87hL//yL7nmmmvm8AwFRxKx2hIIjjAPPPAA11xzDbfddht/8Rd/UfX+aaedxpNPPlnx2uOPP86mTZuc2tKpbDM2NkZvb2/FNgsXLmR8fLxmayvBicXIyAiXXnopHR0d/PznP3ciAzannXYaW7dudSKilMaYLMts3LhxVr9LcOJimibve9/7+NnPfsZvf/tb1q9fX/G+bdC757vx8XF27NjBqaeeOuXv2bRpEz6fryL9VZIkenp6GB8fn5VzERzb/OAHP+Dmm2/mK1/5Cn/wB39Q8V4kEmH16tU1n7vecThb+xGcmLz66qtcfPHFXHTRRfzP//xPlWP8tNNOq1LNf/zxx2lubmbZsmVT2k80GuXRRx/li1/8Ip/73Of43Oc+xw033EAkEuFzn/uc0HY63jjaCoECwYnEgw8+aEYiEfNv//Zv627zgx/8wAwEAuZjjz1mmqZpPvfcc2YkEjG/8pWvTGub3/u93zPXr19vjoyMmGZJrfrMM880L7vssjk8Q8GxwOjoqLlx40bz4osvdtT0vQwODpqxWMz81Kc+ZZqmaabTafP00083b7jhhprb11Pjn8p3CU5c3ve+95ldXV3m1q1b625zySWXmBdddJGj0P/Rj37UbGtrcxT43TRS43/zm99sXnHFFc57zzzzjBkMBs1vfvObs3pOgmOP22+/3fT7/Q3Hwj/90z+Z7e3t5s6dO03TNM1f/epXpizL5q9//etZ34/gxGTXrl1mX1+f+Za3vMUsFos1t3nhhRdMRVHMb3/726ZZelYvWbLE/NCHPjSt/XgRavzHL8LYFwiOIMuWLTP9fr959tlnV/z3z//8zxXbffzjHzcDgYC5evVq0+/3mx/4wAeq2kNNtk1/f795ySWXmJFIxNy4caMZi8XM8847z9y9e/cRO1/B/OQDH/iACZgbN26sGIc33nhjxXZ33XWX2dHRYS5ZssRsamoyzz33XHNwcLBim+uuu848++yzzY6ODrOpqck8++yzzfPPP3/a3yU48bjnnntMwOzr66uaE1999VVnu/3795unnXaa2dLSYvb19Znd3d3mb3/724p9feYznzHPPvtsc9WqVSZgnnXWWebZZ59tPvroo842Y2Nj5mWXXWa2t7ebJ598shkOh82PfexjR/ScBfOPYrFoqqpqxmKxqnH43e9+t2K7d7zjHc5zNxgMmv/4j/846/sRnLhcd911JmCeccYZFePH2zLv61//uhmNRs2VK1eaoVDIvO666yqc6VPdjxth7B+/SKbI5xUIjhhbtmwhn89Xvd7T08PSpUsrXhsbG2Pv3r0sWrSorlLqVLYZHh6mv7+f7u5uenp6ZulMBMcyu3fvZnBwsOr1QCBQlUpaKBR4+eWXiUajrFixouoztca0JEmcffbZ0/4uwYnFxMQEr7zySs33TjnllKpyjx07dpDNZlm3bh2qqla8V2+crVu3rqot3759+4jH4yxfvtxR/Rec2Dz22GM1X1+yZAkLFiyoeG1wcJD+/n6WL19eNbZmaz+CE5NXXnmFiYmJqtebmpqqSpzS6TQ7duygvb29QudkuvuxOXToEP39/Zx++umHfR6C+YUw9gUCgUAgEAgEAoFAIDjOEAJ9AoFAIBAIBAKBQCAQHGcIY18gEAgEAoFAIBAIBILjDGHsCwQCgUAgEAgEAoFAcJwhjH2BQCAQCAQCgUAgEAiOM4SxLxAIBAKBQCAQCAQCwXGGMPYFAoFAIBAIBAKBQCA4zhDGvkAgEAgEAoFAIBAIBMcZvqN9AAKBQCAQCI49TNPk//7v/wCQJIlQKMTChQs5+eSTCQaD095fLpfjpz/9KVdddRXNzc1zcMQCgUAgEJxYSKZpmkf7IAQCgUAgEBxbFItFVFXlnHPOYcmSJeTzeXbu3MnevXt597vfzac//WlCodCU9zcwMMCCBQt44YUXOPnkk+f02AUCgUAgOBEQkX2BQCAQCAQz5v3vfz9ve9vbnL+ffPJJXve617F7925+8pOfOK//4Ac/wDAMFEVhyZIlnHrqqaiqCqUsgTvuuAOAu+++m61bt9LZ2cmll14KQCKR4NFHHwVg06ZNdHd3H+GzFAgEAoHg2EMY+wKBQCAQCGaNM888ky984QvccMMNPPHEE5x11lkA3HHHHei6TrFY5Pnnn0dVVe6++276+vowTZNf/vKXAPzmN7+hubmZtWvXcumll/KjH/2I97znPWzYsIFwOMyjjz7Kpz/9ad7//vcf5TMVCAQCgWB+I9L4BQKBQCAQTBs7jf/b3/52RWQfoFAoEI1G+fSnP82f/umfVn3WMAze9KY3EYvF+MY3vgF10vh3797Nhg0buPPOO9m8eTMATz31FBdeeCFbtmxh7dq1R+RcBQKBQCA4FhGRfYFAIBAIBLOK3++nra2N4eHhitdfeukldu3aRSqVorOzkwceeKDhfv73f/+X9vZ2hoeHuf3227HjE21tbTz44IPC2BcIBAKBoAHC2BcIBAKBQDDrpFIpIpEIANlsluuuu44tW7Zw5pln0tzczP79+xkaGmq4jz179lAoFPjhD39Y8fqFF15IR0fHnB6/QCAQCATHOsLYFwgEAoFAMKs8//zzpNNpNm3aBMC3v/1tduzYwd69e4lGowD853/+J3/913/dcD9NTU20trby/e9//4gct0AgEAgExxPy0T4AgUAgEAgExw/5fJ4/+7M/Y+nSpVx11VVQqsfv7e11DH2AH/3oRxWfs9/L5XLOa/+/nftlaTWM4zj8HToxuCzLE2RitSwLMzxldcW2Nth7MO0taBUWB4JBm+2JayJsca9grCyM0wamwzlwOOfcXFf+ccMdP9x/7u7u8vn5mY+Pj2+zm80mm83mD+8EAP5vTvYBgN9W13WOj4+z2+2yWq0ym82y3+/z8vKSk5OTJElVVXl4eMhkMsn19XXm83kWi0WOjo4O65ydneXy8jLT6TSDwSDn5+e5vb3NaDRKVVUZj8fpdDr5+vrKfD7P+/t7Wq3WX9w5APzb/MYPAPyy/X6f4XCYJGk0Gjk9PU273U6v10u/30+z2fw2X9d1np+fs91uc3Nzk263m9lslsfHx8PMcrnM09NT1ut1Li4uDtf8397e8vr6mu12m6urq9zf33uzDwA/IfYBAACgMN7sAwAAQGHEPgAAABRG7AMAAEBhxD4AAAAURuwDAABAYcQ+AAAAFEbsAwAAQGHEPgAAABRG7AMAAEBhxD4AAAAURuwDAABAYcQ+AAAAFOYHpkq0jAUPLpIAAAAASUVORK5CYII=", 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", 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" ] @@ -652,11 +725,18 @@ "cell_type": "code", "execution_count": 8, "id": "aada924e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:31.310403Z", + "iopub.status.busy": "2026-07-31T11:08:31.310188Z", + "iopub.status.idle": "2026-07-31T11:08:31.968041Z", + "shell.execute_reply": "2026-07-31T11:08:31.967405Z" + } + }, "outputs": [ { "data": { - "image/png": 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" ] @@ -715,20 +795,31 @@ "source": [ "## Sector Composition\n", "\n", - "Sectors are a categorical partition of the column index — each stock belongs to exactly one of the ~11 sectors (Technology, Financials, Healthcare, etc.). A binary encoding of sector membership gives a $\\{0,1\\}$ $N \\times K$ matrix that will reappear in later notebooks. \n", + "Sectors are a categorical partition of the stock universe. If there are $K$ sectors, we can encode membership with a binary matrix\n", "\n", - "Sectors matter for two reasons. First, many factors (value, quality) have strong sector tilts that need to be projected out if we want a clean understanding of what is happening. Second, sector concentration in the universe drives how much idiosyncratic risk (stock-specific variance) a long-short portfolio will carry." + "$$D \\in \\{0,1\\}^{N \\times K},$$\n", + "\n", + "where $D_{i,k}=1$ if stock $i$ belongs to sector $k$.\n", + "\n", + "This matters later because raw factors often contain sector tilts. A momentum signal might accidentally be long Technology and short Utilities, for example. If we want to study stock selection within sectors, we need to project those sector-level components out of the signal." ] }, { "cell_type": "code", "execution_count": 9, "id": "f1ab950b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:31.969856Z", + "iopub.status.busy": "2026-07-31T11:08:31.969674Z", + "iopub.status.idle": "2026-07-31T11:08:32.287296Z", + "shell.execute_reply": "2026-07-31T11:08:32.286748Z" + } + }, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -869,7 +960,9 @@ "source": [ "## Conclusion\n", "\n", - "We have our return matrix $R$, which is a clean monthly panel over ~500 US large-cap stocks from 2005 to 2025, along with some valuable statistics and a sector mapping. In the next notebook, we take this matrix $R$ and ask the first factor question: which vectors $f_t \\in \\mathbb{R}^{N_t}$ have meaningful cross-sectional predictive power (i.e., point in roughly the same direction as the next month's return vector $r_{t+1}$), and how do they behave over time?" + "We now have the project's core return panel $R$: a monthly, current-constituent large-cap equity panel from 2005 onward, plus sector labels and basic market diagnostics.\n", + "\n", + "The important caveat is that this is a convenient public-data panel, not a survivorship-free institutional dataset. That does not make the project useless, but it does mean later claims need to be phrased carefully. The next notebook asks the first factor question: which cross-sectional score vectors have any relationship with subsequent stock returns?" ] } ], diff --git a/notebooks/02_factor_analysis_and_diagnostics.ipynb b/notebooks/02_factor_analysis_and_diagnostics.ipynb index f58b368..e97f8b1 100644 --- a/notebooks/02_factor_analysis_and_diagnostics.ipynb +++ b/notebooks/02_factor_analysis_and_diagnostics.ipynb @@ -15,45 +15,49 @@ "source": [ "## Purpose\n", "\n", - "A **factor** (aka **signal**) is a vector $f_t \\in \\mathbb{R}^{N_t}$. We have one score per stock, assigned at each date $t$. The central question of this notebook is:\n", + "A **factor** or **signal** is a vector of scores for the stocks available at a date:\n", "\n", - "> does the direction of $f_t$ predict the direction of next month's return vector $r_{t+1}$?\n", + "$$f_t \\in \\mathbb{R}^{N_t}.$$\n", "\n", - "In linear-algebraic terms, this is a question about the **angle** between two vectors. If $f_t$ and $r_{t+1}$ point in similar directions (small angle, high cosine similarity), the factor has predictive power. If they're nearly orthogonal, it doesn't. \n", + "The question in this notebook is simple: when we rank stocks by $f_t$, do the higher-ranked stocks tend to have better subsequent returns?\n", "\n", - "We test four standard cross-sectional factors, most of which we compute as price-based proxies since we are using pretty basic data. They are momentum, value, quality, and low volatility.\n", + "In practice we measure this with the **information coefficient (IC)**, a Spearman rank correlation. After ranks are centered, a correlation is also a cosine similarity:\n", "\n", - "> **Spoiler**: Momentum wins, and the rest have negative or insignificant information coefficients over this period. The methodology for diagnosing a factor is the same whether it works or not, and showing *why* the proxies fail is more instructive than silently dropping them.\n", + "$$\\rho(u,v)=\\frac{\\langle u,v\\rangle}{\\|u\\|\\|v\\|}=\\cos\\theta.$$\n", "\n", - "The main goals of this notebook are:\n", - "1. To define and compute four cross-sectional factors (momentum, value, quality, low-vol) as vectors $f_t$.\n", - "2. To measure each factor's information coefficient (**IC**) — the cosine similarity between $f_t$ and $r_{t+1}$.\n", - "3. To examine **IC stability** across subperiods (walk-forward: is the factor consistent, or just lucky in one period?).\n", - "4. To examine IC decay at longer periods.\n", - "5. To quantify **turnover** via rank autocorrelation.\n", - "6. To check **cross-factor correlations** (the Gram matrix).\n", + "So the IC is a geometric question: does the signal vector point roughly in the same direction as the return vector?\n", + "\n", + "We test four common factor ideas using data we can build from prices alone:\n", + "1. Momentum\n", + "2. A crude value proxy\n", + "3. A crude quality proxy\n", + "4. Low volatility\n", + "\n", + "Momentum is the only one that looks useful in this dataset. That is not a universal statement about factor investing; it mostly tells us that the other three proxies are too crude for this public-price-only setup.\n", + "\n", + "### Timing convention\n", + "\n", + "The raw factor formulas use `.shift(1)`, so the score at a given return date is based only on information available before that return was realized. That is why the IC code can pair `factor.loc[date]` with `returns.loc[date]` without looking ahead.\n", "\n", "## Terms used\n", "\n", "| Term | Meaning |\n", "|------|---------|\n", - "| **Factor / signal** | A vector $f_t \\in \\mathbb{R}^{N_t}$ assigning a score to each stock at date $t$ |\n", - "| **Momentum** | Trailing 12-month return skipping the last month; \"winners keep winning\" |\n", - "| **Value** | Cheap stocks (low price vs. fundamentals) may outperform; proxied here by inverse long-term return |\n", - "| **Quality** | Profitable/stable firms may outperform; proxied by a return Sharpe ratio |\n", - "| **Low volatility** | Low-risk stocks may outperform on a risk-adjusted basis |\n", - "| **Information coefficient (IC)** | Spearman rank correlation between $f_t$ and $r_{t+1}$ — cosine similarity of rank vectors |\n", - "| **Information ratio (IR)** | Mean IC / std(IC) — a signal-to-noise ratio for the factor |\n", - "| **Sharpe ratio** ↻ | Mean return / volatility — used as the quality-factor proxy (12m Sharpe-like ratio) |\n", - "| **Rank** | A permutation of $\\{1,\\dots,N\\}$; makes factors comparable and robust to outliers |\n", - "| **Turnover (proxy)** | How much the signal changes; proxied here by rank autocorrelation |\n", - "| **Walk-forward** | Split into sub-windows and test IC stability across regimes |\n", - "| **Return panel** $\\mathbf{R}$ ↻ | The return matrix whose rows (cross-sections) we rank |\n", - "| **Cross-section** ↻ | All stocks at one date — we rank within each row |\n", + "| **Factor / signal** | A vector $f_t \\in \\mathbb{R}^{N_t}$ assigning one score per stock |\n", + "| **Momentum** | Trailing 12-1 return: recent winners may keep winning |\n", + "| **Value** | Cheap stocks may outperform; here proxied crudely by inverse long-term return |\n", + "| **Quality** | Profitable/stable firms may outperform; here proxied by a rolling Sharpe-like ratio |\n", + "| **Low volatility** | Lower-risk stocks may outperform on a risk-adjusted basis |\n", + "| **Information coefficient (IC)** | Spearman rank correlation between signal ranks and subsequent return ranks |\n", + "| **Information ratio (IR)** | Mean IC / std(IC), annualized here by $\\sqrt{12}$ |\n", + "| **Rank** | Cross-sectional ordering of stocks; useful because it is robust to outliers |\n", + "| **Turnover proxy** | How much the signal changes; here approximated with rank autocorrelation |\n", + "| **Walk-forward** | Split into subperiods to see whether a result is stable across regimes |\n", + "| **Return panel** $R$ ↻ | The monthly return matrix from notebook 01 |\n", "\n", "## Outputs\n", "\n", - "This notebook produces per-factor IC time series, subperiod IC stability tables, decay curves, turnover estimates, and a correlation matrix (the Gram matrix of factor vectors). These feed directly into constructions in later notebooks.\n", + "This notebook writes factor exposure CSVs, monthly IC series, IC decay, subperiod diagnostics, and a cross-factor correlation matrix.\n", "\n", "## Notebook Structure\n", "1. [Setup and Imports](#setup-and-imports)\n", @@ -79,7 +83,14 @@ "cell_type": "code", "execution_count": 1, "id": "b6109fc9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:33.898987Z", + "iopub.status.busy": "2026-07-31T11:08:33.898337Z", + "iopub.status.idle": "2026-07-31T11:08:34.945114Z", + "shell.execute_reply": "2026-07-31T11:08:34.944563Z" + } + }, "outputs": [], "source": [ "\"\"\"\n", @@ -108,7 +119,14 @@ "cell_type": "code", "execution_count": 2, "id": "aebef262", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:34.947030Z", + "iopub.status.busy": "2026-07-31T11:08:34.946720Z", + "iopub.status.idle": "2026-07-31T11:08:35.015122Z", + "shell.execute_reply": "2026-07-31T11:08:35.014620Z" + } + }, "outputs": [ { "name": "stdout", @@ -140,28 +158,32 @@ "id": "ac67fd55", "metadata": {}, "source": [ - "## Factor definitions\n", - "We'll now compute our four standard factors. All are cross-sectional ranks (permutations) at each month-end, so they live on the same scale and can be combined later without further normalization.\n\nEach factor below is an economic hypothesis about a vector $f_t \\in \\mathbb{R}^{N_t}$; the rest of this notebook measures the angle between that vector and $r_{t+1}$ (their cosine similarity is the IC).\n", + "## Factor Definitions\n", "\n", - "- **Momentum**: Trailing 12-month return skipping the most recent month ($t - 12$ to $t - 2$). The intuition behind it is that stocks that went up over the past year tend to keep going up for another month or two\n", - "- **Values**: we use price-based inverse momentum as a value proxy: 60-month trailing return, inverted. The intuition is that stocks that went down over 5 years are \"cheap\" and may mean-revert.\n", - "- **Quality**: 12-month Sharpe-like ratio of monthly returns (mean / std). The intuition is that stocks with smooth positive returns are \"higher quality\".\n", - "- **Low-volatility**: Inverse of 60-month trailing volatility, ranked. Intuition is that low-risk stocks tend to outperform on a risk-adjusted basis.\n", + "We compute four cross-sectional factor matrices. Each matrix has the same shape as `df_returns`: one row per month and one column per stock. Each entry is a percentile rank centered around zero, so the scores live on roughly the same scale.\n", "\n", + "The factors are:\n", "\n", - "The following code builds four factor matrices, each matching the shape of `df_returns`, where every cell holds a cross-sectional rank in $[-0.5, 0.5]$. The helper function converts any raw signal to percentile ranks centered at zero. Applied with `axis=1` so ranking happens across stocks within each month, not down time.\n", + "- **Momentum:** trailing 12-1 return. In code this is an 11-month rolling sum shifted by one month, so the most recent month is skipped.\n", + "- **Value proxy:** negative 60-month trailing return. This is not true book-to-market value; it is a rough price-only mean-reversion proxy.\n", + "- **Quality proxy:** 12-month mean return divided by 12-month volatility. This is closer to a recent-return quality proxy than a true profitability or balance-sheet quality measure.\n", + "- **Low-volatility:** negative 60-month trailing volatility, ranked so lower-vol names score higher.\n", "\n", - "**The output:**\n", - "A dict `factor_dict` holding four DataFrames, plus a print loop confirming shapes and non-null month counts. Momentum needs ~12 months of history; value and low-vol need ~60; quality needs ~12.\n", - "\n", - "**Key caveat:** Momentum is the only academically faithful signal. Value, quality, and low-vol are price-based proxies — useful for learning the methodology, but expect weak or negative ICs because the proxies conflate the true factors with mean-reversion and volatility effects." + "The caveat matters: momentum is a fairly standard price signal, but value and quality are usually built from fundamentals. Here they are learning proxies, not production-grade definitions." ] }, { "cell_type": "code", "execution_count": 3, "id": "dbc1fa9f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:35.016900Z", + "iopub.status.busy": "2026-07-31T11:08:35.016702Z", + "iopub.status.idle": "2026-07-31T11:08:35.316517Z", + "shell.execute_reply": "2026-07-31T11:08:35.315893Z" + } + }, "outputs": [ { "name": "stdout", @@ -221,29 +243,66 @@ "source": [ "## Information Coefficient Analysis\n", "\n", - "The **information coefficient (IC)** at date $t$ is the Spearman rank correlation between the factor vector $f_t$ and the realized return vector $r_{t+1}$. From the linear algebraic perspective, this is just the Pearson correlation applied to rank vectors. For two centered vectors $u,v$, this is the cosine of the angle between them:\n", - "$$ \\rho(u,v) = \\frac{\\langle u,v \\rangle}{\\|u\\|\\|v\\|} = \\cos\\theta. $$\n", - "So the IC is $\\cos\\theta$, where $\\theta$ is the angle between the factor rank vector and the return rank vector. An IC of 1 means perfect alignment (zero angle); IC of 0 means orthogonality (no predictive power); IC of -1 means anti-alignment. \n", + "The **information coefficient (IC)** is the Spearman rank correlation between a factor vector and the return vector it is meant to predict. Since Spearman correlation is Pearson correlation applied to ranks, the IC can be read as a cosine similarity between centered rank vectors:\n", "\n", - "The **information ratio (IR)** is the mean IC divided by the standard deviation of IC across dates: \n", - "$$ \\text{IR} = \\frac{\\text{Mean IC}}{\\text{Std IC}} \\times \\sqrt{12}. $$\n", - "This is a ratio of signal and noise, which tells us whether the factor reliably predicts returns or is just noise." + "$$\\rho(u,v)=\\frac{\\langle u,v\\rangle}{\\|u\\|\\|v\\|}=\\cos\\theta.$$\n", + "\n", + "- IC near $+1$: the factor ranking and return ranking are almost perfectly aligned.\n", + "- IC near $0$: the factor is not directionally useful in that month.\n", + "- IC near $-1$: the factor points the wrong way.\n", + "\n", + "The **IC information ratio** annualizes the signal-to-noise ratio of the monthly IC series:\n", + "\n", + "$$\\text{IC IR}=\\frac{\\text{mean monthly IC}}{\\text{std monthly IC}}\\sqrt{12}.$$\n", + "\n", + "A small positive IC can still matter if it is stable, but a tiny IC with a noisy sign should be treated as weak evidence, not a discovery." ] }, { "cell_type": "code", "execution_count": 4, "id": "a7146b0c", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:35.318513Z", + "iopub.status.busy": "2026-07-31T11:08:35.318301Z", + "iopub.status.idle": "2026-07-31T11:08:36.607209Z", + "shell.execute_reply": "2026-07-31T11:08:36.606630Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Computing IC for momentum... \n", - "Computing IC for value... \n", - "Computing IC for quality... \n", - "Computing IC for lowvol... \n", + "Computing IC for momentum... \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing IC for value... \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing IC for quality... \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing IC for lowvol... \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "IC Summary\n", "\n" @@ -335,7 +394,7 @@ "from scipy.stats import spearmanr\n", "\n", "def compute_monthly_ic(factor_df, return_df):\n", - " \"\"\"Compute Spearman IC between factor and next-month returns.\"\"\"\n", + " \"\"\"Compute Spearman IC using shifted signals and aligned return rows.\"\"\"\n", " common_dates = factor_df.index.intersection(return_df.index)\n", " common_tickers = factor_df.columns.intersection(return_df.columns)\n", "\n", @@ -372,20 +431,27 @@ "id": "5659b941", "metadata": {}, "source": [ - "Note that we added a sample size filter with `mask.sum() < 20`. The `mask` identifies stocks that have both a valid factor score and a valid forward return in a given month, and `mask.sum()` counts how many usable pairs you actually have. The `< 20` threshold prevents the code from computing a correlation on a tiny sample, which is statistically meaningless and numerically unstable (e.g., Spearman correlation on 2 stocks is always exactly $\\pm$1). If you don't gate this, early-history months, mass delistings, or data gaps inject garbage $\\pm 1.0$ values into your IC time series, which then contaminate every downstream statistic like the mean IC, Information Ratio, and decay curves. Setting a floor of 20 filters out those degenerate months while retaining enough valid data to produce a reliable signal.\n", + "The `mask.sum() < 20` rule is a sample-size guardrail. The `mask` keeps only stocks with both a valid factor score and a valid return for that date. If only a handful of stocks are available, a rank correlation can become mechanically extreme; with two stocks, Spearman correlation is always $+1$ or $-1$.\n", "\n", - "We do this sort of masking throughout." + "The threshold drops those degenerate months before they leak into mean IC, IC IR, and decay statistics. We use the same kind of guardrail throughout the project." ] }, { "cell_type": "code", "execution_count": 5, "id": "d0fe3ee3", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:36.609261Z", + "iopub.status.busy": "2026-07-31T11:08:36.609030Z", + "iopub.status.idle": "2026-07-31T11:08:39.039239Z", + "shell.execute_reply": "2026-07-31T11:08:39.038702Z" + } + }, "outputs": [ { "data": { - "image/png": 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", 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AIrVPgVuRBkwXLSIiIiIipek6WUREPIECtyIiIiJSKQpsiIiIVC+dW0WkOAVuRURExGPp5kVE3KHuuyIiIlIfKXArIiL1jm7gRURERERExNspcCsiIuLl1CpVdAyIiIiIiNQ/CtyKiIhHUOBJRKThqErPCPWqEBERkYZCgVupFAVYREREpCHRtY+IiEj10oM4kQtT4FZEROQCdFEpIiIiIlLz9KBUpCQFbkVEREQqQQF9EfEkCnaIiIjUPwrcioiIiIiIiIiIV4tN3s8lx5aSEBoLOZ3AP7CuiyRSZQrcioiIeIgWqYexHDbHQtrWdVFExF22E2wbrLouiIiISAN2dA9X7fsQP2cuLdKPwCuH4LqZ0KJDpVdZZz0ZUpPgbCLEtIaAoNrbrngkBW5FROSCqvuixVO6c3pSV/fuJzcz5tBXOLGY23EqcFG1b6Pt2T30j1/OkfAO4OwBDke1b0PEG0WnH8e2LE4FNXN/ofw8+iSsYsDx5WT7BrKw7bU1Um895feyqurL5xAREc8Tnp0EH72JnzO3aOLp4/D6IzByKgy5+oLXvXV5nvLLz6Zl6iFYsAEObIXEOPNGcDgMnQwDxtdqecSzKHArIpVS1onNNz+HDmd/JiGkZR2WrHy6aZTa4JefjdNykO/wc3+htLMMO/IdAA5sxh6cDckjIKJJ9RXsx++5au+HOHDSMi0OvguGcXeCpWaC0oA5nQw9spD+CSsBSApoDCGj4aLh0LhF+csd2AbzX2P4qaMABOVncv3ut2BFtrnBOvfmMCsDjvwMzdpCWFSNfiQREU/kSQ/LpX4JyMvg6j3vQVYyAMdDW+PjzKNpxnFw5sHid2HfZpj8UPVeW1eHs4mMPfAFnc9sx8d2wt5z3s9IgUXvwJo5XBw1mB3R/Sp2jyH1ggK3IlI9bJtJe98nNvUQ+ZYPRByB4VPAP6CuSyZSa1qkHmLSng/I9fHni653uL/g4vcIzM90vQzKz4TP/gG3/wV8q+HibN038O3rOM6dFhQGI26s+vpFvFFeLnz9X1fQFiAy+zQs+8T8a9EROvWF8CYQFkl0+hnyHT4MOvY9bPip1OocOGHJ+xzesIbv2l9Hpl8o4dlJzAjeB5sXQ04mhDWG+5+DoNBa/rAi4s3U+KBuaL97Ph9nHhP3fUxU1ikAzgQ2YU6nW8h1+PGroB2w8kvAhkM74D/3wcWXm9a3TcpoaGTbhOSmkuVb86kJ/PMyYdG7sHYe3fJzS75pOUx6h9BI2L3BlD8tiRFp8+kXv5IlbScBOh4bEgVuRaR6/LyO2NRDAPjY+bByNmxfAePvgq4D67p0UgG6SK2kzDTG7f+cAGc2Ac5sxhz8EpzDwOFz/uXidsGPSwHI9gkkx8efsJwUOLoHlrwPV8yofJlsm4HHf4AN3xdtLrw9rVMOmBc/fGwCSIMmVH4bIt4oOxM++Zvpjgg4sUgIaUXz9KNY2Gae4/vMvwJTy1hNfEgrlsWOp1PyT/SNX4OFTZuU/dzy00skhLSi3dnd5oarUOppc34cM73GP6KIiEi9ZtuMPvgVrQruQQmJ4OsO08guDLyOngYd+sCXz0HKacjPg82LzMPUrgPhkomQlwdHd8PRPfzi4C6C8jNx4oC4ltCsHX2TAjkTFG3ubzefgMxUyEiF7AwICDapDELCaXM2iUy/UE4HxZy3yD7OXHqe3MSg4z9AXoZreqZvMHuienLx5SOhbU8ICjFvJMaZ6/WdawAIy01hwv5P4OwwaHT+bRXfT+3P/kxIbhrxoa3A2f3C9yfiURS4FZGqy8+Hxe+7XjpxmJZHySfh41nQeQBceZf7JxcRb2PbMO9/hOWmuCa1SIuDdfNh8FXlLmbZ+TDvDdfr1S1Hcja4CZN2f2AuENfMgTbdoeugSpTJyfAj39HnxBrXpHXNL2Nty5H0ObGG4UcWmInfvg6BIXDxCPM5kk/RJnkvITmpHAlvX/Htiniw57/ZTnBuKlfveZ+YjHgA8ixfvu1wAwciuxGSk8JdMSdh+3JIOFj+ioLDYfQ0Pj0eDVikhERxKKwT1x2fA2lJhOSm0eHsz0Xz+/qD02m6bK6dZ3LVNYquhU8sUn3U1V1EPEnnM9vpemYbALkOP/xu/gMpW7NLztSuJ9z3HKz4HDYuNL1fsOHndeZfMYXtbB044eQROHmEYcVn2Ee5rin4P9snEJxDoddwaNvDBEid+XDoJ9i2jLu3ryIgv1gZffzYGD2IDc2HkeMbxMXdzvldjWkNU34P8Qc58uELxKYeNHl8v30dbv7DhXdSfh4jD8+h18lNRdP2vg2tOkPrbtChN8R2Of86sjPNPUJg8IW3JzVCgVsRqboti+H0MQCOhbZhcduruS1jmaslE3s2mFaFN/w/c3IQt5R1g6SbJs/U5fQ2OLgKgGxHAAHOgguyJe9D5/7QuHmZy/U+sQ4SDwNwIrg526MHEEgWy2Ov4PK4+WamL/8D9/wLoiowaBIw7JygLWNvZ22SCcRuaTaE4e3CYPln5r2vXjDBpFPHIDfLdfGZ6hcO2f0rNJqtWmyXwbZh11qwnSYI76PLrzqRnkLf+JX0PrG26CFLUCiz29xEfFhrM4t/OAwdCkOvMfXh1DFIS4LUM2zfuZ/g3HROB8UwcPrdprV6/Hbz/QJHw9vDpGc5/OpfaJOyH4A0vzBCh10F/a6AVV/C6q8gPxe+/wgmP1h3+0KqTbdTWxhwfDkHG3WG/G6q3yIitaTr6W2uvxe1m8yVrTrD1u2lZwwKhbG3w/AbYNNCc82beqbUbBm+ISQGNyckN43o7FPmYWsFBeRnwZYl5l9opGk9e2iHuZYASiQR7DUcRt3CqlUnLrzi5u2Y1/Fmpu14gdDcVJNC4ed152/ckZkOn/2DXie3lpyek2nu0w9sNa15+401vWTPTc9m27D1e5j/urmGve0pE/CVWqcrC2kwGlIwwS8/m66ntpLv68POqL41ui3f/Bz44RPX65WxYzkb1ASuexx+WgUL3jQnqqw0eP9pGHsbXHKVBkSSeiMsO4nL4+a5Xi9udzUtUw/TO3Ed5OXA1/+F258uNVhRSE6KyZUJgMX3ba7Cthxgw7bogVweehZ2rjZdsT77J9zxf+DnZs7o7SvoWxC0dWLhmHQ/9B0NxQP/l99sunttWGAuxuL3l/5suSnm4vayGyq1bwTIyTKB8Z2rzetGTeGyKXDRZeDjud3U6s0507ZpkRZHr8QNsHknw/KLbsJS/CMIv+Np4tefLXvZJi1L5MBbmlG0TwaWl6M2tBFfdZ5Gx6SdgMWBRl341fA+5r1h15numVlpsPUHGDzJDFYm3mvnGsYc/AoLm8gTa+DDZ2DK7yr0sEtERCohK4PYgoekqX7h7I3sfuFlAkPMAKKDJpqUfns3QWgjaNUFWnXmtdWJrnvUh67oCqeOsvC7FTTKOk2uw5+hfTqZIHBwmEmTkJVuBg9LT2Hd1r00yjpNu7O78XfmmO2lJcGOFSWKkO0IYH9kN7Y2HcTN140rmOpG4BbI8Q1keevxXLn/UzNh/uvQ7qKyzzlJJ+CDv0DBIKp5lg8/Nh1MeHYSnXOPuwLJgAlmJxwy56/CwduyM+GbV2DbsqL55r4Mv/hnmdevAXkZjDg8Hwsnm5tdqhy81UyBW5H65uAObvnpJSKyzY9xvu0LXFRjm+tzYrXrh39fZHcSQmPNG5YFPS+Fjn1g9vOm1a3thO/eghOHYeK91TPokkgdsmwnVxyY7erytLNxb/ZF9eRwRCfaJe8x9TBuJ2z4tlQe2WFHvitqmdtvDCccrVwt97AsmPRL01X7TLwJqn78N7jpEfDzP3+hEo/AnJdcL39oM5GRfUeXUXgLxt8NOdnmaToWRDaF6Fg2J/vS+8Q601Vs1ZfQ/woICa/y/mpowrLPwpt/hIQDRRPPnoCvXzBd9kbcaH4nlWesRoTkpDBx30c0Sz9W6r1DER1Z3PYa7oqOBcoJ3FaW5WBfVM/S04NCYfj1sPBt001z8btw65+rd9tSa5qnxsHsd4tyIgPs3wJv/wmm/gnCIuuyeCLSAPSLX0GPU5vZ1GxouYGyettbb+8mfO18APZHdjMDernL1w/6jDT/irNOlpynWTt2NUlzTRrav/x9t/aU2c8+zlweaJ9ZFBjOzwOHL3TqA70u47WDQeQ7Kn8PvDeyB4fDO5iePSmnzGCqY28vOdPhnSaPf4bpXZThG8y8jjcTH9YGgM5X9oSzifDzetM7MC8Hju2BV38LN/wW/IPg83+Ze5DiThyCjQtKj41h24w+NKfgoTV0ObMDvtjJm1Z/UgMauWarN8deHVDgVqSe8M3PYejRRbBhHRHFpl96dCFkX1cjrT+CctPpF2+6h2M5WNWyjOBQYIgJNn3/kQlUgBmI6dRRuPFhCIuq9nKJ1ArbSf/45bRMM6kOaBTDsjZXApDrE8Ditldz3e63zXuL3zOj0/sFwu4NXL1nMW2T95r3gsNh1C3w/eGS6w8MNk++3/yDabW5fwt8+ndTb8p56OGXnw2f/g1ys6AgkLw9uj8jy5wb0wr4ml/ByKmm9UBBi94V32zHz5lLr5MbTXeqFZ/DuDuqvs8akOaph5m472PISzcT/IPMCMGHdpjXZ+Jh9nOw7DMYMM7kGC6vFadUXG4OV+39kKYZx4umBYWyKfwidsQM4Gxg47op14DxsO4bkwN+3xY4sA3a19zDVakZjbJOc9W+D83NLnAoohPN0o4QmJ8F8QfgjUdMUL6sUctFirNtorJOkhxQQ4F+2zaDGyUcNNchwXoIWxNCclJod3Y3+9xp9Xku28bPmUOu4wIP5s+1bwuXHl0EwOhDc2BVpEnz01DsKkoHVqn9XkPyHX7Qoy/0GGpSFZyMgyatzHU2kH+4jFQOFWFZfN9mIrfvfMmkXlozFy4aAVHNTe+uTQvhSLEc+01a8mnzG0gOjCqxDiKbmnE42nQ3Qd7kk5CeDO88boLghWki/IPMfMsKWvku/dB8ttCigCxbf3AFbV22L2e6tZofm17iyt8rlafArUg90CL1EGMOfkmj7KIuDzkOf/ydOSYHzoovYPSt1b7dgcd/KNFi8KzVpOwZHQ4TmGraFr76j7nROboHXv09TP0jNG9X7WUTL5OfT1RmIkG56RwvyDXpSXycubRMPUx0RjyNMxPhlXe4/0ScGRygMB3B5IfI2ZHvWuZoeHsTkNuwAHKz4bXfmws4bEp0jh49rfwbqWbt4JbHTJqR3Czz5P7Tf5iAbhl5qEYf/BqSTOvCk0FN+b7NxAunJbGsom5RxaxrMYKup7eaz7jhWzPyrocMMBiUm46FTYafZwY6u5/czMjDc80AcwCRzcwAEjGxphXE9x8VBXBPH4MFb5jgfo8hJs9YbFelk6kK24Z5L7uCtqn+EaxqOZpxU6ewcuHuui2bn795UPLl8+b1onfg7n+YFrgHt8OP35v/uw2CcXeVm06jXdLP9E5cy44m/dQdsZYF5qZz9Z73CCoYDfxIWDvmdryZRtlnmHbkE3PzezbRBG9vfMQMTiNSFqeT8fs/o3PSDo6GtQW7zwV/+x3OPHMeObjd/DtxyJybW3cz547W3Uxr7yO7iwZfSirohh3ZDO76m3rQVLczCUz96WWC89LpeXIjOAe435PGmc+4A5/T5cx2zgQ2geDLofsQcx4737GQmQ5fv1hy2qJ3TOvO4ddX7fN4g9xs2LsZClqTeuK9AwBBIaZOVrPkwMbme/7+I9Oj9eNZJm1DVnrJGdv1gim/J3npeQZbbdHBpD/44lmT89Z2mn8ALTrC9TNNUDjljBnXJjvDHGuTHzLznE00A6UV2B7dn45nfiIoPxNfO4/+CSvpfmoLX3eepuuVKlDgVsSb2Tb9jy9nyLElRV31fP1Z1nwUhyI6cutPLxWMTP819BlV7gBJlRGRdca0xgPTSu+yKbC8dHfUEnoONWX4aJbp2pF62rQmvH4mdBlQbWUTzxeQl0HnMzuISY+HV9+FxDimFbRc2teoG9gX123gyraJzDpFm+S98N6X3HtgO752yQEKiodNNzW/lAFtusOOc56ij55uLizPJkJmWom3Uv3C+bHpJQzrXW57WKNNd7jlT/DB0+ZCdc8G033pht+WGASn94m1dE4qCAYGBDO/403k+VSw9UYx6f7h/BhzCQMSVpgbge8/dnswJcvOx6YCXdbOo1HWadqe3UNU1kl482M4dZRfZKTgxGJp20kedxHY7dQWxhz6qmhCu17muyoMzrfpbnIeH9xuBoQ4XNBCIS/H5D3d+gN0HlB2cF7c0vvEGjjyAxSMMj2n0y2cCm7GuAulGaktvYbDmjmmBVz8AXOzdGS3OS8W2rAAsjLKrnPbVzBx30c4sGmRGgdpV5Zs+SI1xseZy1V7P6RRdsGgNjGtmdfyZpwOX84ExcCdfzW/1ScOmd/8tx8z+cVH3apgmZS2+D3XebtV6iHzcLZz/zJnbZZ2hEHHv6dl6mHYlFvyzYSD5t/6gkFNff1drcFLSEowvXKmPaHzS3VJT4b3nyK4oHdN04x42LTIPLh3x/cf0+WMuXaMyjplBo1d/hnTAxqzL6o7W2PKGXhqwevmPqrg4WRYTrKZvvQDc8024sYKX0d7VUqF/T+aa2LgQKNu2FYDTDk1dLLJP3v6uLnPKC46FvqPg/5j3RswMyQcbn3MtKZdOdtMG3y1aXhV+Fsx+lYz0G5hnv6+Y8zDoq9eMMHcgp5+S9tOYmWrMdwX/DN5q+fia+cRnJfONbvfgRNdoWmbat8VDYECtyJeyseZC18+z9BjRQnDj4e2psWMh/lx7WmwbbY1HUCfhLXmBL7wLdPiq5jo9OP0PrGWY2Ftwe7p/gk+K51xBz4rak02eFJByoMLBG4BmreHX/zDPBk8use0Ivz4r3DF7SZR/AXKUG8GzGnIDu/k1h0vmtbgZeh4dpcZEGvwVbVeNJxOupzexiXHlhbdmJd5srQ4GxDJ6aAYDkV0Zkd0X8p89BAQBFf/ytzI5+WYC6mug/jodGMSg1uAZTHM4UaAs20PkzOxcD0/r4N/zDAPTRwObsvKJzy7WJ7Oax7k7IHgyu4Fl43NL2XA2S1FF2lDrr7gBVff+JUMO7qQ3VE9wdmjavlbt/7ArTtewKfwyX8xDmxGHpoLB/uZ4KgnOHHYlKnAjzGD6H3rb8u+aG7Xy/xLjDPd2rb+UNRSYs8G+OLfcP3/8+gBzDxRbMp+hh1Z6Hq9sN1kTgU3q9MyleJwwJjp8N6T5vVPq8qeb/tyAKyAkWbgQoAdq2D2czgKHtb62nkmyHv5TbVT9vrqbCLsXGO6sl58ednXIvn5XLn/U1qkHwEgzS+M0FseI2dlsRyA4VEw4/9MWpsDW01L6s2LTJfeUbeaIK5yWgsFgwGt/qrktJWzyw7c5mQzYd/HZV83hUZC2llzrBUqHrS1HGZU+5NHzJgUh3fCvP/B1Q+oZ0d5bBtSz8CJOPokrCU4N40j4R1K3yvlZMGH/1c6D2hhV/KCrvHl2rXWlUbOxqy3sCFOZPZpBsSvoMfJzdCnUcmW+7vWmWsGINsngE+73kXnM9sZdrTg3LfsE3PvN+oWj/uOqys4bO1a6/p7X2T1t2j1Cr5+MOEecy1hO80Dm56XQr8xZrC1in73Dh/TC7DwHHhuqp+QCHNMffOKef3Nq2ag3YIeZCn+ESxrbVLG5fgGwZjpvJvajisOfE7LtDiC8jPh3SfMYMuNW1TPPmhAFLiVBs1bg4DBuWlM3PshpB91TVvTciQbmg/nwcYtAPME9sdml9Dp9E/mQm/3BpNPr2PB6NY/fs+UXa/ja+fR/fSPsCzQPJ0tQ4n9NLI9vPeEa7CXNL8wQodUMJ9SaCO47Sn46r/w00pzslnwpnliOO5O954MivexbVj9NSx+j9ASgTgLoppxwBlO+7MF3ZgXvWtaJrboUGvFa5F6CF5/l3HH95V6L9UvnLiIjhwNa8up4Kbccs1lvLNwj3srbtcTHvqfuYhuFA1A4jeVyG/VrqdJLfLh/5mbsqw08w8o0dZu6GTTzfpAFXNoFV54DbvWfB/YsOQDmPqH8hc4tMOVb63LmR1m/jHTK75h24ZVX8HidykV4giN5FS+P00yT5jB0z79O9z9d9ONqw6Z/ML/cLXM3h7dn2VtJtD7Qr9nMa1h/F0wehqLPviYEXHfmPQUu9bAvJfNIHWVvfFyOiH9rLmx97Cbt5oQnp3E+H2fmuMC2NB8WNmDhHmCDr3Nv/0/mteWw5yf+4wyrz9/1uSX276cMY2TWdRuMh2SdsHGz4q6MBba8K2p9/4Btf85vJiPM9fs03e/MLmGCwNf+340ub+Lt0i0bZj7kuscle0IYE6nW5ka0QQ4J2gTGGxS3Kyfb7qx5mSa1rfz/lfUEq9lJ4huVb0fKD+fgcd/oOOZnWxoMbzCvREczjwaZZ8x3bWlZu3faoIeBbJ9AgnIz4K4XRD3M7TuWnL+9d+4grYZviEEdx9Q8PCvp0mTkJVuGkPE7TL/kk6YLs5dB0KnfiaAeGwvvPUnc/3w41JWnPJlc/NLoYL3QF7VKrOCYtKPM/ToQqIzEmCjaUE4vOC9/gmr4K2NJnDVpjvk55veT8fMeAVpfmGcCm5mxi/ITDU9aq68u9xtRWaehC9fc71eETuW3VEXcXfLZNi5Guehn3Bgm5a87z5urhP6X2EGm5r3smu5Za2vJC0ggs3NL2VYz1bmfgpg5RdmsKmLLsM/L6xUflFv+B798rPJL6slbX4e7DG9PrN9AjkS3r72C+cp2l9kHhYmn4SOfU1qhqo659xU/Fix7OY82Ly96S2UeNgMsmreYWG7a8nxDSyxbGpAI+Z0vpXJu98xsYP0syaH7h3/5zHp17yFoiMiXqZJRgKT9n7g6hKT6/Dju3bXsT+qdFL2XJ8AVrYaw7iDBV0evn0D7v2XCaasnVvyB+CHj03AdNh15W47IC/DXDzE74eCi8evOk/n1sBKtOzzC4DrfmNSJyz/zEzbsAA2LTbBrchmXJ7iS3JAJAcbdan4+uuAN1wEVaeyHnwE5abTOnkfuT7+kBRjTsqWZXJxffUf2L3etcyRsHasaTmKKZNHQkAQc7/ZztAj35mLY2eeuSC+51+VG1jPtmmSeYKw7LPk+ARAQigEhJjB8hwO00oiN4vGGScIzMug94m1pqVvMcdCW7M/shvDr7qSN9efLRn48qtggKS6Rhdvf5EJCHz/ock15cwDp5OMrGwcts3BRp3pNvKW6tlWoYFXmsGUUk6blqCHd5qblnNlpsOX/yk5wvqqL02e3l7D3N+e0wnfvQXr5rkm7WjSj5+i+3DjpBEQFMKH87Yyae8HBTdIaSb9yp1/NQGTumDbjDo0B86YB1qJwc1Y1np8xdbhF8DO6L6k+Ycxae+HpkfDliXmmB17e8UCr+nJZtmN35mWhF0GwnUz63VgLzQnmYl7PzQtOgA69WNNxKi6Ltb5XfcbWPmleZjZa1jJwTqn/M7ks3bm0e30VkJzUkwX6YKg9I4m/fBz5pguthkpsPV797vmNnCWnc8lx77nosT1ZjCxc+1YYX7vbnq4KMXJ4vfMwKpAnuXDvE43czLkPA+LfHxMr5GeQ82Dr20FvaPi98OcgryUfgFcH9CUEyEtSfOPgB9PmQBbUBiENy4z93i5Us7AF88y+NhPAIw++BWkXuH+ALBOJ5P2fkCblP3saNIXuNj9bdcG26Zl6iEsbJM/3pudPGIeODpNr7XNTQdzKrgZYw9+ad5fNRtaF3tAmpluficK8ul/0XUG064957ctMMQ8+ClsnFGWlp3gmgfh838CcOnRRZwNbMyBhtpa8Vz5+Yw78BmRWafLnyduJ7z1R/PQLSjUFTwkIJivO04j0zeY6dv/g78zx9zT9BtbZi8l/7wsJu77yFyLArujerGl6RBznh84BAaO540vVzP24GzapOw3x8o3r5hgWWaqOccDdBnArrDeRSu+5Crw8StqEVmQB/luy4fDEZ34ufHFFW6d6va9TV6uue6wbfO7V8VB8Dqd2cHYA7NJ8w+Hy/5ufhML+B/bjVXQNf9Aoy44HQ08pNW6K9DVjRmrzrYcppXv6w+XfGPwVRzLK3vMmhyfQL7qPI3rf36LJpknTFqowuCtBil3m9cd5S+99BL/+Mc/iI+Pp0ePHjz33HMMG1b+DeGyZcuYOXMmP/30Ey1atOD3v/899957b62WWaS6xKbsZ+Lej8wFAUB4Yz5rNeW8Nw+7oy5iXO5PcHS3GQTnhQdK5NE7GtbW5NUCWPK+aWEyeFKp9QTmZTB59zuQYVqWZPiG8EXXGSanW2U5HGaQlqhmMOflgiBUnulydCaewnG2hx1dCLlrYei1ld+W1HwL859WM23Hi64BW3j+IwgIhmZtIflUifxL65sPZ23Lgu6/xQKza1qOolXqIfNU9ky8aaF07a+Lglb5+SZNQNxOExRu0wPLdrq6EYdlJ8Hyz7l1x0IaZ50sKls54xGVNWTfqaCmrIgdS1xEJwCGx7QGK7kadlA1adcT2j1TYtJrxb7bbtXdtd4vAEbcVBRsWPAm3PakuVEsbv6r5ol/QQvlsNwUM/3r/5rRdN0ZhDDPpIAp3nV8dctRbGg+3BwDBS0JbMvBt+1v4MZdr5qccCePwOx/w02P1Ek35J4nN7py1GU7Apjf4UYzqnAlxEV0YkH765hw4HPTsnLNHHODOPyGCy7bPDWOixLXw+adpkVKod3r4b0nTLqN6miN4UH88zJh0bvctm2uq7VzUmBjIq/9DfbSA3VdvPMLDoext5X9XteBJjfyZ/8AZz6xqcUGFukziiU+w4jOSHAdd6yZY7pHqhv+BfWPX8nA+OUlJ0Y2NQ84Ni00eRPjdsLrj5gHZXs2mIdQBd2Zv2t/vfvBw7Aocw7rO8b8RibGFb2Xm03L3DhaphVMO7Kg5LIXjYCrf3nBXkitk/fB//5pAvgF/J055iH9Nb9yr5zbl5sAEdDz1GYzInls7QQCLigz3TV4E8C8Djd5XG5zd7361Vqm7HqVRgVBJzoPYGX4FVi2k8FHl5jz5u4N5jiJKRhsafWXrt41Pze+uGrX3T2HmnuB7z/Cwmbcgc/5tNtdXrs/q9XW711B2yyfIALbdoGY1ixMML+p/eNXmOsNKOopAeDwhRsf5tQuc526sfkwhhxbYs7f375uehgWf/BqOxl7cHbRumLasLjV1aUezmb4h/F151sZemQR/U6sNhM3LyqaISgMJt4Hy4+WWI4B48z12ZL3XdfdvnY+Hc7+TIezP3MstDUM/G117TXjdLzJ1V7YY+27N6H7EFrmdDDp+Cra4+foHsYemI2vbXoB8MnfYcZfXL0gAg8W7f/9evBQaZVucNSqs0n7s3mxeR0dCyNvgfMM/prtG8yXXW7j7iPvmx62SQlmkPKOfcz6WnU2LX11DVOu6hk5pJZ88skn/PrXv+aPf/wjW7ZsYdiwYYwfP564uLgy5z948CBXXnklw4YNY8uWLfzhD3/gwQcf5Isvvqj1sotU2Z5NTNrzgStomxDSEu7++/lbfFAwYvyVd5vu6FAUtHX4sqTNVXzR9Q5WthpTNP93bxUNbgCQdpbWyfu49ue3iSkI2hLSqOpB2+J6j4QZT0P3wRDTBvwCS8+zewO8+SjX73qDtmd3mye64hEC8jJMt97P/lEUtC2UnWFaaBYGbYNCYeqfWNNqdFHOxmKcDl++bX+DCfhSkONx6/emG+Dqr+E/95lAxrpvzLH66m+5Z8tfmbTnfa7f9Tp3bPs3LP2gZNDWTWl+YTDpl3zY4z5X0FYKXHx5Ua6r+P3wxqNwJqHo/e0rXPk4s30C+azbXfzUpKDlT16OyWmdnlLWmkua81JR0NZywKRfsqHFZWVe9Of4BjK30y0QGGom7NloAhW1LDo9nsvivnW9XtzuGjPabxXsi+oJE4s9ZF76Ycnf5TIMj5vPlJ9fp+uZbcWCtpbJeQYmEPP2n0zevnrAx5lLn4RV3L7tOVj1pStom+EbwtyOU+tHgLrbILjht+QX/63sPRKuuh8sBydDWnAkrOCByJl42L2xzorqNU4eYeBxkxvSicXOxr3NYIG/egnG3QG3/wVCCpLPnImHV39rzjUFvm8zgX1RPcpbe/na9oB7njXrH3ObGTX+Qt1Et/0As58zDyzLYNlOLjm2hGv2vOcK2qb6hZPtU3AN9eP3cHz/hcuWk22CPMUtfMczrrMO74T//aboAQUw5NhiV2tVr+J0Mu7AZzTKTjKvm7WD636DbTlwOnzZ3GxI0byrCnLfpiaZnP9AvuXD2paXV70cw28wAyQCfs5cJu35oN6cFyotLxeWfep6ObfTVJj2OFwxg11N+rCrSR/e7/lL8yDk3Ho7+UHTG6rA5mZDzIMgTPoodq4xf9s2HPmZCfs+ocPZn820wFC46eFyB5K1LR9Wth4Hkx8yLWmLm3hP+b25eg0zKbru+htccpW5vi3QMi0O/jeTwUcXm/FSqqjLqa3wysyioC0FqQy2L+f63W8xbccL9E5Yg48z73yrcQnJSYGP/1pyQOBje2B+QVoJZz4BhYFbvwAOh3es8meQShg9zTzca9zCjMfgxuCvGX6hMP3JojqUehq2LIa5L8HLv4a/ToN3/lxUZ6QEr2px++yzz3LnnXdy1113AfDcc8/x3Xff8fLLLzNr1qxS8//vf/+jdevWPPfccwB069aNjRs38s9//pPrriu/O7hITarM060OZ3bCx5+7TmL7G3Xl2w438IC7A4K16AB9RxU9GQtpBDf+nh3bzfo2NR/GpZ2amFxsYE6OP602T8TSkphcfF2hkXDbU5xZn+T2Z3ZLbNei1h22DenJfPLNalqkHqb3ibWu1nst0w7Tcu/hgq58F51/nVJzbBtsJ22T9zHq0NdQbMCMfY26cToohkFh6WaU45SCbmctO5sWZI2iYW/5+VdTAqPgqvtMqgSAea+YYzKnjC6tQEB+Fu2SS+ebPRbahiPh7fB15tG/ZbAJ/mammbL7B4BfIDsSMsj18eNsQGN2NenN/X37Y8dXPTdsvePjY24c3n/K7MOTR0w3qRsfhoho0zK6wNI2E0kNaMT3bSbSIyDdXHAnnzQB92lPlD/Y1uGdJlBBwYjYU35nBmk5Tz7gs4GNzTH1/lOmdcuqL00ah3JG5a4qv/xsmmQkmBYgS7Yxft/PtEo9WHSDMXAC++xKBHXK0m+MOWYXvWNef/sGNGoKnfuVnnfjQvqcKBqog6BQ6DPajCacnWn2T3qyGen+zT+YG9I6zglcFVGZiVy194OiAEhB9/VtMYPY0GI4Wb51lDKjJnS7hHkdb2bg8WUcCW/PwEn3m94qBTY1G1rUGnf1VybYK2VzOmHOS/gWDKy6udlQVsWOpXvbYnmQW3aEu/8GH/zF/M5lF3sYedmNbM+oQv328TEB3GKDDL361VqaZCQQlJfO+C6RBd2gz5rrtfw88yDLchQEbor9diYc4oZdr9O82FgHdOzLh6Fj6HZ6K8OPLDA5exe8aVqqna/F25qvi87ThY78bFrqd3XveGqdvI/g3DT2RFVTy838PPjhEzNY1zk5naOyTplB+i4aXu7iHmnTQlqnmF4AaX5hhN78hxI9jn6K7sdlJ1ea1rXbl8PlN5tzWm42FORNTw2ohrRLlgWTfkn8gQM0Tz9qrq8/mmWOk4qmgaovNi109Rg6FNGJ42Gl0xvYlo95cNZzmEkJsGejGZjpnFRQ+Q4/uGKGGXgZYOHbJs/1+m8hfj+FYUYbC+u63xSci0+V2l4JF48wD88//psJdl08wgx+dj6W5WrJ+GZeP9ok7+WyuG/N9Yszj4Hxy+l8ZjsrW13BidCW5vexHL75OThsJ3nFehL55Wdz+eF5dDu9tWjGxi1MC8pty81vWUF9vezIt6Y3ULdfnjf85OPM5aq9H0KGObefCG5O48yT5hpr8yLz+xzZHJ+CFuh07Ftu0FtqWHA43Fk6/nZBEU1MK/Q5L8Khn0r+vudkmvQePS6t1qLWF14TuM3JyWHTpk088sgjJaaPHTuW1atXl7nMmjVrGDt2bIlpV1xxBW+88Qa5ubn4+ZXuxpidnU12drbrdUqKCRY5nU6c5/lBa3CKPYWv8H6pyrJVUdZ23S1LdZb5nBYM5a6vYL4up7cV5L0y8+2O7MnCdtfitHzMsmWtz7aL/mGbaaOmYWWkgmVhX3GHyRW0bUfRcsOux8rNwVpZ0CL98E+lipTmF0bwbU+ZE7N9puQ2qeb9FBxOQkgrEkJa8WPMIH7Z6gzW6q+wTplAdc9Tm3HuWmu6Nrqrpo89d7/bqqyvgscP1XG8204uObqY3onr8cvPxt5gRr196NzFAkP4rvk4dkf1AstiwJUFN8MZqaald0xr0wXmnM9RZvm6D8Hq8yPWliUlR0cG7I59sfuOhpTTWId/InPvdjN4A3A6MJrIwWN4+1RMiRucvleWPUDRkvk7Srw+b52q7LSylPf5z6237nL3+67Kb17zDnDnX7E+nmXqYUYK9juPQ1QzV64xu+cw9gT2Atsm3/LFecNvsV7/PVbaWTi0A3vh29hXzChje06sBW8W9gvAecUMM8iCO8dKu15wxQwcC94wb33/MXaHPu53zSvjO3vhnOPiV1f2pF3SLq44+KUZQAbgIHQuNk9CcEtiRk8r1VWsvPVdqCxOpxMGT8LKSMVaZYIX9uf/wr7jmaIutJguhdb811z7bkWrsQydPqOopS3A7X/Bev8prOSTkHQC+40/YI++1XynTVqW3TWtus+P7sznTv05/BM37HrdlZvUxoKLhvOu3aeoztt23dbbaj4XHIrozKEIc7T1xypRLw6Hd8SOboV18igc+Rnn4V0Q66F54UudW6rnu3B72fXzcRwxLd2SAhqztsWIomOluPAmMOP/sD77J9bBbWZz/a/AHn4DfFvy2ujc7Vb0u870DXYNrHPFgGK/Cx37Yn36d6z8PNixAtuysK9+APLzsJZ9Cmvm0LzghteJA0ZNhSFXk/XtTrZGD6BX4gYis09D3E6cP602PZrKknoGa+VsrIL1rG45kkuPmYf89uL3sDv2Lfn7UMY+bpu0m6v3md4Onc9sx5nVGfxL95wKykll8LGl+Dlz2R7dH2d+99K/07YT9m3BWvYJVrHWwkdD27CzSV/GHipIWbHsE+zug6vUrdbpdGIX//4vcExV6Tx6JgFr0Tuu3+lFbSdzdVhUibqc6/DHHjgea/ln4MzHXvAG7NmEBdh+gSZlUMG8Vb6+9PFlXsebuXHXq4TnJMPxfdhf/gf7ut+YBwXnU9v19nzLVkXB+nzzc7CXf+76bla3GFnyd+Hc79vhY3LX9htbOLH0fJ36Y7W/GOvAVhMQ/vq/JTad6RvMilZjGd2ht3vXOU6nOV//8nk4cdjkK67A8WhjcSiiM0d6tGNA/AoGnliF5cyjUXYSE/d/bObZ8TzTfSNIDogk3+FDcG469r5c7ktOKkrRB9hbfLnX8sWynSWn9x6JPe4O8A+CUbfCz+s4tugrYgvS8UVmn4b3n2JCo24sjx1HakCjojIXnEPHHPySphnHAUjxb8TXnabROmWfa6wWe/5r0Kro/ObsNgji3Ky37qqNeEFdxCRqo+65u42IaNOQIzfb9Ao5thfr2B5zPZt6BmfLjud9kFCfVOQ7tGzbE/rBXNjx48dp2bIlq1atYsiQoq4kzzzzDO+88w67d5fOqdG5c2duv/12/vCHogTvq1evZujQoRw/fpzmzUu3NnniiSd48sknS00fOXIkvr5eE+euVglni1obNGtUfiuWsuar7mWrMs0dxZc737JlzefutIps15F2Br9TRa0p8kMjyWvcyq2AhG3b5OXl4evri2VZ7pXFtslKOEpEQSsm2+GD7R+I7R+E0z8QZ3BEhS+Sq/U7s218Uk/je8ac1G0fP3JadiYhJbvUsmkn4mmUdYZ8hw8+wWE4A0NwBoSUHCW6jO2Wt5/cnc+dfVCVY+VC66/KsuUdE76nj+KTdv4W1s6gMHIbtyq1f93aRrkrdWId24N/fg42Fs6wSPLDmxCfXnSSa9YoGGwbK88cA7ZvQLn1oyr7qSoqsl136m2l96cb5XPnOHY4nbTMTcSRmVay7L5+5LToXOo3wspKxy9hv+umKDe6Nc6QRiW228I3y/Vbl+PjT3xoLM0iK9DV3bbxi9+HI8cMTJXbtB3OoLAq1dvifFJOuX53yuIMDCW3SauSwdILcPv8kJRBdEYCwblmf9u+/hwNbonT4YOPM4+W6UdNcAfID2tCXuMWZW8wLxe/Ewdw5GaXnG5ZZDv8yfUJIM0/nMgm5Q8UUZVzuk9yIj7JJ8mPiCY/Iqbsz+904nv6KFZ+LvmhUeY4Sc50zROck0Z05omi4IV/EHlNWmH7V2IAwwuoSr290Pqq+/ql+LVChl8IJ0Oa19i1VE1fh5WlOs5xvvm5NE+Lw1Fw7OQ0a49dmGalvOu6pHRCC3qTpPmFVeg3qTquiYNy04nJSHAd7xl+IfjnZ+NbrMux7RdAbuNW2OfkHHdkpOCXaAImeQ5fjoe1wbasUmVpnHGC0BzzGfPDGpMX1QK/hP04Ch7GnQ6KJi0govwyO534H9+DVewBq9M/iGOBzXAWOxe0CMjH99QR128VgDMgmFO+4WT6hmJhE5KbSmRuconfKBvIb9SM/IhoAPMblmUe1J4Kbkq6f1EXcHeur879/A7bidNyVOkYtXKzyTyVSGBeBpl+IQQ1bXlOTlMbv8SDrnNmfmgUeU1alb2y/DwCju4qFezIi4ghP7JZqdmrWh+tnExzfi4IHpwNjHSl+qmte6qqLFsd92M+yYn4JpnUT87gCHJjSre2Pd92yyublZuF37E9FD8jOP2DyA9vjDO4UYmeE+6qyjn43LL5nj7mqktVYTsc5EW14nheyeufwu1aOZlmW8V7L1gW+aGR2D5+4PDB9vHFkZ2BT0E6P9vhILdZB9e53ff0cXxSS7ZKtrHIad3d7fvS6j5PVdd3UdVtuHvfWpltVma7ld1G4TQfZx75lk/F7gG8WF5eHkuXLiU5OZnw8PMP6FehSOTSpUt54IEHWLt2bakVJycnM2TIEP73v/+dd7Cwqjr3gti27XIvksubv6zphR599FFmzpzpep2SkkJsbCxffPHFBXdmfVW8pVC5rYTKma+6l63KNHe42yqqrPncneb2dnesxDH734C5uLP7X4E9/q4LPwkv4HQ6OXnyJNHR0TgcjgqVpVHWafIcvsy4ekjFE8qXsb5zt1ml78y2sT78C1bBwAD2wCv5j3NQyWV3rsbx+b+ADqUXb9zCtJrpOQwsq9R2y9tP7s7nzj6oyrFyofVXdNnzysvB+vxZrD0boHAk0WbtzDFoWeafnz92z2Gm+1g5x0pl6yPA/+ZuonnaERJDWnD3pIFVWl+N7adq3K479bYq+/NC5XP7OB7XDeu7N7E2mMF0bCzs6U9A27KX/+HttxgZZ/L02f6B2Hf9nRcK0q345Wdz396XsdLMb90XnW/jaHj7in+2nWtwFIyWbbfuhn37X6pUb8HkUrMWvo21fj5gWrnaHftid7gYIptD4+Ym9ce5uefcUJHfZN/8HK7f/SZNC/KMHw1tw9edbuWave/RMq1ZwWfujj3t8fMPZJSZSsJLj9Ei/UiZb9tYMHA89sipptXMecpcoe/n2D6sNx7Boi0Aztv/wgs7Suao/NWVPbHmvYxVmNIHsCObsiR8ELsaX0yvkxu57MgC1zrsDn2wb/h/ZZazOlSl3l5ofdV+/ZKXi/Wf+7DSkrCxeKfnr5h+7WVV3m5dXIeVpUL1NvmkyQvavB34+JllbZur975P2xSTg9HuNxZ7wj0X3EZ1nZerdE3cIcO0/D0np6vt44t96XUwdHKZD0yxbdPKvqDF8KqWo9nYfFiJskRnxHPzzlewsLEDgrF/9aLp/npkN463TKOXdL9Q3un5ILk+AWWW2fr+I6wVn5eafjYgiq86TSPNP5whxxbT98QaoGmZnzcpoDF+zhxXkLzQ6cBoFrWbzJQbriiaeHA7jveecC33Xs9fmi7sbl5flfgubJsgO4NMK5hflZe2LDcb4g+aYSICgk1qg8Ic/LvWYP34PdaRn4GiHj52216m5WqICXizeTGOeS+b98IbY9/779KDexbfpwveKDjnFKwvKBT7Vy+VuUy11Mc9G7E+/isW5h55Qbvr2N34olq7p6rKslW+H8tKx/rP/VhZbbEth/luossJqlfC0nfepn/CKk6EtKTT1Teb1AVVuK+qyn11KbYNP6/DitsJSYlw9gQkncAq/uAkKNQcx8ER5ncmNwdys0y9yM2BZu2wr7gdIpud/zuzbdi2DGvxe1jpZ8/zCTua68kbH4YuA4om5+dhvfekKWuBgxGdaPPQX8+zrgvvk6qcp6r1u6jCsu7et1Zmm5XZbmW3UVf3aHUtJSWFyEj3UuBUKHD73HPPcffdd5cZwIyIiOCee+7h2WefrZHAbZMmTfDx8SEhIaHE9MTERJo2LftCoFmzZmXO7+vrS+PGZQ8cEhAQQEBA6fw+Doej3Ivxeq/YCea8+6Cs+ap72apMc8c5J9Nyly1rPnenubO++ANFI7gDXHIV1hUzzvuQouzVWkXHbgXKcjaoiZmnOkanr4nvbOK98NJDkJuNtf5bmnZryYnQWLPs0d3w5X/KL87p41hfPg8bvoVxd5bebnn7yd35ytyoG8tWZH3nWX+Fly1PVoYZUOpQwYnUxxfr+t+WmT/xgkdlZesjkOsbSFyjTiWXren6Xd0quN0L1tsq7M8Llc/t49jXDybcYwL5G77D6jsaq335Oae3xwygefoRup3eipWThfX5P/FtNZ08H3/6J6zEKmjRvb9RV45GdKjcZ+s+2HT7P3UMK26XuZmuSr3NzoQv/m1GlC807Hqsy2/Gqub9ft6yWBZ5vgHM6zSVO/e/CWlJtEo7zLSf/mu6uAKENcaa8jusCw0OERLB593uJDblADEZxxkakQkJB7FPx2OZ2yVYPx9rz0aY9MsSA66cW2a3vx9nPnz7WkHbuYJl572MT5s7TC7Awmm71hTlYS/cXNIJRifNYcixxQQXH/iw90isq+7DOl+QuqqqUm8vsL5qv37xD4BBE2DJ+1jY9Elci8NxedW3WxfXYWVx97s4cRjeeMTkQ/cPgg4X0yOzKX75Oa6gLeGNscbcVroO1+B5uUrXxN0uMQPUffoPfApzAbbpYY7/wgEjyzPuDvjfTLCdDIhfzs4mfUpsd9iRBa5gnXXZFKzQgkHZ2nSDbpfArrWE5KbR58Qa1re8vHSZTx0zeZXBtHq77jew4C1IPU2j7DNM+fl10v3CiM4sdi/WsY8ZmG3tXEg0g0tHZpfMr3s8tDWbmg3lQKMuYJ1TpzpcDG16wOGfiMw+TZczO/i5Se+iferuOdM1zTyEdk3PzYYju821z6EdcHQvuDmoUiHr0HasV39n8rSHNzY5Tgvfu+o+rOCw8y7P4Ktxrl+AoyBFmnXpdeUvUx31setAGHubq5yjD33NmaAYHI6LLrzs+dTCde1DEy8y+Zlt2+TOLG/Z8ta3dq7JKQxYF12G1bQ11emnmAH8FGMCkF3adKv6CqtyX12WHkPMv0IFY4zgdEJIuFvnWNeWLvSd9Rlp7iG+/9gMtmqX3UXcGnUL1rn3Gg4z7kHq8w+5xjzZF9mDdrX1m+zu+qoyX2WXdfe+tRLbrNR2K7uNurpHq2MV+ZwVuuLdunUrf/vb38p9f+zYsfzzn/+syCrd5u/vT79+/Vi0aBGTJxcNlbRo0SKuvvrqMpcZPHgwc+fOLTFt4cKF9O/fv8z8tiKeIDgnFT5+viivZ59RJsl9FVu+1iuRTc2gDQvfBmxGH5rDR93vJTznLHz0JuSbUVJ3NunDsthx3Nfd1wy0cXC7+R+TF5LXH2Zs44tZ3Wo0af4RdfuZPEl6CnzwdNEIsf6BcNOjpYM4IsXzvJ2PZbG0zVVEZ8TTJDMREuMYmT+XNS1H0jehIE+9w5eVsVdcaE3lczjg0uvgq4IHN8s/h6jJF1qqbFnp8M7jEF+QY9HhYwbM6zOq8uWrojT/CLjpEXj7McjLKQra+vjCjb+HwqDLBdiWg7iIjsRFdGRoQSuzl+dspOfJTQw+tgQ/Zy6cTYR3Hzff7Zjp520ddkGbFpUcbRoT8Bngt5y1rcz+DMs+C3OKBrjj0uvMMgfMoCclgrbDroeRU3VOPFf/K8j5/lP8nTn0OLkJEo9ATGxdl6r22DZ8+3rRIJY5mbBrLaPPnW/CPRDoZYPXdbuEOZ1upcfJzRxq1Imx06e5d/w3bWPq8MYF+DtzuOWnF+HsfGjcgsuOprhyTxLZDAZeWXLZUbfCz+vBdtIvYRU7YgaUfN+2zaChhakPhlxtBktq1cUMhnjyCMF56a788/j4mt+SgRPMb3WfUbBvM0fmfEBs6kFsLPY36krH66bz2bYLjHR/+U3mdxAYeHwZuxv3crW6rZLEI/DWH12DKrktpjXLA7pxNrAJow59TUhumhlA6q0/mV4ZBSl86D0SOpUxuOS5ImPYHtOfixPXcyawCVEDx1fu81TE4Ens2Lydnqc24WvnccmxJYAb5/a6tmsdfPZPE1xv0hI69SM2JZLjoW3Id1wgzJGebAK3mOsPRtxYK0X2aJbl9rVEpQSGwPg7YfgNkJQAGSlmHIyMFFPvGrc0A6+VJbQR8zpNZdz+z8jwD2VPZA/G1FxJRTxOhQK3J06cOG/A09fXl5MnT1ZHuco0c+ZMpk2bRv/+/Rk8eDCvvvoqcXFx3HvvvVCQ5uDYsWO8++67ANx7773897//ZebMmdx9992sWbOGN954g48++qjGyihSFT7OPCbu+wjSC1ofxHY1Nxm6QS1t0EQzamnCAZpknmDwscV0TNoF2eaCOy68PUvaTDI51jr1gk59zXL7foTv3jSjRQPdTm+lY9JO5naaClTTaMjeLD8fPvlrUaAlKAxufcwMhODlHiqvK6TUijwff+Z3uJHpu1+D3Cy6nd5K87QjZrRggEsmcDa37N4wbus1DH742AQe928hJmAgiSEXaJV2rvw8+PQfRUHbgGC48WHPeHDRqjNc/QB88WzRtAn3mOlVkOsTwJZmQzjQqCu3pywqGpxy00L4eZ0J4vQeWfEVpyfDkg+KXl/5C1jwBjjz6Z+wgn1RPTgdFM24A59DYf67HpfCqFvMee/Ibg5+/ibtkvfgxOKHNhMYOeqWKn3WeisolK1NBzEgfgW+dr4ZDOfOZ+q6VLVn19qiHiLB4SaweE4AbndUL7p0GVD28h6u8GELwNiKXBNefhPZW34gID/LPAA5/BMc/onexecZe1vpdAtNWpYI+o46+BWk94CQgl6XO1e7HqwQEW0CMRSMGH7HMxx78U+0TDsMBSkPGt/+qOmhUciyoFM/Znf1JzLzJHkOP1IDGvFQbFfYtv38n6ltT/Pv0A7T6vb0dler2ypZ+UXpoG3jFtCmu2nBnZ1pfqeyM0138aZtze9iiw5sKejm+2H3+7g76RuI22mCiQXXmoRGmkYYblrW+kr2RvbgdHBT7vEr3RO02lkW37eZQJuUfYTlJNM+eY/nP/xJSjQPagtbRJ86BqeOcS2Q4/BnX1QPlrU+T9D7h4+LHvT0G20ahUjtCAkv+i2pgMSQFrzb60GC7AzyLPfHFBCpDyoUuG3ZsiXbt2+nY8eOZb6/bdu2Mgf8qi433ngjp0+f5qmnniI+Pp6ePXsyf/582rQxScTj4+OJi4tzzd+uXTvmz5/Pb37zG1588UVatGjBf/7zH6677roaK6M0DDUSALJtRh6eS/P0gsHIwpuYYEFZucsEfHxg0v3w2u/BdtI/YVXRezGt+ablTSUGxnDp2Bva/Rs2fQfffwSZafg5cxl7YDZk6tktK2dD3C7zd1iUGfXTky/cxaskBUWbelsQeGyUfca8ERxubvyXHKjaBnx8Tc7Hb14BYMDx5XzT6Wb3ly9stVcYkAgKgxl/gZjq7T5ZJb2GsWz9TgbEr2B7zAAG9S3VprDSkgOjYPJTJmC76B1zU5uebFL3bPyOZhGXkxBagd+DRe+6uqFy0QgYOB7SkmD5Z/jYTkYd+pq48A60SCu4dmsUY1LhFAamYrswp/OthGedwbYsUgMiqUT4uMFY12IEHZN2Epl1Go7tgbXzgPZ1Xayal5sN371V9HrS/dC5Pxzbx7pvv6V18n4y/UL4oc0EupxvPfVRSATzO0xhQPxyIjNPEZJXclDJI2HtiO1aOgUSACNuJGfzUvydOSaQ999fwujppmv1gjeL5ht/p+mZUygolC+7TKf3ibXYWGyLGcgviwdtz5EUFF3xzzXiJnj7TwAMPbqIpunHYNnP9ExMMwP0BTd3jVrvlvRk+KngOjIwFK68ywSHwyv2MDHDPwxuexIWvw9rvi5646r7ICj0fIuWYFsOjoWXv89qgtPhy5amlzD8yHdmwpqvzYNCT5SfB1/8q+iBX1hjc24p6H7v78yh+6ktRGSdhuwycmX+uBQKcvTj6w/DbqjN0ouIVFiFArdXXnklf/7znxk/fjyBgYEl3svMzOTxxx9n4sSJ1V3GEu6//37uv//+Mt97++23S0277LLL2Lx5c42WSaQ69Dmxhu6ntpgXvv6mS2xNdlepD1p0gEsmwpo5RdNCI2Hqn8hZlVD+cj4+pltgr+EceenPxKYeNINiLHobfIbXStE90tE9pgUCmAHIbvidgrZS/XoNMylLig2+wsipVeuOX1zvkbDsU0hLouPZXURlJnImKMa9ZdfOg40FN60+vuZ32JOCtgV+bDaEH5uZvHTlhFwqz+GAAeNMl96Fb5uWdQDH93Hj8X3sbNybDc3d+J2M22VujilotTz2NvP38Btg5xo4dZRm6cdoln7MTLccJkdmUOnjICUwqvo+Xz2W7/BjUdtruOHnN03u0qUf0qjbfZwNLBl8qne9D1Z/bQYlA2h/MXQZaIL/sV1Y2zKHtS3rLsWJJyjeWvehke3hzHEWLFmHf342u6N6cV95LXhDG7Gw/bWMOfglAfnZkJkGc1+CJe+brs1gfie6DCy1aL7Dj03Na26watr2MEHnguu33onrIHEdhd+0Ewebmw2GHDd7I2xZUpT2oe8ouOiyypfNxxeuuB1iu5hjs8uAkgMtebAd0f0ZdHwZAflZsG2ZOTd7oqUfmmtWCtKn3fMvk5f1wFZ2fr+EDkm7CHBm0zItDj56Bp/Gk1051ZunxsHcYjGDsbdDuM4xIuLZKhS4/dOf/sTs2bPp3LkzDzzwAF26dMGyLHbt2sWLL75Ifn4+f/zjH2uutFIn6t0FfnXJyTZdoE4dhZNHmLh3J5FZp8jxCeC79hVo1Z2dyWWHv+HixPVF06550AQlG5BKH2cjbiJl03LCc86S4/DH/5Y/mVHeOU/gtlBQKIvaTebWHf/F35kDmxcT26UlR8Ib1r6HgoGYZj9XNFjA8Bugdde6LpXUV2Nv5/hP22mRfoSEkJY0q8ZWo/j5m3yLBQOtDDi+nO86XH/h5XZvOKfV3i9NF9mGqlG0GVzn4HbTCrlgIKHup3+k++kf4cSXZkC4boNNLs3iwZ/8fPjm1aLXI28pehDp6weTfon95h9cAyNBQd7KWP3mVFV8WBt+bDqIPifWQl4Oow9+xeddZ5jAuDc4stukM0pPhql/hOgLPDxMPgUrvjB/W45Sg47KOYJCoGUndjfOcmv2/ZHdiQ+NZVjcd3Q9s81MLAza+vrD+LvqbH8vbz2O635+m8D8zFLvOSjoifXyPmKjx3EkovzrOst2FrW+xIL+Vci1Xlz3weafF8n1CWB7dH/6J6w0gex184GL67pYJe37EVZ9af52+MD1/6/owW/PS1l0OIIf049z7e63CczPgkM7mHgmi3mdbiY4N92kpCsM0vcfZ3qCiIh4uAoFbps2bcqqVau4//77efTRR7HtgpFILYsrrriCl156iaZNlR9G6rGUM2aE8d0b4MA21yBYAMUvCSfvfhdS3LjQ2b0BvnmF3ilFI+qubz6cgT2HVnfJ3eZ1gfqAIL7schs9Tm5iT1RPpjavWLfQ1IBGrIwdy8jD8wAYdWgO7/f4JXk+DSx30oI34Uy8+btV56J8dSI1wdeP2V1n0DL1IAmhsdxXkNak2n5/+l9hgjmZqXQ+s53kwEjYlUFYdg6p/hGlAw3xB+HzZ6EwkDj8hvIHyGho2vWCe56FTd+R9d37RUGSk0dg2RHTurlRDIQ0Al9f8PEzXddPFAx81Kw9DDgnENK6K1tjBppWcsCRsLbEXnptbX+yemt1y9H0yTkISSdomXaYixI3sK1ptbfNrl7ZmbD0g4JAUUE9/OETuOG3519u0btFg7kOHK9eIjUgwy+M7zpcT9eJ15kHMqcLWskPux6imtVZuU4FN+e13r8jJDeV4Nw0buodzeI1PxGVeZKLEteb/OlJJ7g26R12Nu7NitbjylxP2+S9RS22O/aBqJpL++cNfmx6CX1OrMHHzoeNC/Dr3pVcn1rIs+uO1CT48rmi16OnlTkGw8mQFnzVeRqTd79LgDObtin7uHLfJ4TlJBcNmNfuIpPmQ0RqldfFGjxEhQK3AG3btmX+/PkkJSWxb98+bNumU6dOREZG1kwJRepaTpbpOrtjZemRscsRnnMW3n+SgJa3kO0bVHqG1CTTgqmwCyqQ6/BjTcuRbGk6hNKdzuR8zgY2ZlVs5Ue/3R7dn85nttMq9TAR2UkMObaY5a2vdGNJ93j8CWrnGtiy2PztHwjX/tqkkxCpQfkOX+IiamjQO/9AuOQq+P5DHNgMOr4MPlnGHUCmTxDp/mH4OvPgZyA3x+TJK2xt3uNSuLwCeXEbgoL0Mu/EN6bbqS10TNpJi7QjRe+fTTT/yjLhF6ZV1DlWtxpNYF4m/vlZLGl7NXeXlZNcKiXPx9+0GH/nz1CQA/RQo6oNYFej9m6Cea8UBc8K7VoLaWfLX+7wTtixwvwdFGbynkrNaX8R3Pdv2L7C/F5WZsDCauZ0+JIaEElqQCR06cVP+0wqv+3R/Rl1+GtapZoB0rqf/pHWKfvhsvYFvbKKXJS4oejFgLKDuw1Jun84uxtfZNK3ZaXT4+QmV2oeAFLPwPzXTGv3xi0guhU0aWX+j2puUkXUBGe+6RmWnmxed+pnzvPlOBEay5zOt3LD/g8gN8vkaS4U1dw8FKqpsoqIVLMK/Vpde617rSFmz55d2fKI1C7bLreLl29+jslNterLoouE4sKiTC61pm2gSSve3JGO0/Lhhp/fICI7CRLjmJTxAV92nl7UejMzHdbNMzlZCxPqA4fDO7C07SRSAvQApE5YDpa0vYbbdr0MeTn0PrGOvZE9iQ/zvNyW1S7ltMlZV2j83Q2+tYnUE4MmcHLtUqIzS6ZNCcrPJCizoNVo9jnLtOoM1zygrtblyPINZkuzoWxpNpSHhrWEn9eaBz9H9xS1eixu6GST57EMuT4B7qWwkMpp18u0PN/4Hf7OHEYf/BLyh3rWQ7n8PJj7clEuZAq63zdrB0d3m0DNliVAGUFnZz58+0bR61G3VGjwJ6kkXz/oU7MB2+p42H02qAlfdJnBQ81PkD3/LQLys8xYBh//Fe54xjVfWHYSbVIKGmVEREOnvlXedn2wqdlQ17gbfU6sYVvMIDPg79E98MnfTPAWSjdoCQyFkTebFATVbfF7cLAgXUdYFFzzK5OT/TyOh7WBqX8g792nTAtsINsnkICb/wDBYdVfRhGRGlKhwG1ERETNlUSkLnz8V9M9PKY1RMfS8YzNmaBoYlMOMCB+OeSWHH2XZu0KBhkYCM3bl7i5T927HYAvO09nyq7XCc5Lp0VaHOP3f8qidpNNDtvn/loiYEtQGIy7g6+ORClQUMfOBjY2rewWvYOFzehDX/Fhj/vqulg1y+mEr14wA45QkI+t9+V1XSqR6hEYzIc97iMiO4nojHgmtMjj0LZtRGfE45+fTZ7lS1BIsAkU+QWYBxYTfmH+bmAqFSgJjzKDPA4s6J1g2yYQl5dr0ghZDt0Y17Uxt5GybS3hOcnEph6ChW+ZnKSeYu28kkHbdhfBVfeaPKP/ud+kTNi0EDp2LJ2jd8sSSDhg/m7WDqozT3YN8fjeN/WN5YB+Y3kvLoTrf36TRtlnzDEz50UINMdL95M/FuXaHjCuzN4BDdGZoBjTonXvJsJzkumY9JPppfLWvKL8sGXJSjOtcbctp3HEaE4Hu5FC0ek0PRszUmDQhLIfwGxcaBrTUPC9XvsbCHEzLtGuF3M7TWXCvo+xsJnfYQqTo1u5t6yIiIeoUOD2rbfecmMuES+ScNB0zTtpunxOKHMmC3oOheFT3MqdlhzYmK86T2PqvncgJ5P2yXu4a+s/8CnshktBMv3el8OoW82Fx9Ht1feZpPIuuYqE1Ytpln6MqKxTDD62FKjHrS/WzoUDW83fYY1h4n16gCD1i2WRHBhFcmAUjOrF11klf2sVSKlGlmVa4/n61XVJpFBAEN+1v45rd79j8lWu+8Z0afaE7uCpSSY/MpjrrEn3Q59RReegjr1h3xY4m0iblP0cLp5WJTMdlnxQ9Hr8XQq4SbnS/cOZ22kqN+581QxEu2MF/VoF8mPMILqcKmjB6eNrjj8pMuQak8YEGHl4LgH5xbqotO5u0mplZ8DJo2ag5uP7zTggAEd3c/OxfWxsdikbWgwn31HOecG2TaB3Y8HgcNuWwc2PlpglNnk/bHyvaMKEX0C7nhX6KHERHXnz4plYtk2WX0iFlhXxZLqObTiU2EUarvw8CAgGhy84y3l63G0wjLjRpEOogJMhLeDmR8l790l87fyioK3DBy6+HIZdV6cDOkg5fHxY3PYabtr5P3ztfPomrIZDO6BtxS4QvULCQVjyftHryQ+qdVwDpYs+kfrreFhblra5ijGHvjIT5r9m8lK2v6huC7bkfcgpSFnSb0zpFrP9rjCBW6BX4oaSgdtln5jWeRTkpG7TvdaKLd7pTFAM37W/jqv2fQTA0KOLicpMJDA/y8zQY6j7LTgbirY9oHkHiN9fMmg78Eq4YkZRftji90gHd8C8l+H0cXzsfAbFL6NT0k981/56oIxrjaUfFAVtwfSCfO1h2rW5loONuhCVmciE/Z8U5aAfPMmkgKmEbN/gSi0nUleqcn2ua/v6R4FbqZfc+rHy8YX7nzMB3DMJcPIIa1ZtJCrzJDk+/myLGcgtN1R+wCva9eK79tdzxYEvsHCyq3Fvek69WwFbD3c6uClrWo5i2NGFpvvcl/8xg3HUJ7nZ8MW/i7q7Dbm67m/iRcTj6UbAO+2M7ktU1kn6JawyAZBP/wF3/RWatDQzOJ00zjhB48wTHA1vX/MFOrqnKEVCYAiMnFp6ns79TR7L1DO0O7ub0Jxk0vwjiMw8CTvnm3l8/WHM9Jovr9QLByK7mQHsfjBd5ruf3lr0pie0Qvc0lgVDr4HP/wVAnuWD71X3nj8tSbuecO+/YcXn5K/4Ah/bSVTWKabseg3WZJvBxApb1a/6ClZ8UbRsZFNIOgE5mVy190PWNx9O1zPbCCgMrncZqPouXkfXTVJdFLgV8fE1I6FGt2L9weod2GJfVA+OhbXBaTnI9g2mp4K2XmFzsyG0Td5jcgImnzQtlALqURe6Re+50oPQrB2MvKWuSyQiIjVoVasx9AvPhj0bTR7Kj56BfmPh8E6I28mtBbnO0/zCYERHaBRdpe2Ve7PqdJpzaqERN5Xd0tHHB/qOgWWf4MCmx8lNrGtxOcPjvjUDkwFcem2VyykNzPAbTI+jn9e5JiUGNyemVdmDKDZ43QezNWYgUZknWdVqNDe5k0vazx9GTuWj0zGMOfglTTNM61u+ewsObDODiv28Hha9U7TM+LvNoHdfvQA7V2NhMyh+WdH7zdrDdb9RShQRabDOPxSjSAPz0IReJf5Vh0y/UHXP8TaWg0XtriXbJ9C83raMTmd21HWpqsfezbD+G/O3r78Z4EE5KUVE6jXbcsB1M81grACnj8PCt2H3+qIBKoHQ3FT44OkS06rV1h+KRqKPjj1/S8e+o12DkvU8uYkOZ3fRNqVg2Yhok4NTpCIcDpj8EKeCYlyTtkUPUH7/8jh8+KHNRGZ3ncGJ0AuP81Hc6eCmfNrtLjY1G1o0ce8mePFBmPe/ommXT4VBV4J/INzwW/O6mFS/cJj6B/O+iEgDpcCtiEgZUgMa8X2bouHqRh6aS0hOSp2WqcrSk01rhkJjbnNrwD0REakHAoLg5j9CcHjJ6UGh7GvUjbMBUeb1ySPw8V/xKS//f2VlZcDiYoMMjb+rKE9mWSKamJQJBQHlKw4U61Y99jbwD6je8knDEBDE3E63cCi8I7sb92JX44vrukT1ltPhy8rYK/iq87SilvUZKSVz1g6/vmgBy4LLbmBux5tJ9wslxb8RczvdAuGN6+YDiIh4CKVKEBEpx+6oixgXdAJ+WklgfiZjD87my85eml/r5FH49O+Qfta87tQPBo6v61KJiEhtioyBGf8HmxdBZDMzsFd0LN98+xMRWWeYsus1gvPS4fBPjEl1sKD9da5Wr1W2/NOic1C3S9zLrd7/CtMqGPBz5pppbXpA9yHVUyZpkFICIvm68zSC7Ayclm6Ha9rhiE5w1b/hy+fhQEFu4T6jYOztZbZ2PhDZjQONuuCwbZxKjyAiosCteD8l/ZYaY1kw8R6I2wWpp2mdcoAep7YAXtY6Y8cqmPNfyCkY4CEkAq5+QF0DRUQaouhWZlT4cyQHRjGn0y3ctPdtyMuhy5ntpPpHsCq2CgO1FjqyG9bOM3/7+sPY0tsvU4feJPs3IiLHBHydWDjG36Xzl4i3CYuEW/8Mu9ZCXg70Gnb+emw5cKqaSw1SDEG8iVIliIicT1CoGUihwID45ZCf7/7y+fmw9EP47J+wbj6cOga2XTNlPVdeLnz7Onz+z6KgbXSsaW0V2qh2yiAiIl7jRGgrk2eyoJVt/4SV9E5YU7WVpiaZHh+Fg4oNvca0/HWHw8GO6P6ulzui+0OztlUrj4jUDYcDegyBi0dooDERkQpQi1uRGqQnefVEh4s5FN6Rtin7iMhOgp9WAZEXXs7phDkvwtbvzeufVgEwwz+CuPCO7I3qTlx4x4qVxZkPW5aYgPDFI0zOwrIkJcIX/4Kje4qmXTTCtCDWAA8iIlJMqeuVK++Gb14B4LIj38LqpjDk6oqvOC/XBG1Tz5jXbbrD8BsqtIotzQYTmXUKh+1kVasxuJFgQURERKTeUOBWRMQNG5sPKxrNeuUX0PqO8+f9s2347q2ioG0x4TnJ9Dy1iZ6nNrGu+WVguxngz82B2f823cwAln8Gl9+EZcdgW6blgo8zF374BFbONl3RwAz+Mv4u6DdW3UvrAT0QkqrQ8SNuGTCOdVt2M+j4D+b1wrfNOYiuFVvPgjfgyM/m7/DGcMPvzj8gWRnyHX4san9txbZbQaoX4kl0PIqISHEK3Eq10UWG1GfHwtpyPCSWFulHIDGOdhF7ONjoPDewyz6FdQX5/CyHCZzmZMH+H03O3HwzyMqg+GWwpAnYF50/qJqVAR/PgkM7iqalJcHcl7klMNqVg3B43HzITiqaJyIapvweWlawZa+IiDRoa1uOJN9yMOTYUjPh+w8Z3Hw4a1qOKnG+Kvf6b9Mi2Pid+dvHD258pNbT9OjaVOozHd8iIg2DArciHkAXXl7AstjYYhiT9n4IQP/jKzgY0aXsYOvaufDDx0WvJ/0S+ow0f186GXKyYcO3sOgdM23lFwxtlsCqVuW0iE07C+8/DQkHzGu/QGjbA/ZuAqBx1kkm7f3gnPI6YNAEGHEjBIZUzz4Q8XL6rRWpmA0tRpDn8GP4EROAHRi/nIFtIsoc3KyEI7th/qtFryfeqweIInVE5z4REe+mwK2IiJsORnTmVFAMTTITaZF+hJaphzgW3q7kTD9+DwveLHp9xR1FQdtC/gFmcBa/ANeNbf+EVThsJytix5UM3iadgPeehDPx5nVQGNzyJ2jV2bTcXfgOHN1dcv1tepj8hE3bVPMeEBGRhmZLs6EM79Ua5r9mJqydax4oXnkXBIeXXiBuF3z6D8jPM68HXln6PCgiIiIiblHgVkTEXZaDjc2HM+7A5wD0j19RFLjNyYKlH8LaeUXzD78BBl9V/voGjjej6s57GYC+J9YQlpNClm8QfDgHUs7AmeNm3QDhTWDanyE61rxu3Q3unMW89z9l0PHv8XXmsbblSMZPu1m5bEVEpPoMvBJ8/WHOS4ANO1bAga0w7g7oNdycc5z5sPxzkyrIdprl2nS/cOtcERERabDUK+DCFLgVEamAPVE9GJe0ApJO0DZlHzHpx/HLz4aXX4KkhKIZB06Ay2++8Ar7jwWHA3vOS1jYdEr6yUw/ec58TVrBtMchoknJ6ZbF/qju7I/q7po0XkFbERGpbn1HQ0CQCd5mZ0BGCsx+DrYtg2HXw5IPIG5n0fytu5kc6xUcjExERERqnwKonktXUiJSL9XUice2fGDINfDNKwBM2PcR4TnJRTP4+sPIqXDJVe63eu07GsvhgK9fLGqlREGe2rBIk/pg/J1ld0kVERGpLT2GmoDst6/DzjVm2r4t5l8hywGXTTHBXB+fOiuqiIiISH2gwK14BHeDbHoKJB6h90jSF35ASG5ayaBtbFe4+gFo0rJS66R5BzibCGFRJmAbEmFSKYiIiHiKsCjTkvbn9fDNq5B6uui9iGi47jcmuCsiIlJNHprQC6fTSWJiIjExMXVdHJFapcCtiEhF+fmzpelgLj26CIBchx9+Y6eZHIBVCbQ2baMBxaRe0kM3kXqo60Bo2xOWfgDbl0On/jD+LggKqeuSyQXoN1lERMR7KHArIlIJPzYdTER2En75OaxtOZLbL7msroskIvWQAizi0QKD4cq7TcBW+dVFREREqp0CtyIilZDv8GVp20l1XQwREZG6p6CtiFQTPbAUESnJUdcFEBEREREREREREZGS1OJWRERqVHW3nFBLDBEREffonCl1QcediEj1UeBWRGqULtxERERERERERCpOgVsRERERkWL00FGqQsePeBsdsyIinkuBWxGRStAFroiIiIiIiGfTfZt4OwVuRUREREREPJSCDiIinkO/yVLbFLgVERERqWW66BcRqV76XRURkfrIUdcFEBEREREREREREZGSvCZwm5SUxLRp04iIiCAiIoJp06Zx9uzZcufPzc3l4YcfplevXoSEhNCiRQumT5/O8ePHa7XcIiIiIiIiIiIiIhXlNakSpk6dytGjR1mwYAEAv/jFL5g2bRpz584tc/6MjAw2b97MY489xsUXX0xSUhK//vWvmTRpEhs3bqzl0ouIiIiIiEh9oxQNnkPfhYjUR14RuN21axcLFixg7dq1DBo0CIDXXnuNwYMHs3v3brp06VJqmYiICBYtWlRi2gsvvMDAgQOJi4ujdevWtVZ+aRh0oVB3qrLv9b2JiIiINBwPTeiF0+kkMTGRmJiYui6OiNfT/ZRIzfKKwO2aNWuIiIhwBW0BLrnkEiIiIli9enWZgduyJCcnY1kWjRo1Knee7OxssrOzXa9TUlIAcDqdOJ3OKn2OBsm2XX9WeP9VZVnB6XRi23bRviu2P6nrferJ3215+8kby1wfePJ+d1cFvp8GVW9r47utD8dPWerD53L32PakOlCOUvW2LGV9Z+5Oc1c1r+9X43tUvizVqa6OAQ/Ybp0f75UtS23U70pso/g8btVbb+SN5+qaVl/qsrvrqw/fWTnqbb31Nl5wbeYNKrLfvCJwm5CQUObT0JiYGBISEtxaR1ZWFo888ghTp04lPDy83PlmzZrFk08+WWr6yZMnycrKqmDJJcjOcP2dmJhYa8uK+SFITk7Gtm0cDkeJ/Ukd71NP/m7L20/eWOb6wJP3u7sq8v00pHpbG99tfTh+ylIfPpe7x7Yn1YHynFtvy1LWd+buNHdV9/o8RV0dA56w3br+zipbltqo35XZRvF53Km33sgbz9U1rb7UZXfXVx++s/LU13rrbbzh2swbpKamuj1vnQZun3jiiTKDpMVt2LABAMuySr1n23aZ08+Vm5vLTTfdhNPp5KWXXjrvvI8++igzZ850vU5JSSE2Npbo6OjzBnylbJlWUSWuaFekqiwr5sRmWRbR0dE4HI4S+5M63qee/N2Wt5+8scz1gSfvd3dV5PtpSPW2Nr7b+nD8lKU+fC53j21PqgPlObfelqWs78zdae6q7vV5iro6Bjxhu3X9nVW2LLVRvyuzjeLzuFNvvZE3nqtrWn2py+6urz58Z+Wpr/XW23jDtZk3CAwMdHveOg3cPvDAA9x0003nnadt27Zs27aNEydOlHrv5MmTNG3a9LzL5+bmMmXKFA4ePMjSpUsvGHwNCAggICCg1HSHw6Efh8ooFliv8P6ryrICBQ88XMfuOQ856nSfevJ3W95+8sYy1weevN/dVcHvp8HU29r4buvD8VOW+vC53D22PakOnEeJelv2DK4/z3teqe7rpoZ0rNTD7db5d1bZstRG/a7ENs6d54L11ht547m6ptWXuuzu+urDd3Ye9bLeehsvuTbzdBXZb3UauG3SpAlNmjS54HyDBw8mOTmZ9evXM3DgQADWrVtHcnIyQ4YMKXe5wqDt3r17+f7772ncuHG1ll9ERERERERERESkJnhFjttu3boxbtw47r77bl555RUAfvGLXzBx4sQSA5N17dqVWbNmMXnyZPLy8rj++uvZvHkz8+bNIz8/35UPNyoqCn9//zr7PCIiIg2dRiAWERERcY+um0QaLq9p0/zBBx/Qq1cvxo4dy9ixY7nooot47733Ssyze/dukpOTATh69Chz5szh6NGj9O7dm+bNm7v+rV69uo4+hYiIiIiIiIiIiMiFeUWLWwpayb7//vvnnce2bdffbdu2LfFaRERERERERERExFt4TeBWREREpCLUrVBERERERLyZArdSo3TTLCIiIiIiIiIiUnEK3IqIiIiIiEdSIwARERFpyBS4FRERERGpQQo+ioiIiEhlOOq6ACIiIiIiIiIiIiJSklrcioiIiIiIiEi9ox4PIuLtFLgVEREREallCiZIWXRciIiISHFKlSAiIiIiIiIiIiLiYRS4FREREREREREREfEwSpUgIuLl1K1SREREREREapruPWufWtyKiIiIiIiIiIiIeBi1uBWPpSc5IiIiIiIiIiLSUClwKyIiIiIiItVCjS9ERESqj1IliIiIiIiIiIiIiHgYBW5FREREREREREREPIxSJYiIiIiIiEgJSnkgIiJS9xS4FRERERERkXpHwWcREfF2SpUgIiIiIiIiIiIi4mHU4lZExA1qsVE3tN89h74Lz6HvQkRERESkYVCLWxEREREREREREREPo8CtiIiIiIiIiIiIiIdRqgQRERERERERKUGpeURE6p4CtyJS63QRKA2JjncRERERERGpDKVKEBEREREREREREfEwanErIiIiIiJSjHpLiIiIiCdQ4FZERERExAMoWCgiIiIixSlwKyIiIiJSDykQLCIiIuLdlONWRERERERERERExMOoxa2IiIiI1Cq1BK1+2qciIiIi9Y8CtyIiIiIiIiJeTg9wRETqH6VKEBEREREREREREfEwCtyKiIiIiIiIiIiIeBilShARKUZdzERERERERETEEyhwKyIiIiIiIrVKD8tFREQuzGtSJSQlJTFt2jQiIiKIiIhg2rRpnD171u3l77nnHizL4rnnnqvRcoqIiIiIiIiIiIhUlde0uJ06dSpHjx5lwYIFAPziF79g2rRpzJ0794LLfvXVV6xbt44WLVrUQklFREREREREpKFSi3IRqS5eEbjdtWsXCxYsYO3atQwaNAiA1157jcGDB7N79266dOlS7rLHjh3jgQce4LvvvmPChAm1WGoRERERERERERGRyvGKVAlr1qwhIiLCFbQFuOSSS4iIiGD16tXlLud0Opk2bRq/+93v6NGjRy2VVkRERERERERERKRqvKLFbUJCAjExMaWmx8TEkJCQUO5yf/vb3/D19eXBBx90e1vZ2dlkZ2e7XqekpEBBENjpdFa47CJ1xel0Ytt20XFr26XeFxHPonorcg4vqAOl6q2I1Lwq/jao3kq9Vaxu1LfjW/VW6pOKHMd1Grh94oknePLJJ887z4YNGwCwLKvUe7ZtlzkdYNOmTTz//PNs3ry53HnKMmvWrDLLdPLkSbKystxej0hdczqdJCcnY9s2DoeDIDujxPuJiYl1VjYRKZvqrUhJ3lAHzq23IlLzqvrboHor9VXxuuGJ58yqUL2V+iQ1NdXtees0cPvAAw9w0003nXeetm3bsm3bNk6cOFHqvZMnT9K0adMyl1uxYgWJiYm0bt3aNS0/P5//9//+H8899xyHDh0qc7lHH32UmTNnul6npKQQGxtLdHQ04eHhFfh0InXL6XRiWRbR0dE4HA4yrZIn7rJasYtI3VK9FSnJG+rAufVWRGpeVX8bVG+lvipeNzzxnFkVqrdSnwQGBro9b50Gbps0aUKTJk0uON/gwYNJTk5m/fr1DBw4EIB169aRnJzMkCFDylxm2rRpjB49usS0K664gmnTpjFjxoxytxUQEEBAQECp6Q6HQz8O4nUsyyo6ds9pea7jWcQzqd6KFOMldaBEvRWRmlcNvw2qt1IvFasb9fHYVr2V+qIix7BX5Ljt1q0b48aN4+677+aVV14B4Be/+AUTJ06kS5curvm6du3KrFmzmDx5Mo0bN6Zx48Yl1uPn50ezZs1KLCMiIiIiIiIiIiLiabwicAvwwQcf8OCDDzJ27FgAJk2axH//+98S8+zevZvk5OQ6KqGIiIiIiIjUtIcm9KrrIoiIiNQKrwncRkVF8f777593Hvuc0UXPVV5eWxERERERERERERFP4jWBWxGpGrVMEBERERERERHxHsroLCIiIiIiIiIiIuJh1OJWRERERDySeouIiIiISEOmFrciIiIiIiIiIiIiHkaBWxEREREREREREREPo8CtiIiIiIiIiIiIiIdR4FZERERERERERETEwyhwKyIiIiIiIiIiIuJhFLgVERERERERERER8TAK3IqIiIiIiIiIiIh4GAVuRURERERERERERDyMArciIiIiIiIiIiIiHkaBWxEREREREREREREPo8CtiIiIiIiIiIiIiIdR4FZERERERERERETEwyhwKyIiIiIiIiIiIuJhFLgVERERERERERER8TC+dV0AERERKdtDE3rVdRFERERERESkjqjFrYiIiIiIiIiIiIiHUYtbEREREREREREvp95aIvWPWtyKiIiIiIiIiIiIeBgFbkVEREREREREREQ8jAK3IiIiIiIiIiIiIh5GgVsRERERERERERERD6PArYiIiIiIiIiIiIiHUeBWRERERERERERExMMocCsiIiIiIiIiIiLiYRS4FREREREREREREfEwCtyKiIiIiIiIiIiIeBjfui6Ap7NtG4CUlJS6LopIhTidTlJTUwkMDMTh0DMaEW+geivifVRvRbyP6q2I91G9lfqkMMZYGHM8HwVuLyA1NRWA2NjYui6KiIiIiIiIiIiI1AOpqalEREScdx7Ldie824A5nU6OHz9OWFgYlmXVdXFE3JaSkkJsbCxHjhwhPDy8rosjIm5QvRXxPqq3It5H9VbE+6jeSn1i2zapqam0aNHigi3I1eL2AhwOB61atarrYohUWnh4uE5sIl5G9VbE+6jeingf1VsR76N6K/XFhVraFlJiEBEREREREREREREPo8CtiIiIiIiIiIiIiIdR4FakngoICODxxx8nICCgrosiIm5SvRXxPqq3It5H9VbE+6jeSkOlwclEREREREREREREPIxa3IqIiIiIiIiIiIh4GAVuRURERERERERERDyMArciIiIiIiIiIiIiHkaBWxEREREREREREREPo8CtiIiIiIiIiIiIiIdR4FZERERERERERETEwyhwKyIiIiIiIiIiIuJhFLgVERERERERERER8TAK3IqIiIiIiIiIiIh4GAVuRURERERERERERDyMArciIiIiIiIiIiIiHkaBWxEREREREREREREPo8CtiIiIiIiIiIiIiIdR4FZEREREquTtt9/Gsiw2btxY10WpNrfffjtt27Z1az7Lssr8N2/evGot086dO3niiSc4dOhQta73fM733a5YsYIpU6bQsmVL/P39iYiIYMiQIbz88sukp6fXWhlFRERE6ivfui6AiIiIiIg3CwoKYunSpaWmd+3atVq3s3PnTp588klGjBjhVlC5Jj3++OM89dRTDBkyhKeffpoOHTqQkZHB6tWreeKJJ9izZw///ve/67SMIiIiIt5OgVsRERERkSpwOBxccskldV2MSsvIyCA4ONjt+T/77DOeeuop7rzzTl577TUsy3K9N378eH7/+9+zZs2aGiqtiIiISMOhVAkiIiIiUitWrlzJqFGjCAsLIzg4mCFDhvDNN9+43k9JScHX15d//OMfrmmnTp3C4XAQERFBXl6ea/qDDz5IdHQ0tm3z61//mpCQEFJSUkpt88Ybb6Rp06bk5uYC4HQ6+fvf/07Xrl0JCAggJiaG6dOnc/To0Rr5zIsWLeLqq6+mVatWBAYG0rFjR+655x5OnTpVat6ff/6Zm2++maZNmxIQEEDr1q2ZPn062dnZvP3229xwww0AXH755a50DG+//bZr+TfffJOLL76YwMBAoqKimDx5Mrt27Sqxjdtvv53Q0FC2b9/O2LFjCQsLY9SoURX6TE899RSRkZH85z//KRG0LRQWFsbYsWMrtE4RERERKU2BWxERERGpccuWLWPkyJEkJyfzxhtv8NFHHxEWFsZVV13FJ598AkB4eDgDBgxg8eLFruWWLFlCQEAAqamprF+/3jV98eLFjBw5EsuyuOOOO8jIyODTTz8tsc2zZ8/y9ddfc+utt+Ln5wfAfffdx8MPP8yYMWOYM2cOTz/9NAsWLGDIkCFlBlPdlZeXV+Jffn4+APv372fw4MG8/PLLLFy4kD//+c+sW7eOSy+91BVMBti6dSsDBgxg7dq1PPXUU3z77bfMmjWL7OxscnJymDBhAs888wwAL774ImvWrGHNmjVMmDABgFmzZnHnnXfSo0cPZs+ezfPPP8+2bdsYPHgwe/fuLVHWnJwcJk2axMiRI/n666958skn3f6c8fHx7Nixg7Fjx1aola6IiIiIVJxSJYiIiIhIjXvkkUeIjIzkhx9+IDQ0FICJEyfSu3dvfvvb3zJlyhQsy2L06NH861//Ijs7m4CAABYvXsyIESM4fvw4ixcvZsiQIRw/fpxdu3bxm9/8BoCLLrqIvn378tZbb3HXXXe5tvnRRx+RnZ3NjBkzoKBF66uvvsr999/PCy+84JqvT58+DBo0iH//+9/83//9X4U/W3p6uiswXGjo0KGsXLmSe++91zXNtm2GDBnCiBEjaNOmDd9++y2TJk0CYObMmfj6+rJ+/Xqio6Ndy9xyyy1Q0Iq1U6dOAHTv3r1EaoazZ8/y9NNPc+WVV/Lhhx+6po8YMYJOnTrxxBNP8MEHH7im5+bm8uc//9m1XyoiLi4OgHbt2lV4WRERERGpGLW4FREREZEalZ6ezrp167j++utdQVsAHx8fpk2bxtGjR9m9ezcAo0aNIjMzk9WrV0NBy9oxY8YwevRoFi1a5JoGMHr0aNe6ZsyYwerVq13rAXjrrbcYMGAAPXv2BOD777+HgnQBxQ0cOJBu3bqxZMmSSn2+oKAgNmzYUOLfG2+8AUBiYiL33nsvsbGx+Pr64ufnR5s2bQBcaQwyMjJYtmwZU6ZMKRG0ddeaNWvIzMws9bliY2MZOXJkmZ/ruuuuq9RnFREREZHao8CtiIiIiNSopKQkbNumefPmpd5r0aIFAKdPnwZgyJAhBAcHs3jxYvbt28ehQ4dcgdt169aRlpbG4sWLad++fYlWn7fccgsBAQGunK87d+5kw4YNJVqVFm6jvHIUvl9RDoeD/v37l/jXpUsXnE4nY8eOZfbs2fz+979nyZIlrF+/nrVr1wKQmZnp2j/5+fm0atWqUtuv6OcKDg4mPDy8Uttq3bo1AAcPHqzU8iIiIiLiPqVKEBEREZEaFRkZicPhID4+vtR7x48fB6BJkyYA+Pv7c+mll7J48WJatWpFs2bN6NWrF+3btwfghx9+YMmSJUycOLHUNq6++mreffdd/vKXv/DWW28RGBjIzTff7JqncePGUJCn9dwg6fHjx11lqC47duxg69atvP3229x2222u6fv27SsxX1RUFD4+PpUeIK345zpXWZ+rrAHF3NW8eXN69erFwoULycjIUJ5bERERkRqkFrciIiIiUqNCQkIYNGgQs2fPdrUyBXA6nbz//vu0atWKzp07u6aPHj2aTZs28cUXX7jSIYSEhHDJJZfwwgsvcPz48RJpEgrNmDGD48ePM3/+fN5//30mT55Mo0aNXO+PHDkSgPfff7/Echs2bGDXrl2MGjWqWj93YYA0ICCgxPRXXnmlxOugoCAuu+wyPvvss/MOkFa4nuL7EGDw4MEEBQWV+lxHjx5l6dKl1f65HnvsMZKSknjwwQexbbvU+2lpaSxcuLBatykiIiLSEKnFrYiIiIhUi6VLl3Lo0KFS06+88kpmzZrFmDFjuPzyy/ntb3+Lv78/L730Ejt27OCjjz4q0Qp01KhR5Ofns2TJEt555x3X9NGjR/P4449jWZYrCFvc2LFjadWqFffffz8JCQmlBt/q0qULv/jFL3jhhRdwOByMHz+eQ4cO8dhjjxEbG+sa7Ky6dO3alQ4dOvDII49g2zZRUVHMnTvXlau3uGeffZZLL72UQYMG8cgjj9CxY0dOnDjBnDlzeOWVVwgLC3Pl6n311VcJCwsjMDCQdu3a0bhxYx577DH+8Ic/MH36dG6++WZOnz7Nk08+SWBgII8//ni1fq4bbriBxx57jKeffpqff/6ZO++8kw4dOpCRkcG6det45ZVXuPHGGxk7dmy1bldERESkoVHgVkRERESqxcMPP1zm9IMHD3LZZZexdOlSHn/8cW6//XacTicXX3wxc+bMKZX2oE+fPjRp0oRTp06VaFlbGLjt06ePKz1AcQ6Hg+nTp/PMM88QGxtbZkvTl19+mQ4dOvDGG2/w4osvEhERwbhx45g1a1aZ66wKPz8/5s6dy0MPPcQ999yDr68vo0ePZvHixa5csYUuvvhi1q9fz+OPP86jjz5KamoqzZo1Y+TIkfj7+wPQrl07nnvuOZ5//nlGjBhBfn4+b731FrfffjuPPvooMTEx/Oc//+GTTz4hKCiIESNG8Mwzz9CpU6dq/VwATz31FKNHj+aFF17gj3/8I6dOnSIoKIgePXowc+ZM7rnnnmrfpoiIiEhDY9ll9W8SERERERERERERkTqjHLciIiIiIiIiIiIiHkaBWxEREREREREREREPo8CtiIiIiIiIiIiIiIfxusDtSy+9RLt27QgMDKRfv36sWLHivPNnZ2fzxz/+kTZt2hAQEECHDh148803a628IiIiIiIiIiIiIhXlW9cFqIhPPvmEX//617z00ksMHTqUV155hfHjx7Nz585SI/MWmjJlCidOnOCNN96gY8eOJCYmkpeXV+tlFxEREREREREREXGXZdu2XdeFcNegQYPo27cvL7/8smtat27duOaaa5g1a1ap+RcsWMBNN93EgQMHiIqKquXSioiIiIiIiIiIiFSO17S4zcnJYdOmTTzyyCMlpo8dO5bVq1eXucycOXPo378/f//733nvvfcICQlh0qRJPP300wQFBbm1XafTyfHjxwkLC8OyrGr5LCIiIiIiIiIiItLw2LZNamoqLVq0wOE4fxZbrwncnjp1ivz8fJo2bVpietOmTUlISChzmQMHDrBy5UoCAwP58ssvOXXqFPfffz9nzpwpN89tdnY22dnZrtfHjh2je/fu1fxpREREREREREREpKE6cuQIrVq1Ou88XhO4LXRuq1fbtsttCet0OrEsiw8++ICIiAgAnn32Wa6//npefPHFMlvdzpo1iyeffLLU9M2bNxMaGlptn0OkpjmdTlJSUggPD7/gExwR8QyqtyLeR/VWxPuo3op4H9VbqU/S0tLo27cvYWFhF5zXawK3TZo0wcfHp1Tr2sTExFKtcAs1b96cli1buoK2FOTEtW2bo0eP0qlTp1LLPProo8ycOdP1OiUlhdjYWNq1a0d4eHi1fiaRmuR0Ojl58iTR0dE6sYl4CdVbEe+jeivifVRvRbyP6q3UJykpKVBG49SyeE3g1t/fn379+rFo0SImT57smr5o0SKuvvrqMpcZOnQon332GWlpaa7Wsnv27MHhcJTbFDkgIICAgIBS0x0Oh34cxOtYlqVjV8TLqN6KeB/VWxHvo3or4n1Ub6W+qMgx7FVH+8yZM3n99dd588032bVrF7/5zW+Ii4vj3nvvhYLWstOnT3fNP3XqVBo3bsyMGTPYuXMny5cv53e/+x133HGH24OTiYiIiIiIiIiIiNQ2r2lxC3DjjTdy+vRpnnrqKeLj4+nZsyfz58+nTZs2AMTHxxMXF+eaPzQ0lEWLFvGrX/2K/v3707hxY6ZMmcJf/vKXOvwUIiIiIiIiIiIiIudn2bZt13UhPFlKSgoREREkJycrx614FafTSWJiIjExMepKIuIlVG9FvI/qrYj3Ub0V8T4Nvd46nU5ycnLquhjiJj8/P3x8fMp9vyKxRq9qcSsiIiIiIiIiItJQ5OTkcPDgQZxOZ10XRSqgUaNGNGvWzK0ByM5HgVsREREREREREREPY9s28fHx+Pj4EBsb2yBbG3sb27bJyMggMTERgObNm1dpfQrcioiIiIiIiIiIeJi8vDwyMjJo0aIFwcHBdV0ccVNQUBCAK73H+dImXIhC9SIiIiIiIiIiIh4mPz8fAH9//7ouilRQYaA9Nze3SutRi1sRERERERERkUp4/pvtrr8fmtCrTssi9VdV86RK7auu70wtbkVEREREREREREQ8jAK3IiIiIiIiIiIiIh5GgVsRERERERERERGpFsuXL+eqq66iRYsWWJbFV199VeL93NxcHn74YXr16kVISAgtWrRg+vTpHD9+vFbKd/vtt2NZFvfee2+p9+6//34sy+L2228v9d7q1avx8fFh3LhxtVJOFLgVERERERERERGR6pKens7FF1/Mf//73zLfz8jIYPPmzTz22GNs3ryZ2bNns2fPHiZNmlRrZYyNjeXjjz8mMzPTNS0rK4uPPvqI1q1bl7nMm2++ya9+9StWrlxJXFxcrZRTgVsRERERERERERGpFuPHj+cvf/kL1157bZnvR0REsGjRIqZMmUKXLl245JJLeOGFF9i0aVOJgKhlWbzyyitMnDiR4OBgunXrxpo1a9i3bx8jRowgJCSEwYMHs3///gqXsW/fvrRu3ZrZs2e7ps2ePZvY2Fj69OlTav709HQ+/fRT7rvvPiZOnMjbb79d4W1WhgK3IiIiIiIiIiIiUmeSk5OxLItGjRqVmP70008zffp0fvzxR7p27crUqVO55557ePTRR9m4cSMADzzwQKW2OWPGDN566y3X6zfffJM77rijzHk/+eQTunTpQpcuXbj11lt56623sG27UtutCN8a34KIiIiIiIiIiIhU3Su/hbSztb/d0EZwzz9rZNVZWVk88sgjTJ06lfDw8BLvzZgxgylTpgDw8MMPM3jwYB577DGuuOIKAB566CFmzJhRqe1OmzaNRx99lEOHDmFZFqtWreLjjz/mhx9+KDXvG2+8wa233grAuHHjSEtLY8mSJYwePbpS23aXArciIiIiIiIiIiLeIO0spJ6u61JUm9zcXG666SacTicvvfRSqfcvuugi199NmzYFoFevXiWmZWVlkZKSUiroeyFNmjRhwoQJvPPOO9i2zYQJE2jSpEmp+Xbv3s369etdaRV8fX258cYbefPNNxW4FRER8UTPf7O9xOuHJvQqd14REREREZFqEdrIjZm8Y7u5ublMmTKFgwcPsnTp0jIDr35+fq6/Lcsqd5rT6axUGe644w5XqoUXX3yxzHneeOMN8vLyaNmypWuabdv4+fmRlJREZGRkpbbtDgVuRUREREREREREvEENpSuobYVB27179/L999/TuHHjOinHuHHjyMnJAXClXyguLy+Pd999l3/961+MHTu2xHvXXXcdH3zwQaVz7LpDgVsRERERERERERGpFmlpaezbt8/1+uDBg/z4449ERUXRunVr8vLyuP7669m8eTPz5s0jPz+fhIQEAKKiovD396+1svr4+LBr1y7X3+eaN28eSUlJ3HnnnURERJR47/rrr+eNN96o0cCto8bWLCIiIiIiIiIiIg3Kxo0b6dOnD3369AFg5syZ9OnThz//+c8AHD16lDlz5nD06FF69+5N8+bNXf9Wr15d6+UNDw8vNz/uG2+8wejRo0sFbSlocfvjjz/y/9u77/A4qnv/4+9dddnqzU1y7703jI1xt+mYbgMhBEIgEEi4kOR3geReuMklBMINNXQImGKqjY3BNrhj3Hu35SJZsq1mq2vn98eRtZLVVtJKuyt9Xs/jx9rZKWd258zOfOec79m4cWOjlU0tbkVERERERERERMQtJkyYgGVZ1b7fqVOnGt8/78J5qlqutm1V5c0336zx/c8++6zs7y+//LLa+YYMGVLnbdeVWtyKiIiIiIiIiIiIeBkFbkVERERERERERES8jFIliIiItBDPLdhW9vf9M/t7tCwiIiIiIiJSM7W4FREREREREREREfEyanErIiIiIiIiIiLi405m5pb9nRAZ6tGyiHuoxa2IiIiIiIiIiIiXsizL00WQOnI4HG5Zj1rcioiIiIiIiIiIeJmAgABsNhvp6enExcVhs9lqnL+osKDs7/x8tdX0BMuyKCwsJD09HbvdTmBgYIPWp8CtiIjUSoNaiYiISH2Vv45A1xIiIi7z8/OjQ4cOHDt2jMOHD9c6f3ZeYdnfOSENCxhKw4SGhpKUlITd3rAAugK3IiIiIiIiIiIiXqh169Z0796doqKiWud9e/nesr/nTujcyCWT6vj5+eHv719rC2lXKHArIiIiIiIiIiLipfz8/PDz86t1vrwSZ6AwODi4kUslTUEJL0RERERERERERES8jAK3IiIiIiIiIiIiIl5GgVsRERERERERERERL+NzgdsXXniBzp07ExwczNChQ1mxYoVLy61atQp/f38GDRrU6GUUEREREWkszy3YVvZPRERERJovnxqcbN68eTzwwAO88MILjB07lpdffpnp06ezc+dOkpKSql0uKyuLuXPncumll3Ly5MkmLbOIiIiItBwXBlPvn9nfY2UREREREd/mUy1un3nmGe644w5+/vOf07t3b5599lkSExN58cUXa1zurrvu4qabbmL06NFNVlYRERERERFPUutsERER3+YzLW4LCwvZsGEDjzzySIXpU6ZMYfXq1dUu98Ybb3DgwAHeffdd/uu//qvW7RQUFFBQUFD2Ojs7GwCHw4HD4WjQPog0JYfDgWVZOm7FPSyr7E8dU6XKfSaUfi7PL9xeYdp9M/rVaZWNXm/1PYq4XaV6W8W5we1Ul8VV3nKsNEW9qANdJ4tbeUs9a+ZUb12k49En1OW78ZnA7alTpygpKSEhIaHC9ISEBFJTU6tcZt++fTzyyCOsWLECf3/XdvWpp57iiSeeqDQ9PT2d/Pz8epZepOk5HA6ysrKwLAu73aca14sXCrFyy/5OS0vzaFm8RfnPhNLPpappddHY9Vbfo0hlX6w/XOH15cM71Wn5C+ttQ88DrlBdFld5y7HSFPWiLnSdLO7kLfWsuVO9dY2OR9+Qk5Pj8rw+E7g9z2azVXhtWValaQAlJSXcdNNNPPHEE/To0cPl9T/66KM8+OCDZa+zs7NJTEwkLi6O8PDwBpZepOk4HA5sNhtxcXH6YZMGy7M5f/Tj4+M9WhZvUf4zofRzqWpaXTR2vdX3KFKZu+ttQ9fnCtVlcZW3HCtNUS/qQtfJ4k7eUs+aO9Vb1+h49A3BwcEuz+szgdvY2Fj8/Pwqta5NS0ur1AqX0uj1Tz/9xKZNm7j33nuhXNN6f39/vvnmGyZOnFhpuaCgIIKCgipNt9vtOjmIz7HZbDp2xT3KPSDT8VTqgoeGdru96ml1Xm0j1lt9jyKVubveumF9LmywcdcvzYe3HCtNUS/qSNfJ4jbeUs9aANVbF+h49Al1+W585lsMDAxk6NChLFmypML0JUuWMGbMmErzh4eHs23bNjZv3lz27+6776Znz55s3ryZkSNHNmHpRURERERERERERFznMy1uAR588EHmzJnDsGHDGD16NK+88grJycncfffdUJrm4Pjx47z99tvY7Xb69as4KEx8fDzBwcGVpouIiIiIiIj3eW7Btgqv75/Z32NlERHxFjo3thw+Fbi9/vrrOX36NH/6059ISUmhX79+LFy4kI4dOwKQkpJCcnKyp4spIiIiIiIiIiIi0iA+FbgFuOeee7jnnnuqfO/NN9+scdnHH3+cxx9/vJFKJiIiIiIiIiIiIuIePpPjVkRERERERERERKSl8LkWtyIiIiIiIiLiO5SPU0SkftTiVkRERERERERERMTLKHArIiIiIiIiIiIi4mUUuBURERERERERERHxMgrcioiIiIiIiIiIiHgZBW5FREREREREREREvIy/pwsgIiLNR/kRgzVasIiIiIiIiEj9qcWtiIiIiIiIiIiIiJdR4FZERERERERERETEyyhVgoiIiIgPUUoSEREREZGWQS1uRURERERERERERLyMWtyKiIiIiIiIiEiTKt+LCPUkEqmSWtyKiIiIiIiIiIiIeBkFbkVERERERERERES8jFIliIiIiIiIiEgFGgxTRMTzFLgVEREREamH5xZsA8sixMolz5bG/bMGeLpIIlIPyrMpIiLeSqkSRERERERERERERLyMWtyKiIiIiIiIiJSjVBEi4g0UuBURERGvpZsmERERERFpqZQqQURERERERERERMTLqMWtiIiIiIiIiIgPUa8kkZZBgVsRERERERERkRaqfBAYBYJFvIpSJYiIiIiIiIiIiIh4GQVuRURERERERERERLyMUiWIiEgFypclIiIiIiIi4nlqcSsiIiIiIiIiIiLiZdTiVkRERESaHQ20IiIiIiK+ToFbERERERERERGRelCqOWlMCtyKiIiISK10UyIiIiIi0rR8LsftCy+8QOfOnQkODmbo0KGsWLGi2nnnz5/P5MmTiYuLIzw8nNGjR7N48eImLa+IiIiIiIiINK7nFmyr8E9EpDnwqcDtvHnzeOCBB/jDH/7Apk2bGDduHNOnTyc5ObnK+X/44QcmT57MwoUL2bBhA5dccgmXXXYZmzZtavKyi4iIiIiIiIj3UcBXRLyVT6VKeOaZZ7jjjjv4+c9/DsCzzz7L4sWLefHFF3nqqacqzf/ss89WeP3kk0/y+eef8+WXXzJ48OAmK7eIiHgnDV4kIiIiIiIi3spnAreFhYVs2LCBRx55pML0KVOmsHr1apfW4XA4yMnJITo6upFKKSIivk7BXBERaWmUw9q76fsREWm5fCZwe+rUKUpKSkhISKgwPSEhgdTUVJfW8be//Y1z585x3XXXVTtPQUEBBQUFZa+zs7OhNOjrcDjqXX6RpuZwOLAsS8et1J1llf1ZdvxUNc3VZb1ZufJyvsxVTXP3stVwZ719fuH2sr/vm9HP/OFr3w8+WubG5qnPxNe+i4bU5bps4/w/LLecB1zebmOuX5qPRv79Lv9bQ/nfmxrWX9dtNEadqvR72xT1tiGaw3m/ic+Ndd6GL17/+tr308DtevX9rSeOgeo+O10j+IS6fDc+E7g9z2azVXhtWValaVV5//33efzxx/n888+Jj4+vdr6nnnqKJ554otL09PR08vPz61lqkabncDjIysrCsizsdp9KZy0eFmLllv2dlpZW7TRXl/Vm5ctLaZmrmubuZavjznrbkO/Rm/himRubpz4TX/suGlKX67qNQAoBm1vOA3XZbmOtX5qPxv79bsp6Vp9lq3Ph721T1NuGaA7n/aY+N9Z1G754/etr309Dt+vN97eeOAaq++x0jeAbcnJyXJ7XZwK3sbGx+Pn5VWpdm5aWVqkV7oXmzZvHHXfcwUcffcSkSZNqnPfRRx/lwQcfLHudnZ1NYmIicXFxhIeHN3AvRJqOw+HAZrMRFxfndT9s4t3ybM4f+PMPuqqa5uqy3qx8eSktc1XT3L1sVZ5fuB0sixArlzxbIfc1sCtkQ75Hb+KLZW5snvpMGrLdKluAN7KG1OU6baO0tW0eIQ0+D9Rpu424fmk+Gvv3u8nqWT2Xrc6F18mubsMT5zJ89Lxf07rcsT53b8MXr3997ftp6Ha9+f7WE8dAdZ+drhF8Q3BwsMvz+kzgNjAwkKFDh7JkyRKuuuqqsulLlizhiiuuqHa5999/n5/97Ge8//77zJw5s9btBAUFERQUVGm63W73upODSG1sNpuOXam7cr0Yyo6dqqa5uqw3u6DHht1ur3qau5etcX02KK27DdKQ79Gb+GKZG5unPpOGbNcTZW5IXa7zNsrV24asr87bVb2QWjT273eT1rN6LFvjastdJ9djP3zm/Ost223ic2Odt+GL17++9v24Ybtee3/rTdc5ukbwCXX5bnwmcAvw4IMPMmfOHIYNG8bo0aN55ZVXSE5O5u6774bS1rLHjx/n7bffhtKg7dy5c3nuuecYNWpUWWvdkJAQIiIiPLovIiIiIiIiIiLiOzRYoDQ1nwrcXn/99Zw+fZo//elPpKSk0K9fPxYuXEjHjh0BSElJITk5uWz+l19+meLiYn71q1/xq1/9qmz6rbfeyptvvumRfRARERHxdropERERERHxPJ8K3ALcc8893HPPPVW+d2Ewdvny5U1UKhERERERERERERH38bnArYiIiEh9lG9FilqSioiIiIiIl1PgVkRERERERERERBqNGlHUjwK3IiIiUoHym4pIVXRuEHEv1SkREamNArciIiIiIiJeSsE9ERGRlkuBWxERkSamm3AREfEU/QaJiIj4DgVuRURERERERKRGyk8pItL07J4ugIiIiIiIiIiIiIhUpMCtiIiIiIiIiIiIiJdRqgQREREREXEb5VAVEXGNzpeeoc9dfIla3IqIiIiIiIiIiIh4GQVuRURERERERERERLyMUiWIiIiIiPiQ8IIM+qetZ09Mf0BdPEVExHXl0wSgVAEiXk+BWxERERERX1FcxFV73iKy4Ax9Tm2CwvEQGOTpUrmFggkiIiIiFSlwKyIi4gVCis4y+dBnFPv5823HKzxdHJFmqVkEBn9aTGTBGQBCi8/B5u9gxAxPl0qkVjG5J7FbJaS3aufpooiIiPgMBW5FRES8wEVHv6Fz1l4AgovyoHgw+Ad4uljiYRr1WCrIz4UfPqo4bc0XMHQq+Pl5qlQitWpz9ijX7n4du+VgYdfrlOJDmpXE7AP0Td/IpoQxOrabULN4GCviAgVuRUSk2fG5C7n0o/Q6vaXsZWLOIfj0ObjmQbBrHFERKbX6M8jNBsDChg0LMk7CrrXQb6ynSydSraEpK/GzSgAYn7wQ8i+H4FBPF0uk4XKzmbXvfQIdhbQ9mwyOyWDXgzQRcR/dDYqIiHja8nnYsSpO27EKFr8OllXdUiLSkuScMa1rgRKbH0s7XuZ8b/VnOleI98o4SZfM3WUvWxflwPIPPFokEbdZ+xWBjkIAwguz4OC2WhcREakLBW5FREQ8KfWwCdICuf6t+Lbz5TjO/zyvWwAr53u2fCLiHZbPg6ICALbGDWd73FDSQtuY907sh8M7PFs+cYvnFmwr+9dsrP+68sPJdQvM758LgotzuWzfe1y1503a5bi2jEiTyD8HPy6sOG3Td54qjYg0UwrcioiIeNKy98v+XN92HAeje/Ndp8ud73/3Lmxa6pmy+YjygY5mFeyQ5uXU8bLAa52lH4ON35q/A0NY32482GxsbHORc57Vn7mnnCLuVJhfduwW2/zZlDDKTLccsPCV2luKWxYTD39Bl8w9JGUfZPbu1+HLFyHvXBMUXqQW6xeZ4G15u9dCbo6nSiSCzSrBVpqaRpoH5bgVERHxkISzx2HPjwDkBISzLW4YgRSxM3Ywk7u2NkFbgC/+CUEh0Gd0g7YXWJJP9zM7SAttq8EzRJqKZcGCl+GnxRAaDrPurntd/u5dE+gCuOgq8nJaAbAvqi/TIr6HrHTYtwFOHoGEjo2wEyL1tPX7ssDW3pj+rOowhU6Z+4gqOA3Ju2DLMhg0sfrlt6+ke8bOitM2fAN71sP0O6DPGLDZGnknxBt5fDyDwvyy9DUObByK7EnXzN1QUgzbVsDIGU1bHhFMK/Abd7xMTN5JDkT1gaOBkNjT06WSBlLgVkRExENGHXd2p1vfbjwl9gCwisyEi66GnAz4cYEJ2Hz8N7jud9BrZP02lpnGdTtfJSY/3byevxsmzYHw6EqzBhedIzbvJOnnu2GLSP2t+NgEbTGD2PDhX6H/xTDjTghp7ZyvuAj2bYT9G83fYAJSJcWwe5153ToKRl0GS/YB4LD7wejLYNHr5v3Vn8NVv27a/ROpjmWZlAilNsePpMTuz/KOM7lq79tm4jdvQc8RFevCedlnYMErZS+3xw6lx5ltJp/o2Qz46GnoPRqu+Q34BzTJLomU2fBN2WCRe6P781PbcSZwS2m6BAVuK/F4sL0lWL+IuLxUALpn7IDXHoEOPWHMFdBrhAbO81EK3IqIiHhAu5wjdMreb15ExrMjdnDFGWw2mPYzKMyDzUvBUQIfPg3XPww9h9dtY6mH4N0/E5Of4Zy2dbkZif7ia2H05VBcaIJD21dy5/4t2HFwMrQtOIaDXZmVROqjx+mtsP7jym9s+wEObYPL7zGtcLcsg+0rIe9szSu85AYIDK44bfAkWP4h5J81rbwuvRnCY9y7I82cggmN5NA2SD8KwPHWSaS3agdAckQ30+p85xoT+Fr6b5j5i4rLWhZ8+YI5roE90f35rvMVrGs3gTsKfjAtbgF2rYENfWHkzCbeOWnRigphlTM9zfp2F3MmJJ7UVu1pc+44pB6ElIPQtkutq4o7d4IeZ7azN7p/s+oNpfOqBxQXVXhYVubYHvPQOKYd3PAIxCV6onTSAArcioiIuElYQSaX73uXQnsQC7tdX/2MlsXocq1tGX8djhP+lXP92e0msONwmECro9hceF3/CPQY6lKZErMPwOsfmgAwkBUYSVBJAcEleVCUb7pgr1sAeTmmZV+5BPgJuSlwdDd07OPyZ1D+Ql0X6dKStcs5zORDnzonXHqLCagufBUKck2LwX//t+sr7NATBl1aeXpQCAyfCis+MeeItV/BlFvdsxMiDVG+te353LbnTf0Z7NtkfofWLzIBh4k3QlhpL5BN35n0H8C5gNYs72gCs2eDIuCqR82Djk+eMfOu+ASGTIaAwKbaM2npNi8153CA3qM4ExIPwI7YISZwC2Z8gtoCtycOMHv3awQ4ihh8cg2sLYCRs5T+Q+pn2w9lx+WByF4cjOzF5NyNkJZs3j99At5+An72JETFe7asUicK3IqIiNTC1VYDw1N+IDYvDYAr9r4D+YMhOLTSfF0zdtHh/MjY0W1hwAQ4sbPSfIDp0nTlvSZdwrYfTHB13l/g+v+oNXjb8/QWJh/6DEoHKEhp1YEvut8MNht3+W023fwsh/Pmo1SBXzBBJfnmxY7VdQrciih4D5H5p5i17338zw8OMmSSSX9is0Hn/iZv9f5NFRfyDzSpUAaMh+g25R7kWGCzm2nVdXEcMdOkSSgpNiOc978Y2nZu3J0UqUnGSWer2PAYDkb2rvh+RCxMuB6WvGWO8U3fmmDsRVeZvLXn038A33W6nHz/cr+lNhv0Hwc7VpmeImczYOMStbqVplFSDCvnO1+PuxY25UJpyoRLjy82vZi2fg+T51b/QCH7NLz/JAEOkxrHzyoxx/3hHXDFvVWnDxGpjmWZ64BSP7UdR2rrRCbPmAsHtsA3b0LaEcg5DW8/Bj/7b+eDMvF66vsoIiLiDkUF9DizvexlXN5J0zq2tBVrmT3rmXbwI+frCTeAXy35pux+cOWvoW/pCPIlRaal3oJXoCCv8vy5OYw/soBpBz8xNwIAPYczv+dt5Ae0MjfAs+6Cu/4GnfqZ91tHmZvenz3F6wN+Q7GttEw7V5sWvyLikrCCTK7Y+y4hJaV1s+sgmHmXswVVeAzc/P/gsl+aBzed+pmb9N++Adc+aB7IxLaHuA6l/xLN65ry0oVFwfDp5u/iQvNwR6Oaiyf9+LUJyAIMn27yMV9ozBUw9XYIKg3KFuXDsvfhn78u6yXCoIkciuxV9TbGl+vZsuIT031dpDFlnTKpPbJKxwvoPhTadS17u9A/2Dn4ZP5Z58OLCxXmw/tPQs4ZALIDI5zv7V4HLz8Ex/Y24o5Ic9Mxa19ZapoTrZNIbV2aDsFmg26D4NYnzLUEQEYqvP14WY5m8X5qcSsiIuIOu390tlI97+AW+PJFE5Sx2WDXOvjo6bJWeHuj+tGj30Wurd/PD65+wLSe3bnG3BCv/xr2/GhGqe8xzHQ1/WkRLP+QQfnlcmUOnQIzfkHxogta9bbpBLf+ybRWahVRFhgq3LaN5IhudMncY96rY7qElkL525oJyyImL40iv0Cyg6Iatq79m7lx50uEFJvWV6dCEoid/Tvwu+CS22Yz9XLolIZtr7xLb4HkXXBiP2SehPl/h5v+oIFIxGU2y4Flq1u7nvD8M4w8sZzYvJMU2wPgTDQEBMHBrWYGvwCTxmD5kSo2aDM51gdMgO/nmZQJlsMZ8A2PMbnevztY9cbbdjat1NXqVhqL5aDNuWN0ztwLL74OJw9XfP/iaysvM/hS09qW0pQf/cZWfN/hgPnPmhy4QFZQFPN6/4KEc8e44tjnJtd5Zhq8/gfnOARBIY22i9I8DE1dVfb3hjZjK8/QKgLmPA6v/948eEg/Cu/+2dwH6PjyegrciohIi9DoQbbNS8v+XN3+Ukac+B5/q9hMj4iD+CSTj89hgrZ7ovuzuMvV9KjLwF9+fnDtQyZv4NJ/Q1GB6Wr37/82N69pyXAmpWz2InsAa9pP5OJZd1WbL+25hedbCR+v8JnsjepnArcoXYI0DY8EwosKmHT4M/qe2kSxzY8vu99cv8FhLIsRKcvhp+WElAadsoKi+Lz7LdxRRbqURhEQCNc9DK/81rSi2b8Jls+DiTc1zfbF4+qdpsSymHDkK/qmb2RvdD/I61x7N+3CAlj5CXO2f2p+6847m1xxvv7joFV4zetqFQ4z7jStxpe8BXt/Mg87rrgPglvVvOz4603gFuW6lTpwlMDJZIiMh5AqjrGSYtj2A7ds/4CY/PSq19HvIkisojV4x74QmWAeoB3YbFrpRsQ631/6nvOYDQrli+43kxfQisORPWHmM/Dx38xgUo5iWP6BeaAx/joYOrnyQ0CR0gHuEnMOmRfRbTkY2bPqGSNiYe4T8MYfzMOuE/vNPcQt/888cBOvpZovIiLSQK0Ls0z+qNJgzfq248gIjmHmgY9My6Hv55kclZZJObArZiBLOl9V55ZNUJo2YfTlJlD75YumVS84bwJK7YwZxJoOl3I2MIKL6zHIxaHInhTb/Ezr4J2rTaunugSZRcrpeXoLvU5vZX9Ub3D09YpjKSL/DLz2KH1PmZsdf6uEaQc/hoyxdRq0I6g4l2kHPyYp29kq8FBEDxZ3uYYC/yZuxRIZZx7uvPOEOd/88BG06wa9RjRtOcS3bPiGgWk/AtDn9GZ44QHTU6TboMrzWpbp9fHNm5CVXnYzaWHDxgUDbAaFmtzOrorrYFqJpx8zDyqj29a+jFrdSl2dPgHzn4Pje821Wfvu0HUgbXPCOB2SQO/Tm+G5f0D2KWIuXLZ9d5Meocew6gces9th8EST9gPLBGJbR5rUVvnnTLAMzLavfYgzewOcy0bGwe3/ZR7Or/7cnMfPZcLCV2DNF6Y+OUrMPpw+wa3JhwgsyWd1h0n1e+gozUL51raMuQJO1nCNFdMW5jwGb/7RtO4+ssM89NJDXq+mwK2IiEgD9T61uaxr586YQWCzsz+6H3QNNTe3UBa0ZdBElvhfXL+gbXlRCebCa8tyWPy6ufgCSOoDU29nyeYqct/WQaF/MEciutM1c7fXpUvQ4Fc+pLgIFr3OtIOLAOiUtQ/eOQSX3+vREY07Z+5m6sH5cEF6k5DiXPjwL2bEZRdan8SdO8HM/R8QUZhZOsXG6vYTWd92nLkp94QuA2DSnNJBn4BPn4M7/+rMbSdSXvrRCgOBAWbwmnefgOHTYPKtZuC81EOm9eCe9aY1YKkSmx+bEkbzY7vxlNj8uW9yd9MatygfwmIgsB6tuOI61G3+Rmp1q9+aZsayzKCsi98wPZYovTY7tgeO7eG6ahY73jqJnbFDmHztFSYA64qBl8CyD8y14dHdVc8z7Q7oPgT2Vuxtgp+/GdRs8KWmde7ONWZ6Rip8+UKFWc+XZsKRhZA507QglhYlrCCT7md2mBeh4TBwAnxTS37khI5wy3/Ca783LbvXfAkjZrh+fEuTU+BWRESkISyrNHBr7Iot10Jp9OUmj9S6Beb1kEkw65dYX+9wz7ZtNhh0CXQbDJuXmZvdHsPM9M3bXFhBzfZF9TWBW5QuQeoutCgH3vrPyjeth7bBi/ebgNCwqdWm8WgMdkcxo04sY3jKirJpGcExfNvpSiYf+pTIgjMm7+CCV5y5qauz8Vuu2/Wvsm7ief6hhNz4O9bv9oKcsmOugOP7TGv5glz46Gm462nlu20ivhLw83MUw8fPmAHtSnuDhBadpWP2ATPD+kWw5ycT5MqrYrC7roN5N2QcmSHluoEHt6o9vYG7qdWt1CYnA774J+zb4JwWmWAC/KUDOlXSfSgf+Q3iRFhHACbXJagVGWeuz8ql0SoTFGqOz5Ezal5HbHuT/ubYXvj2HTi8vdIsJTY7fpbD/A59+47pcSEtyuCTa7BT2jhkxAzXUx607w7DpsCPC82DthUfw/SfN2pZpf7qFLhdunQp9957L2vXriU8vGKuoqysLMaMGcNLL73EuHHj3F1OERERr9T27FGiCk4DcDSsMznlBzey2WDqzyCxt+k612tk43QRbx0JF13l9tUeiuoJyQFQUqR0CVInbc4eZeb+D6DIBHuKbf5saDuWPqc2E1aYZUbUXvCyaTHebTC07UKrwkLOBYQ1XiD3yE5u2vFixXyFfUbzQfAlFPoFs6DbDVy361UCHEXmZrtDDxNYvlBRISx8FTZ9W3YhnRbalq+63sDPug6C3Q1/aNJgNpsJPKcfNf9OHobdPzpHO/cCGtzP88YcW1I22NLp4Di+63Q5JTZ/7o8/bnqLFBdC9qnKC8a0h8lzoOcIMhdWDiZ5hHLdSnVOHIB3/2Ryf583bCpMuQ0Cg00O2oNb2LPie2LyTpIe2pbes2+DNp04saAB5/PLfwVjrzR/B4VCYIjZXl2vozr0MANIHdxqurWHRUNMO4htzyvLDnDrtucILc6F7SthxExIqiLvrjRLQcV59E03DyOKbf74D59etxWMu9YMoFdUAD8tNg1O1GrbK9XprPHss89y5513VgraAkRERHDXXXfxzDPPuLN8lbzwwgt07tyZ4OBghg4dyooVK2qc//vvv2fo0KEEBwfTpUsXXnrppUYtn4iItCx9Tm0q+3tn7ODKM9jtZkThPqN9LuhZ6BfszHF4Pl2CSC16ndrMNbtfp3Vp0JbwGD7udQdr21/Ku/1+ZQIq5x3bYwZfef9Jfr7lae7c/Fdm7J9HcNE5t5UnqDiPiYc/hzf+UBa0dWDnh8RpMPt35jgHToW24dtOVzgXXPgvOLqn4soyTsLrj8Kmb8smbYkbzhc9byInyMu6GAaFwNTbna9Xf2a6Crdwzy3YVuFfS9Uxax9DTpZ2wfYLYFHX2ZTYA0zQf8R0uPsZEzCiNOjUayTM+AXc90+493nzuglby9fqfKtbSn+vzvd0kZbt2F7T8+N80LZVpMmjPOtuE0SldMCmwZeyqOts3ut3L990uQbadGr4tu12iEs0/8JjIDi0/teBNht0HWjykA6fZlLihMdQ6B/C2vaXOudb9Bo4HA0vu/iEfuk/EegwPSZ2xg6ufSDIC4VFwchZ5u+SYjOgqXilOrW43bJlC3/5y1+qfX/KlCk8/fTT7ihXlebNm8cDDzzACy+8wNixY3n55ZeZPn06O3fuJCkpqdL8hw4dYsaMGdx55528++67rFq1invuuYe4uDiuueaaRiuniIi0DP4lhXQ/U9raKDCE/VHNMJVAnzEmpyHAjlWeTZdQmA9bv+fGHZ8TUpxrRs1N97JgWUu3ZTlTDn3qHKSoYx+Y/TtOfm+6ohb6BcPMe8xxteAlEwgtJ7T4HN0zdlDkFwiManBxup/exvjkr2lVfLZsWmqr9nzX6XJOhbatNHDf3pgBTI8vgLVfmrxvb/zRtNqzHCboWVzkzFftH8iixMvYEzOAECu3wWVtFF0HQXxHSDtiAhjJu5TyRAgtOsuUg/OdEybP5dTpNhVnim0Pd/wPZKaboJOfD6TZuORG07Icy3T7HXSJcja2YO1yjsDb/4bC0pz/SX3g+oehVYSni+ZW2+OGMCBtHbF5aWbgs20/mDyn4hZem/qmuIhBJ9dC6eCQm9qMYUB91jP2SvhpkRk4b8tyGHMlxCe6u7TSQHUK3J48eZKAgIBq3/f39yc9Pb3a9xvqmWee4Y477uDnPze5N5599lkWL17Miy++yFNPPVVp/pdeeomkpCSeffZZAHr37s1PP/3E008/rcCtiIg0WNeMXQQ5Sge46DuWYr9m2C2z5wjwO58uYU1puoSmvYEPzz9jRjx/5i+Qf47znbgGpv0I//yRq8M6syV+JAejemLZfCC40FztXAOfPV8WtN0aN5wBcx82A61wQQ7BboPgvhdM4DblIKQc4Mi2rbTPOYK/VWzyRp88YgbQqA/LYuzRxQwrP9JyYDDL20xka/yImgcHnDwXUg7AkZ0meFtQXHme6LZw/X+w56ds727FarOZfLef/cO8Xv25ArctlN1RbLpa79vIdbt+ILS4tFV7tyEm32ZVKQ9sNo8OIlhnCR1hyKWw8VuT23n5PJh1l8uLtyrMJj43hdaFWfDtFqYc3E/rwtKWmhmR4OfPjLRcSmx+HIjqBXhREEcqaJ99iMv3vQelrRHp3B9u/L2zlW0zYtn8WJE4jav2vm0mfPsO9B5VcV8dDvPQ0U9DHDUb21aU9Ww6ENWbzOCY+q0npDWMvQq+e9ccI8v+Ddf/h3vLKg1Wp5rbvn17tm3bRrdu3ap8f+vWrbRt29ZdZaugsLCQDRs28Mgjj1SYPmXKFFavXl3lMmvWrGHKlCkVpk2dOpXXXnuNoqKiKoPQBQUFFBQUlL3OzjY/1g6HA4e6HYgPcTgcWJal41bqrlwQouz4qWqaq8t6wgWBFIfD0SjTyqdJcAycANtLKs9XS/kqfMbn/+GGuuvq91jbtMBgbF0HYdu7Hs5m4DiyEzr2rXazfiVFtM85TGqr9g3fB0cJEw99Tr9TG50tOEuV2Pzws8znnZhziMScQ2QFRvFpj7kufe644xh19/pq2Uad62NTbnffBmwfP4OttDXqlrjhLE+aST+bvfSGsZploxLMvz6j+axgO0NSVzLu2BJsWFjfvoN14+/rXnZHCbYvX6wQtN0f2Zsut/2GLStTKuxLlfXbZodrHsL2xT/NKN42W+k/u/mX1AvrkpvMAEzW9sr11lPHRXX6jsH23bvYcs5g7VmPlXbUtKYs5/lyQbv7ZvRr+HZdmc/Vc62Lnr8g8Fjtfrh5u7Vto87rcvNvcHTuSUadWEZS1gHYYO5xzrc3zPVvRfAV95Y7hutRZlc/u4Z8xnVZdsIN2LavxFaYj7XhG6xhUyG+cs/MC6+T2+QkM3v3685Bfo5A7/ILlGZ+6V76steZrTh2dzUDgjaEt19fXbjdpvoe67s+yyIx+wCX7X/f5CsHrK6DsK57GPwDa04j4KnrXzcsmxzeFavHMGx7f4KcM1gr52NNuAFOn8D240LYsgyKi6HrQKw+owksak2hf0jF7TbR9+PStGpUur910/FY5++2Ca7Navxdtixsqz/jfJ+hDQljoJrPxeFw1P77OGIGtnVfYTubCbvW4ji6F9pXHfNrsKa4RvIRddl3m2W53kzgvvvuY/ny5axfv57g4IpPq/Ly8hgxYgSXXHIJ//jHP+pWYhecOHGC9u3bs2rVKsaMGVM2/cknn+Stt95iz549lZbp0aMHt912G7//vfOif/Xq1YwdO5YTJ05UGWR+/PHHeeKJJypNnzhxIv7+ekIlvsOyLIqLi/H398fmTTnIpEmkZjq77baJDK3TtIZsw91lcXeZ3caysOdmEZCebF76B1LYvmeV+f7Kl5daynxhva1qWU98JvazGQScMi0mcwIjCGzXsdrt+qcn43cuE8svgKI2XbACgur93drPniHg1DFnQWw2SlpFUhIWixUQiN/ZM/jlnMZWVFg2S0l4LMXR7ardxoVq+4xrmubu9bm7PjbVeSC4OI/4syfKguslraMojulQY/7Laj9Ph4PAE3uwFZsb7tTW7Skod3NZ22dnsxzE5Z4kpFyO3KKY9jjC6tYSpS7fjyu/t03x/dS0bHh+BlH5ZhDFs4HhnA6Nr9f6mmK+mpbDDfXR3dt1929hfcpX7bLFhQSm7MNWUlLpLUdwK4qj22EFhlS5aEPK5+7zZV2XjcjPILL0eM/zDyWtdbsK6zo/n91y4LDZaRMZSkDaIey5OS7vI4Bl96OobTdSzjk/38aqy67OV99pTVFXGrKsq+uzFeYRmLLf+XAuNIxjAfFY5c7NjfF76+p8jbmsraiAwBN7wDJd5/P9QwgpriaNj82GI7g1jpAwLD9/sPth2f3M/34BZbl43f39uKqm/S9fb+u6bG1la8hxYSsqwH4uEyuoFY6Q1nUqS13LF1yUS8K5EwA4gkIpalv3IOuF31k7ex4BZ44DkOcfQlrr9vW+hq1pmitl8dj9XRMrLi5m6dKlZGVlVTmOWHl1ikT+8Y9/ZP78+fTo0YN7772Xnj17YrPZ2LVrF//85z8pKSnhD3/4Q0PLX6MLL4gty6oxKFXV/FVNP+/RRx/lwQcfLHudnZ1NYmIin3zySa0fpog3cTgcpKenExcXh93HBkSShqvqKa2r0xqyDXeXxd1ldov0Y9gWvYbt0FbA3Aw6LrkJxlWdgsflVmBV1NuqlvXIZ1KQi+Ovt+NvFXMuoDUhv3sV/AMrb/fobuxvOK8DrIhYrFv/zPOr01wqc4Vp03pje/EBbKc7mHVdPBtr+PTKueksBxzYgu2Dp7A5SrAi47HuewFsNpc+l9o+45qmuXt97q6PjV6nLIsvPviMGQc+JNBh8qFZfcZgXf1Arek0avw8Ny/F/sU/AUhp1YEPe/28LAhc02cXVJzHZfv/TfuzpQ9U/PyxrnrADAxYR3X5flz5vW2K76emZQNL8rl753PYCnIptvnzxoDfkBvQuuL6iovAP6DG9bm63YbMV9NyuKE+unu77v4trE/5qly2pBjbW/+J7ZjJ8WqFtIZuQ7C6D4EugyA0rOHbcGG5mpZtrHOtn6OIudufJ7wwC4DPut/CkYjuFT93yyLEyiXPFsp9YxOwPf8rbJYDKywaa/z1Jq9vRKz53+5nBu4p/Wdb9Bq23esAsOKTeKH9nLJUSY1Vl12dr77TmqKuNGRZV9dne+dxbIfMgzqr10isa35j0j3VsF1PX/+6c1nb4jewrfuq0vQiewD5fiGEFWVXu83zLLsftOkMib1YmB7KidZJ5AaGlW23Id+Pq6rd//L1tpp8s01R9yrIy8H2w8ew/mtsjigArC4DsSbN4fmNzofIdT43FObBgc0mN3Ppte/5+a7a8xZJOQcBcFz3sHNgxjqo9J1N7UnO//6SiMIMAL7qegMzbp7ttuta6ngeaAmys7OJiopyad46BW4TEhLKBvh69NFHKwRBp06dygsvvEBCQkL9Sl2L2NhY/Pz8SE1NrTA9LS2t2m22adOmyvn9/f2Jiam65UVQUBBBQUGVptvtdgW/xOfYbDYduy1VuYdTZd+/q9Masg13l8XdZU5LhtMnICza3JC1inR9hN+CPPh+Hqz9ChzlWi/1GIZ9zBXVr+eCB4W1lblCva1qWXd/Jq4Iac3BiO50y9xFq6KzZrTucddU3K7NBt+9V3Ffsk5he/txWifdwtmgiNrLXH7a7h/NdwXQsS+2iTdR9SNXO/QYCp36wcEt2DLTsKUnm5sOVz6XWj7jGqe5e33uro+NVaeyTpkBLDYv5cozKc7pPYZhu/oBbP7Vj4dQVXkrbXfQJbDmC0g/Sttzx+iWuYv90X2d8124bEkx3c9sZ+SJ5cTkl461EBiC7YZHsHWp11Addf5+av29bYrvp4ZlC/1DsA2dAqs/w98qZmDaOtZ0mGTmSzkEy96HvT9BYk+YfCsk9WrYdhsyXw3L4Y76WBXLgsPbITDE2T3U3fW2qX+Dv/sAjpX2SIyIw3bX3yA0rJrzaD234cJyNS7bSOfaEr9AVnWYwvSDHwEw7uhikiO6VvG5mzQo9k3flg06aBs2Dduwiqn2Krny1/Cvh+HUcWxpyVxa/CWLu5jfxEary67OV99pdbxWqa4cdV6fO3+DD2yBQ6UDSUUlYLv2oep/j7zp+tedy064nrwNS50tbSPiWBE2mB1xQynwC6Lt2WNcF3kSdqyGnNNVb9pRYgY5O7GfmaXTToa25fMecxp+jeSqGvffVrGu1WlZN/5OlRTD+kXm3iDvbIW3bAe3YHtlK1NiBrCm/aXkBEXWrT4W5sNrj0L6UZOWacad0P9isNmIO3eiLGhLdFvsvUa4fi9Tzb4C2AOCWNNhItMOfgLA5MOfYc8c77brWup6HmgB6rKfde7736lTJxYuXEhGRgb79+/Hsiy6d+/ucqS4vgIDAxk6dChLlizhqquuKpu+ZMkSrrjiiiqXGT16NF9++WWFad988w3Dhg2rcZA1ERFpZkqK4Zu34MJWCHY/E8Rt3x2m3m6CuVU5thfm/bXiRW5kvBmoq+eIGruENxfr2l9Cl8zd2LHgh49gwPiKM+zbCMk7zd/Rbc1F5KnjkHmSa/Le4ONeP+NcoIs9V6zSEcHPu/ja2pfpNRIObjF/7/7RBG7FvQ5vhxXzzedsVczLlRzehaTZv6vQYrPe7H4waQ68/yQAY45/y8HIXjjKt+K1LOJyU+h7aiP87a/MyHfeNOX6tyL0tiegXdeGl6U5GTWr9MFTMQPS1nMwshfM+xp2rXHOc3Q3vP4o9B5NpP+I+g924kssywzKsnK+eT37t9B3rKdL1TB71sOqT83fdn+zTy60sPUG7hq1fW90P6YXboFje4nJT6df+gZgUKX5/BzFZjAzSs89QybVvvLgUDN4z6sPQ2E+vc5sJbV1B7YkjHJL2aUeHA4zKNd5E292z++Rrwlpzfyet9Hn1CZOtE5i5k2z2bhoZ9nbKWFJMG0mTLkNju+F9GOQf84EHvPPQu5ZOHkYyqepAhJyUxiQ9iNQ9x4szdLx/TD/784GBmByKA++FPZtgMw0wKL36S10P7ODH5Km1W0ww69fM0FbMN/P/Gdh1zpCgi5mSGq58Z1GX+7WAYP3RA+ga8ZuumfsIKgkHz78X/za30yJvQXWJS9Tp8Dt1Vdf7dJ88+fPr295avTggw8yZ84chg0bxujRo3nllVdITk7m7rvvhtI0B8ePH+ftt82IinfffTf/93//x4MPPsidd97JmjVreO2113j//fcbpXwiIuKFcjLgo6edQcXyHCWQlW7+Je8yIw5fmIx/11r45O9QXJpH1S8ALroaLroKAir30GiuToW2YVv8cAam/QhFBeYGKbj0BtdymMDHeZPmQIee8OYf4UwKkQVnuGbPG3zc82cubatz1l5z4wDQrht0GVj7Qj2Hw8JXzN+718GE6+u+k1K1nAxY8hZs/b7SW0fDOrMzdjB7Yvrz64BA922zxzDo2AeO7CQq/zR9Tm3kSER32Po9Ew+vpH3OYaLzT1Va7FRIPF91u5HbFLStLDwG+o+DLcsILsnjhl2vVHzf7g+OYvP3rjXcYlvHtrjhrG83vsrVNRvL5zmDtgCf/sM8fPJVmenwWbnxRibPhQ49PFkiz7DZYOrP4DUzsPWo40shdzaEVnyA2DVjF5wzKRXoPRrCXGyMFJdoWt5++FcAxh1dRHpo27oFZ8R9dq2BlAPm74ROXvvwxV0PJmpyKrQNPyRNNy/8qgnq2e2Q2Mv8q0puNhzby09Lv2dY6koAumTubqwi+5a8c+bB8tkM57QBE+DSm00DkOIiWP81fP8R5J/F3ypmwpEFcHgUdKp+cN8y21bAptKHSTa780H5rjXc4r+V4OI88zo0HAZe4t59s9n4tvMVxOamElVwGlIPMr5kIUs7Vd1QUppOnQK3ERERLszVeK6//npOnz7Nn/70J1JSUujXrx8LFy6kY0czSEpKSgrJycll83fu3JmFCxfym9/8hn/+85+0a9eOf/zjH1xzTdV5CEVEpJlJ3m1uqs5fXNn9YcR0E4TNPm26fGekmi5JZzPgjT/A1aU5MS3LdNf+5i0oHXSJjn3givsguo1Hd8tT1rSfSI/T2wgpyYNtP9CuV3dOhHWk55ltFQOtvUeZm+Zb/2SCtxknico/zYwD88AaXXMLZcti+IlyAcKLr3WtRXNErNn2if2Qeggy0mpfRmrmKIGfFpsUGAXlBo6IjIdBE2HgJcxffbJxtm2zwaS5ZUGXS458ZVp7b60iLOIfyK6I3uyIHcLxsI7mRkeqNuYKM7p4ea0iTeqTwZfCth9g2QdwLhM/y8GgtHWmpaJ9gmnZE5foqZI3jhWfmG6u5RUXwvtPEdLlZ+QFtPZUyeqnpBg+/puz226vkaaldUuV2JM90f3peWYbocW58MULpqVsOQPSf3S+GD6tbuvvMxrGXgWrPsXPcjDtwEdQNLlFPdT1CiXFFVM1Tbqlfl3HxSk0HHoMY9W+IBKzD5KQe4L43NTGubYqzIdv3jQtVifcYFq0e7MlbzrvK9p0gct+WbHRh3+A+b0cNJFtrz9L//QN5vpl/t/h7mdqXHV4/hn48mXnhCt+BQHB8NVLkJdDaLEzZy7Dp0Og+881hX7BLOh2A9fveoUARxH90zeQ0jqJXbGD3b4tcV2dArdvvPFG45XERffccw/33HNPle+9+eablaaNHz+ejRs3NkHJRETEq6xfBF//y5mPNiwGrvudyeFY3rlsmPc/psVtcaEJ9F56C2SfMus4b8AEuPyeltn1rlSBfyhrOkxi4hGThmh88gI+7H0no48vdc40aY4z0BoRC7f+iewXHia8MMsMGnVgC3Sr3F31vA45h2h7rrSLXnwS9BjuegF7jTSBW4A964BO9dnNZs3l1j4nj8BnzztbMAEEt4bJc2DwpHI3xY0UuMUEXfZH9aFbxk5z01OOAzuprTuwM3YQk264jm++O9h45WhOEjqyM2YQfU5vJtc/lNBLZle8+Rs21eTRW/0ZRSs+JcBRhL9V2pV847emJfSYK6BjX99PEbP684o9BSbPhZ1rTffh7FPM3P8B83vehsNe58xynlFSDAtecea1jYyHK+71/e+pgX5InEZS9gGT83P3OtiwBDAtqqNz08oGMyQu0TycrauJN3N06yYScw6bgZ9++gZGX+bmvZAabV4K5/Otd+wD3YZ4ukTNyoGo3iTklqYE2PMj0NG9G1j+gXlIDCYV042/d+/63engVmdqlcBguPHR6tOshbRmWcfLiMw/TWLOYdNg5IsXoPWMKs/LdkexyctdWNqidsB406LWZoOk3vDlCyYXPVBs88d/xPRG283ToQks7XgZUw+Z3iiXHPmKtNB2nA5tnPGspHY+ciUiIiJSB3s3wIJyT6w79jU5/lpHVp63VTjMfcJcTG1dbqaVv5kH0wJg/HUt/gYYYHvcUCbmmxa28bmpXLXnbSIKSlsedBkIFw4GFRnPysSpzDjwoXn9/TzoWn3qgxHlW9uOu7ZurWZ6jYSlpa1udv8IcQrc1ktuDrzzRMVugIMmmsBWq6btfbUicSpx504QWnyO1FYdSBwynPmpIaS0SiwbxX1ScKsmLVN9NEX3WFd92/lKNrUZQ2ZQNL8aO7TyDEEhcMmNvJWZxKCTa+if/hNBJQXmvb0/mX+jLjM5vn2A3VFCcHGuydd3dI9pPX50T8WWtpPmmpaT/cfDq7+DnDO0P5vMhOQFLO14ufef+zPTTUvb80Hb83ltQ3ysxXAjyA0MY0mnK7l8/7/NhEWvEdXrLjKCY+mTvsk54/Dp9fue/fz4IWkGN+94wbxe+QkMneym0jd/rp4bq52vqMCkOzmv/MNjcYuDkb0Yc/w782L3OohzY+A2+zT8+LXzdVoyvPI72nWczYkwL7uGK8w39wrnTb61+qBtKctmZ3GXa7l5xwtlD4/6d4xjW/yISvOOOf4dbc4dNy+i28LMu5zHclgU3Ph7vnn7HXqf2sz2uKFMb+Trsd2xg5galQMblxDgKGLm/g/4oM8v3LNyy1I9rSMFbkVEpHkpyKsYtB05ywzCUF2eL0q7NV31a4htB0v/7Zxu9zfdlAZOaNwy+xDLZjej277xBwDanz3ifHPSnCqX2R/Vh9PBccTkp5vBjw5tM6MCX6DN2WQScw6ZF9Ftoe+YuhUurgPEtDODRRzZSXBULvn+3tnlzpsCeZUsft0ZtI1LhFl3168lmhtkB0Xx5sAHTY43m537L+nP0QXbPFKW5sKy2TkVWnu6l3OB4axKnMr6duP5ZewJM7BZdmle4bVfmta5se0boYCW+Vefrs5FhaaVeMohSD3IjXt2Ep2Xhr9V2vNiexXLXHKjyVkOEB4NNzwCr/8BSkwX0XMB4aSHtoEtpxlwcj+BjgIyg2I4ENW7YftZFw6HGagmPBZCKj6o6Jy5B17+qzM9gt3f9A5p373pyuflDkX1YmvccAakr4fiQqYd/JhPu8+h+5kdZobA4MoDbtbBqdA27I3qR4+M7SZf7o8LgZb9+TfZb9yPCyHnjPm754jqc7ZKvZ0OiSczKJrIgjNwZCdBUbkUuOva6oePKo4hUVIEeTlcvedNlnW8jB1xVTxc9JSl/4bM0l5GHfvA0CkuLXYuMJwlna/k8n3m/uLi5EWcaN3R2Xq1uIg+6RsZmrrKvLb7w7UPmYeo5dls7IodXJayoPHa25Yz/eek7d5OfG4KUQWnuWLfe1A4wIUFa5ByED78XwgKhcvu1m+VixS4FRGR5mXZ+2awMUpbgE77mWtPdW02uHg2RLeDr140ubaueRA692v0Ivucjn2g3zjYvsI5re9YqGZAKMtm58d245l+8GMz4fsPIf6CwcNKihl39Bvn64uurvtIuTabaXW76lOwHHTO3KOcXHW1bwNsKW15HhQKcx4zg1p5mvLWekyhX7BJjzBypskjufoz88aaL0xuP3favxkWvWYGxrnlP6s9p1TpxAF4/ynIOV02Kb62ZS66xpz3y2vf3QQ+P30OgFEnSnMC74fyw8Akh3eBc/9Z9XqLCiH1oEnRExnn+j5UJTcb5v0FjpQOsBmVAG26MDw7lJCicwxOW+ucNzLetLTVjXAlKxKnMsBKgVPHiM9NYfae1wlwFJk3B0xocF7Nte0voVvGDpPWZdWnBPb+tak7tTl5BPZtNANsxnVoUBlanLxzsOL8wII2MziUuJ/NxoHIXgw9ubr02movu2OrT3nlsjOpFdMO3PWMGWT2wGb8LAeTDn9OVP4psPp5vnXm0T3m4SUmrz6X3VOnh4uHInvBiJnw4wL8rWKmH/iQte0nwiffwp71TD6fHoHStD3eMsBqQCALul7P9bteIbQ4l3Znk+GD/8Ev6gpK6pNGKO8czPurMwD+2u9h2u1gdfD8d+zldBUsIiLNx/F9sG6B+ds/EGbdVfcLgX5j4bdvwG9eVdC2JpPnUmQ3+X4d2GHiTTXOvi+6HxnBpQHAIzton32o4gyLXjMXhEBWYGT9Wz/1cnY/65LRskZA9i8pNC0V6ymwJB++fMk5Yert3hG0Fe/g528GCwwsbQW0ZTmczXTPus9lwfxn4d0n4NQxE6xc/Lrrx/PBrWYgxHJBWwALG2eCYzkY2ZOdMYPMjfPFs00vjNv/2wR6qvqNGDiBn9rUPCp9UvZBeOkh2uY4B0YmN8eMJP7sL+C1R83/r/wOVn5KRP4Z1/alvNMp8K9HnEFbgIyTsGsNY45/VzFo22ukCXwoaFulYr9AuPZBcxwD0fmnnG/WdVCyKmSExLEnprQlWt5ZBqWuqXmBvLOw8F/w0oPw7dvw+qPOVtPimtWfQn7pZzZwgsmLL43iYLkeBl0z3XRttXyecxyK0VdATFu46Y+mp1ypoamrTN7b6pzNNKmdXv89rJwPp467p2zlFRfBF/90DlQ84Yb69TaZPBcSTPqHmPx0Zh6YZwYELRe03RfV1+sGlMwOjuazHnMp8CvNhX9wC9MOfITtfE8WV1kWfPlPZ9AWwFEMC19l2sGPCTifkkmqpBa3IiLSPJQUm9xTlsO8nnC96W5fHy14ADKXRcSyuPM1jEj5nm1xw7k0pl2Ns1s2Oz+2HV820MGIlO/5NLyzeXPDN2UDwRXb/Pi662xuqO930L4HtIqEc5l0zN6Pf0lhWS7UZsuyGHt0McNSV7Evqg84+ta9tTIw9ugSZ1f4LgNh8KXuL6v4tuBWJn/nmi9M99b1i4C+9V+fZcGWZbD4TcjLqfjekZ1wuKrcBhV1PbMT3vvY/AYAdOhhBnRp05kXNp2tUP/7zHC9+/aqDpPJCoohvCCDQr8gxg7szOJdp7FbDsYc+5ZWxWch5zTX7Hmdte0mElp0Fv6+GYryK67oxH44sZ/bgLTQtqzqMAlwoRzJu0wL4vOfS6tIiIqH1MPOrsWUdqudcqtpEa0WSzVr09nkM178etmk462TaJ/gnpyd69pNoPeZbWA5GHJyNVsSRlbqUm6zHPRN3wj/+N+Kx3zeWVj1GUy6xbWNFRfRJWMXbc4d43RwPBR0q9y1ujnLOQNrzECp+PmbYJo0mpTWieT6tyK0+BxJWfvxcxRRYm/AtXJaMmwtHdMgpDWMvtz87ecH0+/ghxNFXHy0dMCyNV9C12pa+H73HhzYbP5O3gXfvsPNIfEciOrN9thh9S9feT98ZFLVALTt6ixrXQUEwrUPUfTig87W/pjf1Z2turM/ug+HInpwvxeex9NbtePz7nO4au9bBDiK6Ja5i8mHPuObzleV9YiqLT1K//T1cKT0gVZwK9NTb4PpadfzzDbiclNY0O0GzoTU2lemRVLgVkREmoc1X8LJw+bvhE71v7ASlx2I7sOBaJP71JUQ356Y/kzNXA0ZqSRlH6RtTml+3A1vlc2ztNPlnGydWP9C2e2m1e2GbwhwFJGUfaBCS5FmafkHDCvNjdY9Y6fpOjp+dq2Lldc++5DJ/wgQEGy6wHvhzYN4gZGzTJdRywHrv8a/V/e6PxyxLDiwBVZ8DEd2OKcHtzItRzcvNa+XfWDSqlRzLPZN38DEw184W0L1GA6zH4IA0zKoeGsD8iHb7GyPd974jx3Rn93pZn2HI7oz/eBHdMg5jJ/lYOzxbystS4+hkHXapEwoFZ+bYvIcHu4BnWro0bF9JXz6D5PvkdJc0zf/0aRCcJSYPN4ph+BMisnr2bZz/fezpRk5k8NrfqBT9n4AtsaNwF2ZmrOCY2DwRNj4LUElBQxJXc2aDpPMm44SOmfuZtTxZcTnpjgXCgiCkhLT8mzdVyYAX52SEji8DbatgF1ruawg1/ne/34JvUbQuaAjR8K74ajHw7smZ1km6Lb2S/Pg5fJ7zcMJV3xfLjfqsGmuLyf1YtnsHIrsSd9TGwl0FJKUfZBDkT3rv8Jl7zvP2xddXSlVyeaEUQw6uY7wwkzYv9EZOC0v65QztVM5sXlpxOalMfDkjzCmvXlgU18Ht8KKT8zfdj8z7kVNY2bUJq4DC7pez4iUHzgTHEe/qTOgc3+WLPb+HmIpYUl82e0mrj7wHpQU0/v0FvwdReQERsLHS8zDlLOZJlf8yJnm97hUbG4KFycvcq7syvvMb33XQfD5/0FBLtH5p7hh58t81/Fy1x5utjAK3IqIiO87kwLLPyh9YTP5Cf30E+dtLJuf6Wr9+f8BcNHRb4goyDA3rMCmhFHuyUlbGriltEvfwajeZjTgLcth4xJz8X31A2YgM0/KzzXBr4aM/L72S5MzuLzlH0BH14PV/iWFTDr8uXPCpFtMHk2RqkTGQb+LTBfP3Gx6n97CtvjhLiwIfo5iepzZBi++BmlHKr7Zb5zJSR4aZvIJnj4OyTvpEHKIY+FdKq1raMoKLjq2xDlh4CVweQNvql2UGxjG/J638uvgbaZ77nn+gTBkEoy6DKJLB4A7kwI715K2+hvic1Pxs0rgg7/Az/+n8ootywSzyw+S2WUgXPc7E9SmNHgQl2j+Sd3Z7Szuei1jj35DQUAwe6P7uneQn4tnw+bl4Chm0Mm17I4ZaI6RnxZzeWZaxXn7jTPdp1d/boK2RQWmdR+jK693+0r4+jU4V016kuJC2L6Sy1lJnn8oO2MHQ2abhudYbgwOB+xdDz98bFqkn/fJ3+D2J2tf/nSK+S2nNDfqxdc2XlmlzIGoXvQ9tRGALhm76h+4Pb4PdpWmeWkdBcNnVJrFsvmxOWGks9XtugVguyB9zZovyq4fGTrFXNPtWmsGwQWCS/Lg7cfhtv9yuWgVWo3mZMAnf3f25Bt/fcOCwKWORPbgSGQPAPp1960A5dGIrjD7tzg++Ct2HKaxAEC57AecPm4GIY5PokfrERyO6M6M/R/ib5V+VyNnmaAtQJ/RkNCR9H/9ibi8kwQ4iogoqEdaoRZAd7UiIuLbSkrgq5edLS9GzlSOP282YLxpKZN5knbnyrWg6NyfFTFT3bONzgPMzVxhPp0zdjPW/xt45i+Qf845z1v/aXJcViOwOJ9ARwFnAyPcU6byUg7Bms/Njbh/oBmEKakeI2FvXgaLnF1+j4V1pEPOEXOT8cnfCen6c/ICqgkKOxzmhnnvT9ywc7kZLRrMiNzDm2SsYp/TZKOk+4LRl5vALTA4dTXb4oZWPYBcQZ5pKZWWzMXJG+l+Zjutiy5IiRDVBqb/3LRQPW/8dTD/7wCMOr6Mj8M6V2h1O/zEcsYcX1qxPJNvrdNgMQ1l2fxg0hxI6gM/LYJ23UzdaRVeccbotnDRVcSPvhze/2/Yv8nk5Xzvz4R0vI28gNKAbGGBeai1Y6Vz2cGXwqy79SDSzfL9Q/mu0xWEWLnuH/gwMt6kE1n/NYGOQuZufx4uyPiRHtKGuBvuMwN9Aoy7xgzSVJQPG5YQ3q8X2UFRzgUObKkYQMIELHeF9eRIRHfa5RxhwNldZTlyQ4pzTW7Q59ZAnzEkOHo3rCeLu1iWCawtn1f5wQ3Asb2lgx/2qHk9y9535kYdcyW0aoTfaakkObyraSFeVECXzD0stRxYVdSfwOJ82LHaDHZ6YLO5Pk/oZIKebTqZa5fzLp4NgUFVbm9H7FBGHV9GoKMQNi8juP9g8v1MOpDg4lzYUhq89w+ES26E1pEw5gr+9ekqZuyfZ64xc7Ph7ceI7DyXzOA65Ox3lMAnzzgflHQdbOqpQK+RLO5yNdMOfoKNC/LQ+wc678fSkpmelkyhPdB8h5Smmpg8t+IyMe2Y1+cXTDiygNaF2fzYbnxVj65aPF0FiIiI7zpxwOS1Pd8VNTy21kGyxMP8/M3F75cvOKdFxsO1v8VaXsWNXH34B0D3obBjFSEleQxLXVl5nuzT8NZ/0jppDmeDyt30WRb909Yz7ugiAhxF7IgdDOfckP/QsuiYvZ8hqatgvbPrNIV58NWLZlChutj9Y1nLZYB17cazrt0lXLXnLRJzDkHOGaYe/ITPesxxBiYsy+QM3fqDuaE6mwHA+VuZYps//lfcW6fgl4KZLVS7rqar/+HtRBWcpkvmHmdKkrOZpsXogc2QlV62SKW29B16mAFpeo+snJO531j44UM4dZz2Z4/QIadcq9tVn1YI2q5qP4mxU27zXGqPHkMrBp2r4+cH1/4W3viDSeuTcZJZhf9mfq/bCCnKhTd+Dynlzg0TbzbnSqUs8T3jrqX4pyXOFmalDkd0Z2u8aYH26/NBWzABp9GXmda2jmJGHl/Gki5Xm/dOHYeP/tcZtO062LTq7jGU3gFBlPWtKC6CA5vZs/hLumbsMtu2HLBjJTewkhOtElnW6TLPdUHOzTaDX+66YNC2Np1Ny+Pv3jXlXfYBsb1/wanQNlWvJ+UQbF9h/g4NV1qsJlRiDzBd23evI7T4HG3OHiUlzFwfBRbn0/v0Zrpm7KLd2SOwyVFx4cPbK+csj4w3x3I1Cv2D2RE7xAzCWFxIv7Sf+KntOAAGnlznzCc+ZJKpQ6XOBYbzeY9buHrPmyTkpsDZDK7e/QYf976j4gORmiyf5yxvWAxcfX+TPhj0dntjBnAmJI6o/NOcC2jN7MnDICzKBPb3bTDnsmN7AcqCtgV+QQTNfqjKcURK7AF81/lK/BzF7n+Y1kwocCsiIj7Hv6SQUceXwk9rnTczNrvJy9mSBufwsHoH7QZOIHvxvwkvzKTIHkDADY9WbqXWUL1Gwo5Vztd+/tD/YjPy9IJXzMj1mWlck/cGH/W6g9zAMEILc+C9/2LikY1li/U9tQn+7176JFzKzthBdb+gtCy6Zuxk1PGlxOalVT1PWrJpsYeLI2If3AofPe089odPZ601Cmw2FnW9ljv3vVo6ONsBhqWsZGfcENNVd+O3ptt2Vdp1w//SW+o3UnITa2nBYq/d3zFXlt3YDkldxcHInrBhCSx5q2Lr9nIc2DgQ1ZvuV88xrburY/cz3VI/MQ80Rh1fysdhnRl0ci2s/7psth8Sp7KpzVjG+kpwMzgUbvoD/Os/IOcM7c4d5bJ9/yY29yQUm9aSBAabVC7nu5L6IK89ZptKeDRr2k9k3LFvyPcLIXjEFBg+lc/XnKp+mTFXmGM77yy9T29hQ9uLOBcQBu8/6axPPYbBDY9UPfikfwD0HM6i/cGEFJ1lQNp6RmVthHNZALQ7d5Qr974NhRdX28Kx0ezfBJ89X/awEEoHEh0/2zxktdlMK/SV88FRzJSDnzCvz12U2KsIVXz3rvPvi69tUdd8XlGveo2E3eugNBVVTmAkLH6Tn21ZRJCjoPL8AUEmzUtOFd3fJ1xf62DAWxJGMThtHWAxMG0dGxNGE+AoYFCaKQN2P1N3LlDoH8JnPW/lruPvQ9oRwoqyuXrPm3zc62e17+P+TSaNB6X3Ftc+qFbdFzDHYjXHY49hpl4f2sbRT98kMecQDux82+lKZtYyaHSVdV5AgVsREfE1SVn7mHj4SyIKy+V5i0+Cy+6BxAYMlCBNxz+AL7rfTL/0n9gdM4Ab2nRy/zZ6jyI5vAsRBRnsihnIqJtudbbIuPUJeOOPcCaFyIIzXL3nTTa1GcPYo99ASV7ZKorsAWbk37wcJh/+jD6nNrG6w6XmZjonwwRYAoKqboVhWSRlH4BX32ZW+Rx+lHadHnUZxHUwKRswXT9Det3r7DZdnU3fmVZL5/O69Rtnupl/bQZ4yg0Ig2t+Y/K6YTH6+HeMOrG0YhdbSruzdR1oBo/oMRTCouv+GUvL1m2wybOafpT2Z5O5cefL8NMFgy4ldIL4RIhL4tNDxaSHtiEvoDX31xS0Pa/vGJO/+dQx2p9NZtLhz8vyKwKsbn8pm9qMrXEVXiki1gRvX/8DFOXTMfuA872oBLjhUUhwQyt/8aiNbS9id8xACvyDuXfqkNKpNQRug1uZQZqWvI0Ni7HHluDvKILsE+b9+CS45sGqg7YXyAtozbr2lzDq9rth2w9kLP6AqILTtCo6ax4SVhHoahRFBbDkHfhxgXNaSGuY9UuT27L8A5cJN8C+jXDyMHF5Jxl5fBmrEydXXN/h7WagKoCIODMomQd4RQDVU3oMxYEdOw76p61n0Mk1YDko/yggMyiayEGjofsw6NTXBGfPZUHqIfMvLRnikkxe8lpkBUebcQt2r6N1UQ7dM3YSUXTG5K8F6D/OtNytQr5/KMx9HN78I5w6TkRBBjfueBEij8HwaRVa6UJpr6Tj+2D+s86B0y69xZnSpB5a7LFis0GXAczvdTtReabnTUaIF+bb9iEK3IqIiO9IPcRVe99xvvYLME/sx1yhHIA+5nRoAt93rGH07IbyD+DTnreVvRxV/gI9LBpu/ZPpspyZRkx+eoXBuc4FtGZJpytJD23LxUcX0fOMGUm+/dkjzN5dmlO2/GD1IWEmGBvTlpGn7OQERtDr9GYScw5XKFJKqw781HYcl910jfPme9BE2LwU8s8x5ti3fNe5mhtqR4m5AV5TbhCxHsPgql9XDhx3GWBaMn3/IXYsczNyXucBJv9iz+EmsCZSX3a76ab8xT8BiM8tF7QdMB6m3l6hlVLymW1VraWG9fuZXLelrW7LB23XtZvA+nbjG7wLHtO2C1z7II73nzJ1FJPnm9m/Nd2/pVnIDQyr2wLDZ8CaL+FsBl0y9zinh4bDjX+oe+vSADNY3oIDcPOOF0w+ypXzYZib8snXJP0YfPhXk+P6vK6D4Yp7zajzF/IPML9nrzwMjmKGpq7kUGRPUlt3gMw0OJMK35a7/rvkhlpba0ojCA3neFgSiTmHnXlLS1Mt7YodxJb4kZwOief+6QMqLtcqwqRZ6Dqo7tscdVlZK98hJ1fTqixPus087KhJ60iY+ycy//k7IgvOEFqcC9/PM/Vg4HiTlzwrHfZuMF38y7cM7jGs6R5yNGMK2LqH7nJFRMR3tOnMnuj+9DyzjaNhnUm87bdmFNkWrsU+0W+IiFi49U/kvPAwYUXZzul9xvBu0MWmpQawqOtses68mswPn3cO4HWhvBw4ngPH9zKqirfTQ9qwusOlHI7oYVohlG8xNWmOGaylIJe+pzayLX4Yaa0uSFeQn2uCV/s2OKeNmAFTf2byZlZl/HUc2ryZzll7ORfQmlYjp8KQS02AWcRdBozn3MK3aHW+m39UG5h1V/1uzqvSdwynF7xDTL4zVy5jr2JtgZvW70k9h7O4yzUMS1nBociejLjlPj2AbOkCg8zDigUvO6f5+cP1/wFRVbcqdMXp0AT2RfWlR8Z2k2t2/ddAN/eUuSq718H850wOd0p7eEy+FUZMrzlnc5vO5mH80vewY3Hl3rexWyXwU0nF+eISzcMh8Yi90f3LHkzn+YcSMnYWr2d1qn4w1Ibq2AfadIHUgxUfEPYaYY6F2oRH83GvnzHu6GK6n9mBHQeUFJn0URu/rXqZqDZwZRUPxkU8RFcHIiLiU75Pms6RiG7sihnE/QraSkNEJTC/1+3M3P8BQcV5rEqczLTZN5O/8IIBNLoO4t1+v6Jv+kZi804SUFJIr7hgKMw3/3LOQHblLrCZQdFEzrqNfx8Jrz43butIc6O6+A1sWEw4soAPe//ctJI9kwJH98CqT52tlmx2mHGn6eZXE7sfX3S/iciCDLIDI7lvUjMIdIn38Q/g285XMuLEcpIjujLy9l+6tyW33Y+17Scy88A8ADYljGbwpDlwYR31UXtjBrA3xrRMG6GgrWAGWsr89iPng8LLftmgrtrnrWs/ge4ZO0yr21WfEdDrPor83NzrwnIw6sRyWL/cOS2+I8x+yLUAG+bBTMq672l77liFFp0VTLnVpZQR0jh2xA3BhkWJzZ+90f341SVDyVtQxx4VdWGzmcH7Pn2u4vRx17i8inOB4SzqOptVHSbzs1YHYOMSKMitOJN/oOn50GOYScEQXEvqqnpSYwupD10hiIiIT8kLaM2u2Erjk4vUS2ZwDO/1vce8sNmYVk1roBJ7AFsTnIMF9brwwruoADJO8uU3a4koOENOYAQHI3tzX/9BkFzLDc2IGWZQp1PHaHvuGLN3vwY7s0zLqPKCW8N1vzOpEFxhs5MZHOPavD6uudwI+eJ+HI7sweHIHgCMbIT0G/uj+/KF/WZsloODkb0YbLP55Ock4hI/fxZ2vY4xx7/jQGRvLh000S2rPRMSz97ofib1T242A9J+ZEPbcXVfUVGBybN++jjEtIf4JDpllpAVFM1Fx76pmOKh70Vwxa9MPnhX+fmxuMs1XLn3HVoV5ZAVFEVsUifTWyS6DXToYVKNiMdYNj+2xY9o2o32HcvZr16ndWmahOSwLiS1717n1eQERcLU20zL9k3fwYHNEBlngrWd+jf9wH11pN++lkuBWxEREWnZ3DEifUAQxCdxMCqr7sv6+ZsBxt55HIB2Z49Wnie2Pdz4e6UGEZ/VkBvOQ5EaeFJajvRW7fi8xxwALnXjete1m0DPjB1gORiauoqt8SPq3up23QLYWtqi9vg+ACplAbXZzaBOY6+s1+9rVnAMbw14wPQ80YOaBmsWn59/AJsSRjPu2DcA/Nj2YpIasr7gUNOKd/Rl7iqhSKNS4FZERETE07oOZFfMQHqf3mJeh7SGDj0hsaf5v2Mf5b8UEZF6ywiJg34XwbYfCCnOZeDJdfzU7mLXV1BYAKs/r3GWfL8Qgm98GLq5IT2POx6qSrOxsc0Y8v1DKPYP4Hh4Z08XR6RJ6Q5ARERExAss6XwlO2KHkBvQmrlXT9CgGCIi4l7jr4PtK8FyMCR1FVsT6tDlfeM3zhQ+vUfDxddCWjLr12wkJj+NEpsfKztM4XZ3BG1FLmSzszN2CCFWrgszizQvCtyKiIiIeAHL5udsRaKgrYiIz/La7umx7aH/xbB1OSEleQw8uQ4YXvtyxUWw6jPn6wnXQ0JHaNuF1cdaRi51ERFPUeBWRES8gtfe5IiIiIg0F+Nn49j6A3ZMrlvO3lL7MpuXQs4Z83evkSZoKyIiTULNOURERERERERagph27Iw16QyCSvJhyds1z19SDCvnO1+Pu7aRCygiIuWpxa2IiIg0KrWmFhERd9LvSsOs7jCJbhk7CS7Jhy3LaNerCyfCqmlFu+0HyEwzf3cbDO27NWlZRURaOrW4FREREREREWkh8gJas6bDpLLXE458hc0qqTyjowRWfOJ8ffHsJiqhiIicpxa3IiIi0mI1l1ZbzWU/vIU+T++h70KkcWyLG0bf9A3E56YQl3eSAWnr2ZIwquJMO9fA6RPm7079IKm3R8oqItKSKXArIiIiIiIi0oJYNjvLOs7i+l2vAjD6+Hfsi+5LbkCYyWt7aBsse9+5gFrbtjh6cCbiHRS4FRGRJqcLQRFp6XQeFBFPS22dCIMnwaZvCSopYNKhzzkbGA5/expys50zdugBnXXOEhHxBAVuRURERERERFqiSbeQv3UVwSV5dM7aW/n9VhEw8y6w2TxROhGRFk+BWxGRZkgtuUTqT/VHpHlQXRZxQasIVneYxMQjXzqn+QdCz+HQdyx0HwIBQZ4soYhIi6bArYiIiIiIiEgLtS1uKMHFuUTln+JwRHemX381BIW4tKwekIiINC4FbkVERMQrtPSbv5a+/yIiDdFczqEe2Q+bnfXtxpe9nO5i0FZERBqfArciIi1Yc7nJEfEWqlMiIiIiIuIudk8XwFUZGRnMmTOHiIgIIiIimDNnDpmZmdXOX1RUxH/8x3/Qv39/WrVqRbt27Zg7dy4nTpxo0nKLiIiIiIiIiEjTu39m/7J/Ir7IZwK3N910E5s3b2bRokUsWrSIzZs3M2fOnGrnz83NZePGjfy///f/2LhxI/Pnz2fv3r1cfvnlTVpuERERERERERERkbryiVQJu3btYtGiRaxdu5aRI0cC8OqrrzJ69Gj27NlDz549Ky0TERHBkiVLKkx7/vnnGTFiBMnJySQlJTVZ+UVEmiM9tRYREREREW+h+xNpjnwicLtmzRoiIiLKgrYAo0aNIiIigtWrV1cZuK1KVlYWNpuNyMjIaucpKCigoKCg7HV2djYADocDh8PRoP0QaUoOhwPLsnTcipNllf3ZpMdFQ7Zb1bKe2o/6KldeailzpXpb1bK++D02xfoaext1+B7dumxDeHNdcfUz8dRnVwde83vr6vnS1ePC3fP5mPum963w+sLzL24+r3jlZ9fY5WuK+l1dHTj/DzfUXW+qK/U9D3jTudZTx4U3bcNTy9Zx/TTludHd9dabeOK3wMXvscrfwoZsoyHzNTN12U+fCNympqYSHx9faXp8fDypqakurSM/P59HHnmEm266ifDw8Grne+qpp3jiiScqTU9PTyc/P7+OJRfxHIfDQVZWFpZlYbf7TFYUaUQhVm7Z32lpaT6x3aqW9dR+1Ff58lJLmS+st1Ut64vfY1Osr7G3UZfv0Z3LNoQ31xVXP5Prh8W7NJ8necvvravnS1ePC3fP1xy4uy57+2fX2OVrinNjVftw/bD4snobERHRZN+jp373XJnmqd+pqnjquPCmbXhq2bqun0Y6fmo6ZgMpBGxeec6sL0/8FjT191jT+rzp/NOUcnJyXJ7Xo4Hbxx9/vMogaXnr168HwGazVXrPsqwqp1+oqKiIG264AYfDwQsvvFDjvI8++igPPvhg2evs7GwSExOJi4urMeAr4m0cDgc2m424uDgFbgWAPJvzR7Cqh2HeuN2qlv35zKYruzuU3wdq+QwurLdVLeuL32NTrK+xt1GX79GdyzaEp44VV3jqM2kM3vJ7W9X37eo0V9fXkPmaA3cft97+2TV2+ZriPFDdPriz3npTXanvecCbzslNcZ3n7dcgnlq2ruunkY6fao/Z0ta2eYR45TmzvjzxW9DU32NN6/Om809TCg4OdnlejwZu7733Xm644YYa5+nUqRNbt27l5MmTld5LT08nISGhxuWLioq47rrrOHToEEuXLq01+BoUFERQUFCl6Xa7XcEv8Tk2m03HrjiVe9DVpMdEQ7brqTK70wUPGGvbjwr1tqplffF7bIr1NfY26vg9um3ZBrh/1oAm2U69eOgzaSxe8Xtb1fHu6jRX19eQ+ZoDdx+33v7ZNXb5muI8UMM+uK3eelNdqe95oJmdk2vl7dcg3nztXMu1qVu2W+Mxa4PSuttseOK3oIm/xxrX19LOP6Xqsp8eDdzGxsYSGxtb63yjR48mKyuLH3/8kREjRgCwbt06srKyGDNmTLXLnQ/a7tu3j2XLlhETE+PW8ouIiIiISMugQW9ERMTd9NsitfGJHLe9e/dm2rRp3Hnnnbz88ssA/OIXv2DWrFkVBibr1asXTz31FFdddRXFxcVce+21bNy4ka+++oqSkpKyfLjR0dEEBgZ6bH9ERERERDzB1RtE3UiKSHOl85uI91B9rJ3PtEF+77336N+/P1OmTGHKlCkMGDCAd955p8I8e/bsISsrC4Bjx47xxRdfcOzYMQYNGkTbtm3L/q1evdpDeyEiIiIiIiIiIiJSO59ocUtpK9l33323xnksyyr7u1OnThVei4iIiIiIe6mlTP3psxMREZHa+EzgVkRERERERERERLybHk66j8+kShARERERERERERFpKdTiVkRERKQRqcWBiIiIiIjUhwK3IiIiIrVQ8FVERESagq45RKQ8pUoQERERERERERER8TIK3IqIiIiIiIiIiIh4GaVKEBERERFphtTdVsR7qD5Kc6VjW6RxqcWtiIiIiIiIiIiIiJdRi1sREREfolYNIiIiIiIiLYNa3IqIiIiIiIiIiIh4GQVuRURERERERERERLyMUiWIiIiIiIiItBBKuyQi4jvU4lZERERERERERETEyyhwKyIiIiIiIiIiIuJlFLgVERERERERERER8TLKcSsiIiIiIiIiIh6nHMwiFanFrYiIiIiIiIiIiIiXUeBWRERERERERERExMsocCsiIiIiIiIiIiLiZZTjVkRERERERKQZUr5QERHfpha3IiIiIiIiIiIiIl5GgVsRERERERERERERL6PArYiIiIiIiIiIiIiXUY5bERFpVMqtJiIiIiIiIlJ3CtyKiIiISJPSAx0RaQo614iIiK9TqgQRERERERERERERL6MWtyIiIiIiIiIiUiO1Yhdpegrcioi0ELrQkobQ8SMiIiIiItK0FLgVERERERERERFpAdQgw7cocCsiIl5LFxUiIiIiIiLSUvnM4GQZGRnMmTOHiIgIIiIimDNnDpmZmS4vf9ddd2Gz2Xj22WcbtZwiIiIiIiJSs/tn9q/wT0RERCrzmcDtTTfdxObNm1m0aBGLFi1i8+bNzJkzx6VlP/vsM9atW0e7du0avZwiIiIiIiIiIiIiDeUTqRJ27drFokWLWLt2LSNHjgTg1VdfZfTo0ezZs4eePXtWu+zx48e59957Wbx4MTNnzmzCUouIiIiIiIiIiIjUj08EbtesWUNERERZ0BZg1KhRREREsHr16moDtw6Hgzlz5vC73/2Ovn37NmGJRURERERERESkoe6f2R+Hw0FaWhrx8fGeLo5Ik/KJwG1qamqVlTM+Pp7U1NRql/vLX/6Cv78/v/71r13eVkFBAQUFBWWvs7OzoTQI7HA46lx2EU9xOBxYlqXjVsTTLKvCy5rqZKV6W4dlfc19050PVBttv8p9fs3psxPv4i2/t01Sp6RlacbnULfWW2/6nOpblmZ8veEpHjsne9Px6Gbe8nvbbOk80KTq8vl6NHD7+OOP88QTT9Q4z/r16wGw2WyV3rMsq8rpABs2bOC5555j48aN1c5TlaeeeqrKMqWnp5Ofn+/yekQ8zeFwkJWVhWVZ2O0+k85apNkJsXIrvE5LS6t23gvrbV2WlcrKf3767KSx6PdWmqvmfA51Z731ps+pvmW5fljFRlKe3g+pP286Ht1Nv7eNS/cdTSsnJ8fleT0auL333nu54YYbapynU6dObN26lZMnT1Z6Lz09nYSEhCqXW7FiBWlpaSQlJZVNKykp4aGHHuLZZ5/l8OHDVS736KOP8uCDD5a9zs7OJjExkbi4OMLDw+uwdyKe5XA4sNlsxMXF6YdNxIPybBUvemrq3nVhva3LslJZ+c9Pn500Fv3eSnPVnM+h7qy33vQ5eVNZxDOa8zGg39vGpfuOphUcHOzyvB4N3MbGxhIbG1vrfKNHjyYrK4sff/yRESNGALBu3TqysrIYM2ZMlcvMmTOHSZMmVZg2depU5syZw+23317ttoKCgggKCqo03W636+QgPsdms+nYFfG0C3p91FYfK9TbOi4rFyj3+emzk8ak31tpju6fNcDTRWhUbqu33vRb401lEc9o5seAfm8bke47mlRdPl+fyHHbu3dvpk2bxp133snLL78MwC9+8QtmzZpVYWCyXr168dRTT3HVVVcRExNDTExMhfUEBATQpk2bagczExEREREREREREfEGPhNCf++99+jfvz9TpkxhypQpDBgwgHfeeafCPHv27CErK8tjZRQRERERERERERFxB59ocQsQHR3Nu+++W+M81gWj4F2oury2IiIiIiIiIiIiIt7EZ1rcioiIiIiIiIiIiLQUPtPiVkRERERERMSb3D+zv6eLICIizZha3IqIiIiIiIiIiIh4GQVuRURERERERERERLyMUiWIiIiIiIiI+DilbRARaX7U4lZERERERERERETEy6jFrYiIiDRLankkIiIiIiK+TC1uRURERERERERERLyMArciIiIiIiIiIiIiXkaBWxEREREREREREREvo8CtiIiIiIiIiIiIiJfR4GQiIiKNSANkiYiIiIiISH2oxa2IiIiIiIiIiIiIl1HgVkRERERERERERMTLKHArIiIiIiIiIiIi4mUUuBURERERERERERHxMgrcioiIiIiIiIiIiHgZf08XQEREREREREREGub+mf09XQQRcTO1uBURERERERERERHxMmpxKyIiIiIiIiIi0kKptbb3UotbERERERERERERES+jFrciIiJeSk++RUREREREWi61uBURERERERERERHxMgrcioiIiIiIiIiIiHgZBW5FREREREREREREvIwCtyIiIiIiIiIiIiJeRoFbERERERERERERES+jwK2IiIiIiIiIiIiIl1HgVkRERERERERERMTLKHArIiIiIiIiIiIi4mUUuBURERERERERERHxMv6eLoC3sywLgOzsbE8XRaROHA4HOTk5BAcHY7frGY2IL1C9FfE9qrcivkf1VsT3qN5Kc3I+xng+5lgTBW5rkZOTA0BiYqKniyIiIiIiIiIiIiLNQE5ODhERETXOY7NcCe+2YA6HgxMnThAWFobNZvN0cURclp2dTWJiIkePHiU8PNzTxRERF6jeivge1VsR36N6K+J7VG+lObEsi5ycHNq1a1drC3K1uK2F3W6nQ4cOni6GSL2Fh4frh03Ex6jeivge1VsR36N6K+J7VG+luaitpe15SgwiIiIiIiIiIiIi4mUUuBURERERERERERHxMgrcijRTQUFBPPbYYwQFBXm6KCLiItVbEd+jeivie1RvRXyP6q20VBqcTERERERERERERMTLqMWtiIiIiIiIiIiIiJdR4FZERERERERERETEyyhwKyIiIiIiIiIiIuJlFLgV8VJPPfUUw4cPJywsjPj4eK688kr27NlTYR7Lsnj88cdp164dISEhTJgwgR07dlSYp6CggPvuu4/Y2FhatWrF5ZdfzrFjxyrMs3fvXq644gpiY2MJDw9n7NixLFu2rEn2U6Q5cVe9feWVV5gwYQLh4eHYbDYyMzMrvH/48GHuuOMOOnfuTEhICF27duWxxx6jsLCwSfZTpDlpqnp73oIFCxg5ciQhISHExsZy9dVXN+r+iTRH7qi3Z86c4b777qNnz56EhoaSlJTEr3/9a7KysiqsJyMjgzlz5hAREUFERARz5syptn6LSPWast6eV1BQwKBBg7DZbGzevLnR91GkMShwK+Klvv/+e371q1+xdu1alixZQnFxMVOmTOHcuXNl8/z1r3/lmWee4f/+7/9Yv349bdq0YfLkyeTk5JTN88ADD/Dpp5/ywQcfsHLlSs6ePcusWbMoKSkpm2fmzJkUFxezdOlSNmzYwKBBg5g1axapqalNvt8ivsxd9TY3N5dp06bx+9//vsrt7N69G4fDwcsvv8yOHTv4+9//zksvvVTt/CJSvaaqtwCffPIJc+bM4fbbb2fLli2sWrWKm266qdH3UaS5cUe9PXHiBCdOnODpp59m27ZtvPnmmyxatIg77rijwrZuuukmNm/ezKJFi1i0aBGbN29mzpw5Tb7PIr6uKevteQ8//DDt2rVrsn0UaRSWiPiEtLQ0C7C+//57y7Isy+FwWG3atLH+53/+p2ye/Px8KyIiwnrppZcsy7KszMxMKyAgwPrggw/K5jl+/Lhlt9utRYsWWZZlWenp6RZg/fDDD2XzZGdnW4D17bffNuEeijQ/9am35S1btswCrIyMjFq39de//tXq3Lmzm/dApOVprHpbVFRktW/f3vrXv/7VBHsh0rI0tN6e9+GHH1qBgYFWUVGRZVmWtXPnTguw1q5dWzbPmjVrLMDavXt3o+6TSHPXWPX2vIULF1q9evWyduzYYQHWpk2bGnFvRBqPWtyK+Ijz3T+io6MBOHToEKmpqUyZMqVsnqCgIMaPH8/q1asB2LBhA0VFRRXmadeuHf369SubJyYmht69e/P2229z7tw5iouLefnll0lISGDo0KFNvJcizUt96m1DtnV+OyJSf41Vbzdu3Mjx48ex2+0MHjyYtm3bMn369EopF0Sk7txVb7OysggPD8ff3x+ANWvWEBERwciRI8vmGTVqFBEREQ3+3RZp6Rqr3gKcPHmSO++8k3feeYfQ0NBG3Q+RxqbArYgPsCyLBx98kIsuuoh+/foBlKUxSEhIqDBvQkJC2XupqakEBgYSFRVV7Tw2m40lS5awadMmwsLCCA4O5u9//zuLFi0iMjKyifZQpPmpb72tjwMHDvD8889z9913N7DUIi1bY9bbgwcPAvD444/zxz/+ka+++oqoqCjGjx/PmTNn3LofIi2Ju+rt6dMoU8ErAAAJTUlEQVSn+fOf/8xdd91VNi01NZX4+PhK88bHxyulmEgDNGa9tSyL2267jbvvvpthw4Y16n6INAV/F+YREQ+799572bp1KytXrqz0ns1mq/DasqxK0y5Ufh7LsrjnnnuIj49nxYoVhISE8K9//YtZs2axfv162rZt6+a9EWkZ3F1vq3PixAmmTZvG7Nmz+fnPf17v8opI49Zbh8MBwB/+8AeuueYaAN544w06dOjARx99VOGmU0Rc5456m52dzcyZM+nTpw+PPfZYjeuoaT0i4prGrLfPP/882dnZPProo41UepGmpRa3Il7uvvvu44svvmDZsmV06NChbHqbNm2g3JPJ89LS0sqeUrZp04bCwkIyMjKqnWfp0qV89dVXfPDBB4wdO5YhQ4bwwgsvEBISwltvvdUEeyjS/DSk3tbFiRMnuOSSSxg9ejSvvPKKG0ou0nI1dr09/yC0T58+ZdOCgoLo0qULycnJbtgDkZbHHfU2JyeHadOm0bp1az799FMCAgIqrOfkyZOVtpuenl6v320Rafx6u3TpUtauXUtQUBD+/v5069YNgGHDhnHrrbc28t6JuJ8CtyJeyrIs7r33XubPn8/SpUvp3Llzhfc7d+5MmzZtWLJkSdm0wsJCvv/+e8aMGQPA0KFDCQgIqDBPSkoK27dvL5snNzcXALu94unAbreXtQ4SEde4o9666vjx40yYMIEhQ4bwxhtvVKrDIuKapqq3Q4cOJSgoiD179pRNKyoq4vDhw3Ts2NFNeyPSMrir3mZnZzNlyhQCAwP54osvCA4OrrCe0aNHk5WVxY8//lg2bd26dWRlZdX5d1ukpWuqevuPf/yDLVu2sHnzZjZv3szChQsBmDdvHv/93//d6Psp4m5KlSDipX71q1/x73//m88//5ywsLCyJ48RERGEhIRgs9l44IEHePLJJ+nevTvdu3fnySefJDQ0lJtuuqls3jvuuIOHHnqImJgYoqOj+e1vf0v//v2ZNGkSlF6QRkVFceutt/Kf//mfhISE8Oqrr3Lo0CFmzpzp0c9AxNe4o95S2tIgNTWV/fv3A7Bt2zbCwsJISkoiOjqaEydOMGHCBJKSknj66adJT08vW/Z8awURcU1T1dvw8HDuvvtuHnvsMRITE+nYsSP/+7//C8Ds2bM9tPcivskd9TYnJ4cpU6aQm5vLu+++S3Z2NtnZ2QDExcXh5+dH7969mTZtGnfeeScvv/wyAL/4xS+YNWsWPXv29OAnIOJ7mqreJiUlVdhu69atAejatWuFFr4iPsMSEa8EVPnvjTfeKJvH4XBYjz32mNWmTRsrKCjIuvjii61t27ZVWE9eXp517733WtHR0VZISIg1a9YsKzk5ucI869evt6ZMmWJFR0dbYWFh1qhRo6yFCxc22b6KNBfuqrePPfZYjet54403qt2WiNRNU9Vby7KswsJC66GHHrLi4+OtsLAwa9KkSdb27dubdH9FmgN31Ntly5ZVu55Dhw6VzXf69Gnr5ptvtsLCwqywsDDr5ptvtjIyMpp8n0V8XVPW2/IOHTpkAdamTZuaZD9F3M1mmQokIiIiIiIiIiIiIl5CCfFEREREREREREREvIwCtyIiIiIiIiIiIiJeRoFbERERERERERERES+jwK2IiIiIiIiIiIiIl1HgVkRERERERERERMTLKHArIiIiIiIiIiIi4mUUuBURERERERERERHxMgrcioiIiIiIiIiIiHgZBW5FREREREREREREvIwCtyIiIiIipW677TZsNhs2m42AgAASEhKYPHkyr7/+Og6Hw+X1vPnmm0RGRjZqWUVERESkeVPgVkRERESknGnTppGSksLhw4f5+uuvueSSS7j//vuZNWsWxcXFni6eiIiIiLQQCtyKiIiIiJQTFBREmzZtaN++PUOGDOH3v/89n3/+OV9//TVvvvkmAM888wz9+/enVatWJCYmcs8993D27FkAli9fzu23305WVlZZ693HH38cgMLCQh5++GHat29Pq1atGDlyJMuXL/fo/oqIiIiId1LgVkRERESkFhMnTmTgwIHMnz8fALvdzj/+8Q+2b9/OW2+9xdKlS3n44YcBGDNmDM8++yzh4eGkpKSQkpLCb3/7WwBuv/12Vq1axQcffMDWrVuZPXs206ZNY9++fR7dPxERERHxPjbLsixPF0JERERExBvcdtttZGZm8tlnn1V674YbbmDr1q3s3Lmz0nsfffQRv/zlLzl16hSU5rh94IEHyMzMLJvnwIEDdO/enWPHjtGuXbuy6ZMmTWLEiBE8+eSTjbZfIiIiIuJ7/D1dABERERERX2BZFjabDYBly5bx5JNPsnPnTrKzsykuLiY/P59z587RqlWrKpffuHEjlmXRo0ePCtMLCgqIiYlpkn0QEREREd+hwK2IiIiIiAt27dpF586dOXLkCDNmzODuu+/mz3/+M9HR0axcuZI77riDoqKiapd3OBz4+fmxYcMG/Pz8KrzXunXrJtgDEREREfElCtyKiIiIiNRi6dKlbNu2jd/85jf89NNPFBcX87e//Q273QwZ8eGHH1aYPzAwkJKSkgrTBg8eTElJCWlpaYwbN65Jyy8iIiIivkeBWxERERGRcgoKCkhNTaWkpISTJ0+yaNEinnrqKWbNmsXcuXPZtm0bxcXFPP/881x22WWsWrWKl156qcI6OnXqxNmzZ/nuu+8YOHAgoaGh9OjRg5tvvpm5c+fyt7/9jcGDB3Pq1CmWLl1K//79mTFjhsf2WURERES8j93TBRARERER8SaLFi2ibdu2dOrUiWnTprFs2TL+8Y9/8Pnnn+Pn58egQYN45pln+Mtf/kK/fv147733eOqppyqsY8yYMdx9991cf/31xMXF8de//hWAN954g7lz5/LQQw/Rs2dPLr/8ctatW0diYqKH9lZEREREvJXNsizL04UQERERERERERERESe1uBURERERERERERHxMgrcioiIiIiIiIiIiHgZBW5FREREREREREREvIwCtyIiIiIiIiIiIiJeRoFbERERERERERERES+jwK2IiIiIiIiIiIiIl1HgVkRERERERERERMTLKHArIiIiIiIiIiIi4mUUuBURERERERERERHxMgrcioiIiIiIiIiIiHgZBW5FREREREREREREvIwCtyIiIiIiIiIiIiJe5v8DcdWhZXTlfaYAAAAASUVORK5CYII=", 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" ] @@ -424,16 +490,23 @@ "source": [ "## Subperiod IC Stability (Walk-Forward)\n", "\n", - "A factor with positive full-sample IC could still be unreliable as it might have earned its entire IC in one lucky subperiod. Walk-forward IC analysis splits the sample into non-overlapping windows and computes the IC in each. A factor that's positive in most windows is robust; one that's positive in only one is fragile.\n", + "A positive full-sample IC can hide a fragile result. Maybe the signal worked in one window and did nothing elsewhere. Walk-forward analysis splits the sample into non-overlapping windows and recomputes the diagnostics in each one.\n", "\n", - "We'll do an overly-simplified form of walk-forward validation. It will tell us whether the factor's predictive power is a stable feature of the data or a consequence of a specific time period." + "This is not parameter tuning. It is just a stability check: does the sign and size of the signal look similar across regimes, or is the full-sample average doing too much storytelling?" ] }, { "cell_type": "code", "execution_count": 6, "id": "fba556cd", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:39.040999Z", + "iopub.status.busy": "2026-07-31T11:08:39.040823Z", + "iopub.status.idle": "2026-07-31T11:08:39.075634Z", + "shell.execute_reply": "2026-07-31T11:08:39.075169Z" + } + }, "outputs": [ { "name": "stdout", @@ -527,7 +600,7 @@ "output_type": "stream", "text": [ "\n", - " Information Ration by Subperiod\n", + " Information Ratio by Subperiod\n", "\n" ] }, @@ -735,7 +808,7 @@ "print(\"Mean IC by Subperiod\\n\")\n", "display(pivot_ic.round(4))\n", "\n", - "print(\"\\n Information Ration by Subperiod\\n\")\n", + "print(\"\\n Information Ratio by Subperiod\\n\")\n", "display(pivot_ir.round(3))\n", "\n", "print(\"\\n % Months with Positive IC\\n\")\n", @@ -746,11 +819,18 @@ "cell_type": "code", "execution_count": 7, "id": "7b0bc01a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:39.077211Z", + "iopub.status.busy": "2026-07-31T11:08:39.077036Z", + "iopub.status.idle": "2026-07-31T11:08:39.399850Z", + "shell.execute_reply": "2026-07-31T11:08:39.399204Z" + } + }, "outputs": [ { "data": { - "image/png": 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" ] @@ -789,20 +869,29 @@ "source": [ "## Factor Decay\n", "\n", - "IC at 1-month tells you the signal exists. IC at longer horizons tells you how fast it dies. This sets your rebalance frequency. A factor with positive IC out to 12 months is slow-moving — you can trade it quarterly and capture most of what you need it to. A factor that's dead after 2 months is fast — you need monthly rebalancing, and transaction costs can play a role. \n", + "A one-month IC tells us whether a signal is useful right after it is measured. IC decay asks how long that usefulness lasts.\n", "\n", - "Momentum is typically slow. Short-term reversal is fast. Low-vol is very slow. The decay curve tells you which is which. In vector terms, 'slow' means the angle between $\\mathrm{rank}(f_t)$ and $\\mathrm{rank}(r_{t+h})$ stays small as the horizon $h$ grows; 'fast' means it opens quickly." + "For a horizon $h$, we compare today's factor ranks to cumulative returns over the next $h$ months. If the IC stays positive as $h$ grows, the signal is slow-moving and may tolerate less frequent rebalancing. If it drops quickly, the signal needs faster trading and is more exposed to transaction costs.\n", + "\n", + "In vector language, a slow signal keeps a small angle between $\\mathrm{rank}(f_t)$ and $\\mathrm{rank}(r_{t:t+h})$ for several horizons. A fast signal loses that alignment quickly." ] }, { "cell_type": "code", "execution_count": 8, "id": "29d1c1d8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:39.401596Z", + "iopub.status.busy": "2026-07-31T11:08:39.401424Z", + "iopub.status.idle": "2026-07-31T11:08:45.405229Z", + "shell.execute_reply": "2026-07-31T11:08:45.404664Z" + } + }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -933,21 +1022,46 @@ "cell_type": "code", "execution_count": 9, "id": "65a97db6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:45.407354Z", + "iopub.status.busy": "2026-07-31T11:08:45.407075Z", + "iopub.status.idle": "2026-07-31T11:08:46.547766Z", + "shell.execute_reply": "2026-07-31T11:08:46.547146Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "momentum: rank autocorr = 0.888, turnover proxy = 0.112\n", - "value: rank autocorr = 0.977, turnover proxy = 0.023\n", - "quality: rank autocorr = 0.898, turnover proxy = 0.102\n", + "momentum: rank autocorr = 0.888, turnover proxy = 0.112\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "value: rank autocorr = 0.977, turnover proxy = 0.023\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "quality: rank autocorr = 0.898, turnover proxy = 0.102\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "lowvol: rank autocorr = 0.997, turnover proxy = 0.003\n" ] }, { "data": { - "image/png": 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", + "image/png": 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" ] @@ -1001,20 +1115,37 @@ "source": [ "## Cross-Factor Correlations\n", "\n", - "The cross-factor correlation matrix is the **Gram matrix** of the factor vectors. If you stack the four factor vectors into a matrix $\\mathbf{F} = [f_1, f_2, f_3, f_4]$, the Gram matrix is $\\mathbf{G} = \\mathbf{F}^\\top \\mathbf{F}$, where each entry $G_{ij} = \\langle f_i, f_j \\rangle$ is the inner product of two factor vectors. Since our factors are cross-sectional ranks centered at zero, these inner products are proportional to Pearson correlations — so the Gram matrix is literally the correlation matrix between factors.\n", + "The cross-factor correlation matrix asks whether the factors are actually different from each other.\n", "\n", - "High off-diagonal entries (near 1) mean two vectors are nearly collinear — they point in the same direction and carry redundant information. Low entries (near 0) mean they're nearly orthogonal — independent signal. If momentum and quality correlate at 0.86, combining them adds almost nothing; you're adding a vector to a near-parallel copy of itself. The ideal set of factors is a set of nearly orthogonal vectors, each with positive IC, so that a combined portfolio benefits from diversification rather than doubling down on the same bet." + "If we stack factor vectors into a matrix\n", + "\n", + "$$F_t = [f_{1,t}, f_{2,t}, f_{3,t}, f_{4,t}],$$\n", + "\n", + "then a Gram matrix has entries\n", + "\n", + "$$G_{ij}=\\langle f_{i,t}, f_{j,t}\\rangle.$$\n", + "\n", + "Because our factors are centered ranks, these inner products are proportional to correlations. A high off-diagonal entry means two factors are nearly collinear and probably redundant. A value near zero means the factors are closer to orthogonal and may diversify each other.\n", + "\n", + "This matters because combining four factor names is not the same as combining four independent sources of information." ] }, { "cell_type": "code", "execution_count": 10, "id": "2d488e92", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:46.549585Z", + "iopub.status.busy": "2026-07-31T11:08:46.549416Z", + "iopub.status.idle": "2026-07-31T11:08:49.782467Z", + "shell.execute_reply": "2026-07-31T11:08:49.781755Z" + } + }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1140,7 +1271,14 @@ "cell_type": "code", "execution_count": 11, "id": "f5fea3fa", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:49.784322Z", + "iopub.status.busy": "2026-07-31T11:08:49.784108Z", + "iopub.status.idle": "2026-07-31T11:08:50.390890Z", + "shell.execute_reply": "2026-07-31T11:08:50.390284Z" + } + }, "outputs": [ { "name": "stdout", @@ -1173,17 +1311,15 @@ "source": [ "## Conclusion\n", "\n", - "The diagnostics tell us that **momentum** is realistically the only signal worth carrying forward, and even it is modest. Its full-sample IC is $+0.006$ with an information ratio of $+0.11$ — positive, but weak. The encouraging part is consistency: momentum is the only factor with a positive mean IC in two of the four subperiods (2011–2016 at IR $0.55$ and 2021–2026 at IR $0.33$), and it decays slowly, staying near zero out to the 12-month horizon. That slow decay, combined with a moderate turnover proxy of $0.11$, means a monthly or quarterly rebalance captures most of the edge. Momentum is not a strong signal in this universe, but it is a *real* one.\n", + "The diagnostics point to one usable signal in this price-only setup: **momentum**. Its full-sample IC is small but positive, and it is more stable than the other proxies. That is enough to justify carrying it forward, but not enough to pretend we have found a large or universal edge.\n", "\n", - "The other three factors fail, and the **why** is more instructive than the failure:\n", + "The other three proxies are mostly useful as negative examples:\n", "\n", - "- **Value** (IC $-0.022$, IR $-0.48$, negative in every subperiod): our proxy is inverse 60-month return, intended to capture mean reversion of \"cheap\" stocks. Instead it loaded on long-term *momentum continuation* — five-year winners kept winning, so the inverted signal predicted negative returns. A price-only value proxy cannot separate cheapness from drift; it needs fundamentals (book-to-market, earnings yield).\n", - "- **Quality** (IC $-0.003$, IR $-0.04$, statistically zero): the 12-month Sharpe proxy is dominated by the direction of recent returns, which is why it correlates **0.86** with momentum. It is not an independent signal — it is momentum with extra noise. Adding it to a portfolio doubles down on the same bet rather than diversifying.\n", - "- **Low-volatility** (IC $-0.026$, IR $-0.38$, decaying to $-0.11$ at 12m): low-vol names underperformed high-vol names in this window, the opposite of the low-vol anomaly. The inverse-60m-vol proxy is too crude to isolate the anomaly, and the period itself was risk-on. Its one virtue is extreme stability (turnover proxy $0.003$), but a stable negative-IC signal is just a reliably bad signal.\n", + "- **Value:** the inverse 60-month return proxy behaves more like a bet against long-term winners than a true value measure. Without fundamentals, it cannot distinguish cheapness from price drift.\n", + "- **Quality:** the rolling Sharpe proxy is highly correlated with momentum, so it is not adding much independent information.\n", + "- **Low-volatility:** the signal is stable, but in this sample it points the wrong way.\n", "\n", - "The **Gram matrix** confirms the structural problem. Momentum and quality are near-collinear ($0.86$); value is anti-correlated with both ($-0.44$, $-0.41$) because it is inverse-momentum at a different horizon; only low-vol is close to orthogonal to the rest ($\\approx 0.1$). So of four factors, we effectively have one directional bet (momentum/quality), its mirror image (value), and one orthogonal but useless signal (low-vol). There is no diversification to harvest from this set.\n", - "\n", - "**What carries forward:** momentum is the candidate signal for portfolio construction in later notebooks. The value, quality, and low-vol proxies should be discarded or re-engineered with genuine fundamental and risk data before they can contribute. The methodology — IC, walk-forward stability, decay, turnover, and the correlation structure — is sound; the inputs were not." + "The factor correlation matrix explains why a naive composite is unlikely to help. Momentum and quality are close to the same direction, value is partly the opposite direction, and low-vol is more independent but has negative IC. The next notebook builds the signal we will actually trade: sector-neutralized momentum." ] } ], diff --git a/notebooks/03_factor_construction_and_composite_signal.ipynb b/notebooks/03_factor_construction_and_composite_signal.ipynb index 8c86471..28e64ca 100644 --- a/notebooks/03_factor_construction_and_composite_signal.ipynb +++ b/notebooks/03_factor_construction_and_composite_signal.ipynb @@ -9,38 +9,37 @@ "\n", "## Purpose\n", "\n", - "Notebook 02 established that momentum was the only factor with positive, persistent IC. The other three have negative or insignificant IC with our price-based proxies.\n", + "Notebook 02 suggested that momentum is the only price-based signal worth carrying forward. This notebook turns that raw momentum rank into the traded signal used by the backtest.\n", "\n", - "In this notebook, our goals are the following.\n", - "1. **Build the headline signal**: sector-neutralized momentum. We winsorize, z-score, and project orthogonal to the sector subspace. The same pipeline that would apply to any factor, but applied to the one that works. \n", - "2. **Tests whether combining helps**: we build a 4-factor equal-weight composite and compare its IC to momentum alone. If the composite IC is worse (it is), that confirms the decision to trade based on momentum alone is the right one. \n", + "The goals are:\n", + "1. Winsorize and z-score each factor cross-section so outliers do not dominate.\n", + "2. Neutralize the signal against sector membership by projecting out sector effects.\n", + "3. Compare momentum-only against a simple four-factor composite.\n", + "4. Save the sector-neutralized momentum signal for notebook 04.\n", "\n", - "### Terms used in this notebook \n", + "### Terms used in this notebook\n", "\n", "| Term | Meaning |\n", "|------|---------|\n", - "| **Winsorization** | Clipping extreme values at $\\pm k$ standard deviations — a soft truncation to limit outlier influence |\n", - "| **Z-scoring** | Centering to mean 0 and scaling to std 1, so each factor vector lives on the same scale |\n", - "| **Neutralization** | Projecting out unwanted components (sector) — an orthogonal projection onto a complement subspace |\n", - "| **Composite signal** | A linear combination $c_t = \\sum_k w_k f_{k,\\perp}$ of neutralized factor vectors |\n", - "| **Sector matrix** | A matrix $\\mathbf{D} \\in \\{0,1\\}^{N \\times K}$ encoding sector membership |\n", - "| **Factor / signal** ↻ | The vector $f_t$ we are orthogonalizing |\n", - "| **Momentum** ↻ | The headline factor we build the traded signal from |\n", - "| **Information coefficient (IC)** ↻ | Used to compare momentum-only vs. the 4-factor composite. Recall that it is the Spearman rank correlation between $f_t$ and $r_{t+1}$ — cosine similarity of rank vectors |\n", - "| **Value / Quality / Low-vol** ↻ | The other factors entering the (rejected) composite |\n", - "| **Sector** ↻ | The categorical partition we neutralize against |\n", - "| **Return panel** $\\mathbf{R}$ ↻ | The return matrix the factors are computed from |\n", + "| **Winsorization** | Clip extreme values at a threshold instead of dropping them |\n", + "| **Z-scoring** | Center to mean 0 and scale to standard deviation 1 |\n", + "| **Neutralization** | Regress a signal on unwanted exposures and keep the residual |\n", + "| **Composite signal** | A linear combination $c_t = \\sum_k w_k f_{k,\\perp}$ |\n", + "| **Sector matrix** | $D \\in \\{0,1\\}^{N \\times K}$, a one-hot sector-membership matrix |\n", + "| **Projection** | The linear algebra operation behind neutralization |\n", + "| **Information coefficient (IC)** ↻ | Spearman rank correlation used to compare candidate signals |\n", + "| **Return panel** $R$ ↻ | The monthly return matrix from notebook 01 |\n", "\n", "## Outputs\n", "\n", - "- `momentum_signal.csv` — the **momentum-only** sector-neutralized signal (this is what the backtest trades)\n", - "- `composite_4factor.csv` — the 4-factor equal weight composite (kept for comparison)\n", - "- Per-factor neutralized exposures\n", + "- `momentum_signal.csv`: sector-neutralized momentum, the signal used in the backtest\n", + "- `composite_4factor.csv`: a simple four-factor composite kept for comparison\n", + "- `factor_*_neutralized.csv`: neutralized versions of the individual factors\n", "\n", "## Notebook Structure\n", "1. [Setup and Imports](#setup-and-imports)\n", "2. [Winsorization and Z-Scoring](#winsorization-and-z-scoring)\n", - "3. [Sector and Size Neutralization](#sector-and-size-neutralization)\n", + "3. [Sector Neutralization](#sector-neutralization)\n", "4. [Composite Assembly and Comparison](#composite-assembly-and-comparison)\n", "5. [IC Comparison: Momentum vs. Composite](#ic-comparison-momentum-vs-composite)\n", "6. [Conclusion](#conclusion)" @@ -59,7 +58,14 @@ "cell_type": "code", "execution_count": 1, "id": "c72adc67", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:52.187310Z", + "iopub.status.busy": "2026-07-31T11:08:52.186272Z", + "iopub.status.idle": "2026-07-31T11:08:53.251453Z", + "shell.execute_reply": "2026-07-31T11:08:53.250854Z" + } + }, "outputs": [], "source": [ "\"\"\"\n", @@ -89,7 +95,14 @@ "cell_type": "code", "execution_count": 2, "id": "0090dab7", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:53.253326Z", + "iopub.status.busy": "2026-07-31T11:08:53.253053Z", + "iopub.status.idle": "2026-07-31T11:08:53.408971Z", + "shell.execute_reply": "2026-07-31T11:08:53.408532Z" + } + }, "outputs": [ { "name": "stdout", @@ -125,25 +138,40 @@ "source": [ "## Winsorization and Z-Scoring\n", "\n", - "**Winsorization** is a statistical technique for limiting the influence of extreme outliers in a dataset by capping them at a specified threshold rather than removing them entirely. Raw factor exposures can have extreme outliers — a stock that returned 500% in the trailing year, for instance. We **Winsorize** at $\\pm 3$ standard deviations, so that we don't have to drop them.\n", + "Raw factor values can contain large outliers. Winsorization clips those extremes rather than dropping the stock entirely. Here we cap each monthly cross-section at $\\pm 3$ standard deviations around its mean.\n", "\n", - "After winsorization we **z-score** cross-sectionally: for each date $t$, the factor vector $f_t$ is transformed to\n", - "$$\\tilde{f}_t = (f_t - \\bar{f}_t) / \\text{std}(f_t),$$\n", - "giving it mean 0 and standard deviation 1. This normalizes all factors to a common scale so they can be linearly combined without one dominating due to unit choices." + "After clipping, we z-score within each date:\n", + "\n", + "$$\\tilde f_{t,i}=\\frac{f_{t,i}-\\bar f_t}{\\mathrm{std}(f_t)}.$$\n", + "\n", + "This gives each factor row mean 0 and standard deviation 1, so later comparisons are not driven by arbitrary units." ] }, { "cell_type": "code", "execution_count": 3, "id": "83a54dbc", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:53.410714Z", + "iopub.status.busy": "2026-07-31T11:08:53.410441Z", + "iopub.status.idle": "2026-07-31T11:08:54.165454Z", + "shell.execute_reply": "2026-07-31T11:08:54.164580Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "momentum: z-scores, shape=(251, 501)\n", - "value: z-scores, shape=(251, 501)\n", + "value: z-scores, shape=(251, 501)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "quality: z-scores, shape=(251, 501)\n", "lowvol: z-scores, shape=(251, 501)\n" ] @@ -184,39 +212,72 @@ "id": "112a7744", "metadata": {}, "source": [ - "## Sector and Size Neutralization\n", + "## Sector Neutralization\n", "\n", - "A raw momentum score will have a different distribution in Technology than in Utilities. If we just z-score and combine, the composite will inherit those sector tilts. To get a cleaner signal, we project out the sector component. \n", + "A raw momentum score may contain sector bets. For example, if Technology had a strong year, a momentum portfolio might become mostly a Technology portfolio. That may be a valid trade, but it is not a clean test of stock selection within sectors.\n", "\n", - "To **sector-neutralize** the factor, we project it onto the sector indicator matrix and keep only the **residual** — the component orthogonal to all sector directions. After this, the signal has *no net exposure* to any sector: it can't be explained by \"being long tech\" or \"short energy.\" What remains is pure cross-sectional momentum *within* each sector.\n", + "To neutralize sectors, create a sector dummy matrix\n", "\n", - "The term comes from quant finance (\"neutralize the factor against X\"), but mathematically it's just an **orthogonal projection onto a complement subspace**. We subtract out the part of the vector that lives in the sector span, leaving only what's left over.\n", + "$$D \\in \\{0,1\\}^{N \\times K}.$$\n", "\n", - "From the linear algebraists' perspective, the sector matrix $D \\in \\{0,1\\}^{N \\times K}$ spans a subspace $S \\subseteq \\mathbb{R}^N$. The orthogonal projection onto $S$ is:\n", - "$$ P = D(D^TD)^{-1}D^T. $$\n", - "This is the familiar ordinary least-squares projection matrix, which is symmetric and idempotent. Applying it to a factor vector $f$ gives the component of $f$ which lies in the sector subspace:\n", - "$$ \\hat{f} = Pf. $$\n", - "The **neutralized factor** is the residual, i.e., the component orthogonal to $S$:\n", - "$$ f_\\perp = (I - P)f = f - \\hat{f}. $$\n", - "This is exactly linear least squares regression of $f$ on sectors, returning the residuals. The residual is orthogonal to the sector subspace by construction: $\\langle f_\\perp, \\hat{f} \\rangle = 0$. \n", + "For one factor vector $f \\in \\mathbb{R}^N$, the projection onto the sector span is\n", "\n", - "For size, we use the log of trailing market cap proxied by price $\\times 1$ (best we could do with our data). This is a weak proxy — a real implementation would use actual market cap." + "$$P_D f = D(D^\\top D)^{-1}D^\\top f.$$\n", + "\n", + "The sector-neutralized signal is the residual:\n", + "\n", + "$$f_\\perp = (I-P_D)f.$$\n", + "\n", + "This is the same as running a cross-sectional OLS regression of the factor on sector dummies and keeping the residuals. The residual has zero linear exposure to the sector dummy columns used in the regression.\n", + "\n", + "The code also mentions size, but with this dataset we only have a weak price-based size proxy. I would not interpret it as a true market-cap neutralization without real shares-outstanding data." ] }, { "cell_type": "code", "execution_count": 4, "id": "b7ce2018", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:54.167456Z", + "iopub.status.busy": "2026-07-31T11:08:54.167259Z", + "iopub.status.idle": "2026-07-31T11:08:56.142616Z", + "shell.execute_reply": "2026-07-31T11:08:56.141776Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Neutralizing momentum...\n", - "Neutralizing value...\n", - "Neutralizing quality...\n", - "Neutralizing lowvol...\n", + "Neutralizing momentum...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Neutralizing value...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Neutralizing quality...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Neutralizing lowvol...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Sector neutralization complete.\n" ] } @@ -268,9 +329,7 @@ "id": "9740141a", "metadata": {}, "source": [ - "Much like in Notebook 02, we note that we added a sample size filter with `mask.sum() < 30`. The `mask` identifies stocks that have both a valid factor score and a valid forward return in a given month, and `mask.sum()` counts how many usable pairs you actually have. The `< 30` threshold prevents the code from computing a correlation on a tiny sample, which is statistically meaningless and numerically unstable (e.g., Spearman correlation on 2 stocks is always exactly $\\pm$1). If you don't gate this, early-history months, mass delistings, or data gaps inject garbage $\\pm 1.0$ values into your IC time series, which then contaminate every downstream statistic like the mean IC, Information Ratio, and decay curves. Setting a floor of 30 filters out those degenerate months while retaining enough valid data to produce a reliable signal.\n", - "\n", - "We do this sort of masking throughout." + "As in notebook 02, we use a sample-size guardrail. If fewer than 30 stocks have valid data in a month, we skip neutralization for that row rather than fit a noisy cross-sectional regression. This mostly affects early or sparse parts of the panel." ] }, { @@ -280,26 +339,40 @@ "source": [ "## Composite Assembly and Comparison\n", "\n", - "We construct two signals.\n", - "1. **Momentum-only**: the sector-neutralized momentum vector $f_{m,\\perp}$, z-scored. This is the signal that we will use in the backtest. \n", - "2. **4-factor composite (just for comparison)**: a linear combination $$ c_t = \\frac{1}{4} \\sum_{k=1}^4 f_{k, \\perp}(t). $$ If the composite's IC beats momentum (it doesn't), combining helps. If it's worse (it is), then the negative IC factors dilute the signal.\n", + "We compare two candidate signals:\n", "\n", + "1. **Momentum-only:** the sector-neutralized momentum residual $f_{m,\\perp}$, re-z-scored. This is the signal passed to the backtest.\n", + "2. **Four-factor composite:** an equal-weight average of the neutralized factor vectors,\n", "\n", - "The linear-algebra operation is the same in both cases: a linear combination of vectors in the sector-neutralized subspace. The only difference is the weight vector: \n", - "$$ \\begin{pmatrix} 1 \\\\ 0 \\\\ 0 \\\\ 0 \\end{pmatrix} \\text{ vs } \\begin{pmatrix} \\frac{1}{4} \\\\ \\frac{1}{4} \\\\ \\frac{1}{4} \\\\ \\frac{1}{4} \\end{pmatrix}. $$" + "$$c_t = \\frac{1}{4}\\sum_{k=1}^4 f_{k,\\perp}(t).$$\n", + "\n", + "This is a useful sanity check. If the composite improves IC, the extra factors are helping. If it gets worse, the added factors are diluting momentum rather than diversifying it." ] }, { "cell_type": "code", "execution_count": 5, "id": "792e547e", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:56.144507Z", + "iopub.status.busy": "2026-07-31T11:08:56.144304Z", + "iopub.status.idle": "2026-07-31T11:08:56.350858Z", + "shell.execute_reply": "2026-07-31T11:08:56.350129Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Momentum-only signal: (251, 501)\n", + "Momentum-only signal: (251, 501)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "4-factor composite: (251, 501)\n", "\n", "Our main signal will be momentum-only. The 4-factor composite is kept for comparison.\n" @@ -352,7 +425,14 @@ "cell_type": "code", "execution_count": 6, "id": "25e7a046", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:56.353026Z", + "iopub.status.busy": "2026-07-31T11:08:56.352756Z", + "iopub.status.idle": "2026-07-31T11:08:58.700588Z", + "shell.execute_reply": "2026-07-31T11:08:58.699933Z" + } + }, "outputs": [ { "name": "stdout", @@ -444,7 +524,7 @@ }, { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -507,14 +587,21 @@ "cell_type": "code", "execution_count": 7, "id": "9c9cc628", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:08:58.702363Z", + "iopub.status.busy": "2026-07-31T11:08:58.702180Z", + "iopub.status.idle": "2026-07-31T11:08:59.630835Z", + "shell.execute_reply": "2026-07-31T11:08:59.630226Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saved:\n", - " - momentum_signal.csv (momentum-only, sector-neutralized — THE HEADLINE)\n", + " - momentum_signal.csv (momentum-only, sector-neutralized)\n", " - composite_4factor.csv (4-factor equal-weight — comparison only)\n", " - factor_momentum_neutralized.csv\n", " - factor_value_neutralized.csv\n", @@ -540,7 +627,7 @@ " factor_neut[name].to_csv(f'../data/processed/factor_{name}_neutralized.csv')\n", "\n", "print(\"Saved:\")\n", - "print(\" - momentum_signal.csv (momentum-only, sector-neutralized — THE HEADLINE)\")\n", + "print(\" - momentum_signal.csv (momentum-only, sector-neutralized)\")\n", "print(\" - composite_4factor.csv (4-factor equal-weight — comparison only)\")\n", "for name in factor_names:\n", " print(f\" - factor_{name}_neutralized.csv\")" @@ -553,12 +640,13 @@ "source": [ "## Conclusion\n", "\n", - "The IC comparison confirms what notebook 02 predicted:\n", + "The comparison supports a simple choice: use **momentum-only, sector-neutralized** as the headline signal.\n", "\n", - "- **Momentum-only** has the highest IC among all signals. Sector neutralization slightly improves it (IR = 0.15 vs. 0.11 raw).\n", - "- **The 4-factor composite** has negative IC — combining momentum with two negative-IC factors (value, lowvol) and one near-zero factor (quality) dilutes the signal. This is the linear-algebra intuition made empirical: adding vectors that point in the wrong direction moves the sum away from the target.\n", + "- Sector neutralization slightly improves the momentum IC.\n", + "- The equal-weight four-factor composite is worse, because it mixes momentum with weak or negative proxy signals.\n", + "- The result is also easier to explain: rank stocks by momentum within a sector-neutral framework, then trade the top decile.\n", "\n", - "**The headline signal is momentum-only, sector-neutralized.** The backtest in notebook 04 trades this signal and tests whether it generates alpha net of costs." + "Notebook 04 turns this signal into portfolio weights and asks whether it produces returns net of transaction costs." ] } ], diff --git a/notebooks/04_backtest_and_performance.ipynb b/notebooks/04_backtest_and_performance.ipynb index 56e02c9..3c2fc4c 100644 --- a/notebooks/04_backtest_and_performance.ipynb +++ b/notebooks/04_backtest_and_performance.ipynb @@ -9,53 +9,45 @@ "\n", "## Purpose\n", "\n", - "This is the central notebook of the project. Given the momentum-only, sector-neutralized signal from notebook 03, we form portfolio weight vectors $w_t$, apply transaction costs, and measure performance. \n", + "This is the main portfolio notebook. We take the sector-neutralized momentum signal from notebook 03, form portfolio weights, subtract transaction costs, and measure the result.\n", "\n", - "A portfolio is a weight vector. The portfolio return at each month is the inner product\n", - "$$ r_{p,t} = \\langle w_t, r_{t+1} \\rangle $$\n", - "of the weight vector with next month's return.\n", + "A portfolio is a weight vector. If $w_t$ is the portfolio chosen at rebalance date $t$, and $r_{t+1}$ is the next month's return vector, then the portfolio return is\n", "\n", + "$$r_{p,t+1}=w_t^\\top r_{t+1}.$$\n", "\n", - "\n", - "The main goals in this notebook are:\n", - "1. To construct top-decile long and long-short portfolios (sparse weight vectors) from the momentum signal.\n", - "2. To track **turnover** explicitly: $\\|w_t - w_{t-1}\\|_1$ (the $\\ell^1$ distance between consecutive weights).\n", - "3. To apply realistic transaction costs: $c \\cdot \\|w_t - w_{t-1}\\|_1$.\n", - "4. To compute performance metrics: Sharpe, Sortino, max drawdown, Calmar.\n", - "5. To run walk-forward analysis: split into 5-year windows and verify performance is consistent across subperiods (not just one lucky stretch).\n", - "6. To regress portfolio returns on Fama-French benchmark factors (OLS projection) to extract alpha (the orthogonal residual).\n", - "7. To test survivorship bias sensitivity: how much return drag from missing/delisted stocks would it take to erase the alpha?\n", + "The goals are:\n", + "1. Build top-decile long-only and long-short portfolios from the momentum signal.\n", + "2. Track one-way turnover explicitly: $\\tfrac{1}{2}\\|w_t-w_{t-1}\\|_1$.\n", + "3. Subtract transaction costs proportional to turnover.\n", + "4. Compute Sharpe, Sortino, max drawdown, Calmar, and active return versus the equal-weight universe.\n", + "5. Run walk-forward checks across 5-year windows.\n", + "6. Estimate Fama-French alpha with an OLS regression.\n", + "7. Stress-test survivorship bias by asking how much missing-name drag would erase the alpha.\n", "\n", "### Terms used in this notebook\n", "\n", "| Term | Meaning |\n", "|------|---------|\n", - "| **Long** | Holding a stock (positive weight $w_i > 0$) |\n", - "| **Short** | Selling a borrowed stock (negative weight $w_i < 0$) |\n", - "| **Long-only portfolio** | Weight vector with $w_i \\geq 0$, $\\sum w_i = 1$ |\n", - "| **Long-short (L/S) portfolio** | Weight vector with $\\sum w_i = 0$ (dollar-neutral) |\n", - "| **Decile** | Top 10% of stocks by signal rank |\n", - "| **Bps (basis points)** | 1 bp = 0.01%; 5 bps round-trip = 0.05% cost per unit traded |\n", - "| **Portfolio weights** $w$ | The weight vector we construct from the signal |\n", - "| **Portfolio return** | Inner product $w^\\top r_{t+1}$ |\n", - "| **Turnover** | $\\ell_1$ distance $\\|w_t - w_{t-1}\\|_1$ |\n", - "| **Transaction cost** | $c \\cdot \\|w_t - w_{t-1}\\|_1$ — proportional to turnover |\n", - "| **Sharpe ratio** ↻ | Mean return / std of return — a signal-to-noise ratio |\n", - "| **Sortino ratio** | Like Sharpe, but only penalizes downside volatility |\n", - "| **Max drawdown** | Largest peak-to-trough drop in cumulative wealth |\n", - "| **Calmar ratio** | Annual return / max drawdown |\n", - "| **Alpha** | Return not explained by factors — the residual after OLS projection onto factor returns |\n", - "| **Beta** | Factor loading — coordinates of portfolio returns in the factor basis |\n", - "| **Fama–French factors** | Standard benchmark factors (market, size, value, momentum) |\n", - "| **Active return / IR** | Portfolio return minus benchmark; IR = active return / tracking error |\n", - "| **Walk-forward** ↻ | Splitting into windows and testing out-of-sample consistency |\n", - "| **Sector neutralization** ↻ | The signal was orthogonalized to sectors in notebook 03 |\n", - "| **Momentum** ↻ | The factor the traded signal is built from |\n", - "| **Survivorship bias** ↻ | Revisited here as a sensitivity analysis on alpha |\n", + "| **Long** | Holding a stock with positive weight $w_i>0$ |\n", + "| **Short** | Holding a negative weight $w_i<0$ |\n", + "| **Long-only portfolio** | Weight vector with $w_i\\ge 0$ and $\\sum_i w_i=1$ |\n", + "| **Long-short portfolio** | Long winners and short losers; roughly dollar-neutral with $\\sum_i w_i=0$ |\n", + "| **Decile** | Top or bottom 10% of stocks by signal rank |\n", + "| **Basis point (bp)** | 1 bp = 0.01%; 5 bps = 0.05% |\n", + "| **Turnover** | One-way turnover $\\tfrac{1}{2}\\|w_t-w_{t-1}\\|_1$ |\n", + "| **Transaction cost** | Cost rate times one-way turnover |\n", + "| **Sharpe ratio** | Annualized mean return divided by annualized volatility |\n", + "| **Sortino ratio** | Similar to Sharpe, but only downside moves enter the denominator |\n", + "| **Max drawdown** | Worst percentage decline from a previous wealth peak |\n", + "| **Alpha** | Regression intercept after controlling for benchmark factors |\n", + "| **Beta** | Regression loading on a benchmark factor |\n", + "| **Fama-French factors** | Standard market, size, value, and momentum benchmark returns |\n", + "| **Active return / IR** | Portfolio return minus benchmark return; IR = active return / tracking error |\n", + "| **Survivorship bias** ↻ | Tested here with synthetic return drag |\n", "\n", "## Outputs\n", "\n", - "Equity curve, drawdown chart, walk-forward performance table, Fama-French regression with alpha, survivorship sensitivity table.\n", + "Equity curves, drawdowns, performance tables, Fama-French regressions, survivorship sensitivity tables, and `backtest_returns.csv` for the risk notebook.\n", "\n", "## Notebook Structure\n", "1. [Setup and Imports](#setup-and-imports)\n", @@ -71,9 +63,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 1, "id": "b2db11de", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T12:20:19.758791Z", + "iopub.status.busy": "2026-07-31T12:20:19.757884Z", + "iopub.status.idle": "2026-07-31T12:20:21.098020Z", + "shell.execute_reply": "2026-07-31T12:20:21.097429Z" + } + }, "outputs": [], "source": [ "\"\"\"\n", @@ -96,7 +95,7 @@ "os.makedirs('../images/04_backtest', exist_ok=True)\n", "\n", "RANDOM_STATE = 3\n", - "TRANSACTION_COST_BPS = 5 # 5 bps round-trip\n", + "TRANSACTION_COST_BPS = 5 # 5 bps per unit of one-way turnover\n", "REBALANCE_FREQ = 'ME' # Monthly" ] }, @@ -110,9 +109,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 2, "id": "9ffbee22", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T12:20:21.100126Z", + "iopub.status.busy": "2026-07-31T12:20:21.099825Z", + "iopub.status.idle": "2026-07-31T12:20:21.176089Z", + "shell.execute_reply": "2026-07-31T12:20:21.175603Z" + } + }, "outputs": [ { "name": "stdout", @@ -143,37 +149,37 @@ "source": [ "## Benchmark Factors and Alpha\n", "\n", - "A portfolio that returns 20% sounds great — but if the market also returned 18%, most of that performance is just the market: the portfolio went up because everything went up. To claim skill, we need to show returns *above and beyond* what standard risk factors explain — that is, a component lying in the orthogonal complement of the factor span.\n", + "A 20% portfolio return sounds good, but it may not be stock-picking skill. If the market returned 18% and the portfolio had high market beta, most of the return may be ordinary market exposure.\n", "\n", - "### What are the Fama–French factors?\n", + "To separate those effects, we use the Fama-French benchmark factors. The regression later in this notebook is:\n", "\n", - "Eugene Fama and Kenneth French identified a small set of common risk factors that explain most cross-sectional variation in stock returns. Their data library (freely available online) provides monthly returns for:\n", + "$$r_p - r_f = \\alpha + \\beta_1\\mathrm{MKT} + \\beta_2\\mathrm{SMB} + \\beta_3\\mathrm{HML} + \\beta_4\\mathrm{MOM} + \\varepsilon.$$\n", "\n", "| Factor | Symbol | What it captures |\n", "|--------|--------|-----------------|\n", - "| **Market** | MKT-RF | Market excess return (above the risk-free rate) — the overall market premium |\n", - "| **Size** | SMB | \"Small Minus Big\" — small-cap stocks tend to outperform large-caps |\n", - "| **Value** | HML | \"High Minus Low\" — high book-to-price (value) stocks tend to outperform growth |\n", - "| **Momentum** | MOM | Stocks with high trailing returns tend to keep outperforming |\n", - "| **Risk-free rate** | RF | The monthly T-bill rate, used to compute excess returns |\n", + "| **Market** | MKT-RF | Market excess return above the risk-free rate |\n", + "| **Size** | SMB | Small-minus-big stock return spread |\n", + "| **Value** | HML | High-minus-low book-to-market return spread |\n", + "| **Momentum** | MOM | Winner-minus-loser momentum factor |\n", + "| **Risk-free rate** | RF | Monthly T-bill rate used to compute excess returns |\n", "\n", - "### Why do we care?\n", + "The betas measure exposure to known return drivers. The alpha is the intercept: the average monthly return left over after those exposures are accounted for. A positive alpha with a large t-statistic is evidence that the strategy is doing more than taking standard factor risk.\n", "\n", - "We use these factors as a basis for decomposing portfolio returns. The Fama–French regression (later in this notebook) projects portfolio returns onto this factor basis:\n", - "\n", - "$$r_p = \\alpha + \\beta_1 \\cdot \\text{MKT} + \\beta_2 \\cdot \\text{SMB} + \\beta_3 \\cdot \\text{HML} + \\beta_4 \\cdot \\text{MOM} + \\varepsilon$$\n", - "\n", - "- The **betas** ($\\beta_k$) measure how much of the portfolio's return is explained by each known factor — just exposure, not skill.\n", - "- The **alpha** ($\\alpha$) is the **intercept** — the return left over after removing all factor exposure. This is the orthogonal residual, the component of returns that *can't be explained* by the standard factors. A positive, statistically significant alpha is the gold standard for \"this strategy actually works.\"\n", - "\n", - "We download these factors from the Kenneth French Data Library (cached locally after first download)." + "These factors are loaded from the Kenneth French Data Library, using the local cache when available." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 3, "id": "5bfbd826", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T12:20:21.177834Z", + "iopub.status.busy": "2026-07-31T12:20:21.177665Z", + "iopub.status.idle": "2026-07-31T12:20:21.192027Z", + "shell.execute_reply": "2026-07-31T12:20:21.191419Z" + } + }, "outputs": [ { "name": "stdout", @@ -274,7 +280,7 @@ "2005-05-01 0.0365 0.0286 -0.0058 0.0024 0.0037" ] }, - "execution_count": 15, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -285,13 +291,13 @@ "Download Fama-French 3-factor + Momentum from Ken French data library\n", "==================================\n", "\"\"\"\n", - "import pandas_datareader as pdr\n", - "\n", "ff_path = '../data/raw/ff_factors.csv'\n", "\n", "if os.path.exists(ff_path):\n", " df_ff = pd.read_csv(ff_path, index_col=0, parse_dates=True)\n", "else:\n", + " import pandas_datareader as pdr\n", + "\n", " print(\"Downloading Fama-French factors...\")\n", " # 3-factor monthly\n", " df_ff3 = pdr.famafrench.FamaFrenchReader('F-F_Research_Data_Factors', start='2005-01-01').read()[0]\n", @@ -327,9 +333,16 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 4, "id": "8691aa8a", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T12:20:21.193875Z", + "iopub.status.busy": "2026-07-31T12:20:21.193696Z", + "iopub.status.idle": "2026-07-31T12:20:21.438807Z", + "shell.execute_reply": "2026-07-31T12:20:21.438161Z" + } + }, "outputs": [ { "name": "stdout", @@ -430,7 +443,7 @@ "max 0.1727 0.4796 0.1008" ] }, - "execution_count": 16, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -516,24 +529,35 @@ "source": [ "## Turnover and Transaction Costs\n", "\n", - "**Turnover** measures how much the weight vector changes over rebalances:\n", - "$$ \\|w_t - w_{t-1}\\|_1. $$\n", - "A portfolio that holds the same stocks at the same weights has zero turnover; one that completely reshuffles has turnover $\\approx 2$ (at least for long portfolios; it can get to 4 for long-short).\n", + "One-way turnover measures how much of the portfolio has to be traded at each rebalance:\n", + "\n", + "$$\\text{turnover}_t=\\frac{1}{2}\\|w_t-w_{t-1}\\|_1.$$\n", + "\n", + "For a long-only portfolio, zero means nothing changed. A value near 1 means the portfolio was almost completely replaced. For the long-short book, we compute one-way turnover on the long side and short side and add them.\n", "\n", "Transaction costs are proportional to turnover:\n", - "$$ \\text{cost}_t = c\\cdot \\|w_t - w_{t-1}\\|_1, $$\n", - "where $c = 5 bps = 0.0005$ for liquid US large-caps. Net return is gross return minus cost. " + "\n", + "$$\\text{cost}_t=c\\times\\text{turnover}_t,$$\n", + "\n", + "where $c=5$ bps, or $0.0005$, for liquid US large-cap names. Net return is gross return minus cost. The first rebalance includes the initial cost of buying the portfolio." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 5, "id": "7f3b7fe9", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T12:20:21.440964Z", + "iopub.status.busy": "2026-07-31T12:20:21.440773Z", + "iopub.status.idle": "2026-07-31T12:20:22.027373Z", + "shell.execute_reply": "2026-07-31T12:20:22.026659Z" + } + }, "outputs": [ { "data": { - "image/png": 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xRT1SUFAAAGhoaEBtbW2vApwsJiUiIiIiIiLaQ8iyDER7BBH1RmFhIQAgHA736nUYmCIiIiIiIiIiIlP6KtuOgSkiIiIiIiIiIsoJBqaIiIiIiIiIiCgnGJgiIiIiIiIiorx28cUX46yzzsr1MJJMmzYN1113Xa6HYWk5DUy9//77OP300zF8+HBIkoRXXnml2+csWbIEU6ZMgcfjwfjx4/H4448PyFiJiIiIiIiIiPpDbxuI97f+HF9OA1M+nw+TJ0/Gww8/nNXjN2/ejJkzZ+KYY47BypUr8Ytf/ALXXHMNXnzxxX4fKxERERERERHlpyVLluDwww+H2+3GsGHDcOuttyISiej3T5s2Dddccw1uvvlmVFZWYujQobj77rvjXuObb77Bd7/7XXg8Huy333545513MibRXHzxxViyZAn++Mc/QpIkSJKELVu24Omnn0Z5eXncY1955ZW4ZuF33303Dj74YDz55JMYP3483G43VFWFJEl44okncPbZZ6OwsBCTJk3Cq6++mvV7/ctf/oIRI0ZAUZS455xxxhm46KKL9J9fe+21uKSfe+65J+7zkiQJjz/+OM4880wUFRXhvvvuM/kXyV5OA1MzZszAfffdh3POOSerxz/++OMYPXo05s2bh3333ReXX345Lr30Uvz+97/v97ESERERERERDTaqqiIsKzn5p6pqn7yHnTt3YubMmfjOd76DL774Ao899hjmz5+fFEz529/+hqKiInzyySeYO3cu7r33XixcuBAAoCgKzjrrLBQWFuKTTz7BX//6V9x+++0Zf+8f//hHTJ06FT/+8Y9RV1eHuro6jBo1Kutxb9iwAc8//zxefPFFrFq1Sr/9nnvuwaxZs/Dll19i5syZuOCCC9DS0pLVe/3BD36ApqYmLFq0SH+91tZWvPXWW7jgggsAAG+99RbmzJmDa665BmvWrMFf/vIXPP300/j1r38dN7677roLZ555Jr766itceumlWb8vsxz99sr94KOPPsJJJ50Ud9vJJ5+M+fPnIxwOw+l05mxsubCrxYdgWMaQ8kIUui31pyQiIiIiIqI8EFFU/PvDDTn53T/87kQ47VIWj8zs0UcfxahRo/Dwww9DkiTss88+2LVrF2655RbceeedsNm0nJyDDjoId911FwBg0qRJePjhh/Huu+/ixBNPxNtvv42NGzdi8eLFGDp0KADg17/+NU488cS0v7esrAwulwuFhYX6c8wIhUL4xz/+gZqamrjbL774Ypx//vkAgPvvvx9//vOf8emnn+KUU07p9r1WVlbilFNOwb/+9S8cf/zxAIAXXngBlZWV+s+//vWvceutt+oZVOPHj8evfvUr3HzzzfrnAwCzZ8/u14CUYKloRn19PYYMGRJ325AhQxCJRNDU1IRhw4YlPScYDCIYDOo/d3R0ANFoaGJqm9Ws2NSIls4gph8wHB5nUa6Hs0dRFC26b/VtiGigcJ8hMof7DJE53GeIsif2E5GtpKpqn2UumdWT353q8WvXrsXUqVPj7j/qqKPQ2dmJ7du3Y/To0QCAAw88MO75w4YNw+7du6GqKr755huMGjUKQ4YM0R/zne98J6txJt5v/GzT3aaqKsaMGYPq6uqk1zaOs7CwECUlJfo4s3mvs2fPxhVXXIFHHnkEbrcb//znP3HeeefBZrNBVVV8/vnnWL58eVyGlCzLCAQC8Pl8KCwsBABMmTIlq/edKr5i5nhsqcAUonWORuJDSrxdeOCBB3DPPfck3d7Y2IhAINBPoxwYnZ1e+HxhNDU1wxnx5Xo4exRFUdDe3g5VVfXoOxGlx32GyBzuM0TmcJ8hyl44HNYDCaKh9blHjs3NYBQZETW7AIYYs7EPkiDLMlRVjbtPvDdZlhGJRKCqKhwOR9xjVFXV75dlGZIkxd0v/l88JhURnEl83cTbRPxB3KYoCgoLC1O+rs1mS+r3FIlE9HF2915nzJgBRVHw6quv4rDDDsMHH3yAuXPnxv3uO++8M+Uqh8bPyOPxpH3f4r0oioLm5uakCjav15v2eUm/M+tH5oGhQ4eivr4+7raGhgY4HA5UVVWlfM5tt92GG264Qf+5o6MDo0aNQk1NDUpLS/t9zP2pbFcQIQRQUVmJ2uriXA9nj6IoCiRJQk1NDU9+iLLAfYbIHO4zROZwnyHKXiAQgNfrhc1m04MJVmiKY7PZYLPZ4HAkhzH2339/vPTSS7Db7XrSyqeffoqSkhKMGTMGNptNb05ufL7xNffbbz9s27YNzc3NeqXWypUrAQB2uz3l7wUAt9sNRVHi7h86dCi8Xi+CwSCKirTqpq+++gqIBn7E704cj5Dq94lxZvNeS0pKcM455+C5557D5s2bsddee+Hwww/XX+vQQw/F+vXrsc8++2T8zDO9b/FebDYbqqqq4PF44u5L/DkTSwWmpk6ditdeey3utrfffhuHHXZY2v5Sbrcbbrc76XaxAVqZceey+nuxIvG587Mnyg73GSJzuM8QmcN9hig7Yh8RQY101Uf5qL29HV988UXcbZWVlfjpT3+KP/7xj7jmmmtw9dVXY926dbj77rtxww03wG63648V18+JJEnCSSedhAkTJuDiiy/G3Llz4fV68ctf/hIwXHunMnbsWHz66afYunUriouLUVlZiSOPPBKFhYW4/fbb8bOf/Qyffvop/va3v+m/K9V/E8eTeLu4Ldv3esEFF+D000/H119/jTlz5sS93p133onTTjsNo0ePxg9+8APYbDZ8+eWX+Oqrr+Iaxqf7vBLvT3XsNXMszulRu7OzE6tWrdK7z2/evBmrVq3Ctm3bgGi204UXXqg//sorr8TWrVtxww03YO3atXjyyScxf/583HjjjTl7D7kkto8clQMTERERERERDZjFixfjkEMOift35513YsSIEViwYAE+/fRTTJ48GVdeeSUuu+wyPbCUDbvdjldeeQWdnZ34zne+g8svv1x/fqbsnxtvvBF2ux377bcfampqsG3bNlRWVuKZZ57BggULcOCBB+LZZ5/F3Xff3SefQbbv9bjjjkNlZSXWrVuH2bNnx9138skn43//+x8WLlyI73znOzjyyCPx4IMPYsyYMX0yRrMkNVddzqIb1fTp05Nuv+iii/D000/j4osvxpYtW7B48WL9viVLluD666/H119/jeHDh+OWW27BlVdemfXv7OjoQFlZGdrb2y1fyrfwix2ob+vC0fsMxfgh1n4vVqMoChoaGlBbW8tZOaIscJ8hMof7DJE53GeIshcIBLBp0yaMGjUKxcXFlsqYGmhLly7Fd7/7XWzYsAETJkzI9XDyTiAQwObNmzFu3Lik4J2Z2EtOS/mmTZuWscP7008/nXTbscceixUrVvTzyKzBxowpIiIiIiIioj7x8ssvo7i4GJMmTcKGDRtw7bXX4uijj2ZQqp9ZqscUxROR7RwmvRERERERERENCl6vFzfffDO2b9+O6upqnHDCCfjDH/6Q62ENegxMWZjeYyrXAyEiIiIiIiKyuAsvvDCuzzUNDBZgW5jImFKYMUVEREREREREFsTAlIWJFnWMSxERERERERGRFTEwZWE29pgiIiIiIiIiIgtjYMrCRI8plvIRERERERERkRUxMGVhImOKiIiIiIiIiMiKGJiyMj1jKtcDISIiIiIiIiIyj4EpC2OPKSIiIiIiIiKyMgamLIw9poiIiIiIiGhP8P777+P000/H8OHDIUkSXnnllbj7w+EwbrnlFhx44IEoKirC8OHDceGFF2LXrl39Mh5JkiBJEj7++OO424PBIKqqqiBJEhYvXpz0vP/7v/+D3W7Hv//9734ZlxUxMGVhkqjlY1yKiIiIiIiIBjGfz4fJkyfj4YcfTnl/V1cXVqxYgTvuuAMrVqzASy+9hG+//RZnnHFGv41p1KhReOqpp+Jue/nll1FcXJx2jM899xxuuukmzJ8/v9/GZTUMTFmYxB5TREREREREtAeYMWMG7rvvPpxzzjkp7y8rK8PChQsxa9Ys7L333jjyyCPx5z//GZ9//jm2bdumP06SJPzlL3/BaaedhsLCQuy777746KOPsGHDBkybNg1FRUWYOnUqNm7c2O2YLrroIvz73/+G3+/Xb3vyySdx0UUXpXz8Cy+8gP322w+33XYbli5dii1btvTosxhsGJiyMPaYIiIiIiIiIkqtvb0dkiShvLw87vZf/epXuPDCC7Fq1Srss88+mD17Nq644grcdttt+OyzzwAAV199dbevP2XKFIwbNw4vvvgiAGD79u14//338aMf/Sjl4+fPn485c+agrKwMM2fOTMq22lM5cj0A6jlJBKZyPRAiIiIiIiKyrr/cCHS2DfzvLS4Hrvh9v7x0IBDArbfeitmzZ6O0tDTuvksuuQSzZs0CANxyyy2YOnUq7rjjDpx88skAgGuvvRaXXHJJVr/nkksuwZNPPok5c+bgqaeewsyZM1FTU5P0uPXr1+Pjjz/GSy+9BACYM2cOrrnmGtx1112w2fbsnCEGpixMlPIxY4qIiIiIiIh6rLMN8DbnehR9JhwO44c//CEURcGjjz6adP9BBx2k//+QIUMAAAceeGDcbYFAAB0dHUlBrURz5szBrbfeik2bNuHpp5/Gn/70p5SPmz9/Pk4++WRUV1cDAGbOnInLLrsM77zzDk466aQev9fBgIEpC+OqfERERERERNRrxeVZPMgavzccDmPWrFnYvHkz3nvvvZSBJafTqf+/qERKdZuiKN3+vqqqKpx22mm47LLLEAgEMGPGDHi93rjHyLKMv//976ivr4fD4Yi7ff78+QxM5XoA1HOxHlO5HgkRERERERFZVj+V0w00EZRav349Fi1ahKqqqgH5vZdeeilmzpyJW265BXa7Pen+BQsWwOv1YuXKlXH3f/PNN7jgggvQ3Nw8YGPNRwxMWVg0YYqBKSIiIiIiIhrUOjs7sWHDBv3nzZs3Y9WqVaisrMTo0aMRiURw7rnnYsWKFfjf//4HWZZRX18PAKisrITL5eq3sZ1yyilobGxMW/Y3f/58nHrqqZg8eXLc7fvvvz+uu+46PPPMM7j22mv7bXz5bs/usGVxElflIyIiIiIioj3AZ599hkMOOQSHHHIIAOCGG27AIYccgjvvvBMAsGPHDrz66qvYsWMHDj74YAwbNkz/t2zZsn4dmyRJqK6uThn82r17N15//XV8//vfT/m8c845B/Pnz+/X8eU7ZkxZGHtMERERERER0Z5g2rRpGZMyxo4dm1XSRuJjUj2vu9+V6nWMysvL4+4Ph8NpH5uuWfqehBlTFqb3mMr1QIiIiIiIiIiIeoCBKQsTGVNMmCIiIiIiIiIiK2JgysL0JSwZmSIiIiIiIiIiC2JgysJsespUrkdCRERERERERGQeA1ODADOmiIiIiIiIiMiKGJiyMJveY4qBKSIiIiIiIsoeryOptxRF6ZPXcfTJq1BOxHpM5XokREREREREZAVOpxM2mw0tLS1wOByw2ZivQuaoqopQKITGxkbYbDa4XK5evR4DUxam95giIiIiIiIiyoLdbseIESOwdetWdHV16QkPRGYVFhZi9OjRvQ5uMjA1CLDHFBEREREREWWrqKgIlZWVKC8vZ8YU9YjdbofD4eiTwCYDUxbGHlNERERERETUEzabDR6Ph4EpyjlugRYmIpMMSxERERERERGRFTEwZWGSnjGV65EQEREREREREZnHwJSFxVblY2SKiIiIiIiIiKyHgSkLE6vysccUEREREREREVkRA1MWxlI+IiIiIiIiIrIyBqYsTCzKyIwpIiIiIiIiIrIiBqYsLNZjKtcjISIiIiIiIiIyj4EpC9N7TOV6IEREREREREREPcDAlIXFekwxNEVERERERERE1sPAlIWJwJTCwBQRERERERERWRADUxaml/IxLkVEREREREREFsTAlIVJDEwRERERERERkYUxMGVh0Uo+9pgiIiIiIiIiIktiYMrCRMYUe0wRERERERERkRUxMGVhNimLBxERERERERER5SkGpqxMX5Uv1wMhIiIiIiIiIjKPgSkLi63Kp7LPFBERERERERFZDgNTFiZ6TAEAw1JEREREREREZDUMTFmYsccUE6aIiIiIiIiIyGoYmBokWMpHRERERERERFbDwJSF2QylfAoDU0RERERERERkMQxMWZixxxQRERERERERkdUwMGVhxriUwoQpIiIiIiIiIrIYBqYszJgvxR5TRERERERERGQ1DExZmCRJejkfe0wRERERERERkdUwMGVxNpE2xbgUEREREREREVkMA1ODBHtMEREREREREZHVMDBlcbZoKR97TBERERERERGR1TAwZXGixxTDUkRERERERERkNQxMWVw0LsWMKSIiIiIiIiKyHAamLE4EprgqHxERERERERFZDQNTFhfrMZXrkRARERERERERmcPAlMVJDEwRERERERERkUUxMGVx0Uo+lvIRERERERERkeUwMGVxNjY/JyIiIiIiIiKLYmDK4vRSvlwPhIiIiIiIiIjIJAamLE7SM6ZyPRIiIiIiIiIiInMYmLI4kTHFHlNEREREREREZDUMTFmcTV+Vj4EpIiIiIiIiIrIWBqYsjqV8RERERERERGRVDExZXDQuxYwpIiIiIiIiIrIcBqYsLtZjKtcjISIiIiIiIiIyh4EpixM9poiIiIiIiIiIrIaBqUGCq/IRERERERERkdUwMGVxNr35OQNTRERERERERGQtDExZHHtMEREREREREZFVMTBlcewxRURERERERERWlfPA1KOPPopx48bB4/FgypQp+OCDDzI+/p///CcmT56MwsJCDBs2DJdccgmam5sHbLx5JxqXYo8pIiIiIiIiIrKanAamnnvuOVx33XW4/fbbsXLlShxzzDGYMWMGtm3blvLxH374IS688EJcdtll+Prrr/HCCy9g+fLluPzyywd87PlCZEyxxxQRERERERERWU1OA1MPPvggLrvsMlx++eXYd999MW/ePIwaNQqPPfZYysd//PHHGDt2LK655hqMGzcO3/3ud3HFFVfgs88+G/Cx5wtJb36e65EQEREREREREZmTs8BUKBTC559/jpNOOinu9pNOOgnLli1L+ZyjjjoKO3bswIIFC6CqKnbv3o3//Oc/OPXUUwdo1PlHdJhiKR8RERERERERWY0jV7+4qakJsixjyJAhcbcPGTIE9fX1KZ9z1FFH4Z///CfOO+88BAIBRCIRnHHGGfjzn/+c9vcEg0EEg0H9546ODgCAoihQFKXP3k8uqaoKeRC9HytQFAWqqvIzJ8oS9xkic7jPEJnDfYbIHO4z1N/MbFs5C0wJUsKqcqqqJt0mrFmzBtdccw3uvPNOnHzyyairq8NNN92EK6+8EvPnz0/5nAceeAD33HNP0u2NjY0IBAJ99C5yp6OjHT5fF1pabGhwR3I9nD2Goihob2+Hqqqw2XK+hgBR3uM+Q2QO9xkic7jPEJnDfYb6m9frzfqxOQtMVVdXw263J2VHNTQ0JGVRCQ888ACOPvpo3HTTTQCAgw46CEVFRTjmmGNw3333YdiwYUnPue2223DDDTfoP3d0dGDUqFGoqalBaWlpn7+vgVbRDjT5JZSXl6O2tirXw9ljKIoCSZJQU1PDAzlRFrjPEJnDfYbIHO4zROZwn6H+5vF4sn5szgJTLpcLU6ZMwcKFC3H22Wfrty9cuBBnnnlmyud0dXXB4Ygfst1uBzKsSud2u+F2u5Nut9lsg2IHtNkkLcNMkgbF+7ESKfqZ83Mnyg73GSJzuM8QmcN9hsgc7jPUn8xsVzndAm+44QY88cQTePLJJ7F27Vpcf/312LZtG6688kogmu104YUX6o8//fTT8dJLL+Gxxx7Dpk2bsHTpUlxzzTU4/PDDMXz48By+k9yxRcse2fuciIiIiIiIiKwmpz2mzjvvPDQ3N+Pee+9FXV0dDjjgACxYsABjxowBANTV1WHbtm364y+++GJ4vV48/PDD+PnPf47y8nIcd9xx+O1vf5vDd5FbEgNTRERERERERGRRkpquBm6Q6ujoQFlZGdrb2wdFj6nPNzZizY5W7DeyAlMm1OR6OHsMRVHQ0NCA2tpapr4SZYH7DJE53GeIzOE+Q2QO9xnqb2ZiL9wCLU5kTCl7VnyRiIiIiIiIiAYBBqYszqbFpVjKR0RERERERESWw8CUxek9psDIFBERERERERFZCwNTFicxY4qIiIiIiIiILIqBKYtjjykiIiIiIiIisioGpixO9JhiJR8RERERERERWQ0DU4MEM6aIiIiIiIiIyGoYmLI4m2h+zrgUEREREREREVkMA1MWxx5TRERERERERGRVDExZnOgxxbAUEREREREREVkNA1MWJ+mlfAxNEREREREREZG1MDBlcfqifIxLEREREREREZHFMDBlccyYIiIiIiIiIiKrYmDK4qJxKSiMSxERERERERGRxTAwZXE2kTHF9udEREREREREZDEMTFmcyJhiJR8RERERERERWQ0DUxbHHlNEREREREREZFUMTFmcWJWPPaaIiIiIiIiIyGoYmLI4GzOmiIiIiIiIiMiiGJiyOL3HVK4HQkRERERERERkEgNTFsceU0RERERERERkVQxMWZzImGKPKSIiIiIiIiKyGgamLM6m1/IxMkVERERERERE1sLAlMUxY4qIiIiIiIiIrIqBKYuTwB5TRERERERERGRNDExZHDOmiIiIiIiIiMiqGJiyONFjSgUjU0RERERERERkLQxMWRx7nxMRERERERGRVTEwZXGSxB5TRERERERERGRNDExZHDOmiIiIiIiIiMiqGJiyONFjSmFkioiIiIiIiIgshoEpi4smTDFjioiIiIiIiIgsh4Epi2OPKSIiIiIiIiKyKgamLM4mekwxOEVEREREREREFsPAlNWJ7ufR4BQRERERERERkVUwMGVxtlhcihlTRERERERERGQppgJTqqpi69at8Pv9/TciMkUyZkwxLkVEREREREREFmI6MDVp0iTs2LGj/0ZEphgzphRGpoiIiIiIiIjIQkwFpmw2GyZNmoTm5ub+GxGZxIwpIiIiIiIiIrIm0z2m5s6di5tuugmrV6/unxGRKewxRURERERERERW5TD7hDlz5qCrqwuTJ0+Gy+VCQUFB3P0tLS19OT7qhiRJkKIr8jEsRURERERERERWYjowNW/evP4ZCfWcJAGqyh5TRERERERERGQppgNTF110Uf+MhHrMJgGyyh5TRERERERERGQtpntMAcDGjRvxy1/+Eueffz4aGhoAAG+++Sa+/vrrvh4fZUGStEZTDEwRERERERERkZWYDkwtWbIEBx54ID755BO89NJL6OzsBAB8+eWXuOuuu/pjjNQN0QCdzc+JiIiIiIiIyEpMB6ZuvfVW3HfffVi4cCFcLpd++/Tp0/HRRx/19fgoK1pkij2miIiIiIiIiMhKTAemvvrqK5x99tlJt9fU1KC5ubmvxkUmxDKmcj0SIiIiIiIiIqLsmQ5MlZeXo66uLun2lStXYsSIEX01LjJB7zEFRqaIiIiIiIiIyDpMB6Zmz56NW265BfX19ZAkCYqiYOnSpbjxxhtx4YUX9s8oKSOJGVNEREREREREZEGmA1O//vWvMXr0aIwYMQKdnZ3Yb7/98L3vfQ9HHXUUfvnLX/bPKCkjkTHFHlNEREREREREZCUOs09wOp345z//iXvvvRcrV66Eoig45JBDMGnSpP4ZIXWLPaaIiIiIiIiIyIpMB6aWLFmCY489FhMmTMCECRP6Z1Rkit5jipEpIiIiIiIiIrIQ06V8J554IkaPHo1bb70Vq1ev7p9RkSnRhClmTBERERERERGRpZgOTO3atQs333wzPvjgAxx00EE46KCDMHfuXOzYsaN/RkjdYo8pIiIiIiIiIrIi04Gp6upqXH311Vi6dCk2btyI8847D3//+98xduxYHHfccf0zSspI7zGV64EQEREREREREZlgOjBlNG7cONx66634zW9+gwMPPBBLlizpu5FR1thjioiIiIiIiIisqMeBqaVLl+Kqq67CsGHDMHv2bOy///743//+17ejo6xE41JQGJciIiIiIiIiIgsxvSrfL37xCzz77LPYtWsXTjjhBMybNw9nnXUWCgsL+2eE1C2biEwxY4qIiIiIiIiILMR0YGrx4sW48cYbcd5556G6urp/RkWmMGOKiIiIiIiIiKzIdGBq2bJl/TMS6jEJ7DFFRERERERERNZjOjAFABs3bsS8efOwdu1aSJKEfffdF9deey0mTJjQ9yOkbjFjioiIiIiIiIisyHTz87feegv77bcfPv30Uxx00EE44IAD8Mknn2D//ffHwoUL+2eUlJHeYwqMTBERERERERGRdZjOmLr11ltx/fXX4ze/+U3S7bfccgtOPPHEvhwfZYEZU0RERERERERkRaYzptauXYvLLrss6fZLL70Ua9as6atxkQnsMUVEREREREREVmQ6MFVTU4NVq1Yl3b5q1SrU1tb21bjIBJExxbAUEREREREREVmJ6VK+H//4x/i///s/bNq0CUcddRQkScKHH36I3/72t/j5z3/eP6OkjESPKWZMEREREREREZGVmA5M3XHHHSgpKcEf/vAH3HbbbQCA4cOH4+6778Y111zTH2Ok7rDHFBERERERERFZkOnAlCRJuP7663H99dfD6/UCAEpKSvpjbJQlZkwRERERERERkRWZDkwZMSCVH/QeU4xLEREREREREZGFmG5+vnv3bvzoRz/C8OHD4XA4YLfb4/7RwGPGFBERERERERFZkemMqYsvvhjbtm3DHXfcgWHDhkES6TqUc+wxRURERERERERWYjow9eGHH+KDDz7AwQcf3D8jItP0jCkwMkVERERERERE1mG6lG/UqFEsGcsz7DFFRERERERERFZkOjA1b9483HrrrdiyZUv/jIhME+WUCiNTRERERERERGQhpkv5zjvvPHR1dWHChAkoLCyE0+mMu7+lpaUvx0dZYMYUEREREREREVmR6cDUvHnz+mck1GNclY+IiIiIiIiIrMhUYCocDmPx4sW44447MH78+P4bFZnCjCkiIiIiIiIisiJTPaacTidefvnl/hsN9YgE9pgiIiIiIiIiIusx3fz87LPPxiuvvNJnA3j00Ucxbtw4eDweTJkyBR988EHGxweDQdx+++0YM2YM3G43JkyYgCeffLLPxmNFNinXIyAiIiIiIiIiMs90j6mJEyfiV7/6FZYtW4YpU6agqKgo7v5rrrkm69d67rnncN111+HRRx/F0Ucfjb/85S+YMWMG1qxZg9GjR6d8zqxZs7B7927Mnz8fEydORENDAyKRiNm3MahwVT4iIiIiIiIisiJJNdkxe9y4celfTJKwadOmrF/riCOOwKGHHorHHntMv23ffffFWWedhQceeCDp8W+++SZ++MMfYtOmTaisrDQzbF1HRwfKysrQ3t6O0tLSHr1Gvvl6ewtWbGrC+CGlOHqfobkezh5BURQ0NDSgtrYWNpvpxEOiPQ73GSJzuM8QmcN9hsgc7jPU38zEXkxnTG3evLk3Y9OFQiF8/vnnuPXWW+NuP+mkk7Bs2bKUz3n11Vdx2GGHYe7cufjHP/6BoqIinHHGGfjVr36FgoKClM8JBoMIBoP6zx0dHUB0R1QUpU/eS86pKlRVhTyY3lOeUxQFqqry8ybKEvcZInO4zxCZw32GyBzuM9TfzGxbpgNTfaWpqQmyLGPIkCFxtw8ZMgT19fUpn7Np0yZ8+OGH8Hg8ePnll9HU1ISrrroKLS0taftMPfDAA7jnnnuSbm9sbEQgEOijd5NbbW0++Hw+tLcpaGhgtHsgKIqC9vZ2qKrKGQaiLHCfITKH+wyROdxniMzhPkP9zev1Zv1Y04GpSy+9NOP9ZhuRi/5IgqqqSbcJiqJAkiT885//RFlZGQDgwQcfxLnnnotHHnkkZdbUbbfdhhtuuEH/uaOjA6NGjUJNTc2gKeVrjbShqCWCktJi1NbW5no4ewSxLdbU1PBATpQF7jNE5nCfITKH+wyROdxnqL95PJ6sH2s6MNXa2hr3czgcxurVq9HW1objjjsu69eprq6G3W5Pyo5qaGhIyqIShg0bhhEjRuhBKUR7Uqmqih07dmDSpElJz3G73XC73Um322y2QbMD2m22aDBPGjTvyQokSRpU2xFRf+M+Q2QO9xkic7jPEJnDfYb6k5ntynRg6uWXX066TVEUXHXVVRg/fnzWr+NyuTBlyhQsXLgQZ599tn77woULceaZZ6Z8ztFHH40XXngBnZ2dKC4uBgB8++23sNlsGDlypNm3MmiIDDMVXJWPiIiIiIiIiKyjT0KjNpsN119/PR566CFTz7vhhhvwxBNP4Mknn8TatWtx/fXXY9u2bbjyyiuBaBnehRdeqD9+9uzZqKqqwiWXXII1a9bg/fffx0033YRLL700bfPzPYEtWvlobn1FIiIiIiIiIqLc6rPm5xs3bkQkEjH1nPPOOw/Nzc249957UVdXhwMOOAALFizAmDFjAAB1dXXYtm2b/vji4mIsXLgQP/vZz3DYYYehqqoKs2bNwn333ddXb8OitMiUwsgUEREREREREVlI1oGp999/H1OnTsUtt9wSd7uqqqirq8Prr7+Oiy66yPQArrrqKlx11VUp73v66aeTbttnn32wcOFC079nMGPGFBERERERERFZUdaBqenTp6Ourg4rV66Mu91ms6GmpgZ/+MMful2xj/oHe0wRERERERERkRVlHZhSo+k4ixYt6s/xUA8wY4qIiIiIiIiIrMhU83ORmUN5RmKPKSIiIiIiIiKyHlPNz++44w4UFhZmfMyDDz7Y2zGRScyYIiIiIiIiIiIrMhWY+uqrr+ByudLez4yq3NB7TDEyRUREREREREQWYiow9fLLL6O2trb/RkM9IsKBCuNSRERERERERGQhWfeYYjZU/rLpfxtGpoiIiIiIiIjIOrIOTLFMLH+JuBQzpoiIiIiIiIjISrIOTD311FMoKyvr39FQj7DHFBERERERERFZUdaBqYsuughut7t/R0M9wowpynebd3fg4293Q2HwlIiIiIiIiAyyDkxR/tJ7TPGin/LUF1ubsb6uHY3t/lwPhYiIiIiIiPIIA1ODADOmKN+FIwoAoCsYyfVQiIiIiIiIKI8wMDUISGCPKcpvkWjUtCvEwBQRERERERHFmA5MXXzxxXj//ff7ZzTUI3olX64HQpSCqqqQZS1jyh+Scz0cIiIiIiIiyiOmA1NerxcnnXQSJk2ahPvvvx87d+7sn5FR1mxclY/ymKKqetDUz1I+IiIiIiIiMjAdmHrxxRexc+dOXH311XjhhRcwduxYzJgxA//5z38QDof7Z5SUkRQNTLHHFOWjiBzbMFnKR0REREREREY96jFVVVWFa6+9FitXrsSnn36KiRMn4kc/+hGGDx+O66+/HuvXr+/7kVJasUX5GJmi/BNRFP3//QxMERERERERkUGvmp/X1dXh7bffxttvvw273Y6ZM2fi66+/xn777YeHHnqo70ZJGcUCU7keCVEy2ZAx5Q/JDKASERERERGRznRgKhwO48UXX8Rpp52GMWPG4IUXXsD111+Puro6/O1vf8Pbb7+Nf/zjH7j33nv7Z8SUhD2mKJ8ZM6YisoKwrGR8PBEREREREe05HGafMGzYMCiKgvPPPx+ffvopDj744KTHnHzyySgvL++rMVI3oglTUKPBKdFziigfGHtMIZo15XLYczYeIiIiIiIiyh+mA1MPPfQQfvCDH8Dj8aR9TEVFBTZv3tzbsVGWjIEo1RCoIsoHckJXfn8wgrJCV87GQ0RERERERPnDdGDqRz/6Uf+MhHrMmCClqmr8DUQ5Fkko3ePKfERERERERCSYDkwBwPLly/HCCy9g27ZtCIVCcfe99NJLfTU2ypLNEIhSVIBFUpRPkjKmGJgiIiIiIiKiKNPNz//973/j6KOPxpo1a/Dyyy8jHA5jzZo1eO+991BWVtY/o6SMkjKmiPJIUsZUkIEpIiIiIiIi0pgOTN1///146KGH8L///Q8ulwt//OMfsXbtWsyaNQujR4/un1FSRnE9phiXojwTScqYknM2FiIiIiIiIsovpgNTGzduxKmnngoAcLvd8Pl8kCQJ119/Pf7617/2xxipG8aOUsyYonwjK1rGlN2mbaks5SMiIiIiIiLBdGCqsrISXq8XADBixAisXr0aANDW1oaurq6+HyF1S5IkPTilMC5FeSYiaxtlSYG2Eh9L+YiIiIiIiEgwHZg65phjsHDhQgDArFmzcO211+LHP/4xzj//fBx//PH9MUbKgijnU8HIFOWXSDRjqqTACUQzppjZR0REREREROjJqnwPP/wwAoEAAOC2226D0+nEhx9+iHPOOQd33HFHf4yRsiBJAFT2mKL8I+sZU1pgSlZUhCIK3E6uH0lERERERLSnyzowNWfOHBx33HGYNm0axo8fDwCw2Wy4+eabcfPNN/fnGCkLNkmCDBUKI1OUZ0TGlNthh8thRygiwx+KMDBFRERERERE2Qem6urq8LOf/QyBQAAjR47E9OnTcdxxx2H69OkYNWpU/46SuiUW5mNcivKNyJhy2CUUurXAVFcwgvIid66HRkRERERERDmWdWDq3XffRTgcxscff4zFixdj8eLF+MlPfoJAIIBx48bpgarzzz+/f0dMKek9phiZojwT0Vfls6HA5UCbL8SV+YiIiIiIiAgw2/zc6XTimGOOwR133IF3330Xra2tWLRoEb7//e/j+eefx5w5c/pvpJQRM6YoX4lV+Rw2CYUuLRbeFZJzPCoiIiIiIiLKB6abnwNAIBDA0qVLsXjxYixatAjLly/HmDFjMGvWrL4fIWVFghaZYo8pyjeyyJiy21Dg1g45/iAzpoiIiIiIiMhEYGrRokX6v+XLl2P8+PE49thjcfXVV+PYY4/FsGHD+neklJFNyvUIiFKLKLGMqYJoxhRL+YiIiIiIiAhmAlPHH388Ro8ejVtvvRUvvfQSampq+ndkZIroMcWMKco3sqxlTDnsNhS6tJX4GJgiIiIiIiIimOkxddNNN2Ho0KG49tprcfzxx+NnP/sZXnzxRTQ2NvbvCCkrNvaYojwlMqbsNkkv5WOPKSIiIiIiIoKZwNRvf/tbfPzxx2hubsZvf/tbFBYWYu7cuRgxYgQOOOAA/PSnP8V//vOf/h0tpceMKcpTevNzu01vfu4PRriCJBEREREREZlvfl5cXIwZM2ZgxowZAICWlhY8+OCD+POf/4zHH38cssxMiFzQe0zxWp/yjGh+7rBJ8EQDU4qqIhhR4HHaczw6IiIiIiIiyiXTgSlFUbB8+XIsXrwYixcvxtKlS9HZ2YnRo0fjnHPO6Z9RUrfYY4rykaqqkEUpn90Gu02Cx2lHICzDH4wwMEVERERERLSHyzow9bvf/Q6LFi3C0qVL4fV6MWLECEybNg3z5s3D9OnTMW7cuP4dKWWkJ0wxLkV5RPSXQjRjCgAK3A4EwjK6QhFUwJ3D0REREREREVGuZR2YeuihhzBt2jT8/ve/x/Tp0zFx4sT+HRmZIjKmVNbyUR4RK/Ih2vwcAApdDrQiCH+QK/MRERERERHt6bIOTO3atSvptmeffRZnnHEGioqK+npcZJLoMaUwLkV5xLginwieFogG6CEGpoiIiIiIiPZ0Wa/Kl8oVV1yB3bt3991oqMf0jCnW8lEeiUQzphz22KGmwK31lepiYIqIiIiIiGiP16vAFIMg+SMal2KPKcorovG5Q1820pAxFeQKnkRERERERHu6XgWmKH9IYMYU5R+RMWU3ZkxFA1PMmCIiIiIiIqJeBabeeOMNjBgxou9GQz3GHlOUjyIpMqYK2WOKiIiIiIiIonoUmIpEInjnnXfw9ddfIxQKAdHm6J2dnX09PsoSe0xRPpIz9Jjyh2Rur0RERERERHu4rFflE7Zu3YpTTjkF27ZtQzAYxIknnoiSkhLMnTsXgUAAjz/+eP+MlDLSe0zleiBEBsZV+YQClwNSNIgaCMt6aR8RERERERHteUxnTF177bU47LDD0NraioKCAv32s88+G++++25fj4+yxIwpykex5uexQ41NkuB2RVfmC7Kcj4iIiIiIaE9mOlXhww8/xNKlS+FyueJuHzNmDHbu3NmXYyMTbNHAFHtMUT6JNT+X4m4vdDkQCMnsM0VERERERLSHM50xpSgKZDl5mfcdO3agpKSkr8ZFJonLfmZMUT6JpMiYgmFlPn8o+VhCREREREREew7TgakTTzwR8+bN03+WJAmdnZ246667MHPmzL4eH2WJPaYoH8WanydkTLm5Mh8RERERERH1oJTvoYcewvTp07HffvshEAhg9uzZWL9+Paqrq/Hss8/2zyipWzb2mKI8FGt+Hh8DF4EpX4CBKSIiIiIioj2Z6cDU8OHDsWrVKjz77LNYsWIFFEXBZZddhgsuuCCuGToNsGhCCntMUT6JpMmYKnI7AQCdgXBOxkVERETUnW1NnSj2OFBZ7Mn1UExRVRVbGryoLi1ASYEz18MhIupWj9ZpLygowKWXXopLL72070dEPWLTa/kYmaL8IesZU/GBKXGS5GVgioiIiPJQhz+EJV/vQqHbgXOOGKevgG0Fu9v8+PCbegyvKMTxB43M9XCIiLrVo8DUt99+i8WLF6OhoQGKosTdd+edd/bV2MgEiRlTlIdiGVPxpXwiMNUVCENW1KTAFREREVEu+YNau4GuYATeQBilBa5un5MvRA9Pf5iLzBCRNZgOTP2///f/8JOf/ATV1dUYOnRo3OyBJEkMTOUIe0xRPpL1VfniA08epx12mwRZUeELhFFaaJ2TPSIiIhr8QpHY5Htju99SgSnR41MsQkNElO9MB6buu+8+/PrXv8Ytt9zSPyOiXmHGFOWTSDSjMrH5uSRJKClwos0X0mYhGZgiIiKiPBI2BHUaOwKYMLQsp+MxQ2SsR3hhQEQWYcviMXFaW1vxgx/8oH9GQz2mZ0yBX0CUPyJyNGPKnlyqV+LRglFsgE5ERESCoqrY2exDKJLbMrS4jKkOf07HYpYISInzMCKifGc6MPWDH/wAb7/9dv+MhnpM0kv5cj0Sohg5mjHlsCUfaoqjfaY6/QxMERERkWZrgxfvrd6JFZuacjqOsCEw1uYLIWihfk0iY0pWWMpHRNZgupRv4sSJuOOOO/Dxxx/jwAMPhNMZvwTpNddc05fjoyzFFuVjZIryh5ips6fImCr2RFfmY2CKiIiIojqjTcebvYGcjiOU0J+pqSOAEVVFORuPGbHAlApFVWOrdxMR5SnTgam//vWvKC4uxpIlS7BkyZK4+yRJYmAqR2KBqVyPhCgmkiFjqkQEpljKR0RERFHhaAlde1cIqqrGLbSUi3EIjR1+ywSmZENvKVlRYUsxQUhElE9MB6Y2b97cPyOhXhEzIQojU5RH5AwZUyWilC8QzumJJxEREeWPsKyVzMmKiq5gBEUeZ7fP6ZdxRANT5UUutPlCaLBQnylj43ZZVuC0m+7eQkQ0oHp8lGpqakJzc3PfjoZ6jD2mKN+oqqo330yVMVXkcUCKpptbqW8DEVG+krkCFw0CxqbjHTks9xfN14dXallSTR0By+xjsqHpOVfmIyIrMBWYamtrw09/+lNUV1djyJAhqK2tRXV1Na6++mq0tbX13yipWyLXhD2mKF8oamx7TLUqn91mQ6FbS9rM5YknEdFg8NnGRrywbCP79pHlGUvo2rtCuRtHNOuopsQDl8MGWVHR5gvmbDxmRAxNzyMyG6ATUf7LupSvpaUFU6dOxc6dO3HBBRdg3333haqqWLt2LZ5++mm8++67WLZsGSoqKvp3xJSS3mMq1wMhijKuBGNPkTGF6Mp8vmAEnYEwassKBnB0RESDS31bF8KygmZvQC+VJrIiYxlaRw4DUyJzy+Wwo6a0ADtbfGho96OqxJOzMWXLGIyySpYXEe3Zsg5M3XvvvXC5XNi4cSOGDBmSdN9JJ52Ee++9Fw899FB/jJO6wR5TlG/EinySJMGWpn1UiceF3fCjkw3QiYh6RY5eiIaZHUEWZ9yGc5kxJQJTTocNNaUe7GzxobEjgH1zNqLsGcv3BkPGVJsvCJtNQmmBK9dDsaxAKAJvIIyaUk4EU37KupTvlVdewe9///ukoBQADB06FHPnzsXLL7/c1+OjLHFVPso3sRX5pLSNzYujs/osPSEi6p1wdDJA9MWhvsEWCQPPWMrn9ee+lM/psKEmmtXd2OG3xDYhy8ZSvvwfbyZhWcEbK7fjrZXbOQHfCx+srcebK7ejtdMa5ai058k6MFVXV4f9998/7f0HHHAA6uvr+2pcZFKs+TkP2JQfMq3IJ5R4YivzERFRz4msiGDY+tkR+cIXCOOlTzbjy61c7GcgGZuf+4KRnGQBKqqq71Muhx3VJR5IkoSuYAS+YGTAx2NWOK75ubWPCb5AGBFZQSAswx/K/88+X4nswxYGpihPZR2Yqq6uxpYtW9Lev3nzZlRVVfXVuMgkG1flozwTy5hKf5gp9jBjioiot1TDRTQzpvpOY0cAXcEItjR4cz2UPYaqqnogSky65qLPlDFry2m3wWG3obLYDQBobPcP+HjMkpXBkzEVCMWOab4AA1M9oaqqvgJ2V5Dn3JSfsg5MnXLKKbj99tsRCiV/OQSDQdxxxx045ZRT+np8lCWRk8IUV8oXImMq1Yp8gmjQ6w9FBkUPBCKiXJAVVV/8xHhBTb0jgny+YIQZ6QNEVlT9sy4v0voJ5SIwJbK27DYJ9mijzJpSrel5Y0dgwMdjljEYJVs8Y8qYJeVjhn2PhGVFv0bsZHCP8lTWzc/vueceHHbYYZg0aRJ++tOfYp999gEArFmzBo8++iiCwSD+8Y9/9OdYKYNYjymeOFF+EBlT6VbkAwCXwwaXw4ZQREFnIIzyIvcAjpD2NJ2BMN77aif2Gl6OfUaU53o4RH3G2OiYGVN9R2TuRGQFYVmBy2HP9ZAGPT1bCkBlsRutnUG056DPVFjW9iOnI3YOU1NagG92tqGhI78zpmRFjZuojlh8VT5/2JAxZYEyynxkLPH2MWOK8lTWgamRI0fio48+wlVXXYXbbrtND4BIkoQTTzwRDz/8MEaNGtWfY6UM9B5TuR4IWU4gLMNpt+kzgonCsgJFUeF2mjshj2SRMSVJEoo9TrR0BuH1Zw5MqaqKrlAERW4ug049s7vNj/auEDbt7mBgigYVY8ZpiNmnfcaYfeYLROAqZmCqv4UNK+GVFWoZU96ugb+QFhlTxmBkbbQBeltnEGFZgdOedeHJgErMkJItfkyIy5hiUKVHggzukQVkHZgCgHHjxuGNN95Aa2sr1q9fDwCYOHEiKisr+2t8lCX2mKKeaO0M4o2V2zCqqhjH7Dcs5WMWrNiGYFjGWYePNTVbLGfRYwrRPlMtncFuG6Cv2dGKFZuacMy+wzC2tiTrcRAJIouPzVNpsIkLTFmglK/NF0RLZxDjakvSrtqaD+KbcIdRUcys3v6mr4Rnt6G0QAtM5SJjKhaYip3DFLodcNptCMsK/KEInNHx5ZvEnlJWz5hij6neiwtMBcJQVTWvj720ZzIVmBIqKipw+OGH9/1oqNfYYyqziKygKxTRT3b2dN/sbIOsqGnT0sOyovd2aPEGMbSiMOvXFidC6TKxhJLo36K7wFRDtNlokzfAwBT1iLh494dkKKqqB/SJrC4cF5jK/1K+Zet2o9kbQFmhC1UlnlwPJy3j58osg4ERSpEx1dEVGvALaT1zKyEryhENTOVzL7fEnp1W7+HJjKneCxgCU7KiNUL3uHoUBiDqN/mZg0qmdXPtT1GfbmjAq59uQV1rV66HknOhiIzNDR2A4UI9kfFkoLnTXLNPcSLk6CbVXTRA725lPnG/nxcH1EPiItO4Og3RYGDMkMjnC2ZBHM+78vx4bgzyMVNjYISjn7nTbkNxgRM2SYKsqAMeGBQ9phIzxUXPqXAeB3siiaV8Fs+Y8hsypjoDXIigJ4IJExZsgE75KOeBqUcffRTjxo2Dx+PBlClT8MEHH2T1vKVLl8LhcODggw/u9zFagZhFYsZUZo3tfqgA1u1qy/VQcm7Tbq9+spLuQt2YPt3sDZp6fTnLjKliTzQwlSFjSlVVPaPKH+aXKfWM8eI93y+IicwwZkTIiprXq3DJiqIHfPK97DA+Y4qZGgPBWMpnkyR98mqgV+ZLlzElfs7nLKSkUr48Hms2AoZJ0ois5P1xIx8lnuPzeEb5KKeBqeeeew7XXXcdbr/9dqxcuRLHHHMMZsyYgW3btmV8Xnt7Oy688EIcf/zxAzbWfMceU93Tghval9vOZl9cWuueRlVVrK+LD86lulCPy5jyms2YEs3Pu+8xBUPNeyr+kKwHupgxRT1lPDlnnykaTBIvPPP5ws044ZHPWSdIyD5jMHtgiG1CZCqVinI+k32mVFXFNzvbsLPZ16NxpOoxBUNgKp8zE5NK+SycMSXKzmC41umu9QMlSwxM8TOkfJTTwNSDDz6Iyy67DJdffjn23XdfzJs3D6NGjcJjjz2W8XlXXHEFZs+ejalTpw7YWPNeNCmFGVPpdYUi+uejqCq2NHhzPaScaewIoM0Xgt0Wm41MdaFuDN51BsKmyp9izc8zZ0wVeRyQoqn6XWmCBcYvUGNKN5EZxotgXmTSYJJ44ZnXgSnD90g4z/thxTc/5zFjIBhX5QMQa4BuMmNqe7MPyzc0YNm39T0aRyhhHIIemMrjoGri8cDKq/IFwzLUaGWIWHyA+6J54rgrtl+eA1E+ylnXs1AohM8//xy33npr3O0nnXQSli1blvZ5Tz31FDZu3IhnnnkG9913X7e/JxgMIhiMlSB1dGg9dRRFgZLHqe6mqSpUVR1876sPieaZwoa6Nuw1rLRHr6Uoiv55W9G6na1QVRVjqksQjMjo6ArBFwgnvZ+uhCympg4/hmXZAD0ckaGqKmwSuv2citx2eP1hdPiCKHAmr/zX7gvq4whFZATDkbxdpplSy4d9RmyTiGboWXX/pT2DmX0mFI7vuxIIhVHiyc/Gtsbs2GBYzuv9MBR3zAghIstcNKGfBaLbst2m7QMlHgdUVUWbL9jttiL2GVmWsXprM1RVhT8YQTgid9tWIFEooo3DYZPifq/dFmt/kK/brvG7DtEgWr6OtTtdAe3c3eO0o9BlR5OqwusPQlGyX4yHtHJIVVVRUeTC7nY/vP6Qfs2Y63MzGtzMbFs5O2tpamqCLMsYMmRI3O1DhgxBfX3q2Y3169fj1ltvxQcffACHI7uhP/DAA7jnnnuSbm9sbEQgYK40KZ+1eIPw+XywyUE0NDTkejh5aXtzF3w+H0oLnPAGwvD5fNiw1Y7SaMaQGYqioL29XQu82KwVIAlGFKzZshuKqqLCVYBtHV3w+bqwa3cTyuzxfaTqG9vg88UaxW/cXg97uDir39Pc2gafz4+OdjsaGjLPzCghP3y+ILbtaoAUSj7Z2FHvhc8XS8ffvrMexXl60UWp5cM+o22T2qx7XaOKYYX5na1BezYz+0xjc/wxsn53I9RAfq52V9fUpY+1sVlFQ3F+XhCpqorW9o64Fgnbd9ajwJU8eUJ9p6lZO+/weW1oaFAR7grB5/NhVyiAhobMKyqLfaaxI4it9S367Tt2mf+7NbW0wecLorPDiQZnLFurq1Pb15qabWhw52c5VEOjDz6fD5KktfhoVUPdfnb5qr49AJ/PB6fqRDgga9tCPVDlzM/PPl81tbTBF4jAVgT4fD7UKyE0NDjy4tyMBjevN/sKpZxf2SUu/ZpuOVhZljF79mzcc8892GuvvbJ+/dtuuw033HCD/nNHRwdGjRqFmpoalJb2LFsmH6luP4p2BlBU4ERtbW2uh5OX6v3NKCoKY+yQUgQjMnY0+9AhuzGxttr0aymKAkmSUFNTY7kD+dodrSgoLERFkRt7jx2BkK0FDX4J7sLipG3H3RhBUZGEYo8TnYEwFEdB1ttXUWMERUEbaqqrUFtblvGxw9sBn9wOZ2EJamurku5f36KgqCh28VJUVoHasoKs3zPlXj7sMwU7AihStOCrq6CQx0rKa2b2mR2dtrhjZElZBWprSwZglOY1BFpQVKRdVBYWJ3/v5IuIrKCwUMuydzlsCEUUFJaWo6aU3z39qbBZRpFfQk11JWprK1BWIeOzHX4AQHllVdIqeUZin/m6IYyioiL99pKyClSWmAvUFuwMokh2YEhNNWqrYxNy1T4bdncBRSWlqK2t6dF77G/N4VYUNUfgcdkRCMkoLHTl7X7WnQ6lHUVFQdRUFmJ4RRHqOlU4CvL3uJGvXBs7UWSXMX7UUNT56mFz2FBbW5sX52Y0uHk82R97cxaYqq6uht1uT8qOamhoSMqiQjTa9tlnn2HlypW4+uqrAUPKrsPhwNtvv43jjjsu6XlutxtutzvpdpvNNqh2QLvNBkmSIEnSoHpffakrKEOSJJQWulBW6MLOli5saezEoRNqepSaLz5rK33eqqpiw24vJEnC3iPKYbfbUeRxQZIkBMJK0nsJRrQvrFHVxfhmZxtafKGs36+iap+R02Hv9jmlhdoYfMFIysf6ghHUdGzD+N0rsa16fwTDwy31uZMm1/uMrMYmQ1Jt70T5Jtt9RlHjJ/oiSv7OfovvFUQXycjXcUbC2jglABXFHjS0++EP8bjR32RF25bdDgdsNhsK3DYUuB0IhGT4gjI8rsxZ7t5ABLta/bBJEtxOOwJhGcEebGdhRZsod7sccc91ObW+mPm8jymqdj1Q4HIiGFYgq8jbsXYnGNH+DoVuJ0oKtHNFf0i27PvJBVVVEZK1z7G6tACSJCEsq4goKhzR60erXc+QdZjZrnK2BbpcLkyZMgULFy6Mu33hwoU46qijkh5fWlqKr776CqtWrdL/XXnlldh7772xatUqHHHEEQM4+vwjAisWXnij33VGl0Yt8jgxoqoYbqcd/lAEda1d3T53sNjd7kdHVwgOuw1jo7PpIr09VfNz0Wh8eKU28+gLhLNezVCsotdd83MAKImuzOf1p07NjrQ04IQvnsDeOz/CiV88gfK3HwN8HVmNwyp2tvjQ2hnM4pHUU3Gr8rHxJw0iXJWv74mxOR02FLq1eVwusd7/YqvyxS5RzDRA/7a+EwAwqrpYb5Yd6MGiKXoT9oR+llZalc8T7dlp5ebngei5aYHLgaJoCweuKGdOWFb0nmPFHoeedegL8DyI8ktOQ6M33HADnnjiCTz55JNYu3Ytrr/+emzbtg1XXnklEC3Du/DCC7WB2mw44IAD4v7V1tbC4/HggAMOiEvZ3ROJiVKVq/Kl1RkNehR7nLDbJIyLBmY21Q+uAEcmWxu1Ot9xtSX6F1OBS/uiT1wRT1VV/YSgtMCl9+Jq9mbXm02cGNmzaFJeXBALTCVuw+GIjMlrXoJTjp2Qlm/4CHjkZ8CXS4BBsM13BsJ476udWLR6J/fhfmS8eA+EZT14SmR14mJeTFLlc2DKH4591+TzOGOrw9lR5Na+o7iSVf8LGT53oaxQC0x1dBOY8gXC2NGqlf3tP6pSD8wEwub/bqHoipGJpYMiMJUYDM4nYmzu6PtPXKXPSsQEaYErth8Gw3Jef/75xrgin91m0wN8DLRTvslpYOq8887DvHnzcO+99+Lggw/G+++/jwULFmDMmDEAgLq6Omzbti2XQ7SMWGAq1yPJT7Ki6ieUxdHsnPFDtB5j25s79ROQwU7MGpYXxcpbxUxwMCRDSVjFRVy4e1x2vT9DS7aBKRMZU2XRUr5QRE468Q9+tQyjmtdqr+kpRtAR7e/R1QG8NA94+U+W3/Dbow25fcFI1hlpZI6qqgjL8dtJIEWWIJEViYs0cTzP5+80q2RMic/QaTdcyDHDoN+FI/HL2iNa7g8AHWmyqoW1O9ugqsCQ8gJUl3rgiU68mf1elRVVP/9xOhIzprRzmnzedvVzN0PGlFUnvUQ2v8fpgMth07cLH4PEWQtGt39XdHsojgb4Onk8ozyT82LSq666Clu2bEEwGMTnn3+O733ve/p9Tz/9NBYvXpz2uXfffTdWrVo1QCPNb2KW1KpfPP2tKxiBCsBuk/TStcpiN8qLXJAVFVsbO3M9xAGhlyYYTvg8TjskSYKaUM4nLh4cdu1EoCoamGr2ZlduJlLHHVlkTNltNpQXaSeezcZytkAXPO88pf/YdMxFePXwn6N+xCGxx3y5GFj/eVZjyldeQ1p6m6/7UgUyT1FV/fgoSkR4YkuDhZgIEIGpfC4z8hsCU3mdMWUoKStiKd+AMZZQCmVZlPIFwzI2RDPg9x9ZARgCM0GTpXzGoJMrMTAVzaDK58BUOCFjSjUEq6wmVsqnnauynM88EZgS+wMzpihf5TwwRX1DYo+pjMQXWJHHqX9WkiRhRKW20kpLZ3ZZQFYnLgKMJ1pag8xon6lg7ORNzDCK+6qivRqy/azEhZI9i4wpRAOFSMzIWvQvOHyt2u1D94eyz5EIuEuw/JALgdN+EnvcO/8AlPzNEOhOZ1xgavD1mYrICurbuqDk8ABlzJYqiV7kpOqrRmRFVsmYkhU1bmwRWYnL1M0nIUOPoViPKR4z+pOqqil7O5UUinL/UNoJ2E27OxCRFZQVODGsohAwXIibzZgS26jDbktaHMcKPabk6PedCEzBwuV8sVI+bR9kWa15IjDljp77F0UrR5gBSvmGgalBQnxtMmMqNXHhX+yOX4hSBF1C4fw9wehLqWYikabPlN/QcBIAKordkKIn5tlc0EdMZEwBQGVxtFRQZEzt3AB8skB7LZsTdUddgEKPOCGRgSknAiMmaY9t2Kb1m7KoTkN5QusgDEyt3t6Cd77cic1NuVtoQO95ZpP07AcGpmiwSApM5Wk2h+j1Y7zUz9cLfOP3JXvbDIyIokKcxRon0MQxWwtspv78xeIpQ8rc+gSkx9WzwFS6xucwnD8llobnk4gSG7+YHLRiA/SIrOj7ofhbMmPKPLH9i0AlM0ApXzEwNUiIL+H8/ZrMLT0wVRC/zLC7h7NpVpXuZKvQlXyhLmapxGfkcthREu3z0NJNOZ+qqqZW5YMhY6q5M6CtuPfao/oW/cXYE+CuGa73iwhFZMiqCpxwYewF3nsWCFuzDK4zOLhL+Tq6tPfX0pm792YMlIqL967gnrHf0+AnLpIL9WNkfl6EihJxj8uhXzDna0lUrPm1DS6HTZ9kYaZG/xHnKJIkxWVb222xzz+YJhswMSsE0b5E6MEkRDhFdrngMPSYytfJ4IjetkGCPbpUuxUzpsR5qN0m6eetxXq2D4Mq2QqG40s7i5kxRXmKgalBwsZV+TLSA1Oe1IGpdCc6g026VWYK3NFSvrgeU7G6fqHKGDzKwNjLIJtV+RANTNkUGWM3LoH656uA+s0AgPaSYVg76hgUFzjhdsTS6v0hGRh3ADDxUO0FOpqA5W9k9bvyiaqqcRlT7V3pSxWsSmRJtHfTuLY/hQ2BqYIUgVgiK7NKKZ/f8L0iLjTzdayxiZxobxuW8/U7Y8N5KaGETgScgmkmEsUEozGYpGdMhWRT36uhNNnlMEzsGSfg8o3eSsFu0wNpucz0W1/Xjs83Npo+tzFm7ovtQWQvcj/MXlKPKUPWuKzk58QA7ZkYmBok2GMqM1+awJTeGHMPyJjKtMqMnjGVssdUrPwx1gA9c2DKODOXbcaUY8uXOOPzP+I7G16DFPABAFR3IZbu/QOoNjtKov3B9H5YIqhwwpxYYcj7/wH8vqx+X74IRpS4pd4jsjLoVkoR+1dnMHcnQZFoRonTLhkypgbX50x7JlVVkwJT+VoeFzD0ixEBhHwdq7H5OYzlL8zU6Dfp2g3AMJGYrvWCMcNNEOd4iqqayswTKwO6UpXyGW7L12w/ffEZm6Sfg+UyiLZiUyPW7GhFY4e5fq6pJkhjK2RyP8xWMKGUz+206xmJPA+ifMLA1CAhxZpM5Xgk+cnbXcZU2NxsmhUZT6ASS/lSZZDElug1ZExluTKfbOjnkzjrmdLK94B/3IOSzt2x2w4+Dl0/nofmkhGwSRIKohcF4r9+8WU6dBxw0LHa/wc6gaUvd//78og4uSpwOVAWXZlwsDVAFxejqgq0d+XmZNKYMZWqdJXIqhQ11pen0NCLJx9nwv2GmXuxulm+lh3qzc8TGwbzQq7fZOrt1F3rhWCKjCmHPVYCGDCxMl/sb29Puk+SYmVl+RqYEqW9DrtNz1rPVcaUrCj659nQ7jf13NjxIjZBamx+nq8Za/lGVIWIfUhb3VD7HAfbRChZGwNTg4Sxx9RgD7CYFZEV/YQkMTAlTmBkRbVk/b0Z4oTPbpOSVsoTwZ6uFD2mPIaMKdEA3R+KZJxlia3Il8UhpmknsOCv+o+NpaPx+XE3A2f9DF57ERDtDSZK+ApEzwjjyen08wF7dJwfvwbs3tr9780TomFrSYET5YWDLzClqmpcRmKu3pveY8pmi23vvMCkQcDYhLnA5dAbi+djwEdkQHhcjvy/uBd9huwJGVNsGNxvErPUjNx6IDNzKZ87aXEX871EM40DhkVd8jXbTwSlHXabnjGVq3PcoCHDzXRgSj9exAKEBS47bJIElZNLWUtsfg7DYlA8nlE+YWBqkDDGGQZ5fMU00V/KabclnWQ4DUsBhwZ5OV9YjvZuSHGiFSvlM/SY0kv5Yl9kTrtNz+rJVM4XazTdTbZUJAy8+CAQ1oIV/v2n4c1Df4KtriGAIdOtxBBQ1PthGYMKFbXAd2ZEXzMEzL8NWLc88+/OE2L7LHI7UF6k9fAaTA3QgxElblGGXK06GGsGa9O36bBhxR8iqzKuOGmTpLzORIo1P7fr30X5OE6kKCsr1Ev5eDHcX7LJmErVeiEiK3r2TOJ5nsj6DpgIYiRmyyWKrcyXn9uuKF132GLNz3O1Kp/x79XY4Tc1eW4s/RW0bB+uzGdGYo8pGDNAeTyjPMLA1CDEjKl44qBbFO1RZCRJ0h6zMl9IX2UmOTVdZJAEwjJkRYWqqobafkfcY6uKo+V8GRqgZ70i33v/Auo2RV94BOynXg5INviCEQRCET2byJjpFivDSvh7TTsPGDo++mb9wLMPAB++lPflrcYVI8tFKV/X4MmYSrwYyFXQzXiR6XLY9RlvP7OmyOKMK07CcGGej03F/dGFEAqc9liPqTy9uA8bGnHDWELELI1+k6nHlMsZLclLca4mLrxtkpR03iHKwExlTCVkyyVy5rg8LhNZUaGosVI+vfl5jmatjZ97KKKYOgfwp+gxBWMDdAZVuqWoqj7x7k4VmOI5EOURBqYGCZsh4JLfl+EDrzOYnHVjlGkWbjDJOBNpWO0uEIogbJh9NM6wAEBliZbV09qZPniiz+BnWpFv4xfAsle0/7c5gHNvgKuwCKUF2t+ppTMYF7QRPOn6A3mKgEt/Dex3VPQGFXjnH8DLfwTC+ZuB1GnICquIZky1d4UHTe8EsV+JI1TuMqbEibo2ksLoiS4vMsnqwmkDU/l30WzMgMj3VfkSJ3MKDU2XrTgBqKoqln5Tj+UbGnI9lLRCGc5TxLlIqu3aWKqUOAHp6UEpn746YIqJPOP48rGUz9hbzmGX4BAZUznqOZd4bt3QkX05X6qWEjA2QGcZWrdChqx148Q0F3OgfMTA1CBh/CK24glTXwhFZHy+sREdXfFBiM5o1o34Iku0p6zMl+mETzI0F+8KRfSLB6ehcahQGc2YaskUmOouY8rXoQWMhBPmAMPGJ71+Z4pSvoyNq10e4Ac3AtN+GLvtyyXAgv+Xdqy5ppfyeZwodGsXa6qqwuvP32CaGeJiQJQpBkKyqZKKvpKYVZLURJ/IomTDipMwXHzkY3m631DKJ8aZjxf3SJG9U2RoLB/M0zFn0uEPY9PuDnyzsy0vG+Oju4wpR/pztdiqY+kDWj1qft5NxlQ+ZvuJSRgpOmltFxlTcu4zpgCgoS37wFSqVflgyKJnxlT3jIsCGPvLxnrm8TOk/MHA1CDBHlPA+rp2rNnRio++3R13ux7cKOgmYypPZ237SnfNPMUXvz8YSZs+DUMD9C7D4xLFVuVLc4h54/8Bna3a/4+fDBx5un6XyMhq6QzqwZmSuIypbjJdJEkr65t1M+DUXgur3gNa6lI/PocUVdVPrEqipaZ6OV8flLyt3NyED9fW5TRYLU5KizwO/UQoF+V8YTn+QkMEOJkxZV1hWUFHVyjuX28nGMwuK58PEjOm9N5NefY+ZEXVM1E8LkesT08eBnm0VQ3jexbZbTb9+8eKWQbGSbukUvg8kamELtO5WjBFqZLQk4yp7s6X8rnHlHESRpJiGVORHGdMiWz4hiz7TKmqauh1mpAxxcbdWUu3b+jBvWBYL/0kyjUGpgaJuNTlPfQAI/oRNbT7407AYs2l0wWm0vctGEwyzUTC8MXfFZLTpk8jemFfEl09Ll3WlJ4xlar5ef0WYPWH0V9aApx1DWAIYImMqfq2Ln3WMq7HVPSEJBiSM3+Z7jcV+O73tf9XFeCDl9I/Nkf8wQgUVYXNkLEmMot6W/ImKyq+3taCzQ1efd/IBWPTzbIC7T3mopwvbcZUnl6gUWZhWcErn2zGf5dvifv3n482ob2r54HPhV/swMufbM7b8rJUjCtOIo9L+QLR/lKSJMHtsOnBh3y8uA8b/v7GrGG9t40Fsww6/MbAVH6OP3aekiLAlCG7XWSwJbYeQFyPqezfs95fLF1gKo+3XRGAEplS4r9yjjKmxN9reGURJElCVzCS1f5jbCmRvscUA1PdSbUiH6LnQJIkQVXjV04kyiUGpgYREZzaUzOmjKtzbNzdYbg9mpGSLmMqQ3r4YCIutFI1P0dCiVwgxQoeRpXF0aymNCvz6UsVp8qYWvJ87P+PnQWUVsbdXRF97aBhpsx4YeB22iFFe6l1m5p/xEzAXaj9/xeLgLb86q3hDcTKTEWPr1jGVO+CN4FwRO8rkMusIPE3cjvt+oxpbgJTotwpPmOKpXzW1NoZRCAsQ4oGYlwOLTtAUdWMK4ZmEpEVNLT7EQzLlloZM3EiQC/ly7Pgmr4iX7QPkDOPm7SHDIFsYw9PMTHSZcHjhnGCIl8D8okN542yKeVLdX4jzmPMvOdQNxlTjjzuMRVbkc8W999cZ0wVe5z6uWNDe/flfOLvpZWgxf8djI2799T2JdnSM6YS9g2bJMWOZ3l6PKA9DwNTg4go59tTD9LGGZiN9R3aShQRWT/pLeqm+XlokM8YZGp+joSeO+lW5BPEyUVzuoyp6ImRPTFjavdWYO1H2v8XVwBTTkx6rsdpj/tbJQYUbZKUvgF60osVAUecqv2/IgNLX878+AHWmWLVQZEx1dsLY38wdqKRy4soPWPKZUdpQe5L+UTfs4IsS/mM5QSUP8RF9pDyQpx39EScd/REjK4uBnoR6DBeuFsp8JCu+Xm+XTTHGp9r37l6j6k8zDrRS8oSAhNWLiEyZkzl60RcKENmt8hulxU1aTW8rEr5TFx8h9OtYqzIwPI3MWL16yjpasrLbTcxO9iR44wpY8bOkLICIOvAlHYMTpW5X+h2QIpuC/x+ziyYYaK5iIEpyjOprzrJ0vbEjClVVfWUXrtNgj8Uwa4Wnz4b4Hba0wZkxInMYP9y0wNTaWYAjauUiew7T4oeUwBQ1U0D9Ei6jCljttTRZ8d6QCWoLHbrf8/iFAHFApcd/lD6HldxjjwN+Pg1IBQAVrwLHPODpCytXOlM8R5FYKozEEZYVtJut90xBlxyWbahZ9857HAXOAGE0OYL6iWMA0XfJkXGVJaZD+vr2vHJ+gYcs+8wjK0tGYCRUjZS9Z/rbVm2ccEBK/UeiyT0T0tXyqeoKj7f2IiqEg/GDykd8HHqF5pOEZjKz5JDZJjIEZMmXRZsutzRZcyYys/xZ+ox5Yz2TFJVFcGwHJdJHd/8PH7/j63mJ0NW1LgG0KnISqyELO7vH/QDLz0ErFuOIQDOAtC6eW8gfDqwz+GAI/Xk50BLzKC06xlTOSrli8QCUzVlBcCO1qwCU4mBbCO7TWt/0BWMYGN9h34OZbdJGF5ZmL6/qYXsbutKyvIrLXTq7S6ylSloW+xxYrfFJmJocGNgahCxSRJkqFCx50WmAmHthEMCMHFoGdbtasOG+g795DtVcEPY45qfp8uYMmQhiYBB2oypaINyXyCMYFhO+sKTU2VM7d4KrIlmSxWVA4edlHasVcVubG/qBNIGphwAgtml5heWAt+ZoWVLyWFg2SvAKZd2/7wBkKoxv8dph8dlRyAko90XQnWpuZMQwXjhkcuTDtHXw+2ywwY77DYJsqKi0x9GabRX2UBIvHg3bu+qqiYtMS5sbeyM/tfLwFQeEdlNcYEpR++yX70Ba2ZM6aU7ovm5PXUpX0ObH9/sbNMu3ioKU2Yi9KfERsbOPC6HipW+D46MqVBEjvtOyNvAVIaMKUmS4HbaEAjJCEZkFCG27xsnQID4v42x/D8YlvVJibRjMGyP+jjam4Bn7wfqN8c9tqJhHfCfddp5xmlXar0tc0xO6DknsoQTs8wGijFjRwR227tCCITltO0iYNhGC5yp/17FHie6ghGs3NwUd/uBYypx8NjqPnwHA6+h3Y+3v9iRdLtNknDOkePSnpunkikwJVYr9zFjivKE9UPKpBMXVpao5IuEgaadwM4NffJy4gK/wO3AXsPLAAA7mn16r5FMgalMDTUHk+6an8cySORue0y5HHb9gjBV1lTKjKn3X4ieGgI4+qy02VIwNEBHmt5gBdmW8glTzwAc0SDIZ28BnW3ZPa+fpWvMX17Y+wbo8Rchudu2Y9uS1kerr5q7mxVOuHgvdGvbtrZSWOoTdlVV0dKpHUN62reI+odXD+rGgpuuXh7LjRklVmqqG0koU02XidQezQiTFRXr69oHfJyJpTkuw8pm+bYqVOIqnoJVm58nLoCRvz2mMrcciPUETSjli6S/+JYkCW59Zb7u/25iv9H7i9VtAp64JRaUchei/dDT0FFQFXtSVwfwnz8A6z/P8p32H5EZJTLDxHeenIOMKVVV4/pMepx2lEUnpLrLmootwpP6PPTA0ZUYVlGIoeXaP9GftK61q4/fxcAT5xsel11/f067DYqqpq1USCdd83MYzrVbOq3TU5EGNwamBhHL9JhSVWDuhcDDVwMvz+uTl/RF0+qLPU6UF7lRVeKBqqpYt7NNvz0dt+FiJu8/u17orvm5CPaEIrJ+UZbuhABxX2jJF+yyHJ9KjobtwNfLtP8vKgMOOyXjWEVGFtJlTEWDCllnNRSXA1OiGVqREPDWU0APG4FuafDi1eVbUr5vs7wpMqbQRw3Q43tM5eYiWyu50D5nUWZVHj0pHejAVKzvRqy8QewL6QKc3kBYv0jxBbMsHaV+p6qqvvJqaYqMqZ5mv3oD1i7lS+4xFf85GFer/WZXm75IxUAJJFxoGidJcpXNkY6eYZzwfSkyDMSKqlbRkbBSZSDN9i0rKjbWt+ckI0xVVcPnnrn1QmLwOVNWCIwr82URkIsbw84NwJO/ALwt2p3lQ4DLf4OuY87Hf4+4EcuOvAqYNEW7T5GB538H7Pg2y3fcPxKzg0WAKhf9sIxBZ/G3qY32mWrsJjDVXa/T4ZVFOOGgkThxsvZv+v7DAQDN3mBe9v4yQ6wsO2lomf7+hlVoC/kk7svdyRS0FX8LbyAy6NuZkDUwMDWY6IGpXA+kG5IElNVo/9+6W/sy76VY5on2BTZxqFbCJ76cij3p017FCZCsqDmrwR8I3c1EaiufaBtRYslFKrGV+VJlTIkZu+jvev/5WLbUUWcBrvTZUuL3VpV44Hba9SBN4v0w2cwUR58Vy5r66n3g9b/0aGdZX9eO9q4Qdrb0blYuIiv6+BODbxV90AA9rpQvR7PjoYiiB3tF9l15cTToNsAzdIkn68iiz1Titt3UwaypfBAMywjLCqQ0PaZ6mjE1eJqfi1K++Isz4wVNICRjS0PngI5T/16JHgvstth3Tr71mQql6cnocdphkySoFlvRsyO6bYvy6XQZU9savVi2bjc+39iU8v7+ZAwmpMvsTtV6QfScQsbAVPa9REPGc6WFfwPC0e+BkXsDl/8GqBml3SfZUF8+ATj/tlgJXzgI/PM+rSIgR/TzL3tixtTA72NiYspuk/Tv3prSaAP0ju4ypkRgKv0EqVGRx4kitwOqqlr+u1osVGBsdyAyzdrNBqYyVEB4nHZ9srC7QCHRQGBgahARfYEsMYtXOUz7rxwBOlp6/XKJTaTH1pbENbjMlDHlNCwHPZjL+dKdaAuSJCUFojKdEOiBqRSZQ3GlJU07gdVLtTsKS4HvZM6WEk4+eBTOPmJcygyvbFdUi1NaBZxzHSBF3//nbwNvzDcVnFINadShXm4rYpt12m1Js8P6ynxdPc8qimt+nqMllcVFgNMeW+65og/em1myouplDMaGuaLhf7pMqKaE8j2W8+UHcZFd6HbENbmNXbSavwCLyEpcMMpKGTGxoGtyKZ9xvxef2/DozPs3O1sH9LiQapWtfO0zlW4iR5IkuEQA1EJ9KcWFrlgVLd0xT5R79iZbt6fEZ26TpLTNq2N95GKffcRwfE8bmHJlH5gSmYa17VuALau1GyuHARfdq2VfG86jwrIK2OzAOdcDYw/QHuv3Av+4B+hozvKd961IQo8pu95jauCPZ6mCIiJLp9kbzJgp6ddX9M2+n5J47d3t1i7nE8Gnsj4MTKXbN2pMrJRI1N8YmBpE8qnHVLM3gC0N3rh/jcbZkcqhsf9vqcvqNTu6QmlPlkTpmWis6HLYMaYm1qg4U2BKa6g5uPtMqWpseeV0KfIwZJAgIZiQighMdfjDSU1244IAn7xuyJY6E3Bl18zbOMOWqEBf/tnkjPV+U+ODU5++rs2IZrnT+IIR/b32doZfD6YWOJMab5dFs8QCIdn8e4wyzuYrqtqji3VESzt3t3VlvIBt6Qyk7MkT0C9EYydEYnbO6w8PWLq9cabYGJgq6GapZBGIqok2oG+0+CzsYBFbkS8+mzLVRWv2r6ltvy5HdOWvPG4QnSiSEHQVF82KGrtglxVF30enTKiB3SahpTM4oBcj/hSrbMWCaPn13SuO76m+L9NlpOUzsX2LC3dZUVMef8Vqg15/eMADs931wUSaVZTF/m63SXqftUR6xlQW2cPi7zpx/cLYjcd8H3DGjjeiJDwsR4O/Difww1uBoeO0B7Q3As/8KpZtNYASS3vFWCM5yZhKDooUexwozCKzyWzGFAAMKdeC7lYOsgTDsr6dGjOCS3sQmDL20EwXmIoF86z7mdHgwcDUICK+jnM9y9sZCOONFdvwwdq6uH9vrtweO6CKjCkAaK3v9jVVVcWbq7bjjZXbU57AdgZjPaaECdFyPkmS9L4Q6Qz2wFRYVvS1GtMFe5BQutfdyYDH5dBLJ1sTmjHqzc9DXcCq96K/2BPr89RLhXrz8x70BTvwGODMn8Z+XvZfYMnzWT21xZAx09vZ8s5A8jYrOO02/faelPPJiqqfuItswJ72mfpkfQPe/mIHdrb4Ut7fFYzgjRXbsfDL5BVkUs2WelwOPVA1ULPy4oJHm4mPXbiI7SjVZ2NsMrrXcG2WvKUzMKj70FlFqhX5YDiOh2XFdKPfDkOwS2TSid6F+S7xQtRpt+nnA+KixOsPQ43eV1bo0lesXbtzYBaC0C6QkjMgnI7Y3yyfZAqSuEUwzSLnC8aebJXFbn07SRV4FU3dFVUd8FLFUDftBpCmXFd817kc9rSrq4ptLpvm52FZQaV3J6p3r9FuKKsBDjo27jFijKoh+AtPEXDBHVofKgBo2Aosfq7b39fXYhODCaV8OciYStV4W5Ik1EbL+XalaVSuqCqCeiDbfMZUU0cgJ6WLfUF8FxW6HXEVA2WFLkh64Cq7fVMcc6UMk9K1ZdrEW2tnMO8mCGjPw8DUIJJmomjAtXeF9BNgsZqEOCC2iwvRuIyp7gNTwYiCYFhGRFaSVqRQVdWQMRX7AhtSVoCDxlThOxNrMmb+YA9YmS8+RT79hmIMRmWTPh1rgJ4QmIqeAJV+syQ2Y3jwdKCguGdvIIEYW48zgQ4+DjjtJ7GfFz8H7NrY7dOM77O3X+CJ5aeJREZacw+arIuTb5skxXqKBM2PV1VV1LdpJ47pVoJp8wWhqCq8KTLn0q0G0xc9tMyIyMllfDBkTKX6bDq6QojIChx2G8bUFMNukxCKKHo5FOVOusCUy2EMyJjb3sVrlhY49cxbq/SZSlyVT5IkPeAjPge9WXyhC5IkYZ8RWrB1R1Nn0opt/UEckyRJ0gM7MFzg51v2kSjnctqTJ2isljEVSOjJFss4Tt5HjEH6gdgujLprfA5jVqThs++uVAk9yJg6YOt7sRuOPhuwx58PGYNncUHVkgpg9u2xxy/7b1bnFn1JjEdflS96/puLHqrpGm+PrtHOBTfWt6cMIAXDMtRoQCXT3zVRaYETHqcdsqKiOUX/UyuILewRnxHssNv076Zss6bEvuGK9sZLpcjtRJHbDpVZ4ZQHGJgaRKQ86TElIvk1pR59NYkhZVp6ragZR4W5wJRxZjIxOycYliErKiRD83NEP4/JY6uwdzTbIZNUDTUHE+Psb7oZRSSU8mUzSyVWz0sMWsiKAkmRUfTF27Ebjzi1J0NPyW6LlV/2eFb3sJOA6bOjP6jAgv/X7Up98YGpXpby+TMHpqpKtKBfT5p4ikBLgcuu7xM9WWWsKxTRT+Q701ykeA0lfJ0J5XyxjKn4bSkWmBqgjCl9Jj5+28/Uq0yU8VUWu2G32Xr196C+1ZGmlE/r/9OzSYZYsMsVy6TLcSlftsG1cIrAq7HPFAC0GwJviPaxG15RCDXaayqTYFhGR1co7p/ZbAR9RT5nfFZLrFdP8uvlcvY+lCFjSny2VjlfEBexRR4n7DabHqRJzJhSVVXPmELCsX0ghLvpg4noBTYSSvkyNXcWzDQ/tzVtx5jGaG+p4grgkOOTHiNJsVYDSdtu7Sjg2Fna/6sK8N+HgcjAfZYiM8qZUMoXl901QNL9bUZVF6PI7UAgLGNLgzfpeWLbdLvSB1RSkSQpL0vT2nxB/XurO+1dYqGC5HNDs32m0k0OJqqKToSmKoHs8Id6HaT2BcKmV7L2+sNZf2ZCmy+YdB5K1sLA1CCSLz2m/CnSbwvcCSdCZTVaw0hk12PKeDKRGAQRJVEFCY1wzRDp4YN1udRsUuSR8DfLdJInpGuAHpFVjG5aDbs32vxzr8OA6hE9GXqGsWZuXJ2Vo8+KjWvHOm21vjRUVY1rft3bMg5x0l+SJjBVHQ2ENKfJVMpEXFAXuB16sLEnn1OzIQiT7iLFeMKSGLxKd1Ikmru3DlBgKrHUSYiV8qUKTGljEwEp8d+eNEDf0dyJV5dvie+zRz2iRrPzYAiyGIlsHLOBqViwy9ntao0DYUNdO55ftgnbmrtv4ptqxUkRPBEX+96u5FWe9hlZAQDYWN+RNtDU5gvihY824b/Lt8T9+99nW01d5OqBqYQScZfDhpKuRlR8/ALw+l+BTq20cH1dO55buhEb6zuy/h19KZxFj6l8a9ieTuL+4jGUwhuJST7B7EVhb4VTbMeJxHmJMWiZLisn7nkm+lIOWf1G7IejzozrLWXkyNS4/+izgSFjtf/fvQVY+nK3v7eviFYKdr35eezzHOjytnTZbDZJ0kvk1+1qTyqRb2jXvmfNlPEJQ8qjzbzb8qMBelhW8MbK7Xhz5fasEgc6UjQ+F8wGprIJ2gJAdXS15MTAVFcwgtc/34Y3V27rVVDznS934I0V27M+Dw2EIliwYiveWLE9Y4N8o1BExoIV2/DWqu09HiflHgNTg4iYVMh1D5RUK++ILxe9ZMZuB8prtf9vre82mmY8CUkMgojouDFbyiyRHj7YS/kypcjDcKGOLBtOilK+dl8o7stDVhTss/3D2AOPPL0nw85I36Z6E5hyOIEZl8d+Xvh3IJg6eNAVisTP0vbioiRd+amRyEbzBcKmG6DHmoY6YllBPbjINgbF0s1CGYNRicGrdBejYiYwXRZWXwunCUyVFjphkyT4Q5GkgJMooRQziSJQmLhSXzY27faivSuEbU2dPX4PpAkmlCUl6mm/wNjFuysvAlNitr+pM/MFiKqqSc3PEVdupn0OYrU1Y3nI8AqtzD4sK+joSr0v7m73Q1VV2CQJLodNbw7f4Q+nzHRIRz8miQukSBj4eikOWDQPZ33ye9R+uQBY/gbw5C+A1t2oi/ae2Z2ji8tMkzlWW5WvIyEomW7xEF/C9j7QpXwhvXyy++bnqXpMiQnGVAr0HlPd9KVsqUPNluUAgIi7OGNfzEzZfrA7gDOvji20suQFoGFb+t/bh2ITMdpFgU2KTVwP9Mp8mTJ2Jg4rg90modkbiCsh6/CHsHJzEwDovfDMEBlTjR2BnFeQIHqeE5G1diTZfC+lOlYLZhug64HBFKtbG1VFA1NNHYG4c/l1u9oQkRUEwnJW/dlSEa0eFFVFe5btG77Z2YZQREEoImeduez1hyErKrqCkayDWZR/GJgaRGx5kjEVSLHyTsoggugzFQoAvvaMr2k8mLd3heMOOr5g/Ip8PSG+NEPhwXkwy2a1GyQEELLpMVXo1hpZqwnZL2UtW1HbET0Jqx0DjDuw54NPoyDNrK9pEw4G9jlC+//O1rSN0FuiGTTi90Z60GBZCEYU/W+SrpTP5bDrJyFmeyWI8sYCl71XF9nGsjV/MJJyttUbiJ1oJAavAmlm60Rp30BlKKbKKIG+eqfW62KdoQm0rKh6yXBVaXzGVGtn0PSss1hFbqCbCQ9GosdXYZoMWRGQMRM4DsuK/t1kzJjy9XDBgL4g9tfujm+KquoX2g57comcKEnrSFEeIhl60KXLjhFBjX1GlOO8oyfivKMnYvLYKgDAmh2tWU+EiX29wOUAAj7giVuAF36P0vp18Q9sqQPm3wZ195aM4+pvmb4zrdZjSnyGscBU6u/OxJVV+7IkJpvvyqxW5TOUqIqgQzY9psR96VYj1F7ID7z5FKToUjEdk08C3AVpX9OZKWMKAIZP0LKyAUCJAP99BFD6/zsvMVAtSbHVCgd6Zb5MgRGP046xtdrq2eL7V1FVLP2mHhFZwZDyAr0XnhnlRW494J7Y+iMXjPtRdxOpshLLCO7LjKnuSvmK3HYUuBxQVFWffAvLCr7dFTsv6kmfUkT3VbH3Z/OdGorIWLcrdk2Y7XmT8bMdrNUvewIGpgYRkTGV6xkCfziWrSGkrO83rszXTTmf8SJDVdW4psndNZHORqoliAcTMRPpStHI1cjYYyqbUj4AqIpmTe1s8aG1M4iWzgD23v5B7AFHnhbbOPtQn2RMCSddDNij28/H/wOadiY9RJSQDi2PnaiGezhjLjKFClyOpCweI5GtYzZLR8wwFbocPS55VFU1LjtRNZTNGh/Tk1I+MbMtK+qAzGxFlOQLd2Hv6InvlkavnkHQ5gtCVlStzCh6XCn2OOB22uNW68uG8TPqTRA1EIpg+YYGPVDQE2FZwfINDZZeStubpr+U0JOFLMRruhx2uJ12FLlz3/xcnMB3t82EDRkQ6XpMBcKy/h2QOAsvfk63XSVm2wDAXsPK4LDb0OYL6plN3TH2jMGbTwL1m2O/o6Aamw8ylFV3tmLq0j+jpm1LThYbUNTYccmV4oLaaqvydRiyARGXPRS/fYvvDVFq7fWH+yQDv80XxHNLN+DzjY0ZHxfrBZj+3MNl+C4Rn38wOqGY6ZzFabfp+0fK87xta4HHrwe+1bKlQnY3/AdnXkVYBKYyfocdOwuoGq79/85vtcypfiYnLIYAAPbod99Ar8ynT06lycAXgaetTZ3wBcP4amsLmjoCcDlsOHrvoab6Swk2SUJNaf70mTJmInZ3PO8MaPucw26LOx8Xyou0fbgrGMmqB1+2Paa03lzaubw4P9hY3x4XfO/pubbxuzjxHDKVDfUdce8t2/Mm4+MGa/XLnoCBqUFEHL77upRvS4MXa3a0Zh3w8qco30mZtWGiAXriQcZ4wSyW9O6LwJRVUvPNyjZjymm36Su5ZFvbL/pMfbW1Bf/7fCs+fXcJxjR+BQBQC0uBA7/Xy9GnFgu49MHfrHJowszmw8Daj4H2Jj0FUWxzVSWeXq8kJWawi9OU8QnVpT3raxQw9HnracaUtsqeArtN0mfpEgNP/lB8T5L0zc/jT4qcdpt+wjkQJxDpSvkQLdGrKvFAVlSsj/azEYGnymKPXgIhSVKs75eJv4coPUMvm2mv2NSEb3a2YdWW5h6/xrbGTnyzsw1f9OI1ci3dinyCqwfHcr2ML5pNpPdlC0ZyMtGjqmpcxlSm7/SIYQUu40WcsZRPBJeK3MmB8FjGVOoAUGLTdES/LycO1Ups1uzI3DhdEBdINXVfAauiK565CrDz1Bvx3yNuxOZJJwCX3A+MmKTdFfHjhC+ewP5rX0V41RKgcfuAZJsgIQMmVVmZvuKhBcpFlBQ92WLNzxMzprRtbkhZAaTottUXk3WNHQHIiorNDd6M23I2q/KJklIYtqlsy5U8TjvscgjB1hatl1lnG+BtAd75B/DUL4HW3QCAiN2FZfvOgqOwJOPrpW1+Hvcgd0JJ3/PA+hUZX7e3Ui2GEFuZLzcZU+mChpXFHtSWFUBVVXy6vgFfbWsBABw+sbZXVRCxPlMZAlOyrPX++tudwBeL+63cxJiJ2F1bhnZ9RT5nyoWKXA67fm2VrvzaKJhFmaugN41v80NRVazdoWVLie+Vnh4LjOd43WVMyYqKtdHvFLH9ZhsQY8bU4NDzpjyUd/Tm5334mv5QBB9+U68tG9/ahe/uOzTlDKKROPAav4j0xpPR+n5JkmKlfMgiYyp6kJEkKZrJkdz7pjc9pnoyy24l2TY/15YRr0CzN4CKaMCpO2NrS7C1qRN2bzMO/PZ1jKlfGXu9w05O2zi0t9LN+vbYd78PrFoEdDQB278BnvtGu724Ahi9L7pqTwCkIlQWu+Fy2hGWlR4HMjfv1gIgtdHVKtMxNtzW95ssxDU/N/TWkBVVDzx2RwRfKordKHA50N4VSuohJTJN7DYJsqKiM6BdyNui+2m6Uj5J0lZV9Ef7dvXmBDQb6Ur5xFj2Hl6OZevqsX5XG/YfVaGXMIrAoFBV4sHOFp92f5a9/I0X/D0t5QuEItgc7efT1IsG6iIgmssStd7q6CYw1ZMeU3qwyyOaQ9v17xp/KKJnUA0UYxPqSLT0yJ4miyRdY39j83MRmEqVZSaCFalK5mRFQVdABO3in7vvyAqs29mGutYutHYGu/2+CIRkuEM+DP/477EbZ1yGyMiDgDV12sV9USlw4T0I/vN+uLethkMJY//t7wPbo4tSON3AAccAJ18CeDIfO3sjbAj2pTpeWiljSmRg2G2SHnDVvzvT9JgqKXCi0OOELxCG1x/uUQNqI/F7/KEIOgORtPtuNqvyIbqPa/1ntMenbX6uqtp3+fZvgLrNOHHLOhR1NkJ6P8NZ8qh98PaYs9DsrMDkbs51M/aYMhq9L3DcbODdZ7Qz9JceAq54ECivyfy8HhKl5nGBqRxkTCmqqu8jmTJ29hlRjoZ2P3Y0+4DoOeW4HvSWMhJBloYOf+pzp4ZtwMt/Auo2aj9v/grYsBI47cr48s2mncBnb2nHnu/9oEfns2YyplJlqCYqL3SjPtSF9q5g0jlKomybnyMakAaAxg4/tjZ40RkIoxghHFb3IVq7IggMPwdAWbevk8gYJPJ1kzG1eXcHuoIRFLgcGFlVhPV17SYypgyBqb6YsKacYMbUINIfPaaMM1w7W3x4a9WOpD4ERrISO1mIL+VzQIrOBOsXDMbAVHSmKh3xnJroQVgEplRV7dNSvmB3jTEtKtvm5wBw6PhqnDh5ZNYBjHIncKbvE5y2bG5cUArDJwJTz+z5oLuhl1/21ReQyw2c+n+xmU2hsxVYswwHrn4RiAZqYhcm5mcfvf6wXv4ycVjmk6/KYjckSUIgLJvKeBIBkEKXVn6mz3iZyNgR5YPVJR79gt2bcPEqLuhrSgtgkyQoqqr/7lBE0felVGn8A1k+myljCgDG1hbD7bTDF4xge1NnUuNzoScZbMZSx7CsZJV+n+jbunY9c8cXjPS4xEyMpSsYyfvjnNcfxjc725J603gzNIZFXNAged8MRWSs2dGatM11JJQH2iQJhdFtNhflfIlNqDOdzKfbto19kDoy9CwxlvIlbhNefxhq9Hsj8cKm2OPEmBotoySbrCl/MIzDv30ZDn+0d8hehwEHH5fUpB3uAmw54RpsHDolxZsNAivfAf7yc2Dnhm5/Z0/pTbjTfF9aqceUMSgpLs6N2cbGv7ner9PtSHvM7wnjhWWmMuJsJ9DE55+UMWXcRrev07KgnvyFtqjJ6g9Q3Nmg949KYnMAJ/wIuOQ+tLoqtXFkkWGObAJTiK7St9dh2v/7O4EXfqctANAPRIPzuFK+HGRMGXsLZZrQHlVdrAdNi9wOHDGptte/u7LYA7tNguRrg3fnNqCjBfD7tGPIhy9pxxARlBK+eh94/Abt2NKwDfjPg8Aj1wAfvwZ88B9gwV97dIFlnAjqLvsn04p8QqzPVBYZU9F9qrtSPvG6LocdsqLis42NqG7filM/nYdRa9/CQVvfxbg35gLehGO9HAEW/Rv445XAG/O1nsGJY8gyY0pVVXwd/S7Zd2S5PmGZ7XlrXCnfIK1+2RMwY2oQ6eseU6qqYmO9dhK51/AybGvqRJsviAUrt2H6/iNSRupFkMCYbo3ozKPbadcusEMRrbF2+ZBoAaLabcaUOGEZVlGIhnY/2nxBbTYmEmtAnW51s2y4nXZAVeHxtUBe0wRH1TBg6Ngev16+ybaUzxRVBb5eCrz9Ny3LSCgo0WYHDz1RW32xn/RLltve3wFueALYsU47Odm1Qes9EQlhdNPXGB2sg8uxV/LFlAkb6tuhRrfldBfXgt1mQ0WRCy2dQTR5A1llFsmKamg0rGV+FLi0oEtXKJJ1dpKxnE2czCaW6omfSwqc6AqG0eHXZtiLPE7976KVh9qgJJwQD2SWYuIqRYnsNhsmDSvD6m0tWLOjVe9hJzLWBPFzhz+MYFjO6mTP2Bwe0WNkd1mnRrKiYl20AanI4mnyBjDaXZz1a+hjiV5kyoqKYETJuo9cLqza0oQtDV4Ew7LebNvYryttKZ/e/Dx5u/p2VztWbm5CQ5sf0w4Yrt+eWOqEaDmfLxiBLxBBTe8m701LDEx1hSKoSvNYkQHhTNi2Yz2mZHR0aY8xNj4XSgqckKLfscGwHLfoRXuKoIbRfqMqsKXRi80NXhw8riqWWda4A9i4Eigs075Hq0agZvvnGBst8UZBMXD6TwBJStlAuiOk4pt9Z2Hl+FNQ0bkL+9lbMSy4G9j4BRDs0lbynX8bcMIcbcXXFE3we0OfyEkTIImtyqeYymTNhY4U27aYKBDnUOI4JgKgRR4nSgqcqG/rm5X5jIHgxg4/JgxNvUOFM2S2GonjVig6kRgXmGragfK3noJt86qk5yk2B1oLh8BZUROfkVJYAhxxKjB0HGQl1lS9u4k8R3fNz41sNuDsa4G/3Ai07QZ2rgfeekqbDOtDsqLq47enyJgayFX5xN/F5bBlnOi0SRIOHVeNr7a14Mi9hpj6fkyiyMDODbB/+xlO/2IZSjp2AUszPL56BHDICcD7LxiOLbcCipJcf7LyXWDEXsBhmXuPJeqMK+XLfL6TaUU+IbYyX/e9LrPtMQVDn6kdTV6M37AYh2x+CzY1tm0Xt2zVFq2YfTswZAzQXKdl/+1crz3gk/8B6z/XtvNRe+vPCyZkTKU7Zm5v6kRHVwguh3Y+tj26inG2LRCMQb/BWv2yJ2BgahCJ9Zjqm9dr6QyizReC3Sbh4LHVOGBUJRZ9vQutnUG8/cV2nH7Y2KSLA38o/oLYyONyIBCWY1FtpwsoqwbaG7sNTImDa1WJBw67DRFZK08QQakCV+oVmrrV0Qx8sRiOHetw7qa1KAh3Ah9Dy5o57UpgyonmXzOVxh3a7xp3AGAb+ItBvfl5b77wjXZtBN6crwVtBMkGHD4TmHaeduHRz2J9wfr44qCkAtj3SO0fAHz6hjZTBuCgjW8COFY/YTWz8heiJ40bRLB3WHYp0VUlHrR0BtHsDeoZCpmI2SVbtFwOhovsbLM/FFXVs4KqSz36iVViYMoYJPAFnFpgKhDG0CxOiAY2MCUu3tMfI/YaVoavt7fqZXwepz2p+ajHaUdJgRNefxjN3gCGVxZ1+7sTL+y6QpGMafqJtjZ6EQjJKHA5MLyiEBt3d6CpI4DR1eb2scRG9V3BcF4HpsRJ5re72nDA6ArYbTYEov26pAyBqUzbldh+dzR3osMf0k/+Y9tx7O9S5HaiEYFe9QXrqcT9NJuMqcTvP2Pz8y5ZlGglb3cOu00v2+rwh+MCU6mCGkZVJR4MKS/A7jY/vtnZhilD3MDif2ulL4YLGtXuxHeMJyanXgmUVCaNUxABMWdFNXa5S+GqKcGw/YZpmdX/eVBrIq1EgLefBtYtByZNAYaNA4aOA4rMl5ok0jN30nxfiu9RVVURUdSkoGA+SVUaZLfZ4HLYEIpoq1G6nVqWhPjuKHI79P2rLwJTxgvGxgylyN1OoAW15xp7gkYUNXYe+Ml/IS1+Fh7Dtoeq4Vrm9shJ+MJXgNU7OrDPiHJ8Z2LqrBzjdphpYRKYKeUTCoqBWTdpQVU5DCx/Q9umx+ynXcgPn6RlbveCccVY43YpekyZXVG2N7JdEQ4Axg0p7XX5HlYvBd74f/oq3xnPliQbMPUMRI49D01+BbX7HgHbi/OixxbDd0dhKUITpsD11SLt5wX/Twu2j9wrqyHJihIXjPJnaD2hqmpWGVPlJlbmM/M3AIBhHhl7ffkURrR8G3uNoRMRaW1EUbBdu1578hfauf7H/wPCCRlSLXXa/d89R2v873DGfRcrqgp/SE46t1JVFau3a9lSew8vj/bSMrfytvFxg6GUb0uDFzabhCFlBVn//QYDBqYGEb3HVB9FpjZF++CMqtLKXNxOO06aPBJvf7EDrZ1B1Ld1oaQg/iRQnIB4UvQkKHDZ0eZLSMusGKId6Pyd2r80AQ1jnXRlsRsN7X40e4P6LFB3TaSTqKo2+/HWU0CwCxKAuEWBVQV47VGtMeaxs3q+qpyvXesrsOJdbfZl3IHA2dcBpZU9e70eynYmsluRsHYx8Okb8bNJEw4BTrkEqBnVy5FmTxyoVbWfsz8OPQH+xS+ioKsZFbu/ATavhtup9YYwmzG1vakTgZAMj8uOEVXZBRaqSjxYX9eedfmY3l/KEBw2u4Jhu08L+jrtNq0JZ/R2sUqTeF1vIHZB3xkIA62xi//uVuPpSS+gnuqulA/RLIFRVUXYFp2lqyrxpAx2VpV4ehSYiuaGmioNU1UV30SX0d57aBHKW7Zis1yEJq/5PlPBiBJ3AdUVjKCy/+PHPSYuEANhGZt3ezFxWJn+WRZ6nGknIvRslhTblfjsVQDf7GjD4ZNqEZYVfb8oSciYQs5K+ZKDmemk65+mZ44ZyoDTXeyUFmiBqfaukN6bBQC8WVwk7TeyAk3NHZA+/h8iW96BI5S8Sp8kh/WTTXX/oyEdcLR+n7i4j8ixCQYREBtZWYQ1xt52FUOAS38NvPcvrWkxAGz9WvsXFSqqhPOg70KaPD1l1vOWBi/sNgmjMgR2u/u+dEQbzYseOr3+Xu1H3oQV+QSPy4FQJKRdwBVFy3sN2e16KV+G1g3ZMmZMtflCabNN07YcUBTgo1e1v7vDiZqjLsUmjEIwrOj7+X47PoR9/Wv6U9TiCkjTfggccryeue2JlgllutANG8oJu1sRLlW2X7eGTwBmXg689pj284YV2j9EywmnzdJ6GfWQmISRDO09EP27wrBC7UAwk63Tazs3aNk7CQskNJWMQldxNUaXubQyvnBQu8747jnAqH3w5aYmfL29BVP3GoKJ4tiy7L9AYSlw9FmQDz0RL3++C4e0R7DXtg+0gPhzc4Erfg8Ul3c7rMRJhUzbnrZ6qjbxkiq7VSg1LEYjK0ra70JZUfTvh+4WBgC0oNLE138DR6uWKKBCgvS9c9Fx6BlY8uk3OH7131DRsUPLLPvgP/rTQmVD8NHIE3HY7mUoatqkXT998B/gi0XA8ImotVehE5VoLBsNv7sMvmA4KTClTb4GYLdJ2kqNTTtR+8rDOKWjC1/tfTqAMRmHrqpq3LXlYMiYWrG5Cb5AGCdNHokh5f3XUzHfMDA1iNj6sPm5rCjYtFtrtjvekHbtcthRW1qA1s5gUvYEDF9EBSkuRgtTRb8rhwFbVmv/31IPjJiY9DxVVfUAgNsQmGr1BfXfY6o5bXuTFnTasDLu5pCzEI0lI1FZWY6CDZ9qNy7+txacmvl/5srSZFmbNV70LyDgi92++SvgseuAs36mlY0NkGx7N2TU2qD1Rdhl6O1RNVxrRDtpSs+Ddz1kt0n6rG8wLPdfYMrhxNcTT8JhXz6r/fzeP+Gcdj3Qgx4j6+u0mbxJQ8uy7uFVVaLNoGbbAF3PWjR88ccusrP7shb9pURwpsijBadEmaAIdBmbRnujFzJi5b7umm4OZI8pvZSvm6zKvUeUxwWmUqku8WBLg1f/jDLRspS0i/vyYjdaO4Omllxu7Aig2RtAUbgT+7/5GGy7t+C0whos/M7VUNSRppbSTuwVk4uAixnGfWvtzlZMGFqqv4eSDOWomTIpjZ/9hvp2TB5bpQeBxOSLkMvAVFcgtoCIL2FVp0TiQjOxTFVc3IvvabtNSrtASFmhC3WtXUkN0Nv9KZqmR8LAioVa+Ubrboxo3Y3zva1xvXvCdhfWjjwGss2BCl8dKjrrUNrVhI6SoShLKF0S41SjASGbJOkN10dUFWHNjla9/5UkSYDdAZx4ITDuIG31VG/8CpMuX4sWxPjoVWDIWGDyNGDCwUD1CARk4MO1dbDZJJx39MS0x+BQNz0ZpWirAnEh2X14OnfE3zTxQrfAZUdHV+z42xXdDwrdDkiSpP/N+6SUL7rfiUUyGjv8GJliYiYWEDR8Z3hbtCbVm77QfpbDmLT4MezedxaCQ49FMCxjfN3nmGIISnUecgoKT/4RpIQG+R79Oyf9Pt3d395InE9FzK7OeOiJWr+jpS8Dfm/sdiWiBUbKarTttgf0VTrttrhjn6OnY+2FbFdL7P0v8gMvPhgLSo07EDjwewiMnYw3vmqFBOD8Yyal3N9bo/0kvYFw7NhyzPcBlwew2REIaqsTfzpuBsYrjXDs+EY75rzwe+DCe7q9LhBl2SKQnalfksiAKsow8YLovivOfTu6wmkXnhBBMXGunImzfiOkt/8KR5eWkBApKIXjBz8Hxh+EwkAYfncp3jrkSpxX/19I65bHnnjICVg18TRsawoC+xyOY1s/BhY/p/0tOpqBjmaMAjAKgCLZsGLCTPj2+QFqSuNSAdAabR1RU1oAz+6NwL9+DaffixoA05Y/CsVVB9v0HwKO1N/9xrYuGAQ9piJybOGRsqLeZVFaDQNTg0kf9pja2exDKKJdgA6riP9yF72cUgWm/Hq2RvKm5UmVtRHXAD11YMqYqu122lER3UlbOgMoL3RHx5RlYOrLJcDrf9Ui/sLBxwHfPQdLdiiob/fju/sMxbjx+2mZQQDw+dtaw7/Trswq00ndthbBVx6Fp2VH7EZXgfZF19mqnYg8e7/W0+DYWdrMTArf7GyD0y5hwtDelyZku9pNWt9+Brz0RyCgXbTD7gSOv0BL503zRTEQxOo8/Tk7EgzL+KbyIEwsfBflXQ3A9m9QVb8GwHBTqzJ1dIVQ36Zl503KsowPAMqL3LDbJIRlrYlxpuwFpNkHzWZMieysymhQzG6TklZpCoZlPWBcXOBEiT9+hl2cgKWbLR3IjKlYVknmQM6QsgJUFrvR0hnUl5tOJAJWTR3dBwqDhpWjREDfTKDjm51tKPa34JTVT8LW2QgAKOtqxFFfPoO2KXehsjT7S+KkksI8D0yFo9uWFM2y0AInmftLwXARpKraanbG8mWReSRO6jfUt+uLZiS+ZiwwNfArGIpxVpd60NzWkTFjKl02YOKxviTN8uMwBJ46DKUhWlmJWJFP9I7aDrz4EFC/WX+c8RVVSGiZeBR2HXIW5EItm6A1+k9SZIyoKQUK4/cru82mX7SFo/uLaLheU+qBFH2PxoA4AGDiwcB1f9HGVL8Z8q6NaFq3BtUd22BXo8eU3Vti3+N2BxyVI3CkvRq7qvZGIDgGRWn6uKQtKQuHgC1fAetX4uCmLiwfeXxeXwCFZUXfzxMzpgqc8d8J4gJaTPIVR/eHUETOup9eKsYFcYZVFGJHsw+NHYGkwJSiqrHjtPjc1y3Xgo/Ri2VBUmV8d82/sd6tAB3DMXVdLHtD/d4sdO43HYWu5ImF2GqE6f9m3TW+NxLfJ1mX8ulvQAK+ezZw9FlA8y6tUfumL7Tm24CWTTV0nNbHx6SIviJf/L4ugjKJi0n0JzMrwvXKm/Nj7UCGTwTm3AnYHXCrKuw2bQENXzCcsm+TT1+sxbBNeGLfq+LcWbXZ0XLKz1D7719q5/FbvwbefEKbtM5wDiCukypL3GjqCEQDKKmznLIp40M0MF5W6EJjRwDtXaH0ganoeyv2pD/2AwC+XorK1/4EKVryjZpRcFzwS6BcK3cV125hmxPBc26E5+NXtEn2w2cC+02F90vtWicgQ8v2m3iotuDAjnVahlqUTVVw2Ib/odnZBZx7VVxQT0yCjGldC7z2BBCJfRfZoAJLX9IyC8+5LuV+kXhuOxATnv2pvSsENXqenM8tF/oDA1ODSF+uyrcxWsY3fkhp0qy8OJHPFJhKVb5jXAlGVzks9v9p+kyJi3+bJMFhk/SL5dbOoJ4B0W0pnxzRyvY+XWB4IxXAGVfpK6W4G7TfH4zIwFFnave/8mdtFuvb5cAfVwGHnqClAJekCFD5O4F3/gHp87cRd0o0ebq22ovdDvz3EWBdNBvrk9eBTxZoqd3jJ2uzuqP3AewOdAbCWL6hAZIkYWxtSc/6ZxmIE6dsZgHjKIqWNfb+C7HbKoYCs27WenrkmNtph9cfNrXanFmtnUGokg3r9p6BI1b+DQAwdMXLwIE/MZUxJbKlhlcWZR9IjW73lcVuPXum28CUuBBRAlqj1ZY6jB8yCQHnSIRxALBXbbfNgpsNK/IJJdHAVGcgjNqyAj3QUeBywGm36RcySaV8ztT7pmcAM6bCYpWibjIGJUnC9ANGoM0XxNA0qdPGlRJ9wUjG1UBFhk+hoWdLtv0SfIEwOrasx8mrnkBByBt33/DW9Wh95xngnCuyei2k6XWVr9RokAIAxtSUYEujF2t3tOo9fzL16HLYbXpmRjAcC0wpqopg9LPff1QlVm5uwjc72/QgcWIWlghMJTYiHwgiQ6qmtADrugkiyt2U8gmZmumKY0qHYRvRgqra51XqcQLL39S+QyMJfU0KS7XvhOoRkI44FVXDJ6Rt1J6OMfsoluHjgt1m6H/VFUqe8LLbtXK9oWPROOYILCzaAVfYh6mhDRi98zOtX4wgR+Bo3IqJ2IqJ9Z+jy9kEnHFFymNhrKTMrs38r14KrP0I2LBK76kyCUA4FEbooOz3wYEmjj+J2YAwnKOJ41EsMKV9xk67DQUuB/yhCLz+cFzp/OrtrShw2jExiwkWcXyXJAkjq4qxo9mXcmU+Yzmc027TyqlEUBHR87WzrtH+Dp+/DQkq9lr5HNQv7JBET6nDT4V67CygsTHlWMR77gyE8d5XO/Xb3U479h9VgfIit+FcqfuLQXE8Mh2YEiRJa75dPQI45Dhtkm/lu9o+9vxc4Me/AzzmSnhiK/LFb9cOMysI9pEBKeVbvVT7zADA6QG+f4OW+RTd5oo8TnR0hdAViCQdA1VV1bf7dOWYxnO8DnsRamfdDDx9h3ZdsPxNbbs8dlba4YmM3IoiN1q8Qb3HUrEn+bjTnqIfXDqlhsBUOuJcLF2mLADgo1dhe+up2M/jJ2t90AzBOVHeGwzLCERUeI6dFfeexWeof08NnwBcdI92/dC6G58s/RTl9d9g710fAwCq1r4H/LsdOPfngFubqOjw+jFp58eYtP6V2EXs2AOw2jkK+254W5ts2L0F+OuNWnBq/1g5OAzHMbFAjNV7TLVnGaQcjBiYGkRE/Ki3Pab8oQh2tmgZReOHJLcPzByYEqV8qXpMpcjaqDBkTLXUpxyP8ctNmylwwyZJCEX+f3vnHSbJVV79U1Wd4+S8OeeosMoSklBCCYRIMgKBycHYxvBhW9gYg7ENGGySsREiCowEQggJ5bhKG7XaHGZnd3Ls3F3dXfX9cetWV1dX9/SEnp7ZfX/PM4+0PT0dq27de+55z6voQZolF/rRMeBX/wp07c/dtv5S4NoP5GVaObVsEn2hvP4SVkN+378whxUPq9zxGIQNl8HjqQPqG9kAHo8AT/0CiI3pjzfsa0P8Te/HvE2Gttfv+Bx7jEfvYY8HlZXG9RwFnv8N4K8HLr0NIx3nAtp3GU1mpjQ4qVrnHUy0lE9V2cTwpZxFHivPA276BOCeHcULLkPZTqUY1qzeySXnAH3PA73H4BruwvzBfUjWnFfWY2QVRRd7l7dN3AFX73fpwtTicQJCueAw7+CjejtkT9cebMUe4NgfgFdqWEeslecWfa2jxq502SwQG4PPmZvUw9SRD4ZxgTupcuet9TFXFcdUGcKsx2nL5R9kM0yY3b+dTYQuuQ02mx11PieGI0kMhZPjCFO5z4g/ZlmuNVVF986XcdXO78GZ0RZxDR3AJbdBfeBbENQsavc+AixdycayMuCL1KDHgVBcnrRjKqso2HtyBAsafajzWZc79o3G0R9KYO38urJLVo2ks7k24+sW1OHkYAQ9o3H9fC9Vygft2IqnMpqwwltOZ6FqE9eV7TU40D2KeCqDQ1qGl3kxwJ0jCTkLRVUnVDaJ3uOsg9zqbfmu4DJQVBVx7TraqHW+jaeKdzLioqv5c7aJgj5Jt3p/Rni4eTSR1t8r371vUCKw/epf2OYMp6GDOYhbF+sLi6lgt+WC7cOmTCSefxVJpNFcItJlUBPTZbsXJ9svwvzrbgOGuoGDrwB9x4G+TqjD3RC0z8Oz+08AMsBbPlpQjqO7ZgSVzR0Ovmz5nCu6t+P06C3ABBsRzBTdwyxGoMZb+N3n3EOaYyrJ89tycze/264JU7Lehbl7JIbdJ4YgCgIWtxRuXJrhC0SXXdLzy4YjSWQVNe+YzYX4C5D6jgOP/yT3ICvOBW78GOANAEs2ICq44HvtQUBz4gFA/4Jz0XzN+0u+Fo/TpovW3SOxvN91DkSwfkGdfl0qZ640qYypUlz3QTZ29J1gTqoH/xO47a8nFJNQrAOtjTumqtCVr1jO5JQZG2CxHJzrPwjUt+bdxeu0IRyXLdcrciaXwVRsk9Eo5MVSGWDhSuDGj7BNa4DN+z0B4JxrLP/e2OmSd0dOyNYbWuU6poz3KSVMcVGs6ProlT+yzQYNddObINzwYV3YM+J2SHpeYY2htMwo7iVk03VKFIH6VnTWr4EcXAXHwpVYsP1e1unvyA4Wkt40H+g/iUsHT0NUDXPBNRcBt3wSXXt70VmzHFcfvx+O4VNsTvb777FSbk9ufcrnVUGPA2OxFOTMJK7bs4iJHAtnGiRMnUHwE3CqpXwnBiJQVRUNAVfeAMThA2pSziKTVfJcCHyS47bYIck5poqU8hURpvgFgy9wJVFAjdeBkWhK/13RxeHpwyyokOdQSDbg+g8x55MJp81iobx4PfCJ/2J5Fa/8ke2WKhkIux5HMXkga3Ni58Krcah9G+pUL/LiwAWB2V8XrQd2Ps7s2/2dud9HhoGHvodm3/9hcccVONG8EdEyyrdKkVVUfXEyoVK+Z3+dE6UEkbXmvuDmGc+SKsVM5BSNaLXvdQE3K1/86T8CANZ3PoHnF24Z568ZXYNRpNJZeJ22sgKzzfDysXIC0BOpDJxyFPWHnra+Q2yM5TF85JuWi+aRaAqqqsJlE+B942k9Z+0cpw+twcXIRNcB3m3IdPViYf9xLBiKAicTcDR2wGVbjWRGRTSZNixISjumZraUbwLHf2iI5UicPsT+/eyvgQMvATd/EvV+P4YjTChc2FS8908kkYagKlhy+mU07zuCrQk7YnULgHaB5bOZ3RrDPcDrzwH7nsfyIUMpcPsy4N1/C3gCGBseQe0zzLmHB78D1LdblkAXvBZtYt5c456SMHVyMIp9XSPoGorixq0LCsSSrKLg2QO9SKWzcDskLG8bPyDWDB/X2VjvREeDjzUO0I6VUqV8MAhTxnEhYWgKYJNErGyrwe7O4dxj8mtI73EgPAxXx3Jd2EnImfJzDPdvZ53jlAzLGNx2E8ssKVPAMU7u67QSDeb+ylo2FSl2bPMcJH5+lXJMGRfs0UQaAY8D8f5enHvoASzrfRUwLhjOuRa4+r2AffpyL5g7JQ05k811kdO+44DbkVfGWYwhQ7c3/dhuaGclUxo7D3VD3vkkzjv0ACsP2f0kICeAW/8iryQ9nVUAVcGCF+8BjhpEKU+AOazTMvDG85DULIKv3A8s+6tp+yymi0xW0RsnLLOIA+DjLz8v4qZSPmjn2UAokReA/obWOUtRVSRSmXHdv0lDp+aA2647L0aiybycGS7uOAUF+O1/5fKCLriZ5f7wcUYQIF/2TuwYzWDLMeaAP12/Cr0X3IlmUWQujSI4bBLevHEexmK58iJVBbqGokxw6xzWxbKySvn0rnzTJPbYncyt8v2/Ypuh+7ezzmfb3lL2Q+Qy5/Jfv8QzpqrRla8SGVOZNHD/N3OxHGsvYtUJJvjawMr5amwyUcxJZhQd9ay/jVewzWju6PvDD5hIYnLxGJ/D57TB5bBpwpT1nGe8LqhGyhKmDKJYAbuf0rtNA0B0y/XwXPc+CEUys9g8Ti547UZxL6sUls9nldzGODZcjifidly27yewZ5Js7aOtf/KO1m03Ale9FxBFuB0STvva0HXL32Pp8z9kmwTJKJuPGYRoPo7VeBwIxVJQtWobq2vmXGCMhCniTGA65AJVVXGsjzk7lhRxZjjtueC9aDKdJ17xQcu6K59F+LnTDXhr2GK5SCmfVbvTOp9LFwxgtqqmZba7u/spFnDObd7+OuD2vyna5jXn4DBdoHw1bGJ0wc2aQPUHQC4iEKw8D3tW3ISDYfZYw5EkRqOpwhrwxg7gzXey/4+MMoHqjRdYlhMAZ3QIFx78FdZ0PY3h5s8A9Susn68M+EVBmMjC/OU/sJ0gzls+YinmVRtdmKpgWRKfxNZ5ncC8jUDHCuD0IdTG+lDTfwjA+CWNh7UyvqWtwUnt4PCSupFoqmCn2UxczmLV6ech8pKbc65FevPV2P3kU1jQvwdN4ZOs7v/332HhnabXMxxOoG34IM7rfARCOHdO2lJRLBzYCwzsBV79GZYCMMshW5dchufnX4toIq1nrxTtypeOw5aVkRKcZYW6TwXLHB5VZbt2Lz/MSicWrmE29vZlLD/h/m/mB9NCy9j54d9g+cZrcdS/bdwAdKFrP6577Reoi7LPcRUAnH4B2PtzljnnNIiUqsKyK0woizdAvP1vdGHDfcH1OHL4AJb1vsJe9y+/Atz2V8D8VSVfC3dvNQc9ONwTmrQwxSeALDMtUZBBeKI/oo/Zh3pYqdxEv1tzJt7qjlqc0kLphXKEKW1ibMyA4++XX4eWtdXg9a4RPXPF7xCBP/0YePG3gDZRvtHXjN7AImQ95wArN1qXcBvZ9QQTC/k1J5thTtg9T7Fy7sUb8u/vDRaIk/x1ejQBjW/IxFKZksKUVZlqnjBl1eUpmwH2PA1h8BS2DqcQyUrIqgeAaC/m73wcoqltOm76eEWadhidJ8ZSPhi+a3MwuxFVVTEUzp2LxcovY1kRJ9vOg2zz4OIDv2Dvb/92dj2//kOs6x+AdDqLcw//DjVa6QkkGys7WXEOIEpAIorMkZ2wyXEEj74IDHSxnf9ZxLH+MJLpLLwuOxZYiOf6fCzNS/ly4eccvTOfNnYMhBJ5ZXixMoQpPd5Bc7w3Blx6OV+eMKUdx2tPPA4MnGQ3Ni8ErnhXwTXKaZewf/6lGKxZhKWOOF52LMWaMoXjer+roLHFkpYATgxE8NqxQf18KWeuZDNkTE3bNayuFbjlk8Avv8r+/diP2bG1ZEPBXV89OoDBcBJXrm/XxYBskUYf1XBMVayUT1WBh74HdB1g/w42Atd/2HLT1KuXZBcK28aOecU6LBc4pjgX3MS6br/wAKt8+M03WfXEko15fx81OaZQZL6aySq68FWeY4qtKcJxuagzSHdMmUv59m9n2W0a6oW3ILruanhKHL98XDA3DjB/rgk5mydMyYasyFqfE321S/HYOR/HtQfuhTA2wJ5fEBFyNyDsb8W8i6/K69rKx6lYRmBVLkd3sXnPK39kmySaQ46vKz1OG+w2SXftz1VhihxTxBmBoDumJv8YI9EUxmIpSKKABY3FnQBelx2y1pmPC1N8ZxnFSvm0gS2TZW3L9Qt/XQsTpqKjbIJoCq3kC1zjYFfndwKawcqlTeAxNsgWAfuez++EB7BF221/Dfhri76ncUuLvAHmGrrwZiidbyA82IeAywExnWRi2LyVwJINGN17GkBc34E+0hvCucuaij4v/LWsA8uGy5jD66lfAsdYx8Ca+AC8D34ZqP0iK5uYBMYg17ImTnueBv74w9y/r75zVopSmCHXTdJoRxcEtnv5a+aiWXziGQDWFm5OOC5jIJSAoE2AJ4PfbdfF4FA8VbSECgCysTBWnH6R/UO0ARfdCnuwAccWXIyjLVtx+55vQQwPMfFl52PAlqtzfxwaQsvv/x0r+w/mP2jbUijDPRBTha3gjSw69jSG4UVk8a15JRwcKTwEHHkR2P8CPL3HcbsgYigwH1n5HNiWrmcTcId7WgP1FTXXPEFfvPeeAP70I/YZcE6+ATzzK7ZrnZZz/U2DjUxUePG3zE2jKqjd9Qfc4HkFO5bfBGWDRXe88DDw2L1Yz8NsrZCTxQVuAP3BhRhYcC7W3fi2vM/D5bTjjfVvQzA+gKZQJ+ta9b9fYM0U3vTugvEThgBjaI4paOOCnMmWlaVixCjcH+oeyxOmVFXFge5cOfNYTEZ/KFGQ15XOKth5fBAtNR7L64xsGvMbA2wxORxJwjNOxyLkdeazdkxBOy4XNwdwpDcEf3wIdb/6gV76yglE+xGI9gM9LwF/0BaNC9cAC9YwAbOuNScsvfR74JH/zf3xvJWsRDub0TqL/Ufh+wy2wPGuz+WFufLFEl8IeBwSZG1RVG9xSS4Wfg6DEwlWjqmhbia+al1W9e0a7SPgj5a1OyGdfwNzfnmKzwmmAhcg5axicEw58v5r7ippJJrMd8fFUxnLxRpfRHU1rcMbdUGs2/7fbJFzdBfwHx8G5q8GNlyGRQcOYhEXpQSRzR2Mpc9uHwbWXYu2Hb9hpYFP/BR45/+bts9jqiiqiv2as2l1R63lotXlKOaYMpby5X/2/DE55YjbuU7N7HGbgm4WgB5KwmgnT2cU1IdPYdnxJ9kNoo0JNBbXAi48Dwbmw17rgWIo850MgiBgcXMAbbUevHJ0EF2DEb3ssBR8Dqtq1xhz+dykWXkecOEtTPRQsixO4n1fzsv1DMVl3RE3EMp1OeSOKUkU2Dn+xE+BBathW3CJ9vsqOKamW5h67jfM7QgANgfbmCkSL8GFU6MIxTEKTcUzprKG+5vErSvvYMH8u55gDtmffom5fS57B+BwsrJsfl65bNab8xqRRFpr+lCYB2eFz1XocjXDRbG8ipIjO5ijV89luw7qFe8umsvG4eOF+Zw3f64JOT96hB8DDrukv45hVyPkD/wbnN0HgZomnFT8eO7wEJqCbsxfm1djkj9OBZuZIPjsr9nn/fi9zGxgyjd2OaS8OIm5RlZRdfccCVPEnGY6MqaOazk48+p9JQdHn9OO0WhKH/igTZD5AtDKJWGXRNgkEZmsgkQqAzs/4epagFPaQni0v6DjglVnj1qDS8vnsgMn97OLt6l7CwINbOF94c3jLnatFjOWuH3AinOQrB1AoKkwSDqqDdwr22vxxqkRnBgIY/PihnGDlwEAHcuRvP3/4Zk/PoHzDj+Amlg/7MkIC1t8x+eBRWvHfwwT6XLzpRQFeP7+fKfUxW9lF4JZykwIU3lBuACw8nyowUYIoUG0Dx9EdqALUond8iN9htDzcsuBTLCyHhf6xuIYjhQXprKKikUnnoUjq7kJN10BBBsATRgOZ50Yu+Iu1P32X9jvH72HdVAJNrDF2W++gRqjS6h9GbNUL1wDOSXjqUefRevoEax3hNAZEzDqqMWy1SsQSI2wLiwAth77A440tyDlYi4/VzYJvPIkhN1Po7HnSN7rFVWFiSvPdwLPGwL2JTtzCNW1sOdfsLq8DyoeYQOhITvO2B7bngwDD/2Eia8oMk4aushg+VYWuOvxs7ygFx4Anv4VoGQQjA/iit0/hBzbA8d1HwBqm4C+TuDlh4C9z2oZctpraFoA25vfh2fe6EZgtAtrbCNwDJ3Kuw8AljG3eht2elbgjbCE1R21luNWfdCHZ9begesP/wyewePsvbz8EHOK3vjxgnGCOx5cDgluh00XOeOpzISFKeNE/fRwFLFkWp/894cS+sZGR70PJwcjONwTKhCm9nQO43BPCH1jiSLCVH6zBkEQsHZeLZ7Z34tGf3FRlsP/ziiixeUMfIlhLN//JNDvA5webBAccHb1Yl3n4xCzmvAh2li+4EAX1N7juWBlaA06RnpZGTa0sN2Whcz5ZMwhOu964M3vZ9ezP/2IdRezep2hPqg//nsId35Jd9zwjDg+VrjtEuR08e6A/Jpr1XGSj/kuY/i1qgKvPWodZG4gIzlxoP0CNLz57Whtn1hO1kRxaK8zmmCt2Y2uOL8n59op5krhZXz1fpdeipyUs3nuH5gWVd11y7HuPX8H/OIruXKgrv1A137dA6tCgHDLpyzz+MbWXongvsfgTYXZ99t1YFzX4kzRNRhFNMkCy4tthvBFciqtaLmA7Dg3OqD4dxBJphGKyzg9zFyLvHOplQvFDF8wOh1cZGaCz2A4kfd9ppMJXHDgVyx/BmDhyi3WbmRjg4OQNrZNR7mYy2HDJatbkck2lzVfM86p0qZYiynzpncDQ6fZsSUngJ99CfjAV/VOaYcMGwChuIwOreMAv9451Qzwi6+x8vADL8FzSzsAnx6OPhNUpCvfvheAJ3+W+/ctnyxaBYHxHFOG24pmTOWV8pkylAQBuOEjrPHRwZeZ2PPib5kj6YYPIdGxVr+/22ErEION5MKux+mgB/7UAgIeB0ajKYTicoEwlSeK8XHwxD4WbaJoz7/xCuCau8Z9LpToaGl2p5p/b3TN2SQRLrvEGsfADqfmvh3rHAKKlJt7zM974S3s+hsdZdEKnW8AC9fov/c4bHDZJYTLXBeoqorBcBL1fueUm0xNF5GEDFVVYZPEgmvY2cDZ947PYPhYNtmMqayi4sQAW5QuHsfZwTtwxQzZA3wAskliURHE7ZAQSShIyJncQGoOQC8iTBkXULU+JwRtabm4dwfw0M/ZzjS0Ov1V24CNlwML147bgYwzHSKHqqr6Z7K0NYDOwQhiyTRODkbLdsuMRFMYqFmERzd9GJfvvYeVXqXiLNvobX8JrCovcJtTVvvjRAz47X/kL6DOuQa44t0Teq6ZhucXVWpnJKvk3Db65ydJLCfsMZbzo25/CLjpo0X//rhWGltOB6NS1Pu5MJXUO4mZSUbCWHn6Bfa6BBHChbl8FY+DhYCOta5G3aY3sV0+OcEs8W1LmVtIE2uizho4rn0fHBsu1gcWp8OOUN1CDAXnY97m+XhhZxcAYO2GJYBdAuQU8Mx9AIAlL92DoWU3oTHUBc9ze4GMXFhq3LIYkUgE/pjFTl02DcTTTGi+927glk8DawvzG3RUNdc1TFWZoHrxWwGbXZ+It40chvi9r+Q1KEBNM3DVHaw888Re4Lj2k4yxXc8LbsqNH5KNtUJecS4rgzzNOn45jrwK/NceJlCcPpz3slI2D3YtfjO23PZOwG5HeLQGXcGlaFrXjvYSWWOnXu0EIBfdtW8IuNA56MNLF38aV0R3Ak/+nIkMo/3Aj/8OWLQOOP8twLItgCjqwhSf+HmcNsgZuSDItBz4+MjH38O9IWxaxMTPg6fZZ7u4OYAVbTU4ORhB11AUsVRaF1pGoyl9p79Y5xyrZg3zG/24brN93DI+FBnL5UgUV+3+b/iSOdeHG8Am4x/Wt7GuTm1LAAC7D57C8P7dWKP0ojV0Aug+mpvUAyxz8JTJXXjp7cBlt7Pzpr6VOWmO7mblfJroGU2mkR04hWB8EEI8DPz474E7vwQ0zsuFUGsTUrdDQihdvDwtFxotAqkEc+s1tANaxhSMweeRUeDB/2K75vp7bgeu+jP0RtI43NmLeiewen4dfjtUh4Tdg1vqJ9pjb+LwsZWXxnpcdn2Rz1udZxW20LIqHRvUyvgaAy4k5QxiqQxiqXTepJ5nInHiqQybH3z826zsf89TzGFiIHrlXfCvv8TyNTtcbuxdeBW2HfoNu+HxnzBXS5UzGFVVxRunRgAAK9pqis7FXA6JncOqilEtFsFhk/Luz8+1pJzFns5hqNqmpd9jx0g0NSnHFFsAsq6mEe70UFUEt/8aNXFW2oPWJXnZYFbwHLm4dr5MpyunXIFJEATYJRFprQpg6m0ADIgS8Na/BO79e3ZdiY4yR877/xmyw6M3UwGAcNywCaLNVxbv+y0TpTRqdz0ELHoHsjPkmMoqij42Teq72fk42+xy+9j1bNE6tllldJ6+6T2WuU5GfAbHlFnYjps21q1clsZSPt5RL08skCTm2Hr+AeDZX7F1yFg/8NN/hNvhxu1ZBYIgQHxBxPLadhxe8k4k5cJmCSGTU7QcggZhap7pdwlDwwy308aOoV98ObcZseZC1pV8nFw2DheIzN18zYKf+fdmcdLrsiOZziKazKBO+xhCWrOdoEWTBpf5eZ1u4PJ35kLvH/0R8MGv6b93O2y6SF3OuuBIbwgvHxnAugV12LiwYdz7zwTGMr5KRlzMVkiYOoPQB9RJboh0D0e1wFpbQW6IGZ/WucUYimkMuSyG22FDJJHOt7LWGbpoWORM5ezAucmCXRLhd9uwZN+DWNH1TO7Oizcw8WYSJQfGUr7J5gUk01lkFRWCtuO9rCWA3Z3DONoXKluYGtUyjTw1NXh84wdw6f6fo33oAFus/+prwNs+M+7F2Mi47Y/7OpnbbJSHzwtsYXXJbVWfZI9HpTu7GSclxgm7sOUqpJ/8JezZFKTXnwGueg/LYDHRPRzV6tylkkJEOfAgZGO2mhn11Uf0Lm7C+kvzws3zmg9c/T7gyE422T2yI2+herp+FV5aczvetjE/00IQBPjdbEHSO8ocBg6bmJt0XnY74sP98Ox7GqKSxbZD9xe8vnR9B6QNl0JceyFQ14rndnYhMdiHy7wjqB8+yhbOcoItsGMh9vqyGeD//g0ID1m79+QU8NB3gb2GceCZ+4D9LwI3fgyZYAe2HH0Iq089l/u9y8uO73OvyzmSNl7BflSV/RQTtJsXAO//CjqfeAjNr/4abjnKJntGUcrpQWLdFXjQtRmSN4jz7ew5PA4JYzHkLZDNJOWMPjEpJUwBwFBUhrrtRgjLtzLBgWdunHid/dS3Aee/BfEAc3LwzBiPw4ax2OQC0LmjdH6jHycHIzjSG8L6BXWIp7K6o2Jlew1qvE40Bd0YCCVwpDeEjQsboKoqXj7Sr7t600U656SzhZsRMDQBGA+Hhft13qu/yBOlCtj0JrZ7bAgpd/l86K1fAUfjVrSubmXH2ulD7HPuO8F+tJwMAMCb38dKOcws3ch+NPYc7MOp0/24cs8P0RA5zY71H98N3PklxFPss/C6csIUYtm8RZQR7pBwxUeAn/4TO08WrQdu+LD++fndduDAy2wyb3QVn3MNcNWdgMMJeziJrngXBh02zF/RgUSoE5IolG41Pk1wYYo3dzCG/4qCAL/LhnAijXAibSlMcUGrMeDGcCSJWCpTcGwnUpm8qVGcd1v01zER+6JbWVnjnqcxcnAvDrWeh9Wbryr6mh02EcdatmBd93PwRQfYMbHveWDdxVP+PKZC31gCI1HmWlzRXrzxgCiwFvDJdFb//Lyu/O+alxWl0lmcHGSblqvn1erXoLKEKVNJtySKqPe79LyqgMsGPPq/qN33JwCAItog3vwJy85gRrgwpRr+XQ1sXJiqRGdghxN45xeA//kcmxsPnQZ+8c8YXHoJmobTyEguRNy1CMVz42Imq6Bl5AhaD+c3P/F07kJN42XI+BZYPNH0w92qgkEgL4tslokNr/yB/TsRYe99x5/y77fxCnbOjoPbaYMgCJaikllUSWeUguPI7KSKJtOFLhbJBlx6G7DmAuD332WxAABEOQFdaskAnr4j2Cg+iuMN7yp4nboYYSHOFIOXeYUtAtD5RobHaYPY38k2tnl0wLItwC2fYuJnmbiK5GPx65Kof8bWwhQXi7xOG4Yj+Q5gnh8YtBDlLHO5Nl3BcnAHTrLy++fvh5SaB9iDeikfylwXnNY6lw6Gxm8sNFOEzuJ8KZAwdWYx1YwpvgOzuHn8FsC5XYjc4FIqX4qj20GNAXp5nfkKhSl+YfCHeoDel9nFeagb1/Z2wpEI5e54zjVsYTHOhKYY/IKUVVRkFNWyNGI8eL21W+tytLglgD2dwxjQSlzKcSeMRNikb16DD6/HZDy15g68c+QRSK8/y6zCv/8eyzjxldftysp9oHN0FwvZ5Lsobh/rULRs8wTedfWodFe+tKE7WF7guMuLkx3nYunJ5yBkZGDHY2xxY+Ko3kggWDKwvBy4MDUWSxUu5lMJYLgH7tfYZE6FAMH0ejyaYyWeygDuOtbu/Zdfyd1BEBG98O14yrYBXpf1BdHnyhem/MaJhCBAve5DON3bj47hA7nbnR5gw2VQNl2JYcGNJkP5q8suYdhVg5ElK1B/kSmrK5thbq5dT7B//+ke1invwluYeGB3MjH1vq/lwnK19wFVYUHl//N5eIONWB0yiAdLNwM3f6L4+SMI4wuyoghx0xX4nbgQ53Q/jSWdz7IskPo24LwbgA2XoT+UgXygF02GRTb/Dqys/Jx+LVy4xussutiq8zkhCgJS2s6jv6EduPOfgF2PAy/8NjeODvcAf/g+VkFAs78N2YXrAMc2eG3MBWPe3SwHvuBY0hzAUDiBWCqDzoEoRrVOOK21Hn2cW9FWowtT6+bX40R/GIPhpF7SrWrjk7nUw1zKN1EKOqzuex6tXa8AABSHG+Ktn2LiYyoOpJIsu8WiDIsvQPRJtMPJOrUuXp+7UyLGOgu5fQVu32IMhRNI2914YsNduP6N/4Vv9BQTYe/5O7S3bkXaMx9egQWtu+0SpGwC4uBJ4GAX0NDBHFEamawCKSuj/uHvMlEKYO6/734ay7fchFOONVi/6yHgoEGY9dUCN32MLVA0eDh6Qs7o1yC/e2Z2bPXgZm3yYp6Q+90OTZiSCzbNMllFF0oaAi54h+wYRLLAYRYzlLXE5Wxht0VBANqXQW1bioe9R6AC2Fji+HPYJaiihP3Lr8O5O7XuXL/5OvDKw0xA50HpM8y+LuaWWtYaHLeEyuWw5QtTFiKk323Xz6OmoBtNQbd+vS3m4jPC53rGeWFjgAnWQ6MRLH3+v1njF41TW96GBWWcR+bSvWktF5sAdpuIhFy8q9uU8QaA9/w9E6diIeDUQbSfOoh2w106WzdDXfwRCMEGqIkoLjhoKIvvWK5vmqzpehqvN95RmddpIidIlJltCq0U///+nTUD4ohSrkMjZ+FaNn8p43FFQYDHaUMsmS4QlczHbzpbKEyZBceSYmxDO3O+7noC2PEYkrEIknIWDpsIT2wYyKaxtOcVdA1fAiA//mEyXdi4uypkkb/H12ZN6WHg3m/ncncXrWOdHyeY4+kpko/Fxb1anxPDkWTBpps5Z4xvLPAYGEVVdceflShnfF7dMCBKbBPoJ19kd3ryZ7gBQEa0Qzy8AAsaVuJY7bZxhSlFVfVmDqW6G840Z3NHPpAwdWYxlYyphJxB9whbbC5uHt9t5NMHF6MwNX49uVsP0DM6pgzCVPcRdvH15kqVlGgI2w7cj/l9O/Iei5+yqiBCuPYu5n6YAjZR0FX/VDo7sdbyGvzz4J+P12lHe70Xp4djONoXxtYljeM+xojmmGoMuOF22JCQgdErP4QGJcsmcMko8Mf/AW77y7JekzH8PI++TubA4qJU6xLg7Z9lWTlzBL4zwtvUTuY7K0WpMsjTSy/DkpPPQ4DKFiPbbsy72MeSafSMsMnA0kmGnhvxu+36gj4yMoLg0ZeAg68woTbCFiP8zBuYtxnNDe15f+9xmsIrV57L2ivveYp1xnzbX2IssBDY11O0kwkv4eUXc7/JveB2O/H7te/G1sO/hScZwtCCrVh/3Y0skFtRgIGBvPuXLJ+VbMCNH2Ph40//kt328kPsB2DOPkHIhXg6XKxrWF0b8OB/sqByqJA0USorSpCuvpPl/0zDYrvB70La5sL2Bddg/g23wx4bY5lcmugWTgyzz8ggTLn5d1BCEOKfbVOwuDtIEkXU+Z0YCicxFE6w5xBFlqe36UrmgNv+INC5T/ukVNRHuoHXu4HXH8EmXwOiS25CvHXiHdZkQ7fFZW012H1iCAe6RxHVygVXtecaTMxr8GljWAZHekPYe5J9JusX1OH1kyMsgD2dLSpMldOy3QrurpUzChMzH/qe/rv4le+Hb2V55dA5YarEYsTtZYHoZZJMZ/VgU9nuwfbzPoKrdv2AiVvRUSw98hiWAlBfvwdomo8N4RFsjRs2YESJLcq0hhTpjIJtB38D+0Bn/hNlZNS//GvcJj4AwVh+uPI84C0fZQteAw4b22lOyjnnWzlty6cD87jtN+2cBzwOdI/E9JJUIzxTyuWQ4HXa9O/MuGkGY7i3oX18PJUpyP1La4Kp1esywkXTkw1rcO7STWyTB2ClnfcdZE7w6z4ILN1U9DEmykg0yTJUiozPw5Ek+sbiEAQBqzqKN3rhuDUH5wgvobQSplx2vePhmnnsMb3lnBcaVvPCpqALh08ksOTxHwAjLG1fEUS8tOJWeFa/CeXIu2YBoVqOKWNHyYpR1wK8+++Ae/7WsmHGwt6dUP/z48DFb8P8Y4fhTWnjxaL1wDs+B3zzQ0AigoUDe3Aoem1ZnYSnyoSCz6NjzK34yP/mNlVEG3DDh4C1FwFdB5nY3nWAbSi95aMTEla8mjBlPF6Npb2CIEBVVe3alv+4sube1YPGk+PkqgkCG5s3X4ndh/vZpsyCOmw89QzwxE8hQsXq/b+D+qZtBjOBqjcYsAoxL4bRMWWu8oimMnCnQjhn13eAhOaS7VjBsmrtEyvfh2GunTY1r+LiXoPWnMQsXJk7M/JqGz4+RxJpKKpa1J3LxzpFVZEybmIt2cCuZYZsR5uSBnqOorXnKK4MvoHjNR8BUHw9MxpN6eujhJyZVDOYSnA2d+QDCVNnFoKW4jIZw9SJgQhUVUVDwFWWq4dP7uRMrrNTciKOKePCzO1nu82JKCuN+OaHgK3XABfcCBx+DZc8eQ8c6YTFg/mA5oUQLrktfwd7kgiatT0hZ5BKZ/M7WRiIJtM40jOGBmfhRMSqPevSliBOD8dwvD+MTYvqSwbspbMKItqgVOdzwu+2IyFnEE1l0XDdB1n+TSICvPE8sOFSFs5s4HDPGHwuO9oMZWOW4eeRUeDnX85Nclaex7JV7HNrILRpTqasMnkxsRSlyiAzwSacaliN+UNvMGFo/4vA+kv13x/tC0PVuqBNZLJRDAHAolQ3Go88B/9zrxcNLs4KEgbW34Bm0+25jjCGc++mj7FJVPNCwOVBspdNaIuJy1yI4jl2PtPCVRQEuL0ebF95GwCw8kWLLnGccUsxBa2sNFDPLPLGIGpoJXcAc5Hc/lmgUUta+MDXgJceZB0uMzLGPE14/Zw7cfH524q+loni0RbB8VQGI6IfzfPyP3G+iDYusvXdv1TxnTwuTDUHS5dTN/hdTJiKJLGo2SAyiCJza6w4h3Uf3PsMxva9ippILm/EGR3ClXv+B32je4H2j+VtBIyHMfNvWUsAezuH9YyagNuOtrrc65ZEActag9h7chivHR2AqjnBVrbX4nBPCOmsgmQ6C7Nsa+7KVxZyku22d76BpsYF2GhrRbhxKfD6S/pu8YmmDWjZcOm4D8XRhSnZuuRwMvCgbh5APyDbkL3ji5B+8+95XSIFJQv0nUDBVUjJsrLN4R7gTe/BshNPYtHAbvY7hwv4s39gJWUv/wFQlZwo5XAB136QZS8WeR8BtwNJOaFvUk3HuFUOZmecWRDj/7YqWRnUPs9GvxuCIOjlaAXdo1K57C5VVS3L/WC4XoqCUNLlyh07ckaF+o7PQdj7LBODB0+xO4z0skwgXhY/xWDdUFzGwzu6UOd34brN1s02jmjj94JGX9H5ixE+zufcZIV/w8evoMehl6Pz+U1SziCrqEU/J0VVIaezmD+wF8GffJc5YgC0Q8WtiTgcGTb/yIh2PLP2PeipX4nNZYrRxmgHSRSmN3h8AvA5R6ZSjilO2xLgk9/F/icfRzwcQqtHQLtLgbzrSTjSCQjpFPDkz8BTcrION6SbP8HcxeffADz1C4iqgmXHnwKumPiGxEQxCxJ5KApwYDuw5xm2gRQZzv+9J8C6rfGmJ6ZS6InCj9e8jXStDFQUBPi0UmGrAHQ+HtR4nVqZ8PiB/xwuvnidduD8t0B97VEIoUG0jRxG+vAO2Fds1V8XP4/KOW85AY8dgrYOS2oxLPpzx1O4aP8v4UxomZoti5i46ZxcEprd0HAgIWdgdzvY/2tjBy+zz6uGsYhi4a5xPuaExslTkkRBLydOypn8uelb/wI4vAOxriMYOXYYtfF++OIjAFQ0h07A9/i/AvP/gWU9WsDnWpxwPI2GQHWFKVVVqZSv2i+AmD7ESTqmVFXFMb3kqDxnh93QXSGSSKPeL+lKeemMKYuuFILAwuwe/REr30mngO2/Y5M8qDlnlNMD4aJbgXkr2ELUG5z2DCSjMFWMnceH0DkQxvygiHbTeMcvfMYcjPZ6r76APTUUw8Km4o600Sgrh3E7WGtZn8uOgVCCPW5THXDN+3Phjw99H/jYGv1CMxxJ4uUjA3DYJLz9gsX6IF/g+kmnWAkXL/toW8rK9+aYKAWDmBhPZZCUi4uJk6VUR0OHTcKBeRcxYQpgx+vaiwFRhKKqOKZ141vWMrXQcwBAzzHgT/fgfM0Bk4fbzyzk9W04qQbwun0+FjQXLlzcjtwiW0eU8jrelZxMmtsOWzimYCi7QRnlFWVnhG2+kp3zu55gwmwqwbKo0ilg4TrWwcg44ZIkVvK39mL0H9qPJ6L1aKibumvNTL3fhXgqiqFIEs2mznN8B9T4GbmLBIhy5ExWF3nGa1feoE0EefCzJa2LkG5agN+7LoBLjuLmxgjsu/7EupgCaOl6FfjPT7CsjmAj4HQBDjfgr83P/tPIGDqvOu0iHDYJCxp9etOMFe21BZPLZa1BvN41ol+XzlvWpE82o8l0Xpc/TnqipXwZLX9Pc604ug9iHQ4CJ5/S7xJzBvHqiltwW4mNEzNuh03fTc8r+5oC3H0yr96HrqEo0lkFYbhQ+95/RGxoADuffBotY8exNHUawnA3si4fhh11iLgbsKDOBduBF9kDvfAA1FMHsb7LEL5+y6dZ6U7HcpZ19OB3mBNr/irWXbKudHe9oMeBgVBC/04mEsQ7Fczjq1UpHwznlBH+efLcNS7+mst04gbxhR2KSWthyuAwLlWCxK+niqoiK9hg23wlyyk7upN1tz25n4nnT/+SlVLd+ulJZV9yekfjUMcpOeHzj7ba8vIMzZuIVo6Fpa0BhOMyVnXkzm2nXcrLlCl23U3G4jj30P1Y3vNy3u2CwfEuO7zYef6HkKlbiA6biIUWXTqtMJbyVauMD0bHVKWFKQBh0YMdwfUQgsDycxcCbgdearoYTbt+hxU9L0EwzP0HLngPWrWOvDj3OqgvPABBTmJRz6tsY9I/vqNuKiTTFhUUqsrG6Cd+BvQdt/7D5oWsYUTN+NUF5aJHjxhEJV2Mddlgt0kA0pauN1kXphxMmCqS9WcFfw6fyw7YHRCuvIOV+wIQH/sxc1NKkh7+HfA4JrT5IYkivC47olrXTOP53LD3YbSMaZ9xoB64427m7p0kglYSGUmkkZSzCLjZOo6LezxqIm7adDOHn5sdU+WIMG4HE6YKmrXYncCaCzDUtA7PenrRGHDhmiYZ2Z/9E6R4CN7YIPDDvwHe+XnLUv1+kzAVisv6daRaxFJM7BcFoWDj92yBhKkzCKMtdCKMRFN6i2+r1t3F4N0VYsk06v0uXWwqZjNHXocF02Lk3OtYt6sXfgvsfExzg+Tex/HmTWi7/SNw1U3fxcry9Y2zUFYNNclj8cKdE37RMk7UREHA4uYA9nWN4FhfqKQwxbMy+CBfUDK5/lLW6v74HiYsPfEz4LoP5P2tnMkikkzri4o814+iAA98i5VMAkCggU0CHBO39s4WXJowVYkA9FJZNw6biJPBRYjXz4dnuIvt/D30XeCGj6B3jGXvOGwS5jcWdmApm9AQa42855m88yFtd8O++Qpg81V5uTZH957G6GgcKy0m6nzRYezWYma89s7mC6VVhzTjse8sIVJjohlh81eyn4kQbEBk3gZkD/fDVoFWwA1+F04NRTEcKQykzzmmjBlTpUtgBkNJqNrfjNcmuFETrnh3rGL35yV2iicI+8bNwIZLEH/5UUiP38uC8hMRvcNkHusuYaWRhpIJHibOu1EBwIr2GpwYiMBhEy3LwD1OGxY0+tA5EMGSloAuuDm1c8rqvNXPu3JcEIoC/PZbuVIqnjFmQIWAF1bdDsnrn1BmkigI8DikomVfk0HvIBd0I5JMYyCUwEg0iVqfE1GbD53NGzG04BwsO28RlLSMweERvHAihricRWDTPDQuWgv88YfMDdWVy3LLXHI7bMaOre3LgA/9O3NzBurL2sQxn888d6rSGJ1xkigUHMvcMRVJZgqcazwfiS8o+KZQgWMqmQsD5nMkq/Ow3HwzuyTqomUqk2WOHUFguV1LNrG28U/8jB2LR3cC3/9L5kqe6BimMajNOzKmUhojetncOOMux3w/q2B5r9OOi1cbROpUAsLup3DuiSOQMwqEsB9w2pig3TSfXY9qm4HhHjju+1csHzqV+1tfLTs/OU3z4bj2LpxvKjsvB+PmiaOawpStPGFqx7FBSKKAjYsm3/nrUA9zv7TVeXMdVmvr8Orym5HZeBXW7v8tcGIvDrWdD9sKQ4Mctw/ZzW+G7aXfQVIyULc/COHq91o+RyqdxStHBrCsLYiWmtKu3VIUlPKdPsy67GnB4DpOD9C6mP20LWXu/WneJPUaOvNxjN1PRX0T18IxpX2vtZogUq5jSlVzZX+64Lv2Iow88X+oG+uCNHSaZUJufXPJ8O+ijPQBz/wK1xzZi8NNmxBe8o7c93XqIBYfeJi9DkGAcOtfTMgVXQzevIqPm3FDwDofs+VMFllF0StDUpn8zoz8GppMZ5HJKghp0SWlhCnWfVsuKBPk5MY9G9A+H+F3fwnCz7+Mmlg/m9/8+G5W/r7pCv1vVFVlY6qqoFWMo1fx6N9DNcmJlPZpcWjPRUiYOoPIZUxN7O+Oa6Hn8+p9E6rT97vsGI4kEdUGp1xb4PEdU+bODgBbROK6DwAX3wq8+Dtgx2NQgo14vP0a9NcuwbtrK9/KU3dwWOziQxuIuQA3FrMQpixK+QBgSQsTpnpH43mt082MFghTWvdDnq0hCMBbPgJ851PMLfLKw2xnfN4KvaMRtAB1PnHJCz9/+pes5Axaace7vlDxnbNKU8kAdH333GIR4LRLgCDg1MZbseLJb7EFyM7HAcmOowuuB7S8tlKlm0VRVeCFB4Cn78sr2csGm/FS6yXob9+EWy5aWbDI5oKve7xa/XTWUkAezzHldTLrOB9irHZ0jLeV65iqVHg9AGSUEuH/U4S7lnh5FkfOZPX3lJcxZShltioN69fzpca32/tc9ryudxsW1lveT3du8dchCJC2XIUHI43YeuT3WDSwx/Lv8PqzLJD79s8BLjbhlbXgc2OgbWPAjSvWtcPtkJjAkEmzkrTQICutbFmEc5c2obXGg0UG4SrXOa/4YmDcjClVZSLNvufZv20O4D1/D6WuBc8/+hQaQ51YbgtjbPG56FeXoH4CbimOx2krWvY1URRVzQkpfhfGYilNmEphSV4OkvY6tUYeHi2wO5bMoPHca5nz6df/xsLbAZxsXIt5l91W+ISiyK6rZWJ2SM2YY8rwPVsFrnu0ZiI844W/rliKLZIEQdBLSTxFysy4S9E7jjBVyiVrhHcbS6Wz7G+MezuiyFyIbUtZeWk8zM6H//08y3k591pg9QUTyskZMIwxvJTGTC5ovLx5XDmOKZ1MmjX5eOY+IB7GMn77KYv72p2AqsKmXbuyoh3SWz7EOqlN02LLKGaag9BnknIyppJyBvtPs46gy9qCkxK4VVVF1xDLflvelhMZ+IK+39mIte/9Bzz62nEMxDK4xDx2nv8WZF/5AyQlA7z2KHDx2ywdNMf7w+gcjCCWSuOaTdYlo+WQt8l1bA/wsy/lh5i3LGZO56WbKt79mR/Xlo4pp113AfNusByWO5UvTEWTxTf2jKQyOXexPp4LAo6suxXnPfdN9u8nfw6suUgXI8rqyBcaAp79NXOPK1m4AWzofBzR3x4D3vXXgCcA9TffgKhtzsjbboVzAhmIpTA3r9JLFV02OGyiwUWZhc8lavfNn1M6bCLsWifLWCqju+tLCVN8TC/WNIbfzt2y9voWPLT5I7h038/QOnqEdTT/3bdZRtl1HwDsToTjMhp6X8fGE39CbbQXI75WHHXeBUxBOJ4OxuJcqJu7ZoGpQsLUGQQfKCciTGUVVS/DWDzBgGa9u4I2sJTTlY8PHKl0tng2gb+OdVy46r0IJ9Lof+2kPuhVGl4HXWyhbCybSWg1zx6tgxnbISl0TEGb4PNF5PH+CNbNr7N8/FFt96C2mGMKYLuRl7+TdSmDytwCd/6T/rcAMBxN6s4sPmEK9h1gFzRoroK3/SXQsnCCn9DsIyduTH3haCZdIvyc76gPNa7Aird+hlm0VQV49Y9o6o+ia/H1WFxmaWwB2x8EHv9J7t8uH3Dp26FuuRontp+EqqCg9TEsLtBGJFHQy2/jcsZSmBrPMSWJAjwuO2LJNPt/i8cwHvvjdoUqt5RvCqSzbECsRAZJnd8JQZvkJuSMPvZFDKWMxgWUyyHpLoukxfc3EGJCQ3MZwhQMXe8O945h7fw6y/E0kix0bjlsItKuAJ5f8y40vvnt8I2eZqWRcpLlMe34ExO+T7zOAnff/XeAv1YX7M0OhfYaF9D5BvDkc8CBl1heoI4AZ0MblrYuYXb6hWuAho6S331ZGVOKwoT2V/+oPY0I3PZXwMI1EAH0tm3Cyab1aN26AMOhBHBkwPJ4HQ+WiVHY5Y2jqiqe2tdTkFdht4m4bE2bLphA66iZ0dwuQa8DdT72O+521Vt8m16nx2kDIqlcCejSTcBdX0HmT/eiMypg54qbsGAaOsAZFwcuuzRjgdJGd5LVAkUQBPjddozFZETiOWGKl/HVeh26QOAqUmZmdEjwBaNVSW3Zoqgm0KbS2eLj1+L1zLX263/VO6Ph9CH28+iPmCtx/ipg3sqSG0Tm4GZeSmNEUVWk9KDx8o5z41xNKLKhAUVhTVee/Bkw2l/W4yKdm4uMeZtxYNtd2LZpenONjNeWapby2coo5TPOJwfGEljUPHFhiovjgiDklY1zN2FIu+akBQlApuB6JwXrcKRlC5b3vAxBTrAmIpfdbvk8ADAcYWPVZK+b/JzwJYaBB/8tJ0rVtwGXvwtYvW3KuWvlYuwEx0Ul4yYy/37Mjqmsouol6DWaaJTJKpAzhd37zPDxxuWQ8jYnU63L0dm0HgsH9jLB+r8+geCiKyDWbS6+EaCqbMzY8wwTpLKFm+K+oRPA9z4DNC2AMMYavgwEF6L+isLveLKYm1cZxT1BEODW3MXGcdfsnOM5gGMxWS9BBIBgiWxjl1UEjIHc2pPdz2WXkLa58cT69+Gdkacg7XyM3XHX4yxk/8Jb4Hz+d7h8IFdOWhftxZbH/gWQ3sc6vE9wvTkYTiDocUw5PF3vUHiW5kuBhKkzC74mCcVTegjmeITjMlJaaJ65DfN48IVONJnWF1oYx0butBsWZulxSiNEUb9QzNQEebzMm0GTM2IkmtKFqVRG0QMw9R0SA0tbAhgIJXCsL4S18wqzWLKKanBMsQULd5/EUiaHxXk3AK8/B/QeA4Z7oP7oC0iteh/gYDtpI4bSonRWgVOOoemJH+Se7Mr3FASnz1VyC9zpz3iQS4SfO4wt6dddyCYLD3wLgIpVXc8hCxtqLv7YxJ/0wEvAnwylVeddD1x6O+Dxw6blEITiMkajqTxhgwfAo9gCQ7s9qdXq11lUGOq5ECXOYb8mTPnddstdQ3+eMFX6EjMTwhQ/J23S9AvbDpuEoNeBsZiMoXAS8xrYh2pVxgceDu+QdOel8fvLZBW9JLCxTGHK2PXu1FDUskzY6rUIAuuAE06kEa1fCN+S1fl/tOZC4Of/lGtI8T+fBy69DZkA6+bktIlswXrqIHMr7d8OxMaKvEoVGOpmP68/y27yBrGsYSnCwfVItRZ2yCtaTqVk2a7n/u3sJzqa+93Nn2CB7xouuwg5w0QDLkDwrogTodbrwMlB4NRQFKstOp2dHo6hW+u+aSSdVfB61wguW9Om38aFlHq/Ky+XY1TrLKcvlkwbG/w6mddprmk+orf8Nba/dnLaro9el12/Ps9U8DlM7qRinQD9bnaehRMy2sGcHuZ8KRiyUKLJNGLJNHwuO7KKoo9tXlfOIWElNuqiqDT+Z8qzaaxKgHSCDcCd/wTsfoo5nAdOsttjIeCl37MfAKhpZnl/F94CNM3LewguevoSw5BtbkvHuZzOQtUEpnJL+YzOKrehpEmn5xjw8A9yohpnzUU43HYujg/FsaDRj1XtNez99J9kmWb9J4FkFCMLtuKRpjdhfr21m3MqOAzh59XqyIcyS/mM17eBcCK/WUWZ8GOgzufMO1/4AjaeTLMMQH69M21SCIKAAwsvw9LeV5mb5qXfs1B0V75rio8x3N052XK+ZDoLWyaF+Y99n3WSBtic8/a/0Z2gMwV3TBlFJV7WZxwPzK43fl4L2jHGN/aiyXTZwpR5k9rtkLBr8bWYN3oEUjoBREex5vXfYIHzCYiutwPZFYYXkAQOv8ausaHB/CdwuIELbsJo3SJIf/wBAolhdv/Th9if2lx4dcO7cP0EXJnj4TI1rzJXiLg1dzEvrcsquTWRUTz2Otkmw2AogUxWgSCUDn3POc2t54lJYymfJhZLooAsJMSv+iD881exzrwZmY1P938DxiQp1eGCICchKWk23h3ZAVz/obJzzo73h/HCwT4savLjolXWQevlEoqPX9p4pkPC1BkEV+UHw8nSgbgWLG4OTNiRZOx0kcoouj2+1GJUEJhrIyGzsOrxLM18MjlTLTydttIiB/9c+cRgJJpCRwNbDBbbIeHMb/TjlaODiCRYrog5LDkcl6GoKhw2US/h8zhyJQzxlCFkVJKAt/81cM/fAaFBCCO9uHLHd/HYxg8i6q7XW2gLgoB0OoNth/4PUlxbOC7ZCGy7aZo+sepTSXGjVFmH0zwh3XAZC+9/8L8AAGu7ngK+c5iVbWy4XC+HKkn3EeA338gVy116O3D5O/LuUutzIhSXMRJLob0+N6nkkwVREPTXZsbjsGEUqaK1+vwxSk26/G47+sYKJ1ycvFK+MjOm0lqodqkuWJMlU6Icczqo97swFpMxHDEIU9pYYLUD6nawRgjxVAb1Bh1pKJKEoqpwO2yWofJWGLveHeoZKy1MufJfi8dpRziRti5Rm7cCeP9XgJ/+I5sQj/UDv/tPdAC4xVWLWMMi4KmTQHi48G/tLiYQdSxnHcp6j7PJYNbwPLEQamI78CbswFExAix/t/4rY/mE3SYC2SzQuY+VIB98mS2AzVzzfnb+GXDYJSCRRiqd1bsglnLzFmNJSxB7To5gIJTAcCSZ54ACgAPdTBxb2V6DFe01AIB4MoPH9p7G6aEoopo4AmO+lCakBDwOSKLAurEaXDHmkqpi2WSZaXYDSqIAv9ahyio/rlJIoqC7nPxFXAP8XOoeienvlwuCDf58IdfrYsIU/7y4ACWJbGwslbc3UccUDGJWUWx2YOvVwJarmLD6ysNsA8JY2jTWz35efxbYdiNw6dv1jqaJI3tw1a7fomXsOJJ2D/qaPwc0rst7Cj6mOzTHWDkYXbN5x1w8whxSr/0pv8/zonXAlX8GtC+F2jOGwcwAXAEfVi3QxNfV+V1PTxwbRPb06KTOu/EwXqOqKkyVUcqXJ0yFJjY3z/0d79aaf6y7HDa9a1k4IZd0CKd8jTjevBlL+15jztiX/8COMwNGsXYwlJi0MJVKp3HhgfvgGO1mNzS0swYAMyxKQfsszKJSzJCPxOc9ZoHZ3AhBz9U1XbutiBbpdOly2BB112Hv1Z/FpuN/Yt0JAfhSY8CjP7B8rDzsTpbJe+HNgCcAt5zBA4OfwjlHHmTfq8b2FW+FWGvuzTw1PCbnklHcAwC3Pb/kjq/fjJmUMGzc94xq3V/d9pJzP884TWPiFpUCLu07TqYz8G+8nGWY/epfgeFu/T4j3lbgineidt05OHTvt7HitBZzcmQH8M0/Z90hm+YDTQvYnGj1BWztZUBRVew9yeZBE113m6GOfAwSps4gFjT6MBJN6jkg5eKwiVjdUTPh5zOWmfGB3WGTxl1cepxsh7+YLdOIPE5p0XRTyjGVySp6ycWiZj92hSMYjuacScV2SDh2iXWcOdoXwrG+cIEwNRLVyhJ8Tn2izC+G4biMaCKd/9i1zcD7/xn48d8DI73wJUdx7a7v4bGNH8SYpwnRZAZ+tx0dnS9g3hDrwgVPgHVnmiEL9UzgMtW9TyfpTPFFSp5jirP5SgyORND4/L3s38PdLAPniZ+yfI1LbgN8Rc61sUHgF1/JZUqtv9TSal/nc6JzIKK76zj6Rd5j7WRCGeHbuU46xS8NDX4XjvSG0GiuJdGwSyJqvE5EEvK4XRIdNkOAcLqwtG06SOuOqcoc8w1+F471hfXsIFjlOhnwOGwYtrClGxceEwno5l3veIg2d1uO91rGOxbQ2AHc9RXg5/+c10HJlxyF7/Ro/n0lO7B8C7D2ImDZ1sJmCpk0c16dfIOV/HUd0PORlu7+P6A5AGx7C7urVj7REOqC+49/Ag69wgJMzUg2FjK95ao8pxRH32TIKGWVmRfDGN5+qHsMF6zMdbYbjiTRP5aAIAhYPa9WX4QE3A601HjQNxbHoZ4xbFnMdl55Fhk/dyRR0NuQj0RSeYslI7mMFLMwZe2OmApBjxPhRHpGJ8aCIMBhF5GUs3rJTOHrYp9t72gcvdpYx2k0dVIyd+YzCn6CIMDlsEHQFhXmNuu5zbDxxwt+DSjpmMp/o8wVtWA1EAszx+Gpg8CpQ6zEJCMzseqFB5hL4sJbgP0vYrWhG6srHUfbH78OzP/XvPywvIxPRWGbHAdfZg6Khg527THljTkN46++gD62G/i/r+efcw3twDV3sU0tbWwab7EIwxhXroNrIrhmmTCVKeWYMhwfoVgKqXR2wq+ZXx+s3LS8m2Y4nkZWKX69s4kCXl94BZb074SgKiwy4Lzr81xTRlfmQCjBNh+e/Dk7NtdcyML9y3DhLD74SK5jsdMDvOPzBe6smcQsKsUNbp9Igp/H+fP+XEdrSb/vcMTkXC2CMX/JiFv73kddjcDtn8XgwTeQ+tNP0TFy0PJxAK1MfclGdn1deW7e5+i0SxBdHmxfdRtazrkIvv1Po7d5Lbqc67BgGpp1GDE3rzJfr8wld7kyvvwOp3ys4bm4411rXKWyiYuMMy4Hc2/p8/PmBcCf/yvw+E+Q6e/Ci74N6Gpej9s3LoMgiTiy6e04XbcClx35DaQEy11GPMw2xTr3Aa/8gW0o3PoXQG2T/jwnByL65l9Mcy1Odq6ZTGdZQ4kZbDwyGyFh6gzC7bDhghWl20FPJ14Xm9wZS9DKCd3kE4pirg0jPNNkxkv5LHZAuQvJ5ZAwv8GHXUdyYhIMOyS+EheDJS0BHO0LoXMwgq1LG/OcYFz0qjXVWvs1YSqSTKPg2w02AO/7MhL/87dwj/XAlQrj+le+gcHAfGSjG4EFS7Hh0IO5+9/08Tkfdm6mVHevqaJ3NLS40PBSAvOx0r30YuwN27C173kEB7QSCDnJLmoHX2FdEFsX5T9YdAz4+ZdzpUnzVwM3fsyyzp0fH6OGY09VVb1jz5KW4t1X+ALMShTmriUYstas4J3VSjkq3ryxA5msOu55K2jurqSW02JckCuqikgirWc8lIvPZc+bGEy3q8QMd9AMR5IY03LexmJcDLJwTGnlZOYFXf+YFnxeU14ZH8fjtGF+gw8nByM43BPC+ctzi/SsouiLcnOJ1HiBouyP6oEPfo0tbjv3IXxgJ7wDx1iArijlJssrzi3tCLTZmYOqYzlbbCtZhB/5KQKv/Jb9/tH/ZflsF9yETN9JXPb6j3Niet7jOIBlm9nO5bItJZ/TuMlgtaM6EVa216BzIIITAxFsXtygT9APdrNzbkGjr2BnfGV7DfrG4jjaG8aGBfXIKKoe9GosPavzMWFqNJYyCCjliYiVEF3XLaiDyyFhyWTz8SbJ1sWNGI3JenmjmXkNPiwNJQs2IBr8roKyQ3NnvlxHPna7JDJxKiEz56JRmOJl8DUlMk84xa4BZeENsEXmynPZv9Mp4Pn72U82w5yKD+c7KBRRgqhk4YiPMTfj+7+iB1gn5Azqw6ew/sQu4On9+WWuJ/ezbr7b3gJc9FbAycYYo4Pd47IBg6eB+77G8uaguR8vezuLDrBZH5OlFunlbHRMFmNXxGpmTNm1EvFyS/lULRKio778br3JdFZ3Ulg1xgi4mTA1Fk/lrncWYrUkiQi765FcdTHc+58pcE1lskpeHpbzyCtQH34AAi/F2/c84PYBazSBxG44RzIyMHAK6O+E2t+J1b1sM0MVBAhv+0smblYRo6gkZ7K6mOx12eGw8Y7WJseUyTGv59WV0QhDdxOZxnLzHGzY34ZXN7wPa9QBbE4dAdKGznACgOZFwKrz2XhhgSAICHrsGAxnMTRvM3xbLsXpowNA95hlpMhUMDeviptK+XKNJ9gxxCtPzOenecNyPGHKo39mheOsMVvPOH+0XBc43cD1f46TfWGcPNSHhoBL/26Dbgc661fiyNovYmXX86yMeaArfxw9dRD43l8A1/85sP5SqKqKfadGICoZeJOjiLlqEU7IBZuD5cJD8H1u++SaJp0hkDBFTBpJFOF2srIU7hYoZ0e61OLYDB/Yytm9nA74ABqzaEvN86Ua/W598hxLZpBMZ5ltlIerlrgYNAbYJDocl3FyMIplrTkRYcTUkY/DLy7RYhNAfy1evuCTWPfct1Ef6YaoKmgOdQKvdgKv5k7y7NZrIVk4C+Y64+WCTYVSZR1cVDRb+KOJNHrqV+DU1gsRdEaYILXnaTZxCw+xzky3fIqVPagqsOcpFoTLA6PrWoF3/E3RXUkejB9JpPW24YPhJEajKUiiUHJBWUqM4JMJSRRKlr0JgjBu/ozDJqFcDcCpWezN39/zB/pwctDCKTMOXpcdN52zUHdu6gH2FciYgraAlUQBckbB7187mfe7Yo4pmESGTFbRx5dyg8+NrGgL4uRgBMf7w9i0qEE/JyKJNFRtYm0WCT1FXDgFSJLu8tjXcglOdA/j3BoZy1YuBTzj1DMUQ5SQvvh27BmIYkPn4+y2P90DHNkBV+c+zDOKkXYXc2Ot3gYs3awvqsfDuMmQSE0+Ywqaw6ne78JwJIkjvSGsW1CPWCqtNw6xyp5qr/fC57IjmmT345P6gMeR913w83kwnNDPgWKOKXOnuUqUqdb7XQXlijPBouYAFpX4vcMmYduK8kpTcsc2u2YaO/IZ75OQM3llOYqq5q7zgfE/g2LXgElhd7KmJusuAf7wA+DEXv1XYXcDDi+7GrXrz0HT/30J/uQIK5O976vAe/4eGDqNxofvxeKu3cUfPyMDz/2GdY69/F3A5isBUYTbwYQpv5Bhj8dFqSUb2UZWwDofiot/yXR+e3gjiTJyRyeLcVOjuhlTuXL0YpivbQOh5ISEKe6WCnocliIcd1eMxWQ9UkMq4pgCgNDWG+E+8BzbDDC4pvg1yamksPXwg1jc+1rBYyARBV57hP0UwXilVa54D6Rlm8t+r5XCKCrx9+mwSbBLYq4c0/QdmrMOx52LGyjWoTvnKmLHBN/EEhasBBZdNKn3FvA4MBhOIqy5o4uJYlPF2LyKu3tgLOUzzW3Mwef645g+k3IdU+msos95OUk5l61nVd5r1chqwKL7MZ/Tjqoe4Mo7cneOhdnG3B//h5Vap+LA/d8E3ngB8YyAS3pPwJ8YhqgqCLkbEG7/LOqWLyt4znIIJaiMDwDOXkmOmBbM2RnlTED44qA8YWpmS/lqvE647BLkTBY9pkBbPR8k6ILDJukXHO4Wi45TygdtMsWFg+P9Yf12VS0MPuf4TN0Pzaiqiv60DY9t/CASW2+AHCx0zY16W4Cr/qysz2CukSvlm35hqlR3MD5ZySpqno0/7zhoXgC85SPAJ78LtGsXq3QK+NXXgMfuBX7yD8Bvv50TpbxB4N1/y0oui+B22OB22KAajr3DmltqYZO/5CS9lFvReK5NpJRsqlhNIBRVxelh9pk4bKImdI3/I2g7+GOGDpUZpbKOKUkUsLK9Fk57/mtpqfFYui50Yd4gCJ0YiCCrqPC67JOalDQF3ajxOpFVVBwzjCvG4HPzd2olkI2HnFagSHaoLYsmL0ppOGwS9i66CnsXX5278cTrELSFVcIZAG74MPDZe1i3vTUXli1KwbBjmpRzoudkHVPQHFAAcKgnhKyi4nBPCKqqoinothRyREHQM6cOdo/qQd2NpvvyjQiePWOTxIKNGKedlcirputm7tieufN1LuA1OcysNo2sXGij0RSyCst5LOc8dFTCrdvQDvzZF1nX3HWX4OTF78eD534GqdUXw17TgCc23AXZoZXydO4Dvvtp4Hufgd8oStkcrLz1po8Dn/gOy5QUtfceCwEPfZc1N4iOoSnohgAVC1/4X9agACxYH2//bFFRCtr5xQVS3qXLDHe3leOknwyNATdskli0/HMmmEjGFHetmpvojMegxULaCD9WRwzl5FYbMfwamPI35jL5uGsKQDwUwtKeV3D9K9/MF6VWb2OleOsuYcfWOKiCgJCnEbuWXgfxwpsn9F4rhVFUymUjsdv4eZw2OR/TJuGfCz3lXDOLdeh2G+ariqrqYlLRjnxlwL9/7qrjophvmh1TevMqQxkeF/dgdFRpxzv/r9NW2jE13kanXcqNNeZ1I/+305GfrVdqXWDV/Zh/hvz70PEG2Fj64a8D6w05lodehffYKwjGB1kzAQDBxBBaf/OPrDR7EnDH1Ew2HpmNkGOKmBI+l13LNuGlfBNxTJVfymduT14pJFHAwiY/DnaP4VhfWN/VUg07qQ3awqLGY8eYzAbo1lqP7jwoVcoHLWh+d+cwBkIJvHioj4W+KirSWQWSWOhG4UHIkSK7NPFUBnImC8HugePy92E0+m48s30v5oWPY6Pajd7BMexedj1ucs78LvhMwIUNOTP9Adqlws+NpQSpTFaf9PHvKc8tE6hjnZl+/x1g7zPsthceyH/AdZcAb35f8QwqA3U+J7pHMhiNpRBw23FykIk4K9pK/22pXCG+iJjp3WeXhTA1FmMLRLsk4u0XLClbKHt872n0jsbzQqornTEFAJsXN2Dz4oYy7pnrmMhzGoxlmCvagpMSBQVBwMr2Grx0uB/7T40irE1Q+UTVqqSw2LEQTaZxvD+MVR21Bcd9qoRQO1F4ueieBW/CmgUNkJ76OQBAcXqwu/1S9K+4HNdundzOIwzHcSgusx1VQZjSsb2g0Y8dxweRkDM40R/G4R4Wwr6qvfg5t7QlgN0nhjAWk5GU2f0bTE6cWq8TgnYcQPtezMcA7zQXSeQ6zSGv4yTtMRoxuwGtSiTN4hXywunLy3mbcMZUuQgCK5FdexEO7zkNdSyOxoALboeEiKcBL2y+C5e/pnWZGu7R/yzmDGBsy01ov+w6PTgdAPDmO1kL9Md/wpoIAMDRXcB3P42tN30Cm9RjsB3VhAiXF7j9c+OKwHnHZKowLN9YYlOJUj4AuHRNKzLadaJa5LryFS855+NmR4MP+08xkbqYy8yKAVM2nZmgR3Pwa8eyoAnjZnJOSxW45G3Myc1dUz3H0HxkB1oMgfyy5MTxzbdj5XU3s2Ny5blAKsGC+wdPMcc3RxCA+jageQGGXU34475+eJ02bJolJUlGUcnsZip2Huc2JifmmEpnFf1vzeV0Trukj/dyOquLEVMRV7moxa/70Qo5pgRDV2EuTBnfX7mOKbcmIimqCqEMh5BxrEmkMnkiXqJImX6xSoqEnEE4kYZgOp+465DPmQpweYBbP8WiBB76np6RmRVtEBo7kInH4IgMwpaKstzft/4FK8GcAPy5a0iYIojJwwclPqkuZ2fMbZ+4Y8qsuFeSJS0BHOwew+nhmB5SGUuxLoKCIOgLXiZMZXVRrljYoRmP04b2Og9OD8dwrC+c97s6n7NAWOFdzoplOfAA9qDHAUkUUetzIOGuxSHXZrSsuQHPvtFTka44swWnTWQXem0iMZ3vVS4Rfi4IAhw2Eal0lglYTjYh4SVxBc45u4OV8DUtYAsE3u0o2Mha0y7fUvbrqvU50T0Sw2g0hbTWEbOcEpxc293CUtWZdidyrCYQw1rOS73fNSGhpsHvQu9oHEORJJZrt1W6K99E8ZrKKY1lmEtL5IONx8ImP3ZqwsmR3vzOdVaTXk+R8rDnD/RiMJyEwybpLiGOPI2Zf0ZhN3n+zfC2LgLGBnCqZRPeOBFBi2viJY1G+GKDu+fcjqk5ASVRwPLWGuw9OYxXjg4gq6jwuezoaChekuOwSVjSEsDhnpAuvJoXlzZJRMDj0Cel5tIPDp+Ym0tAAcA2SxZ/swW+IEuls8hkFctQ+Zwwm7uuTqSMD0anRYkyrqmQVXIbYk1Bty5A9no7oL71MxB+9TUmLHgCOLz0SrxasxnnrerIF6U4dS2sk+/RXcAD3wJiY0AsBOHn/wSbXnwlsHDf+vJannud9oJjkiOncyU2lSjlg97tq7puQZshY8rc4ZHDr20NfhdcDglJOYuhcLKgAY4VmayiXw+bi+QPel257s3QyvisXgcfJ7KKwiIDNlwG7H6SuaYOvZJXgie3r8Af5t2MTLARK4zleU43sPHykq95WNtocc2ieSefl8eSaX084OOEQ++uWbwrHwxzulQ6W1BSZoTP1bnb24gkCnp8wVhM1q8LU3HJGB1TLD/LWhSbDnhXYe4ANl6vXAbHFG9oA4s5JWvqxK5nXlMmaKnnjSTSBYYG/m/zvF/f8DTdn2d51vicefMYLnbxMsWi8+B1F0NdshGvPfcSehQPOpYuwZZlLRjqHYB439fQMnaMbRjc9zXWLfj8G8Z9b5xcR77x8w3PZGbPqEHMScxtzcvZGXM7y3dMTedCqFzqfC7UeJ0Yi6XQORjBirYa3Upd53PCJolQFAU1HjswlmWdEDOF9dalOG9ZMxoDYT0PANqFf35jYXmMz5DlIGeyBRc6Xs5Vr5WESCKzto9GU+gfY6p+Oa2v5yrcCZFMZ5GUp0+YUtVciV6xjDOnJkzxCzAvt3TYJOtjVhCAi24BmucDL/yWlfddctuESpRgCEAfiab0bnwr2sYXNVwOSRfxknJ+2LgeVDvDk0mXhTDFJz1md8l4GIPIOTnH1Owod3IbchqyiqIHaC9qCkxpnLNLIt60rgM9o/klyDZJxNKWwtJQlz23a5mUM/C67BiOJHXXiJUQPpGOZePBzltRL7XzLt/KnqNnDEBkymMWP674Ym06xoXlbUHs6xrRH3NVR42lM8HIirYa3V1ll0QELUTCOp9Tn5QW60zJFlGJvEywXGv42XFszxYcWplZVlERN2TKGBdRuoPCsEHGr/PFnClmnFadWacR7hzlpYX8uMsqKtLLtsLxgX9hjqkV5+DIvgEo0dT4ItDSTcBHvgH87j9ZW3Qgt0ly+TsntEFSyoHL53cOuzTuOTKX4eKEqqrIKqrluWh0jjQF3OgaimKgTGFqKJyEqqrwOG1FRWtREOB3O3QRvth4IGm38xJgXPI2YO+zgMK+P9kVxNGGdXBsugyLNm5E/IXjUOQsIsl02aVmiqrigNa1dXHz1Mq9pxPjPFrfBNCEG36tUVQ1z8mW1jOmJP2/dklEOqsglkwXbZBgbrZgxuWwIZnOok8rKfM6bVPaOPO67BAFNt4NhniJXaEoNh3w6yrPFTa6stxat1MuShVzTMEgapcbXZBz1uePNckinT+LNbIydj82YpNEeF12xJJphOMyXCWyPvtlCQddCyCJAlbNZ075QG0tfrfh/dh28DdY3L+TjamP/A8wNgBcfWdBJ3RFVdEzEtObFWQVVd+sPJs78oGEKWKqmEWYshxTBtdGsR0mjl6jXKJLWCVY0hLAjmODONYXxoq2Gn0QNu6k1njsAJKIJNK6OOS0S2VdYDxOG9bOryvrtXCRI5XOIpbMwOHL/4x5Z8BaQ2h6nc+J0WgKfdruwEyFx1eLYgHa4xGOy3j2QC/WzqvDwqb8SVQ6q/DpetHv1GGXgERaFyUty/isWLaF/UwS/l0b6/wXWIiaZkQh140qIWfyhSl55kVgFHFM8fNtoiHMXMgKxWRdxK10V76JYlw0D0dS6BrSyjDbJ++W4jQEXGWLeYIgwO206TvIXpddX1DAwtHKSg+su+xMFqeNuQf448IicHayOEzXjKnkS3HcDhsWNPpwYiACh00s2QGTU+N1orXWg97ROOr9LstFeq3PqQepFyu/sBIBZpsbcLbAduRZN1vuIkARxxRfRMZS7DwQANSXeQ7ZizgtposBg1AmCAJsEnPpyhkFCTkLR/tSoH0pUMI5YImvBnjXF1hjjj/9GMimWdnJxW+d0Oszh8wb4XO3SuVLzRaM5166SKv4pGHcbK7RhKmxOFDGHJCX8TWNU14a9BiEqSIOSt0xxR1+da3AHXezoP2Fa/FMuAZ9oSQuaG+BJEloCLgwEEpgYCxRtjB1aiiKSCKtuUWnfk2bLoyiEheg+Vhrl4yuewVuR74wZfyOvS4bxmIyYqlMUWEqkiyd8eR2SBiL5dw7Uw27lkQBfrcdobisb0yVs0E+Gfg5z+drRleWaNgkjssZw/qtcAzgQmG57z13/cuf4xfruGu14YlxymKDbiZMheJy0Tw3APpG05KWgP66vE4bRLsDL6x6O9oWL4Rr+/3szi/9HggNsTJAQxfLA6dHsfP4kP5vQcnCnxyFzeOx7AJ+NkHCFDElzI6p8jKmcjvZckYpuhBmqju7MMxkKR8ALGryY+fxIQxHkgjF5bzsCY7DJsLrsiGeyuqLy1LB51PB57IjlWY7V7Wmrn2jFt386nwuHENYn6ic6QuXUh04SnF8IIzRaArH+8OFwpQ2KREFoWhuFb+A8IVPOQH404Hfbc+z7i9tCZQtvPBuVGbhodqlfPy7S2cVhLTjtmGCwpTbwXaVY6kMRqIpNAfd+iR8tpwDLKfBhmgyjT2dw3qA9mRbDE8FryZMcVcJzyqDhaM1o6i6w3O6Mv+szlvzLvWkH9v095PtyGdm3YJ6jMZSWNFWU/YxtXFhA+KpPj0M3Uy94bsv7pgqFAEoY6o4XqcN4bisi9x2Kd9BYBT6VDXnNKj1Ocv+XnP5hpVxTOnClGGR5HLYIGdkJOSMvqhTVTXneC333BQE1o1txbnAYBfrwjfBklCrnC4Ov75UKl9qtsAEQxEZrWOYeTmrqKoequ20S/occjCcLCint0LvIFakjI9jXOAXc0zZzI4pAFi0lv0AiL1yAjAIKo0BNxOmwgksbR1fZFJVFW+cYpsbK9qCs+aay/G67BiLpfRzhY8BgiDArgm+TJhi97faJPE67RiLySUD0HkIfW0R4Yqvk7gzfDrCrnk5eO9ozoVVCczOJPNGCneDJeSs7layWuOtaA8ioyhYVobTH8Yw84Lwc2unv9OilC+dVTCmrZeshKeAx4Ge0bie1VWMUc0MML8ht2YQBAFBjwPDkSQGNt+M+Q3NwEPfZ6XWB7YD946yBgIeP9Kjgxje+QrWhHrQLA8iEOmFN9IPUXMu4iU/a0DBfzqWA62Ly/qczgTO7CsGUXHcWlDrRDKmJJFNEOVMFgk5U1SY4jX7qIKLw+2woa3Wg+6RGA73jOk5UmY3Qr3PhXgqhlOaMFWpi4FPK7Mxhy4m01m9tMMoWNX78y+Is22CMN1YBWiXw4iW3WD1d8Z8gWI7lQ7dLqw5phI8bLqywpQoCKj1OfWJTbkXd5TozFdqd6uSmC3Xw5EkVG3SWGyRXop6vwuxVBRDYRaAzqfgs2nx7nEyYapvjJdhjh94X5HXwcNK5QwO945BUVV9V9ksXMra8SEKgt52fKo4bYULe3Pg7KQf29yieppKVIMeB96ydeGE/qYh4MKN5xT/G+PYXSpjCmbHVIU7Ts5l+OfFM5rMeSt8cahoZSeD4wRMW2HszDqRMOtyUFXV4JbJzTtcdglh04IrlcnNlSac51TTyH4mQelmGnzBeGY7pqDNrzJZxbIzn5zJOa+5+52PsWOxVMkNCUVV9U3RpnGOy3xhyvo45MdnxiITTVVVQ7c6Nn9pCrrwxqlciet4DIQSGI4kIYlCURG+mnidtryOvcYxwW6TNGEqX8iAhWMK4wSgD0ZKRxHwc4Jv9BRzXk2EoMeBU4acoukOPueYzQfmOZpHc4Ml5UzJzc46nwsXryovy44/Liyc3EndMWVdypfO5poi8bmlt8jc0tzd0IqsoiKinSdmt1fAzdZpoUQa2HI162r6q38D0kng1EHgvz4BKFnYkzFcUurNJiLAyTfYDwCsvxS49dOl/uKMgmYzxJQQBUGfSAsT2EnX24qWyJnibilJFKoy8V6i5bIc0tqCczeGEe5S0sMUK+iYgiHDiMPdUn63PW83mHd64lSi1nw2UawDRylUVdVL4az+Ti7RkY+TC83Mz5iqtGMKhmOvrdYzoVbDxRYTE95xnybMIZXDkyzj4/DJ4FAkqU/ABWDaxJTpwDi58zhtmFciQLuS8GMhmkjjiGZPXz2vFrCaAPJ8KXtxoXaiWJ23pTphTgSbocU0piljqlI47RJqfU4IglB0kcJ31UcNu/W58PPZc2zPFvjCjIv3ZmFSEgV9gRhLZQyu6PLHHV4CBMN8ZbqIJlnDFdHQcAV5XY1z5ydfnLEy4ZmbK/HPOFbSMXVmzz2Q15mv8BhIGXL5JFGAKAj6MTYQShbc38hoNIVMVoHDJo7btc2YS1OslI8HxWeNjimNZDqrd0njx1hjwA0BQDiRLqtZEXdLLW4OzMrx1ihEcecyh19vjOKiOfwchrldsWZEqXRWd9w0FBETzZ/NVEv5YAjv5lQi+BwWr72Y4B+XS2dMTRSX4XGNxIs5pgwbyvx15LJLrb8Xfo0NJ4oLU5GEDFXbwDMbMYLatZs7/rFsC/C+fwJ8tdqLDbNGA2YEEWjoAFZvY85Vfn9O47yir+dMZPaNHMScw+eyI5pMw+koP+TS7bAhFJfzgkfNVCP43EhHvVd3dkFb8JoXZHWmsrpiNeVThTtwzLs0er6UaTFj00J2x7RWtGdy+DlKBCOWwlgDbyVMpcvIutEdH9qihH8/5hLXSrCqoxbpjIJ1C+on9HdWCxtUcYfbmAWgqqo+eZjIAtFIgyEAnU8si3UpqhbGCc3y1mDRUtFKw4WpY/1hZLIKPE4blrfVYE/nMOSMgowhM4ULtdO50OQ5UKmMMWNq+gLWnXZJF3HcFXKzThdXru+AnMkWdQkG3A40BVlpzeHeMWxc2GAI9j+zx/fJwD9Hvgi32jTyOu1IyllEEmndFd1YIlvEDCsBkvROWMW+u6yi4nDPGFprPUWFx+FIEicGIrrziV9L6vzOvO/X7Sh0vCaLLM4qjTFvJmPKV0pNJPNqjmMlanBywlRu3GwKutEzGsdAKFHQ+dSIOWOsFAG3Q89JKnY9kaTijinulnI7bfrfO+0SglojoIFQomSO5Vgshe6RGAQAqztqi96vmhg3DD1OW956xaozX+5alPvuSomxMORj+t32otdKt+n26SjlM4tblXNM5V67WdyDYQyKJGR97J2ONVzOzJD73FWtcQssxhlBEOC0iawpUprlqY7XVId/htFEuqgDlouOAY+j4Jy0dFy1LQE+8FXgl18F+k4g7a3FgLMJ0WAblm1YB7FlIROlbKbvKx4GBk4BA13A/FUT+KTmPmf+FYOoOHywn8gEhA8y4bis7zxIkpg3kCctLugziSSKWNjk04PurBbKZmGqUhcD3TFVIEwV5vDy9JsAAC8XSURBVEvlXpsrJ0yd4QsXLqZMZNeat2CGbvfNvxBZ7ZaZcRjK0BRVRVSb3PkqXMoHbSJ60QSs0JyijintAl+tUj5FVZHOKlN2TNX5XRC09xeJs/NltnUt49+BKAhYVkZ2R6VfB1+orGirgdMQzp6Qs/C7NfGoAuOxVUCprJ93U38eh80gTM3ykiKXXRpX9FvRVoOBUAJHekJYN7+Ows9LYHY3W4lGHocNwwBODUeLuqLHw2kX87ryWnGwmwXdBtx23HjOwoIFjaKqeGZ/r6ULozmY37lNz1oxbMJUy+1q7n5oXGCfVY6pEoKPVYk8Fz8HQomSDYD6eb5UGWKpTRLh0bqKFRsPuLMyY+GY4nNL8/HfFHSVJUzt1xpndDT4pkVoqQTG+bn5fTosXG/Fws9RwjGlix8l5i/GtVI54345mDu5Vcq17zG5vc1mBI+W5cjXHpI4PaX/Hn3cy+qleXJG0cUvj0WGZK4pEit1Hoyw86nYd+OyS3pziUjCuutiSKuKCFjM8Y3CVN55XdME/Pm/IZuW8fvdbJw/Z2kjxPYSAq4nACxcw37OMkiYIqYMX4SbdwFKwQfm17tG8HrXiH77hStbsLiZldDJVZpsGVnSHDQIU4WTA5chbBkVDj+HNnkwDng5YapwoK33O3G8n/3/Gd+VbxJtu3lAJSeVVuBx5j4nfbdMKn78OQw7pfFURg8znUw20kzhtqjVz2RzF/iZPt94yVVWUfVuN0IRsbUc7JKo7/LyDKfZtnDnC43lbcEZdzkYMR6nkihgaWswL5w9IWd0t6buYJ1GYcphcd5OV1c+mI7l6cqYqibzGnzwOG16UH0u/Hx2Ca+zAfMYbCU48fucHmblFY0WrujxYMdwumgAelZRcbB7DNBKok4NxzDfVLp7ciCCWDINp13CUkMnM7skYLkpf85tKn2GYSyfaXeSsfuhWZjKdeWb++fdeOiOKQthymoe26B150zIGew/PVr0+jQwAWEKAGo8DsSSaUhFxgPJ3JXPAG+qYN5cbQq4cbgnhB4tb9UKRQVO9LOuorPVLQVT2Zl5PLCbGtmgRPg5tI0vLpAYGdJy4YqVi8HkSp8uEc9hk+BySPq4UKlSPuNrtxpTebMDnuXlsEnT4lZ32iU9zziZzsDrtOvjXrESZpdDQijO5hexVK40utjcUhAEBDwODIVZ0ysrYYo7pqzKL/1uO0SBzWVjqUz+elAU0TmaQiyZhss0zhP5nPlXDKLidNR5cbQ3VNDVrOTf1HtxvD+sX8hVle0a7j89qgtTqWks6Zgs9X4nFjb5kUpnizo46nwsbBkVvBh4nDYI2iQ3IbOSgXRWQUQbJM2d+mASq2bbwny6sdpFHo/hAmEqvxRjoo6pXL5U4S7SbCJXymcoBTEEW1fjWHHZJcRSGXSPsAVi0OuYkjOnwe+a1cJUY8CNt1+wpOqCsVGsWdwc0BdPboekC1OclCFjarpw2ngp3/SHnxsfg7exnutIooDlrUHs7hzGoZ4xpLMUfl4Mc+mex8LNbHYMTqSMj8OPsWJu3c6BcJ479cDp0TxhSlVVvKG5TVa114xbmm2ZMVXFTTyPg3U/NJc25bplzf3zbjzsFmVgHKvOZDZJRL3ficFwMq9lvBWSKBQ0sylG0ONA90is6LXTsiufBj9GPaY5LO8GGE6k8fKRgZLP3xR0ly2iVYM8x5RpfOCfGXdJZRVFDyc3zgHdWmSJoqpIyPnig6qqGNKc+KWiCIxi7XTkSxkfKyknWH5ehcYCY/OqUmL/dJbxQRONXHYJCZkJTLwMGyVKmPkmWjKd0Z1stT5nyetl0M2EqWKd+UKGUj4zoiDA77YjFJcRisl5x4aiqtinmTBWz6ula3YJSJgipkytz4lbzls0ob9prvHgtguW6P9OpbP4v+3HMRpNYTjCumnxiV41FxSCIIzbOaLO78Sp4SgcNrFiZYeSKOg27R3HBuFySCyTR5v4WTl0an1OPXOg2gvgSuPUFsvlduVTVRXDmtuM78KY3VblhJ87DRPSSHLmgs+nAp8UJeWcwytpWERUI4vJaRKmJlvGx2kIuHC0L6Q3B5iNk4DZIJS4nTa9Q5Qx68Rq8ctzoKbzdfPHki3Cz6djLHUahLbZlDE2FZa1BrH35AiGwkk9eHs2Ht/Vxi6JelkGxnFMcSaTa6cvaLOF1x5VVfVA6JXtNTjUE8JAKIGhcFLPOekZjWM0moJNEgvcUVa4LDKmco6pmR9TvHppeK60ibkazh5hSg98LtGd0Dxubl7ciIPdY7r4UYz5Db6yA+1XtNcgoyhYXqQ8nIeiW4Wf84wp8/zF67Rj65JGvaywGJIoYO28urJeZ7UwikrFSvn4eMH/K5jmgMwlaEMkkUY0mc77vMIJ5pyUxOJNLPhz8dcxXqj9RAi4HegfS8CrdUuvFB6nJkxZzHXN5/t0CmQepw0JmbmFI4m03km1mBuan3PJNMsRRBljfKBEZz5VVUs6pvjtobiMUEJGO7z67V2DUYQTaThsUlXjG+YCJEwRswKnXcL8Bh86ByM41hfWhKnqhp+XC69XrnRdPbdpdw5GLJ/fjLGkqZrlQjNBrruXUjKzgRNLsVa2oiCgzu/EUDhZIGqVE37usOUW1jyjYSbypaaCyyHpgmVSc9+Vaus7I69Je14uJJXKZygHvsPMp9+zzTE1WxAFAZevbUNWUfMm0lauOi4eTWcpX27imNul5oum6Qo/xxwIPp8ILocNCxp9LChbu41K+azxOO2QMynt/0sLU5IoWJbEj0cpx9Tp4RhCcRl2ScSGhfWQMwqO94dx4PQoLl7NNrz2a8LV0pZAWXMdvvBLas0ihLyNhZk/zrnDxuiYSmUUPcSdl/acyei5Q6nC3CF9HmsazyrhLvK57DhvWXPR3/MSP8vw85R1xhS0RiurZnGJXrkIggCfy4ZwIl1Qsmg3dVjm8z+bReMUr9OOSCKN0WgKLTW5DDjuyqn3u0o2NBEEAW6nDbFkGkHP5CILrOAiV6W6g3PcDhvGYrLlsWIu3Z3O9RsX3t84NWJ5u5lc9mxWjz0Zb25ZSphKprNIZxUIhoZUZoJeBzAEhGK5v1dVVY+sWdVRc8Z3SZ8qZ/4Vg5gzLGkJoHMwghMDYWxZ0mBpgZ6NtNZ6cP7y5ikvpsdj69JG1Pe79AkfAIiigEVNgaJ/c9HKFgxHkpPucDZX4MKGqqqQM4p+zKhaoLb5QsDL+Gq8Dv1CmjJlhJRXypfrKsZ3ZPzu2Rn8yREFAS4H23lKyKxbCS+BrNa5Zn7eYl1TyqXG69Rzq0COkpI013gKbuNCTiJvsTn94zF/rLTWPMDY1Wo6vrNcaeKZNdVZ2V6LEwO5DQoSXq3xOm0Yi6XgtEuWx5NRmBpvMVkMfXPCImOKB0Ivb2OLkVUdNTjeH8bJoSg2JdNIyln0jcUhCELZ2Txc6OEuX5fDVtVSPmPmDiepZ79IVes4OpP4SnRqm00brNwxZVXKx197pRr4zBY2LmpAz0gcLbX5omDuPM53TFltkMxr8KJvLI4D3WNY3lajH+ND44Rr572OhfUYCCXQXDN94uTCpgCGwkksrbAjZ+28OrgdNsy3CMO3S6LuwoahmmE6WDOvDoqiwnj4SqJQVDTl42E8lcGIVmJZKvsLhk3N0WgKciabt3bgYpXPbS/qYgxq8/9wIidM9YzEMRZjrtgVZbhiz3bOrNkaMadprfXoQeJdg9GiO02zDWGGOmsF3A5sWFg6f8JMrc9pmT91piGJuYthKp3VJ4E7TwzhwKlRXLm+Ay21uQU478hnLBkrLOUbvwsZ/52qqhjVwh5neykfDJboeCqDen91M0pgmrRLojDlXURREFDvd+nhseQomRgei4D8XFe+6RuP9fNH26E2ls9OR07bgkY/hiPJskqk5hINARfq/S5dYCfh1RouPBVrRmHc8Z+sGM4XXuZ8oYFQAgOhBERB0Mtk63wutNR40DcWx8HuMb2z16Imf9kuB0lkeWmpdBYJWROmeAe8KpTN8TKaWJ4wdfaU8SGvU1uhMCVXoAR6svDroDn8nM+bUMGc1NnCgka/ZXdBc4B9qY3JpS1BvH5yhFUwDISxRAuy1jvylTGWLG4O6Hm604XLLk2qU/NEaan15M2nzbgdEtKJ6T/um4JuvGl9R9n35+7u/rEEFFWFyy7BN87x7XXa4XPZEU2mMRhOor0uV47Hy/gCJTafg5prbSyW68y3T3N4LW8NzopxYLZDsxli1iAIgj7AH+0Lz6oLOjH7MdaTQxOWDnWPQQX0rkickWjOcp0rAyzimCqx6LOJgr6A5kH0/jkgTJk78+XyQKozKTUKYpN1Lpgx7lqSo2Ri8OMgbijlq0TGlCTmwvZT6ey0duSDJkhctKp1VgfyTha+8yoZxiAiHy48WZWcQNvQ4MezVdfdcijmmOLlJoubA3nCGHdGHe4Zw6kh1jRlzbyJlUm5TeV8vOS2Gs5ALmTkOaZmQUflmYR/v3ImW9CZr1jGVDWQijimuEDKctmq/zqrQWHGVPGNSZsk6i6dfadGmds3q2BUK9+aquN7rmOcR1ZzDOCvg5+TDWV2XW3W5gsDply10Dj5UtBEK0E7fpLpbP4GRceZtUFWKWi2TswqlrQEIADoG4vr6vTZeqEkJoYxewMAjvdH9FKu7pGYfruqqrpjqs7nLBC0OOWEnwuCoJfz8anebM+YgkWGUEqu7uTZaRKmpgPj45CjZGJ4LMLPK5ExBVNnS74YsNOYPy4Lm3xY2OTHmlkeOFxNFjT60RhwlXQ0r+6oRWutB20lHAClMC9oobVKPz0cg2AhOrXVeRD0OJBVVKgA2uu8JYOSreDlfAk5g3Q21z2sKl35DKLMcCSJ0WhKz3M500poi+GwSfpxwEUeTqpC4+ZkMDqmjJEQehnfGe6WKkWuK19+xlSx+d/ytiAcNgnhuIyuwShGIkmoqgqP03bGl0OOhzGMvJrHvbmMsNy4laYiwlS4REc+jk0SdfdrKC7rGYKLmv1n/XFRLjRbJ2YVPpddzzzhKvfZsutGTA1+AUxpu8hHeplLStC6n3RqmSzRZAZyhgWf13ideX9nRJ+YjOPeMAqnLoc0J9w55q5r1d7hNgpT05XVZty1JGFqYrgNoaFZRWV5NtzNNI2ZETAcc5VwTJ3JSKKIi1e1Tri8+2wi4HHgmk3z0VHvK3qftfPrcOX6jkmPEVaOKZ4t1dHgK1jECEJ+JspE3VIwOV755oJdEqsyzhmf9+GdXXhox0ndLXY2zd08FjlTqqrmBP1Z8FlwJ7Jq6swXP0vypUphNzumxskYddgkvUT39a4RDPIyvgpnzc4FjCW81TzuC7NLy3PFNmm5X0PhJLJKbsMhpOXIlnJMGX/fNRjFqeGotkFBG0jlQrM/YtaxtCW/7toxCy7oxOzHuMAdDCcxFpMhiQLWzWcXhBMDYcAQfF7rYwHZrnFK+cZbJBsz0OZCGR8Mu9x8QlrtcoNKOKa8Tpv+3dopY2pCOO2SbnlPaq4MvsM+7Y4pQ1ezcs85gpgtmLvyJdNZfROkWKD54mY/2uu8WNYanFSZKd9YSMrZquZLQRPaVrbXwOWQ4LLnfnwuOxY2FWb5nKl4nYUljXJG0Z3Us0GYMgqXxnI+7vI6ux1TWpmjdq0bzzEFACvba2CXRIzFUjjQzcTos72MD6ZMv2oe90ZhXDAEm4+H32WH22GDoqp6blgmq+jnyXgd2LkwdbiHbY53NPjGFbOIHFWf/X3nO9/BokWL4HK5sGXLFjz33HNF73v//ffjqquuQmNjIwKBALZt24ZHH310Rl8vUXnmN/r0i4RAixSiTJyGUj5+QVjY5MfytiAEQcBQOIlQXMawli9Vp4XCW2VM8e5+mKBjai4En6NUxlSVJhFc0PM4beOGU5aLIAj6ou9sKSmZLgRBMBwjWX3RLYnCtLsynBalfFS+TcwVzI6pY30hZBUVdT5n0W64kijiinXtOH95c1mZJ2ZcFo6pao5xmxY14LZtS3DbBbmfW85bdEZmuxWDizpRQykfn1PYJXFWdCcUhVwenXG+c7Z05CsFX2eo2qZkOdcip13SG2vwwH9yTOVKjVFl1yRvigQtlLzceYUgCAU5U7zLnlMT3kvBRSgu/a4lt9SEqOqK/7777sOnP/1pfOELX8CuXbtw8cUX49prr0VXV5fl/Z999llcddVVePjhh7Fjxw5cfvnleMtb3oJdu3bN+GsnKockiljUxFxTDrtEwa5EWfCLRTguo0sLlV3eWgO3w6bnh5zoD+ttY7kzxypjipcvoYzgbKNw6i/RrWM24dbDrdmENKWHn1dnEuF12fGmde1407r2SS3UinHusiZcvKq1ZCkPYY3xGOGT9ErsfnIHlpzOlrVLTRCzCT7+ZxUVmayCwz0hAMDytpppHcuM5DKmslV3uxIMvillzJiajd9NY5DNe472hfTbYinNMVWkScDZgCTmxEM5o5TsymdkdUeN/neC1g34bMc9S0r5jM/f4J+YSM7L+fq5MBUvr4wPhs58ANBS4yEX3QSp6uzv61//Ou666y584AMfwKpVq/DNb34T8+bNw3e/+13L+3/zm9/EZz/7WZxzzjlYtmwZ/vmf/xnLli3D73//+xl/7URlWdYahCQKqJ1gKChx9sIXuN0jMX3Hmlt3eVve4wMRQ0c+7pjKX1jAUMYnlLFINl5454pjilutUzLrIpSZBXlubZMIAR4Pt8OGhU3+WbFbPdfQO3/JGV24rISTySgMU8YUMddw2ETw0eXEQATRZBoOm4RFFSxjy3Xly+ilfO4qbSoQDK9FxlRqFgpTvLz0SE9I33CIJXn4+dyYv1QKPtdLZ5Syr0Uuh01vrlDrdVCepWF+aatS7p0RPi5OVBxq0vKoBsNJZBVV78gXKGPz2SheTSZD8GynavK4LMvYsWMHPve5z+XdfvXVV+PFF18s6zEURUEkEkFdXXGbXCqVQiqV0v8dDof1v1UUpejfEdUl6LHjhi3z4bCJs/J7UhRWhz4bX9vZisMm5nWaWdIcgKoy51NbrRs2UUBUs+NKogC/yw5FUSAJTIBSVBWJVBpelx1JOQNVVWHXHtP4uGbskqD/3uuU5sQxwTKXVCgqMBJJQFVViIIASUDFXj+dM3MLl12CqqqIJdOQRHaMOyRh2r8/fv6k0hmIAvt/WwWeZy5C58zcwCYJkDMK9nUNQ1VVLG72QazgWOrUrkuJVAbxVBqqqsI5S+dKM021zhm3g30n0aSsPzefR1Ri3JwsrTVuBNx2hOIyDnWPYVVHDaJJGaoKuO1n9zFkkwSosopkOgNZa6JjK+M8XjuvFulMFgsa/XPy85vuc8bvsmFlWxB+t6Pqn8eaebXoHIhgfoN3Qq8l4LbBLgms22g4gbEY67rod9vGfRybKGDz4nqkMwqag66qfwazgYl8BlUTpoaGhpDNZtHc3Jx3e3NzM/r6+sp6jH//939HLBbD29/+9qL3+cpXvoJ/+Id/KLh9cHAQyWRyEq+cmEni1X4BRVAUBaFQiC3oRdohmQ1EozJisRigXRi8QgIDAzlROujI4uQQO6JqvQ4MDw3qv0unEkims+juG0CNx46RGHssxSFhYGCg9POGo/rzJiJjGEhFKvQOp5dMKolkOovjp/oQi8XgsksYHBws4y8nB50zc4tkLIJYLIa+QRVxtx2xWAxJe3bc82GixCIJxGIxDIoZSKKAWCyJaNiOgYF0GX99ZkPnzNxATsYRS2WhXQZQa/NO+3liJJXOIhaLIQbApqYQi6WQiNowMJAt46/PbKp1ziRk9p3E40Bffz9EQUDfIJsbJJ1KRY+HidLiUdEzGMOOQ0l4kEA0GoMgAJHQCGLhs9ddnErEEIul0dc/iOHRCGLxNCKhMQwI468VF9cIQDqKgYHojLzW6aQS58w8PwCkqn7c2wEsqxMxOjw04b91Io3RWBKHTvageziBWCKNTNxZ1tyk3s6evJJz6rlEJFL+uqjqBcXmGnxVVcuqy//FL36BL37xi/jd736Hpqamovf7/Oc/j8985jP6v8PhMObNm6cHqBPEZFAUBYIgoLGxkRYMswS3Pw3vaVYPvqw1iPbW/HFhk8OHoUQ3AGBhWzBv3KivTWAsJsMXrEFTrReZ0Ri83gRqvI6S4wsARFUXjo9mIYkC5re3VCxXZLpprEtiJJqC4PDC682g1usc971OBTpn5hahrBOnwgocbi+8fie83gwa6wPTfoxkbDF4B2Q43U7YJAHejITmxno0NZ49Hb2KQefM3KCuJglE2SZIW50Hi+e3VfT5FFWF73gUqgqokg1erw2tzY1oaqAsvWqdM4qqwneCfSf+mjp4nXZ0x0R4vVk0NdSgqalxxl7LeNQ3KOgKq0jIGQwkbfB6vfC6bGgxGQXONur705ARhz9YA1dIgVdIo7m58YwP8afrjDXLZDsix4cgCy6oNgVerwOL5rXMmSzZ2YTLVX4pZdWEqYaGBkiSVOCOGhgYKHBRmbnvvvtw11134de//jWuvPLKkvd1Op1wOgtzS0RRpBOQmBKCINBxNIvwuOysFEgLnjV/Ly21XvjcDsSSaTQG3Hm/dzvsCMXTSGfYjlFWYd+v024b9/t1O+wQBAEBjxOSNHuyJMbD67JjNCZjLC6zLmzO8d/rVKFzZu7gcbLjOpVRkM6yDSOXwz7t351LO3/krAIIUsWeZ65C58zsx2m3QRBYmfjK9tqKf1eidt1JyBnE5SwEQYDHSecMpxrnjAjA62Lzi4SswO8WIWf4uFn5a+tEEEURqzpqsevEEI71hSEIAnwux6x6jdXAYWPXn4wCZPg1r4w54JkAXWcKaanxQhCG0TeWgKKygHy/x0kNuSbBRI6rqh2BDocDW7ZswWOPPZZ3+2OPPYYLLrig6N/94he/wJ133omf//znuP7662fglRIEMRewSyLOW96Mc5Y2WXZGEQQBF65oxqqOWiw0BdMaW9ZDC79Emd3BWmrdWNleg82LG6bpncwMvOvaaCzXBpcgODzANJ7KGEJ8p3/KoJ976SzSeotumhwTcwd+vPpcdrTVeWfkOc1h59XqqErk8LnYmMk7883G8HPO8rYg7JKot7Q/mzvycXhzj3QmW3ZXPuLMpc7vhE0SoWgZsgGPnUSpGaCqI9FnPvMZ3HHHHdi6dSu2bduGH/zgB+jq6sKHP/xhQCvD6+7uxr333gtootSf/dmf4T/+4z9w/vnn624rt9uNYDBYzbdCEMQsgHdHKUZzjQfNNZ6C2/mCm7d3licwKZFEEecsrVwJXKXgwsNs6MhHzD5cFl35nBXpypfripmQ2fOUIwgTxGyhzudE11AUq+fVztjCxTxe840GonqwznwJvTMf3+iqxLg5VRw2Cctag9h/ehSgjnyAQWBOprPIKmrebcTZhygIaAy40DvKsmmphG9mqOqV7Pbbb8fw8DD+8R//Eb29vVi7di0efvhhLFiwAADQ29uLrq4u/f7f//73kclk8LGPfQwf+9jH9Nvf+9734p577qnKeyAIYu5jdG1ggo6puYp5IUPCFGHE7bCB9W4EQgnmAHBU4BixSyIErRsf35mkxQAxl1g9rw7zGnyo8RbGRlQK4/gticIZfa2aK3DXUSyV75iardfWle01ONA9BlVVyTFluO7Ekhn9NhudV2c1zUG3LkwFPSRMzQRVH4k++tGP4qMf/ajl78xi09NPPz1Dr4ogiLMJPnFMao4NWS8pmp0TyumgsBSk6pcDYhYhCgKcDglJOauXpjgrIBgJggCnTdTdigBgP4PPO+LMQxKFGRWlYBqvyS01O+CuIy5szOZSPmivd828WhzpDaG1dmZKUGcz3CHPhUW7JFLp1lmOMfg+QI6pGYGuZgRBnPVwq72eMXUW5AuYFzOzdfJMVA+3w6aLtajgMeK0S7owJYkCJJEWAwRRCuPGAuVLzQ48umMqA1VVkdKc144KZPNNF5sWNWDjwvo50024kti15jW8FPNMnv8R5dEQcEESBWQVFTVeEqZmAhKmCII46zkbS/k8TirlI0rjcdgwipT+70oKU5wz2aVIENOFcWPBbaep/GzAqzeMSCOdVaBqpcmz/dpKohSDl/LxeSCVlBOSKOK8ZU0IJ9Ko882sK/Zsha5mBEGc9fCFsR5+znc6z+CJicsu6dk+mAOTZ2LmMTsxKiUaGcOBz+RzjiCmCxc5pmYdvJRPziiIaLl8NkmENIFW6UT1MF97zuSNSaJ8lrRQc7WZhM46giDOely6Y4rtcuqlfGfwxEQQBCoHIUriMbgy2AKrMjvrxlIXEqYIYnyMjikau2cHdknUN7lGosxpSiXycwdz6R65dwli5qEZIEEQZz188shFqXTm7LBy88WNKFBXJ6IQY8ByJRdYxsem45AgxsfocHVRKd+sgZfzjUSTADmR5xRmIYquRQQx89BZRxDEWY9NEvW2wMl0FvJZEH4OQ4CuUyvrIwgjHoMToxId+XKPTRlTBDERHLZcxzBzh1WienidrJyPO6bO9M2tM4mCUj767ghixqGzjiAIwhSAngs/P7Mn/NwxReUGhBVGx1QlBaM8xxQtBghiXARB0BtYmDusEtXD62LfxSiV8s05bBIJUwRRbehqRhAEoVnuY8k0EnIWWYUFgp/pu518YUPlBoQVxs6NFS3lsxkzpuhYJIhy2LqkEYPhJBoDrmq/FEKDj5l8DkHX1rkDjzTgGaMOKuUjiBmHhCmCIAjD4jiSkPXbzvQds3ofW9BQG1zCCmOJkNNewVI+O3XlI4iJMq/Bh3kNvmq/DMIAL+XjkGNqbuGw5YQpO22SEMSMQ8IUQRCEYQIZTebaPItneO5Se70XN527ED6XvYx7E2cbkijCYZMgZ7J5OVDTDQlTBEGcCZivpeQAnVvYbRKQyrD/J8cUQcw4dNYRBEEYhakEE6bOlklJwO044wU4YvJw15Sjgjv/Dgo/JwjiDIBnTHGolG9u4cgrKz875oAEMZugs44gCMIwgYxojqkzvYyPIMqBOwA8FQxYzgs/P0sEYYIgzjxcdilvo4dK+eYWxusPzQEJYuahUj6CIIi8Uj5m46bdMoIANi9uQFPQjY56b8WeQxIF2CQRmaxCiwGCIOYsgiDA67IhojmvK5nNR0w/5JgiiOpCwhRBEIRBmFJV1k2HnBsEAdR4najxVj4cvyngwlAkiaDHUfHnIgiCqBRep90gTJFjai5hLCW3S/TdEcRMQ8IUQRCExQSSsm4IYua4fF07sopKgjBBEHMarzO3tKpk0whi+rGTY4ogqgoJUwRBEBYhpbRAJoiZQxQEiBKF8BMEMbfxaAHovESZmDsYxSgqKyeImYfOOoIgCAvHFE1KCIIgCIKYCD4naxhBHfnmHtwpb5NE6lZMEFWAVl4EQRAWpXtk4yYIgiAIYiIEvSwnz+e2V/ulEBOEO+XJMU8Q1YFK+QiCIDTbvcMmQs4oAE1MCIIgCIKYIA1+Fy5b04ZaX+WbRhDTC3fOUzdFgqgOJEwRBEFoOO2SLkyRY4ogCIIgiIkgCALmNfiq/TKISdAUdGNpSxBtdZ5qvxSCOCshYYogCELDaZf0Ns/kmCIIgiAIgjg7kEQB21Y0V/tlEMRZC628CIIgNIytne3U5pkgCIIgCIIgCKLikDBFEAShYeyiQ6V8BEEQBEEQBEEQlYdWXgRBEBpOgzBFpXwEQRAEQRAEQRCVh1ZeBEEQGk5yTBEEQRAEQRAEQcwotPIiCILQyHNMkTBFEARBEARBEARRcWjlRRAEocGFKVEQIIk0PBIEQRAEQRAEQVQaWnkRBEFo8PBzKuMjCIIgCIIgCIKYGWzVfgEEQRCzhTqfE/V+F5qC7mq/FIIgCIIgCIIgiLMCEqYIgiA0bJKI6zbPr/bLIAiCIAiCIAiCOGugehWCIAiCIAiCIAiCIAiiKpAwRRAEQRAEQRAEQRAEQVQFEqYIgiAIgiAIgiAIgiCIqkDCFEEQBEEQBEEQBEEQBFEVSJgiCIIgCIIgCIIgCIIgqgIJUwRBEARBEARBEARBEERVIGGKIAiCIAiCIAiCIAiCqAokTBEEQRAEQRAEQRAEQRBVgYQpgiAIgiAIgiAIgiAIoiqQMEUQBEEQBEEQBEEQBEFUBRKmCIIgCIIgCIIgCIIgiKpAwhRBEARBEARBEARBEARRFUiYIgiCIAiCIAiCIAiCIKoCCVMEQRAEQRAEQRAEQRBEVSBhiiAIgiAIgiAIgiAIgqgKJEwRBEEQBEEQBEEQBEEQVcFW7Rcw06iqCgAIh8PVfinEHEZRFEQiEbhcLogi6bsEMR50zhDExKBzhiAmBp0zBDEx6JwhKg3XXLgGU4qzTpiKRCIAgHnz5lX7pRAEQRAEQRAEQRAEQZyxRCIRBIPBkvcR1HLkqzMIRVHQ09MDv98PQRCq/XKIOUo4HMa8efNw6tQpBAKBar8cgpj10DlDEBODzhmCmBh0zhDExKBzhqg0qqoiEomgra1tXFfeWeeYEkURHR0d1X4ZxBlCIBCggZwgJgCdMwQxMeicIYiJQecMQUwMOmeISjKeU4pDxaQEQRAEQRAEQRAEQRBEVSBhiiAIgiAIgiAIgiAIgqgKJEwRxCRwOp24++674XQ6q/1SCGJOQOcMQUwMOmcIYmLQOUMQE4POGWI2cdaFnxMEQRAEQRAEQRAEQRCzA3JMEQRBEARBEARBEARBEFWBhCmCIAiCIAiCIAiCIAiiKpAwRRAEQRAEQRAEQRAEQVQFEqYIgiAIgiAIgiAIgiCIqkDCFHFW8pWvfAXnnHMO/H4/mpqacPPNN+PQoUN591FVFV/84hfR1tYGt9uNyy67DG+88UbefVKpFD7xiU+goaEBXq8XN954I06fPp13n8OHD+Omm25CQ0MDAoEALrzwQjz11FMz8j4JYrqYrnPmBz/4AS677DIEAgEIgoCxsbG833d2duKuu+7CokWL4Ha7sWTJEtx9992QZXlG3idBTBczdc5w/vCHP+C8886D2+1GQ0MDbr311oq+P4KYbqbjnBkZGcEnPvEJrFixAh6PB/Pnz8cnP/lJhEKhvMcZHR3FHXfcgWAwiGAwiDvuuKPouUUQs5WZPGc4qVQKGzduhCAI2L17d8XfI3H2QMIUcVbyzDPP4GMf+xheeuklPPbYY8hkMrj66qsRi8X0+3zta1/D17/+dfznf/4nXn31VbS0tOCqq65CJBLR7/PpT38aDzzwAH75y1/i+eefRzQaxQ033IBsNqvf5/rrr0cmk8GTTz6JHTt2YOPGjbjhhhvQ19c34++bICbLdJ0z8Xgc11xzDf7f//t/ls9z8OBBKIqC73//+3jjjTfwjW98A9/73veK3p8gZiszdc4AwG9+8xvccccdeN/73oc9e/bghRdewLve9a6Kv0eCmE6m45zp6elBT08P/u3f/g2vv/467rnnHjzyyCO466678p7rXe96F3bv3o1HHnkEjzzyCHbv3o077rhjxt8zQUyFmTxnOJ/97GfR1tY2Y++ROItQCYJQBwYGVADqM888o6qqqiqKora0tKhf/epX9fskk0k1GAyq3/ve91RVVdWxsTHVbrerv/zlL/X7dHd3q6Ioqo888oiqqqo6ODioAlCfffZZ/T7hcFgFoD7++OMz+A4JYnqZzDlj5KmnnlIBqKOjo+M+19e+9jV10aJF0/wOCGJmqdQ5k06n1fb2dvWHP/zhDLwLgpg5pnrOcH71q1+pDodDTafTqqqq6v79+1UA6ksvvaTfZ/v27SoA9eDBgxV9TwRRSSp1znAefvhhdeXKleobb7yhAlB37dpVwXdDnG2QY4ogAN2uWldXBwA4ceIE+vr6cPXVV+v3cTqduPTSS/Hiiy8CAHbs2IF0Op13n7a2Nqxdu1a/T319PVatWoV7770XsVgMmUwG3//+99Hc3IwtW7bM8LskiOljMufMVJ6LPw9BzFUqdc7s3LkT3d3dEEURmzZtQmtrK6699tqCkkCCmGtM1zkTCoUQCARgs9kAANu3b0cwGMR5552n3+f8889HMBic8vWKIKpJpc4ZAOjv78cHP/hB/OQnP4HH46no+yDOTkiYIs56VFXFZz7zGVx00UVYu3YtAOhlds3NzXn3bW5u1n/X19cHh8OB2traovcRBAGPPfYYdu3aBb/fD5fLhW984xt45JFHUFNTM0PvkCCml8meM5Ph2LFj+Pa3v40Pf/jDU3zVBFE9KnnOHD9+HADwxS9+EX/7t3+Lhx56CLW1tbj00ksxMjIyre+DIGaK6TpnhoeH8aUvfQkf+tCH9Nv6+vrQ1NRUcN+mpiaKWSDmLJU8Z1RVxZ133okPf/jD2Lp1a0XfB3H2YivjPgRxRvPxj38ce/fuxfPPP1/wO0EQ8v6tqmrBbWaM91FVFR/96EfR1NSE5557Dm63Gz/84Q9xww034NVXX0Vra+s0vxuCqDzTfc4Uo6enB9dccw1uu+02fOADH5j06yWIalPJc0ZRFADAF77wBbz1rW8FAPzoRz9CR0cHfv3rX+ctLghirjAd50w4HMb111+P1atX4+677y75GKUehyDmApU8Z7797W8jHA7j85//fIVePUGQY4o4y/nEJz6BBx98EE899RQ6Ojr021taWgDDTgNnYGBA33VoaWmBLMsYHR0tep8nn3wSDz30EH75y1/iwgsvxObNm/Gd73wHbrcbP/7xj2fgHRLE9DKVc2Yi9PT04PLLL8e2bdvwgx/8YBpeOUFUh0qfM3yDY/Xq1fptTqcTixcvRldX1zS8A4KYWabjnIlEIrjmmmvg8/nwwAMPwG635z1Of39/wfMODg5O6npFENWm0ufMk08+iZdeeglOpxM2mw1Lly4FAGzduhXvfe97K/zuiLMFEqaIsxJVVfHxj38c999/P5588kksWrQo7/eLFi1CS0sLHnvsMf02WZbxzDPP4IILLgAAbNmyBXa7Pe8+vb292Ldvn36feDwOABDF/FNNFEV9l5sg5gLTcc6US3d3Ny677DJs3rwZP/rRjwrOH4KYC8zUObNlyxY4nc68FuHpdBqdnZ1YsGDBNL0bgqg803XOhMNhXH311XA4HHjwwQfhcrnyHmfbtm0IhUJ45ZVX9NtefvllhEKhCV+vCKKazNQ5861vfQt79uzB7t27sXv3bjz88MMAgPvuuw9f/vKXK/4+ibOEaqevE0Q1+MhHPqIGg0H16aefVnt7e/WfeDyu3+erX/2qGgwG1fvvv199/fXX1Xe+851qa2urGg6H9ft8+MMfVjs6OtTHH39c3blzp3rFFVeoGzZsUDOZjKpqXfnq6+vVW2+9Vd29e7d66NAh9a/+6q9Uu92u7t69uyrvnSAmw3SdM729vequXbvU//7v/9Y7Vu7atUsdHh5WVa2z5dKlS9UrrrhCPX36dN5zEcRcYqbOGVVV1U996lNqe3u7+uijj6oHDx5U77rrLrWpqUkdGRmZ8fdNEJNlOs6ZcDisnnfeeeq6devUo0eP5j0On5upqqpec8016vr169Xt27er27dvV9etW6fecMMNVXnfBDFZZvKcMXLixAnqykdMOyRMEWclACx/fvSjH+n3URRFvfvuu9WWlhbV6XSql1xyifr666/nPU4ikVA//vGPq3V1darb7VZvuOEGtaurK+8+r776qnr11VerdXV1qt/vV88//3z14YcfnrH3ShDTwXSdM3fffXfJx/nRj35U9LkIYi4xU+eMqqqqLMvqX/7lX6pNTU2q3+9Xr7zySnXfvn0z+n4JYqpMxznz1FNPFX2cEydO6PcbHh5W3/3ud6t+v1/1+/3qu9/9bnV0dHTG3zNBTIWZPGeMkDBFVAJBZQc1QRAEQRAEQRAEQRAEQcwoFNxBEARBEARBEARBEARBVAUSpgiCIAiCIAiCIAiCIIiqQMIUQRAEQRAEQRAEQRAEURVImCIIgiAIgiAIgiAIgiCqAglTBEEQBEEQBEEQBEEQRFUgYYogCIIgCIIgCIIgCIKoCiRMEQRBEARBEARBEARBEFWBhCmCIAiCIAiCIAiCIAiiKpAwRRAEQRAEQRAEQRAEQVQFEqYIgiAIgiAIgiAIgiCIqkDCFEEQBEEQBEEQBEEQBFEVSJgiCIIgCIIgCIIgCIIgqsL/B1DD8wTjAusfAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -545,8 +569,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Average long turnover (one-way): 0.286\n", - "Average L/S turnover (one-way): 0.295\n" + "Average long turnover (one-way): 0.289\n", + "Average L/S turnover (one-way): 0.596\n" ] } ], @@ -556,20 +580,32 @@ "Compute one-way turnover at each rebalance\n", "==================================\n", "\"\"\"\n", + "def _equal_weight_vector(holdings):\n", + " \"\"\"Equal-weight vector for a list of holdings.\"\"\"\n", + " if len(holdings) == 0:\n", + " return pd.Series(dtype=float)\n", + " return pd.Series(1.0 / len(holdings), index=pd.Index(holdings), dtype=float)\n", + "\n", "def compute_turnover(holdings_series):\n", - " \"\"\"Compute one-way turnover between consecutive rebalances.\"\"\"\n", - " turnovers = [np.nan] # First period has no prior\n", - " for i in range(1, len(holdings_series)):\n", - " prev = set(holdings_series.iloc[i-1])\n", - " curr = set(holdings_series.iloc[i])\n", - " # One-way turnover: fraction of portfolio that changed\n", - " turnover = 1 - len(prev & curr) / len(curr) if len(curr) > 0 else 0\n", - " turnovers.append(turnover)\n", + " \"\"\"Compute one-way turnover from actual target weight vectors.\"\"\"\n", + " turnovers = []\n", + " prev_w = None\n", + " for holdings in holdings_series:\n", + " curr_w = _equal_weight_vector(holdings)\n", + " if curr_w.empty:\n", + " turnover = 0.0\n", + " elif prev_w is None or prev_w.empty:\n", + " turnover = curr_w.abs().sum() # initial buy into the portfolio\n", + " else:\n", + " names = prev_w.index.union(curr_w.index)\n", + " turnover = (curr_w.reindex(names, fill_value=0.0) - prev_w.reindex(names, fill_value=0.0)).abs().sum() / 2.0\n", + " turnovers.append(float(turnover))\n", + " prev_w = curr_w\n", " return pd.Series(turnovers, index=holdings_series.index)\n", "\n", "df_port['long_turnover'] = compute_turnover(df_port['long_holdings'])\n", "df_port['short_turnover'] = compute_turnover(df_port['short_holdings'])\n", - "df_port['ls_turnover'] = (df_port['long_turnover'] + df_port['short_turnover']) / 2\n", + "df_port['ls_turnover'] = df_port['long_turnover'] + df_port['short_turnover']\n", "\n", "fig, ax = plt.subplots(figsize=(12, 5))\n", "ax.plot(df_port.index, df_port['long_turnover'], color='steelblue', alpha=0.5, label='Long turnover')\n", @@ -589,9 +625,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 6, "id": "d3cfa6a1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T12:20:22.029262Z", + "iopub.status.busy": "2026-07-31T12:20:22.029060Z", + "iopub.status.idle": "2026-07-31T12:20:22.047668Z", + "shell.execute_reply": "2026-07-31T12:20:22.047015Z" + } + }, "outputs": [ { "name": "stdout", @@ -629,20 +672,20 @@ "
\n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -653,21 +696,21 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -681,13 +724,13 @@ ], "text/plain": [ " long_net short_net ls_net\n", - "count 238.0000 238.0000 238.0000\n", - "mean 0.0163 0.0169 -0.0006\n", - "std 0.0561 0.0702 0.0479\n", + "count 239.0000 239.0000 239.0000\n", + "mean 0.0162 0.0169 -0.0007\n", + "std 0.0560 0.0700 0.0479\n", "min -0.1797 -0.2208 -0.4088\n", - "25% -0.0126 -0.0213 -0.0220\n", - "50% 0.0177 0.0143 0.0024\n", - "75% 0.0496 0.0488 0.0248\n", + "25% -0.0117 -0.0209 -0.0217\n", + "50% 0.0174 0.0144 0.0019\n", + "75% 0.0496 0.0488 0.0247\n", "max 0.1725 0.4797 0.1005" ] }, @@ -701,9 +744,8 @@ "Apply transaction costs\n", "==================================\n", "\n", - "Cost = turnover * one-way cost in bps / 10000.\n", - "Round-trip cost = 2 * one-way, but we apply one-way cost to each side's\n", - "turnover, which effectively gives round-trip on the changing portion.\n", + "Cost = one-way turnover * cost in bps / 10000.\n", + "For the long-short book, long and short costs are applied separately.\n", "\"\"\"\n", "tc = TRANSACTION_COST_BPS / 10000 # 5 bps = 0.0005\n", "\n", @@ -735,11 +777,20 @@ "source": [ "## Performance Metrics\n", "\n", - "Standard performance metrics, all derived from the portfolio return time series $r_{p,1}, \\dots, r_{p,T}$ are the following. \n", - "- **Sharpe ratio** = $\\frac{\\bar{r}_p}{\\text{std}(r_p)} \\times \\sqrt{12}$ (annualized) — a **signal-to-noise ratio**.\n", - "- **Sortino ratio** = like Sharpe, but denominator is downside-only standard deviation (we don't want to be punished for making decent returns).\n", - "- **Max drawdown** = $\\max_t \\left(\\max_{s \\leq t} V_s - V_t\\right)$ where $V_t = \\prod_{s=1}^{t}(1+r_{p,s})$.\n", - "- **Calmar ratio** = annualized return / max drawdown." + "The main metrics are:\n", + "\n", + "- **Sharpe ratio:**\n", + "\n", + "$$\\text{Sharpe}=\\frac{\\bar r_p}{\\mathrm{std}(r_p)}\\sqrt{12}.$$\n", + "\n", + "- **Sortino ratio:** like Sharpe, but the denominator is downside deviation rather than total volatility.\n", + "- **Max drawdown:** the worst percentage drop from a previous wealth peak:\n", + "\n", + "$$\\text{DD}_t=\\frac{V_t-\\max_{s\\le t}V_s}{\\max_{s\\le t}V_s}, \\quad V_t=\\prod_{s=1}^{t}(1+r_{p,s}).$$\n", + "\n", + "- **Calmar ratio:** annualized return divided by the absolute value of max drawdown.\n", + "\n", + "These are descriptive statistics. They do not prove a strategy is good, but they help us understand the shape of the returns." ] }, { @@ -747,117 +798,69 @@ "id": "6ab72d21", "metadata": {}, "source": [ - "## Sortino Ratio\n", + "## Sortino Ratio and Max Drawdown\n", "\n", - "### The Problem with Sharpe\n", + "### Sortino Ratio\n", "\n", - "Sharpe uses **total** standard deviation in the denominator:\n", + "Sharpe uses total volatility in the denominator. That means upside and downside moves both increase the denominator. Sortino replaces total volatility with downside deviation, so months above the target return do not count as risk.\n", "\n", - "$$\\text{Sharpe} = \\frac{\\bar{r}_p}{\\text{std}(r_p)} \\times \\sqrt{12}$$\n", + "With target return $r_{\\text{target}}=0$:\n", "\n", - "This penalizes **all** volatility — including upside. If your strategy occasionally returns +15% in a month, that's great, but it inflates $\\text{std}(r_p)$ and *lowers* your Sharpe. You're being punished for making too much money.\n", + "$$\\sigma_D=\\sqrt{\\frac{1}{T}\\sum_{t=1}^{T}\\min(0,r_t-r_{\\text{target}})^2},$$\n", "\n", - "### Sortino's Fix\n", + "and\n", "\n", - "Sortino only penalizes returns that fall **below a target** (usually 0, meaning only actual losses count):\n", + "$$\\text{Sortino}=\\frac{\\bar r_p}{\\sigma_D}\\sqrt{12}.$$\n", "\n", - "$$\\text{Sortino} = \\frac{\\bar{r}_p}{\\sigma_D} \\times \\sqrt{12}$$\n", + "Example monthly returns:\n", "\n", - "where the downside deviation is:\n", + "$$[0.03,-0.02,0.08,-0.01,0.04].$$\n", "\n", - "$$\\sigma_D = \\sqrt{\\frac{1}{T}\\sum_{t=1}^{T} \\min(0,\\; r_t - r_{\\text{target}})^2}$$\n", + "Only the negative months enter downside deviation:\n", "\n", - "### How $\\sigma_D$ Works Step by Step\n", + "| Month | Return | Downside part | Squared |\n", + "|---|---:|---:|---:|\n", + "| 1 | 0.03 | 0.00 | 0.0000 |\n", + "| 2 | -0.02 | -0.02 | 0.0004 |\n", + "| 3 | 0.08 | 0.00 | 0.0000 |\n", + "| 4 | -0.01 | -0.01 | 0.0001 |\n", + "| 5 | 0.04 | 0.00 | 0.0000 |\n", "\n", - "Say your monthly returns are: $[0.03,\\; -0.02,\\; 0.08,\\; -0.01,\\; 0.04]$ and $r_{\\text{target}} = 0$.\n", + "So\n", "\n", - "| Month | Return | $r_t - 0$ | $\\min(0, r_t)$ | Squared |\n", - "|---|---|---|---|---|\n", - "| 1 | +0.03 | +0.03 | 0 | 0 |\n", - "| 2 | -0.02 | -0.02 | -0.02 | 0.0004 |\n", - "| 3 | +0.08 | +0.08 | 0 | 0 |\n", - "| 4 | -0.01 | -0.01 | -0.01 | 0.0001 |\n", - "| 5 | +0.04 | +0.04 | 0 | 0 |\n", + "$$\\sigma_D=\\sqrt{(0.0004+0.0001)/5}=0.01.$$\n", "\n", - "The +8% month contributes **zero** to $\\sigma_D$ — Sortino doesn't care about it. Only the two negative months matter.\n", + "Sortino is useful when the return distribution is asymmetric. A large gap between Sortino and Sharpe usually means the strategy has more upside volatility than downside volatility.\n", "\n", - "$$\\sigma_D = \\sqrt{\\frac{0.0004 + 0.0001}{5}} = \\sqrt{0.0001} = 0.01$$\n", + "### Max Drawdown\n", "\n", - "Compare to regular std which would be inflated by that +8% outlier.\n", + "Cumulative wealth is\n", "\n", - "### When Sortino > Sharpe\n", + "$$V_t=(1+r_1)(1+r_2)\\cdots(1+r_t).$$\n", "\n", - "A strategy with occasional large positive surprises will have Sortino noticeably higher than Sharpe. A strategy with symmetric volatility (gains and losses of similar magnitude) will have Sortino $\\approx$ Sharpe. Big gap between them tells you the return distribution is positively skewed.\n", + "The running peak is\n", "\n", - "---\n", + "$$P_t=\\max_{s\\le t}V_s.$$\n", "\n", - "## Max Drawdown\n", + "Drawdown is the percentage distance below that peak:\n", "\n", - "### The Formula, Deconstructed\n", + "$$\\text{DD}_t=\\frac{V_t-P_t}{P_t}.$$\n", "\n", - "$$\\text{Max DD} = \\max_t \\left(\\max_{s \\leq t} V_s - V_t\\right)$$\n", + "Example:\n", "\n", - "where:\n", + "| Month | Wealth $V_t$ | Running peak $P_t$ | Drawdown |\n", + "|---|---:|---:|---:|\n", + "| 1 | 1.05 | 1.05 | 0.0% |\n", + "| 2 | 1.08 | 1.08 | 0.0% |\n", + "| 3 | 1.02 | 1.08 | -5.6% |\n", + "| 4 | 0.95 | 1.08 | -12.0% |\n", + "| 5 | 1.01 | 1.08 | -6.5% |\n", "\n", - "$$V_t = \\prod_{s=1}^{t}(1 + r_{p,s})$$\n", + "Max drawdown is the worst value in that drawdown series. It answers: how far underwater would an investor have been at the worst point?\n", "\n", - "There are three nested pieces here. Let's go from the inside out.\n", + "Calmar then asks how much annual return the strategy earned per unit of that worst drawdown:\n", "\n", - "### Piece 1: $V_t$ — Cumulative Wealth\n", - "\n", - "$$V_t = \\prod_{s=1}^{t}(1 + r_{p,s}) = (1+r_1)(1+r_2)\\cdots(1+r_t)$$\n", - "\n", - "This is **compounding**. Start with $\\$1$. Each month, multiply by $(1 + r_s)$:\n", - "- Return $+5\\%$ $\\rightarrow$ multiply by $1.05$\n", - "- Return $-3\\%$ $\\rightarrow$ multiply by $0.97$\n", - "\n", - "$V_t$ is the value of your $\\$1$ at the end of month $t$. If $V_t = 1.50$, your $\\$1$ has grown to $\\$1.50$.\n", - "\n", - "### Piece 2: $\\max_{s \\leq t} V_s$ — The Running Peak\n", - "\n", - "This is the **high-water mark** — the highest your portfolio has ever been *as of time* $t$.\n", - "\n", - "Example with a wealth path:\n", - "\n", - "| Month | $V_t$ | $\\max_{s \\leq t} V_s$ |\n", - "|---|---|---|\n", - "| 1 | 1.05 | 1.05 |\n", - "| 2 | 1.08 | 1.08 |\n", - "| 3 | 1.02 | 1.08 |\n", - "| 4 | 0.95 | 1.08 |\n", - "| 5 | 1.01 | 1.08 |\n", - "\n", - "At month 4, you're at 0.95 but your peak was 1.08. You're underwater.\n", - "\n", - "### Piece 3: $\\max_{s \\leq t} V_s - V_t$ — Drawdown at Time $t$\n", - "\n", - "This is **how far below your peak you are right now**. It's the pain you're feeling at month $t$.\n", - "\n", - "| Month | Peak | $V_t$ | Drawdown |\n", - "|---|---|---|---|\n", - "| 1 | 1.05 | 1.05 | 0.00 |\n", - "| 2 | 1.08 | 1.08 | 0.00 |\n", - "| 3 | 1.08 | 1.02 | 0.06 |\n", - "| 4 | 1.08 | 0.95 | **0.13** |\n", - "| 5 | 1.08 | 1.01 | 0.07 |\n", - "\n", - "### Piece 4: $\\max_t(\\ldots)$ — The Worst Drawdown Ever\n", - "\n", - "Take the maximum of the drawdown column across all months. In the example above, max drawdown = **0.13** (13%), occurring at month 4.\n", - "\n", - "### Plain English\n", - "\n", - "> Max drawdown is the largest percentage drop from a previous peak to a subsequent trough, over the entire life of the portfolio.\n", - "\n", - "It answers: **\"What's the worst pain an investor in this strategy would have had to sit through?\"**\n", - "\n", - "A strategy that returns 15% annualized with a 50% max drawdown is psychologically very hard to hold — you'd have watched half your money vanish at some point. The same 15% with a 12% max drawdown is investable.\n", - "\n", - "### Why It Matters for Calmar\n", - "\n", - "$$\\text{Calmar} = \\frac{\\text{Annualized return}}{\\text{Max drawdown}}$$\n", - "\n", - "Calmar asks: \"How much return am I getting per unit of worst-case pain?\" A Calmar above 1 is decent; above 2 is strong; above 3 is excellent. It's return-adjusted-for-disaster-risk rather than return-adjusted-for-volatility (which is Sharpe)." + "$$\\text{Calmar}=\\frac{\\text{annualized return}}{|\\text{max drawdown}|}.$$" ] }, { @@ -867,18 +870,31 @@ "source": [ "### Equal-Weight (EW) Universe Benchmark\n", "\n", - "The **equal-weight (EW) universe** is a portfolio that holds *every* stock in the universe at the same weight, $w_i = 1/N_t$. Its return each month is just the average of the cross-section — the mean of a row of $\\mathbf{R}$:\n", - "$$ \\bar{r}_t = \\tfrac{1}{N_t}\\mathbf{1}^\\top r_t. $$\n", - "In linear-algebra terms this is the projection of the return vector onto the all-ones vector $\\mathbf{1}$.\n", + "The equal-weight universe holds every available stock at the same weight:\n", "\n", - "Why this benchmark rather than a cap-weighted index like the S&P 500? Our long-only portfolio is **equal-weighted within the top decile**, so comparing against a *cap-weighted* index would conflate two separate questions: \"did we pick the right stocks?\" and \"does equal-weighting beat cap-weighting?\" (a known size effect). The EW universe holds the weighting scheme fixed (equal weight) and isolates the first question: **did selecting the top-momentum decile beat just holding the whole universe equally?** The gap $r_{p,t} - \\bar{r}_t$ is the **active return**, and its signal-to-noise ratio is the **information ratio (IR)**." + "$$w_i=\\frac{1}{N_t}.$$\n", + "\n", + "Its return is the row mean of the return matrix:\n", + "\n", + "$$\\bar r_t=\\frac{1}{N_t}\\mathbf{1}^\\top r_t.$$\n", + "\n", + "This is a better benchmark for this project than a cap-weighted index because our portfolio is also equal-weighted within its selected names. Comparing equal-weight to equal-weight keeps the focus on selection: did the top-momentum decile beat simply holding the whole available universe equally?\n", + "\n", + "The difference $r_{p,t}-\\bar r_t$ is the **active return**, and its annualized mean divided by tracking error is the **information ratio**." ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "id": "4845c9f1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T12:20:22.049879Z", + "iopub.status.busy": "2026-07-31T12:20:22.049541Z", + "iopub.status.idle": "2026-07-31T12:20:22.070626Z", + "shell.execute_reply": "2026-07-31T12:20:22.069944Z" + } + }, "outputs": [ { "name": "stdout", @@ -920,28 +936,28 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -951,9 +967,9 @@ ], "text/plain": [ " ann_return ann_vol sharpe sortino max_drawdown calmar\n", - "long_net 0.1955 0.1945 1.0054 1.4074 -0.5713 0.3422\n", - "ls_net -0.0072 0.1661 -0.0433 -0.0437 -0.7029 -0.0102\n", - "ew_universe 0.1594 0.1683 0.9471 1.2657 -0.4747 0.3359" + "long_net 0.1948 0.1941 1.0036 1.6436 -0.5713 0.3409\n", + "ls_net -0.0081 0.1658 -0.0491 -0.0590 -0.7029 -0.0116\n", + "ew_universe 0.1594 0.1683 0.9471 1.5203 -0.4747 0.3359" ] }, "metadata": {}, @@ -965,10 +981,10 @@ "text": [ "\n", "Active Return (Long-Only minus EW Universe)\n", - " Monthly active return: 0.00300 (t = 1.92)\n", - " Annualized: 0.0360\n", - " Tracking error: 0.0834\n", - " Information ratio: 0.4315\n" + " Monthly active return: 0.00295 (t = 1.89)\n", + " Annualized: 0.0354\n", + " Tracking error: 0.0833\n", + " Information ratio: 0.4243\n" ] } ], @@ -980,15 +996,16 @@ "\"\"\"\n", "def performance_metrics(returns, freq=12, rf=0):\n", " \"\"\"Compute standard performance metrics.\"\"\"\n", + " returns = returns.dropna()\n", " excess = returns - rf\n", " ann_return = returns.mean() * freq\n", " ann_vol = returns.std() * np.sqrt(freq)\n", " sharpe = ann_return / ann_vol if ann_vol > 0 else np.nan\n", - " \n", - " downside = returns[returns < 0]\n", - " downside_vol = downside.std() * np.sqrt(freq) if len(downside) > 0 else np.nan\n", + "\n", + " downside = np.minimum(excess, 0.0)\n", + " downside_vol = np.sqrt(np.mean(downside ** 2)) * np.sqrt(freq) if len(downside) > 0 else np.nan\n", " sortino = ann_return / downside_vol if downside_vol and downside_vol > 0 else np.nan\n", - " \n", + "\n", " cum = (1 + returns).cumprod()\n", " running_max = cum.cummax()\n", " drawdown = (cum - running_max) / running_max\n", @@ -1033,13 +1050,20 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 8, "id": "e9999e92", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T12:20:22.073634Z", + "iopub.status.busy": "2026-07-31T12:20:22.073011Z", + "iopub.status.idle": "2026-07-31T12:20:22.927406Z", + "shell.execute_reply": "2026-07-31T12:20:22.926772Z" + } + }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1089,16 +1113,23 @@ "source": [ "## Walk-Forward Analysis\n", "\n", - "A single full-sample Sharpe ratio doesn't quite tell us whether we have a robust strategy. Walk-forward analysis splits the sample into non-overlapping 5-year windows and computes performance metrics in each. A strategy that's positive in *every window* is far more convincing than one that earned all its returns in one lucky period. Stability of the signal-to-noise ratio across sub-windows is evidence that the angle between $f_t$ and $r_{t+1}$ is a persistent feature of the data, not a single-period artifact.\n", + "A full-sample Sharpe can be misleading if one period did all the work. Walk-forward analysis splits the history into 5-year windows and recomputes performance in each window.\n", "\n", - "This is pure consistency checking; we're not optimizing any parameters or anything." + "This is a consistency check, not a parameter search. The question is whether the strategy works across more than one regime." ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 9, "id": "a0a6cf4f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T12:20:22.929561Z", + "iopub.status.busy": "2026-07-31T12:20:22.929356Z", + "iopub.status.idle": "2026-07-31T12:20:23.333476Z", + "shell.execute_reply": "2026-07-31T12:20:23.332807Z" + } + }, "outputs": [ { "name": "stdout", @@ -1151,13 +1182,13 @@ "
\n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1196,14 +1227,14 @@ "text/plain": [ " lo_sharpe lo_ann_ret ls_sharpe ew_sharpe active_ret \\\n", "period \n", - "2006-2011 0.3117 0.0712 -0.5382 0.5729 -0.0482 \n", + "2006-2011 0.3108 0.0703 -0.5493 0.5729 -0.0495 \n", "2011-2016 1.4347 0.2105 0.6602 1.3470 0.0420 \n", "2016-2021 1.2810 0.2515 -0.0018 1.1172 0.0592 \n", "2021-2026 1.2279 0.2447 0.2942 0.9738 0.0883 \n", "\n", " info_ratio active_t \n", "period \n", - "2006-2011 -0.5058 -1.1120 \n", + "2006-2011 -0.5230 -1.1596 \n", "2011-2016 0.7802 1.7445 \n", "2016-2021 0.7994 1.7874 \n", "2021-2026 0.8852 1.9795 " @@ -1214,7 +1245,7 @@ }, { "data": { - "image/png": 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", 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" ] @@ -1294,57 +1325,234 @@ }, { "cell_type": "markdown", - "id": "79a4d8c5", + "id": "14f058aa", "metadata": {}, "source": [ - "## Fama–French Alpha\n", + "## Reconciling Walk-Forward IC vs. Walk-Forward Sharpe\n", "\n", - "The **Fama–French regression** is an OLS projection of portfolio returns onto a basis of factor returns. Given a matrix of factor returns $\\mathbf{F} \\in \\mathbb{R}^{T \\times k}$ (market, size, value, momentum) and portfolio returns $r_p \\in \\mathbb{R}^T$, we solve:\n", + "Notebook 02 found that momentum's monthly IC was positive in only some subperiods, while the long-only portfolio Sharpe can still be positive across all windows. Those are not contradictory.\n", "\n", - "$$\\hat{\\beta} = (\\mathbf{F}^\\top \\mathbf{F})^{-1} \\mathbf{F}^\\top r_p,$$\n", + "IC measures cross-sectional ordering: did higher-ranked stocks beat lower-ranked stocks? Portfolio Sharpe measures the return level of the selected stocks. A top-decile book can make money in a window even if its ranking skill is weak, especially if the selected stocks had high market beta during a rising market.\n", "\n", - "- **Beta** ($\\hat{\\beta}$) = the projection lengths along each factor axis — how much the portfolio loads on each factor.\n", - "- **Alpha** ($\\alpha = r_p - \\hat{r}_p$) = the **residual** — the component of portfolio returns *orthogonal* to the factor span. A positive, statistically significant alpha means the portfolio earns returns not explained by exposure to known factors.\n", - "\n", - "We run this regression on the **long-only portfolio** (excess of risk-free), which is the main portfolio. We also run it on the long-short for comparison." + "The table below compares mean IC, portfolio Sharpe, and realized market beta by window. If high beta lines up with weak IC, the Sharpe is probably being carried by market exposure rather than stock selection." ] }, { "cell_type": "code", - "execution_count": 22, - "id": "10c3bb8c", - "metadata": {}, + "execution_count": 10, + "id": "508e0b07", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T12:20:23.335403Z", + "iopub.status.busy": "2026-07-31T12:20:23.335208Z", + "iopub.status.idle": "2026-07-31T12:20:23.360707Z", + "shell.execute_reply": "2026-07-31T12:20:23.360069Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Regression data points: 238\n", + "IC vs. Sharpe vs. realized market beta, by walk-forward window:\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "
TickerAAAPLABBV
count238.0000238.0000238.0000239.0000239.0000239.0000
mean0.01630.01620.0169-0.0006-0.0007
std0.05610.07020.05600.07000.0479
25%-0.0126-0.0213-0.0220-0.0117-0.0209-0.0217
50%0.01770.01430.00240.01740.01440.0019
75%0.04960.04880.02480.0247
max
long_net0.19550.19451.00541.40740.19480.19411.00361.6436-0.57130.34220.3409
ls_net-0.00720.1661-0.0433-0.0437-0.00810.1658-0.0491-0.0590-0.7029-0.0102-0.0116
ew_universe0.15940.16830.94711.26571.5203-0.47470.3359
2006-20110.31170.0712-0.53820.31080.0703-0.54930.5729-0.0482-0.5058-1.1120-0.0495-0.5230-1.1596
2011-2016
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mean_icportfolio_sharpeportfolio_mkt_beta
period
2006-2011-0.0130.3111.144
2011-20160.0281.4351.105
2016-2021-0.0081.2811.127
2021-20260.0171.2281.171
\n", + "" + ], + "text/plain": [ + " mean_ic portfolio_sharpe portfolio_mkt_beta\n", + "period \n", + "2006-2011 -0.013 0.311 1.144\n", + "2011-2016 0.028 1.435 1.105\n", + "2016-2021 -0.008 1.281 1.127\n", + "2021-2026 0.017 1.228 1.171" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "If portfolio_mkt_beta is visibly higher in the windows where mean_ic is negative, that is the mechanical explanation: the decile's Sharpe in that window is being carried by market exposure rather than by the ranking signal actually working that period.\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Reconcile factor-level IC (notebook 02) with portfolio-level Sharpe, by window\n", + "==================================\n", + "\"\"\"\n", + "df_ic_momentum = pd.read_csv('../data/processed/ic_monthly.csv', index_col=0, parse_dates=True)['momentum']\n", + "\n", + "reconcile_rows = []\n", + "for start, end in windows:\n", + " mask_port = (df_port.index.year >= start) & (df_port.index.year < end)\n", + " mask_ic = (df_ic_momentum.index.year >= start) & (df_ic_momentum.index.year < end)\n", + "\n", + " sub_port = df_port.loc[mask_port].dropna(subset=['long_excess', 'Mkt-RF'])\n", + " sub_ic = df_ic_momentum.loc[mask_ic].dropna()\n", + "\n", + " if len(sub_port) > 3 and sub_port['Mkt-RF'].var() > 0:\n", + " port_beta = sub_port['long_excess'].cov(sub_port['Mkt-RF']) / sub_port['Mkt-RF'].var()\n", + " else:\n", + " port_beta = np.nan\n", + "\n", + " reconcile_rows.append({\n", + " 'period': f'{start}-{end}',\n", + " 'mean_ic': sub_ic.mean(),\n", + " 'portfolio_sharpe': performance_metrics(df_port.loc[mask_port, 'long_net'])['sharpe'],\n", + " 'portfolio_mkt_beta': port_beta,\n", + " })\n", + "\n", + "df_reconcile = pd.DataFrame(reconcile_rows).set_index('period')\n", + "print(\"IC vs. Sharpe vs. realized market beta, by walk-forward window:\\n\")\n", + "display(df_reconcile.round(3))\n", + "\n", + "print(\n", + " \"\\nIf portfolio_mkt_beta is visibly higher in the windows where mean_ic is negative, \"\n", + " \"that is the mechanical explanation: the decile's Sharpe in that window is being carried \"\n", + " \"by market exposure rather than by the ranking signal actually working that period.\"\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "79a4d8c5", + "metadata": {}, + "source": [ + "## Fama-French Alpha\n", + "\n", + "The Fama-French regression is an OLS regression of portfolio excess returns on benchmark factor returns. Given factor matrix $F \\in \\mathbb{R}^{T\\times k}$ and portfolio excess returns $y\\in\\mathbb{R}^T$, the fitted model is\n", + "\n", + "$$y=\\alpha\\mathbf{1}+F\\beta+\\varepsilon.$$\n", + "\n", + "The least-squares beta estimate is\n", + "\n", + "$$\\hat\\beta=(F^\\top F)^{-1}F^\\top(y-\\alpha\\mathbf{1}),$$\n", + "\n", + "or equivalently the full coefficient vector is estimated after adding a column of ones to $F$.\n", + "\n", + "- **Betas:** exposures to benchmark factors such as market, size, value, and momentum.\n", + "- **Alpha:** the intercept. It is the average return left after controlling for those factor exposures.\n", + "- **Residuals:** the month-by-month unexplained returns around the fitted line.\n", + "\n", + "So alpha is related to the residual, but it is not the entire residual vector. It is the average unexplained return, annualized here by multiplying the monthly intercept by 12.\n", + "\n", + "We report the usual OLS t-statistic and a **HAC/Newey-West** t-statistic with three monthly lags. HAC standard errors are a useful check because monthly portfolio residuals can have mild autocorrelation or changing volatility. If the alpha only survives under plain OLS and disappears under HAC, the result is less convincing.\n", + "\n", + "We run this on the long-only portfolio as the headline result and on the long-short portfolio as a comparison." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "10c3bb8c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T12:20:23.362870Z", + "iopub.status.busy": "2026-07-31T12:20:23.362667Z", + "iopub.status.idle": "2026-07-31T12:20:23.581378Z", + "shell.execute_reply": "2026-07-31T12:20:23.580746Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Regression data points: 239\n", "============================================================\n", - "LONG-ONLY: Fama-French 4-Factor Alpha (HEADLINE)\n", + "LONG-ONLY: Fama-French 4-Factor Alpha\n", "============================================================\n", - " Alpha (monthly): 0.00496\n", - " Alpha (annualized): 0.0595\n", - " Alpha t-stat: 3.95\n", - " MKT beta: 1.189 (t=39.18)\n", - " SMB beta: 0.263 (t=5.01)\n", - " HML beta: -0.045 (t=-1.12)\n", - " MOM beta: 0.251 (t=7.94)\n", - " R^2: 0.889\n", + " Alpha (monthly): 0.00497\n", + " Alpha (annualized): 0.0597\n", + " Alpha t-stat (OLS): 3.98\n", + " Alpha t-stat (HAC): 4.17 (Newey-West, 3 lags)\n", + " MKT beta: 1.189 (t=39.27)\n", + " SMB beta: 0.262 (t=5.02)\n", + " HML beta: -0.045 (t=-1.12)\n", + " MOM beta: 0.251 (t=7.95)\n", + " R^2: 0.889\n", "\n", "============================================================\n", "LONG-SHORT: Fama-French 4-Factor Alpha (comparison)\n", "============================================================\n", - " Alpha (monthly): -0.00246\n", - " Alpha (annualized): -0.0295\n", - " Alpha t-stat: -1.41\n", - " MOM beta: 0.905 (t=20.56)\n", - " R^2: 0.704\n", + " Alpha (monthly): -0.00246\n", + " Alpha (annualized): -0.0295\n", + " Alpha t-stat (OLS): -1.41\n", + " Alpha t-stat (HAC): -1.37 (Newey-West, 3 lags)\n", + " MKT beta: 0.144 (t=3.42)\n", + " MOM beta: 0.905 (t=20.62)\n", + " R^2: 0.704\n", "\n", "============================================================\n", - "SUMMARY: Long-only alpha is positive and significant.\n", - " Long-only alpha: +0.0595 (t=3.95) — SIGNIFICANT\n", - " Long-short alpha: -0.0295 (t=-1.41) — not significant\n", + "SUMMARY\n", + " Long-only alpha: +0.0597 (OLS t=3.98, HAC t=4.17)\n", + " Long-short alpha: -0.0295 (OLS t=-1.41, HAC t=-1.37)\n", "============================================================\n" ] } @@ -1357,64 +1565,143 @@ "\"\"\"\n", "import statsmodels.api as sm\n", "\n", + "FF_FACTOR_COLS = ['Mkt-RF', 'SMB', 'HML', 'Mom']\n", + "HAC_LAGS = 3\n", + "\n", "# --- Robust alignment via year-month period index ---\n", "# df_port has month-end dates, df_ff has month-start dates.\n", "# Align both to PeriodIndex('M') so they match regardless of timestamp conventions.\n", "df_port_pm = df_port.copy()\n", "df_port_pm.index = df_port_pm.index.to_period('M')\n", "\n", - "df_ff_pm = df_ff[['Mkt-RF', 'SMB', 'HML', 'Mom']].copy()\n", + "df_ff_pm = df_ff[FF_FACTOR_COLS].copy()\n", "df_ff_pm.index = df_ff_pm.index.to_period('M')\n", "\n", "df_reg = df_port_pm.join(df_ff_pm, how='inner', lsuffix='_port', rsuffix='_ff')\n", "\n", "# Resolve any column-name collisions (Mkt-RF may exist in both from cell 10)\n", - "for col in ['Mkt-RF', 'SMB', 'HML', 'Mom']:\n", + "for col in FF_FACTOR_COLS:\n", " if col + '_ff' in df_reg.columns:\n", " df_reg[col] = df_reg[col + '_ff']\n", "\n", - "df_reg = df_reg[['long_excess', 'ls_net', 'Mkt-RF', 'SMB', 'HML', 'Mom']].dropna().astype(float)\n", + "df_reg = df_reg[['long_excess', 'ls_net'] + FF_FACTOR_COLS].dropna().astype(float)\n", "\n", "print(f\"Regression data points: {len(df_reg)}\")\n", "\n", + "\n", + "def fit_ff_model(y):\n", + " \"\"\"Fit FF regression with ordinary and HAC/Newey-West covariance.\"\"\"\n", + " X = sm.add_constant(df_reg[FF_FACTOR_COLS], has_constant='add')\n", + " ols = sm.OLS(y, X).fit()\n", + " hac = sm.OLS(y, X).fit(cov_type='HAC', cov_kwds={'maxlags': HAC_LAGS})\n", + " return ols, hac\n", + "\n", + "\n", + "def print_ff_model(label, model, model_hac, show_all_betas=True):\n", + " print(\"=\" * 60)\n", + " print(label)\n", + " print(\"=\" * 60)\n", + " print(f\" Alpha (monthly): {model.params['const']:.5f}\")\n", + " print(f\" Alpha (annualized): {model.params['const']*12:.4f}\")\n", + " print(f\" Alpha t-stat (OLS): {model.tvalues['const']:.2f}\")\n", + " print(f\" Alpha t-stat (HAC): {model_hac.tvalues['const']:.2f} (Newey-West, {HAC_LAGS} lags)\")\n", + " print(f\" MKT beta: {model.params['Mkt-RF']:.3f} (t={model.tvalues['Mkt-RF']:.2f})\")\n", + " if show_all_betas:\n", + " print(f\" SMB beta: {model.params['SMB']:.3f} (t={model.tvalues['SMB']:.2f})\")\n", + " print(f\" HML beta: {model.params['HML']:.3f} (t={model.tvalues['HML']:.2f})\")\n", + " print(f\" MOM beta: {model.params['Mom']:.3f} (t={model.tvalues['Mom']:.2f})\")\n", + " else:\n", + " print(f\" MOM beta: {model.params['Mom']:.3f} (t={model.tvalues['Mom']:.2f})\")\n", + " print(f\" R^2: {model.rsquared:.3f}\")\n", + "\n", "if len(df_reg) == 0:\n", " print(\"ERROR: No overlapping data between backtest and Fama-French factors.\")\n", " print(\"Check the indices of df_port and df_ff.\")\n", "else:\n", - " # --- Long-Only Alpha (headline) ---\n", - " X_lo = sm.add_constant(df_reg[['Mkt-RF', 'SMB', 'HML', 'Mom']])\n", - " model_lo = sm.OLS(df_reg['long_excess'].values, X_lo.values).fit()\n", - " print(\"=\" * 60)\n", - " print(\"LONG-ONLY: Fama-French 4-Factor Alpha (HEADLINE)\")\n", - " print(\"=\" * 60)\n", - " print(f\" Alpha (monthly): {model_lo.params[0]:.5f}\")\n", - " print(f\" Alpha (annualized): {model_lo.params[0]*12:.4f}\")\n", - " print(f\" Alpha t-stat: {model_lo.tvalues[0]:.2f}\")\n", - " print(f\" MKT beta: {model_lo.params[1]:.3f} (t={model_lo.tvalues[1]:.2f})\")\n", - " print(f\" SMB beta: {model_lo.params[2]:.3f} (t={model_lo.tvalues[2]:.2f})\")\n", - " print(f\" HML beta: {model_lo.params[3]:.3f} (t={model_lo.tvalues[3]:.2f})\")\n", - " print(f\" MOM beta: {model_lo.params[4]:.3f} (t={model_lo.tvalues[4]:.2f})\")\n", - " print(f\" R^2: {model_lo.rsquared:.3f}\")\n", + " model_lo, model_lo_hac = fit_ff_model(df_reg['long_excess'])\n", + " print_ff_model(\"LONG-ONLY: Fama-French 4-Factor Alpha\", model_lo, model_lo_hac)\n", "\n", - " # --- Long-Short Alpha (comparison) ---\n", - " X_ls = sm.add_constant(df_reg[['Mkt-RF', 'SMB', 'HML', 'Mom']])\n", - " model_ls = sm.OLS(df_reg['ls_net'].values, X_ls.values).fit()\n", - " print(f\"\\n{'=' * 60}\")\n", - " print(\"LONG-SHORT: Fama-French 4-Factor Alpha (comparison)\")\n", - " print(\"=\" * 60)\n", - " print(f\" Alpha (monthly): {model_ls.params[0]:.5f}\")\n", - " print(f\" Alpha (annualized): {model_ls.params[0]*12:.4f}\")\n", - " print(f\" Alpha t-stat: {model_ls.tvalues[0]:.2f}\")\n", - " print(f\" MOM beta: {model_ls.params[4]:.3f} (t={model_ls.tvalues[4]:.2f})\")\n", - " print(f\" R^2: {model_ls.rsquared:.3f}\")\n", + " model_ls, model_ls_hac = fit_ff_model(df_reg['ls_net'])\n", + " print()\n", + " print_ff_model(\"LONG-SHORT: Fama-French 4-Factor Alpha (comparison)\", model_ls, model_ls_hac, show_all_betas=False)\n", "\n", " print(f\"\\n{'=' * 60}\")\n", - " print(\"SUMMARY: Long-only alpha is positive and significant.\")\n", - " print(f\" Long-only alpha: {model_lo.params[0]*12:+.4f} (t={model_lo.tvalues[0]:.2f}) — SIGNIFICANT\")\n", - " print(f\" Long-short alpha: {model_ls.params[0]*12:+.4f} (t={model_ls.tvalues[0]:.2f}) — not significant\")\n", + " print(\"SUMMARY\")\n", + " print(f\" Long-only alpha: {model_lo.params['const']*12:+.4f} \"\n", + " f\"(OLS t={model_lo.tvalues['const']:.2f}, HAC t={model_lo_hac.tvalues['const']:.2f})\")\n", + " print(f\" Long-short alpha: {model_ls.params['const']*12:+.4f} \"\n", + " f\"(OLS t={model_ls.tvalues['const']:.2f}, HAC t={model_ls_hac.tvalues['const']:.2f})\")\n", " print(\"=\" * 60)" ] }, + { + "cell_type": "markdown", + "id": "0a7da761", + "metadata": {}, + "source": [ + "### What Does MKT Beta = 1.19 Mean for the Headline Alpha?\n", + "\n", + "The regression reports a market beta along with alpha. A beta around 1.19 means the long-only decile had about 19% more market exposure than a beta-1 portfolio over this sample.\n", + "\n", + "That matters because part of the raw return may be ordinary market risk, not stock selection. This is why the factor-adjusted alpha is more informative than the raw active return. The regression subtracts the part explained by market, size, value, and momentum exposure before estimating the intercept.\n", + "\n", + "To make the idea concrete, we compare the portfolio to a beta-matched benchmark: the equal-weight universe scaled to the same market beta. This is not a tradable recommendation; it is a diagnostic for whether the outperformance survives a simple market-risk adjustment." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "519db558", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T12:20:23.583614Z", + "iopub.status.busy": "2026-07-31T12:20:23.583280Z", + "iopub.status.idle": "2026-07-31T12:20:23.590261Z", + "shell.execute_reply": "2026-07-31T12:20:23.589581Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Long-only ann. return: 0.1948\n", + "EW universe ann. return (unscaled): 0.1594\n", + "EW universe ann. return (x1.19 beta-matched): 0.1895\n", + "\n", + "Outperformance vs. unscaled EW: +0.0354\n", + "Outperformance vs. beta-matched EW: +0.0052\n", + "\n", + "If the beta-matched gap is still clearly positive, the outperformance is not simply leverage on the market factor; this is a second, more direct check on the same question the FF alpha answers via regression.\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Beta-matched benchmark comparison\n", + "==================================\n", + "\"\"\"\n", + "beta_lo = model_lo.params['Mkt-RF'] # MKT-RF coefficient from the FF regression above\n", + "\n", + "ew_scaled = df_port['ew_universe'] * beta_lo\n", + "\n", + "lo_ann_ret = df_port['long_net'].mean() * 12\n", + "ew_ann_ret = df_port['ew_universe'].mean() * 12\n", + "ew_scaled_ann_ret = ew_scaled.mean() * 12\n", + "\n", + "print(f\"Long-only ann. return: {lo_ann_ret:.4f}\")\n", + "print(f\"EW universe ann. return (unscaled): {ew_ann_ret:.4f}\")\n", + "print(f\"EW universe ann. return (x{beta_lo:.2f} beta-matched): {ew_scaled_ann_ret:.4f}\")\n", + "print(f\"\\nOutperformance vs. unscaled EW: {lo_ann_ret - ew_ann_ret:+.4f}\")\n", + "print(f\"Outperformance vs. beta-matched EW: {lo_ann_ret - ew_scaled_ann_ret:+.4f}\")\n", + "print(\n", + " \"\\nIf the beta-matched gap is still clearly positive, the outperformance is not simply \"\n", + " \"leverage on the market factor; this is a second, more direct check on the same question \"\n", + " \"the FF alpha answers via regression.\"\n", + ")\n" + ] + }, { "cell_type": "markdown", "id": "b8020d29", @@ -1422,16 +1709,25 @@ "source": [ "## Survivorship Bias Sensitivity\n", "\n", - "The universe is reconstructed from the *current* S&P 500 — stocks that were delisted or went bankrupt between 2005 and today are missing. This biases returns upward because the stocks we *don't* see are exactly the ones that went to zero.\n", + "The universe is based on current S&P 500 constituents. That means stocks that disappeared from the index, were acquired, delisted, or went bankrupt may be missing from the historical panel.\n", "\n", - "We can't fix this without using different data (maybe CRSP), but we can ask: **how much return drag from missing delisted stocks would it take to erase the alpha?** We apply a synthetic annual drag to the long-only excess returns and re-run the Fama–French 4-factor regression at each drag level, tracking the alpha t-statistic. If the alpha survives a plausible drag (e.g., 1-2% per year), the result is robust to survivorship bias." + "We cannot fully fix this without survivorship-free data. Instead, we ask a sensitivity question: how much annual return drag would we need to subtract before the Fama-French alpha is no longer statistically significant?\n", + "\n", + "This is not a perfect model of delisting bias. It is a breakeven calculation. If a small drag erases the result, the backtest is fragile. If a large drag is needed, the result is less likely to be explained only by survivorship bias." ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 13, "id": "5a4bcd3f", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T12:20:23.592778Z", + "iopub.status.busy": "2026-07-31T12:20:23.592552Z", + "iopub.status.idle": "2026-07-31T12:20:23.971023Z", + "shell.execute_reply": "2026-07-31T12:20:23.970290Z" + } + }, "outputs": [ { "name": "stdout", @@ -1467,8 +1763,9 @@ " annual_drag\n", " alpha_monthly\n", " alpha_annualized\n", - " t_stat\n", - " significant\n", + " t_stat_ols\n", + " t_stat_hac\n", + " significant_hac\n", " \n", " \n", " \n", @@ -1476,56 +1773,63 @@ " 0\n", " 0.0%\n", " 0.0050\n", - " 0.0595\n", - " 3.9511\n", + " 0.0597\n", + " 3.9777\n", + " 4.1656\n", " Yes\n", " \n", " \n", " 1\n", " 0.5%\n", - " 0.0045\n", - " 0.0545\n", - " 3.6193\n", + " 0.0046\n", + " 0.0547\n", + " 3.6444\n", + " 3.8166\n", " Yes\n", " \n", " \n", " 2\n", " 1.0%\n", " 0.0041\n", - " 0.0495\n", - " 3.2875\n", + " 0.0497\n", + " 3.3111\n", + " 3.4676\n", " Yes\n", " \n", " \n", " 3\n", " 2.0%\n", " 0.0033\n", - " 0.0395\n", - " 2.6238\n", + " 0.0397\n", + " 2.6446\n", + " 2.7695\n", " Yes\n", " \n", " \n", " 4\n", " 3.0%\n", " 0.0025\n", - " 0.0295\n", - " 1.9602\n", - " No\n", + " 0.0297\n", + " 1.9780\n", + " 2.0714\n", + " Yes\n", " \n", " \n", " 5\n", " 4.0%\n", " 0.0016\n", - " 0.0195\n", - " 1.2966\n", + " 0.0197\n", + " 1.3114\n", + " 1.3734\n", " No\n", " \n", " \n", " 6\n", " 5.0%\n", " 0.0008\n", - " 0.0095\n", - " 0.6330\n", + " 0.0097\n", + " 0.6449\n", + " 0.6753\n", " No\n", " \n", " \n", @@ -1533,14 +1837,23 @@ "" ], "text/plain": [ - " annual_drag alpha_monthly alpha_annualized t_stat significant\n", - "0 0.0% 0.0050 0.0595 3.9511 Yes\n", - "1 0.5% 0.0045 0.0545 3.6193 Yes\n", - "2 1.0% 0.0041 0.0495 3.2875 Yes\n", - "3 2.0% 0.0033 0.0395 2.6238 Yes\n", - "4 3.0% 0.0025 0.0295 1.9602 No\n", - "5 4.0% 0.0016 0.0195 1.2966 No\n", - "6 5.0% 0.0008 0.0095 0.6330 No" + " annual_drag alpha_monthly alpha_annualized t_stat_ols t_stat_hac \\\n", + "0 0.0% 0.0050 0.0597 3.9777 4.1656 \n", + "1 0.5% 0.0046 0.0547 3.6444 3.8166 \n", + "2 1.0% 0.0041 0.0497 3.3111 3.4676 \n", + "3 2.0% 0.0033 0.0397 2.6446 2.7695 \n", + "4 3.0% 0.0025 0.0297 1.9780 2.0714 \n", + "5 4.0% 0.0016 0.0197 1.3114 1.3734 \n", + "6 5.0% 0.0008 0.0097 0.6449 0.6753 \n", + "\n", + " significant_hac \n", + "0 Yes \n", + "1 Yes \n", + "2 Yes \n", + "3 Yes \n", + "4 Yes \n", + "5 No \n", + "6 No " ] }, "metadata": {}, @@ -1548,7 +1861,7 @@ }, { "data": { - "image/png": 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", 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" ] @@ -1581,15 +1894,17 @@ "\n", "for drag in drags:\n", " y_adj = df_reg['long_excess'] - drag / 12 # monthly drag on excess returns\n", - " model = sm.OLS(y_adj.values, X_factors.values).fit()\n", - " alpha_m = model.params[0]\n", - " alpha_t = model.tvalues[0]\n", + " model = sm.OLS(y_adj, X_factors).fit()\n", + " model_hac = sm.OLS(y_adj, X_factors).fit(cov_type='HAC', cov_kwds={'maxlags': HAC_LAGS})\n", + " alpha_m = model.params['const']\n", + " alpha_t = model.tvalues['const']\n", " sensitivity.append({\n", " 'annual_drag': f'{drag*100:.1f}%',\n", " 'alpha_monthly': alpha_m,\n", " 'alpha_annualized': alpha_m * 12,\n", - " 't_stat': alpha_t,\n", - " 'significant': 'Yes' if abs(alpha_t) > 2 else 'No'\n", + " 't_stat_ols': alpha_t,\n", + " 't_stat_hac': model_hac.tvalues['const'],\n", + " 'significant_hac': 'Yes' if abs(model_hac.tvalues['const']) > 2 else 'No'\n", " })\n", "\n", "df_sens = pd.DataFrame(sensitivity)\n", @@ -1598,7 +1913,8 @@ "display(df_sens.round(4))\n", "\n", "fig, ax = plt.subplots(figsize=(10, 5))\n", - "ax.plot(df_sens['annual_drag'], df_sens['t_stat'], 'o-', color='steelblue', linewidth=2)\n", + "ax.plot(df_sens['annual_drag'], df_sens['t_stat_ols'], 'o-', color='steelblue', linewidth=2, label='OLS t-stat')\n", + "ax.plot(df_sens['annual_drag'], df_sens['t_stat_hac'], 'o-', color='seagreen', linewidth=2, label='HAC t-stat')\n", "ax.axhline(y=2, color='coral', linestyle='--', label='t = 2 (5% significance)')\n", "ax.axhline(y=0, color='black', linewidth=0.5)\n", "ax.set_xlabel('Synthetic Annual Return Drag')\n", @@ -1613,10 +1929,217 @@ ] }, { - "cell_type": "code", - "execution_count": 24, - "id": "93b4cf77", + "cell_type": "markdown", + "id": "2b3dbdb0", "metadata": {}, + "source": [ + "### Concentrating the Drag in Bad Months\n", + "\n", + "A flat monthly drag is easy to read, but real delisting losses are not evenly spread through time. They tend to cluster during market stress.\n", + "\n", + "So we run a harsher version: apply most of the same annual drag during the worst equal-weight universe months and only a small residual drag elsewhere. If this concentrated drag erases the alpha much faster, the flat-drag test was too generous." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "1a9a235a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T12:20:23.973287Z", + "iopub.status.busy": "2026-07-31T12:20:23.973043Z", + "iopub.status.idle": "2026-07-31T12:20:24.014214Z", + "shell.execute_reply": "2026-07-31T12:20:24.013558Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Concentrated (crash-weighted) drag sensitivity:\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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annual_dragalpha_annualizedalpha_t_olsalpha_t_hacsignificant_hac_t_gt_2
00.0%0.05973.97774.1656True
10.5%0.05383.59213.7664True
21.0%0.04793.20423.3635True
32.0%0.03622.42272.5481True
43.0%0.02441.63641.7237False
54.0%0.01270.84840.8946False
65.0%0.00090.06180.0652False
\n", + "
" + ], + "text/plain": [ + " annual_drag alpha_annualized alpha_t_ols alpha_t_hac \\\n", + "0 0.0% 0.0597 3.9777 4.1656 \n", + "1 0.5% 0.0538 3.5921 3.7664 \n", + "2 1.0% 0.0479 3.2042 3.3635 \n", + "3 2.0% 0.0362 2.4227 2.5481 \n", + "4 3.0% 0.0244 1.6364 1.7237 \n", + "5 4.0% 0.0127 0.8484 0.8946 \n", + "6 5.0% 0.0009 0.0618 0.0652 \n", + "\n", + " significant_hac_t_gt_2 \n", + "0 True \n", + "1 True \n", + "2 True \n", + "3 True \n", + "4 False \n", + "5 False \n", + "6 False " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Compare the drag level at which significance is lost here against the flat-drag table above. A meaningfully lower breakeven drag under concentration would mean the flat-drag result overstates how robust the alpha is to realistic (crash-clustered) survivorship bias.\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Concentrated (crash-weighted) survivorship drag sensitivity\n", + "==================================\n", + "\"\"\"\n", + "crash_mask = df_reg.index.map(\n", + " lambda p: df_port_pm.loc[p, 'ew_universe'] if p in df_port_pm.index else np.nan\n", + ")\n", + "crash_mask = pd.Series(crash_mask, index=df_reg.index)\n", + "crash_threshold = crash_mask.quantile(0.25)\n", + "is_crash_month = crash_mask < crash_threshold\n", + "\n", + "concentrated_sensitivity = []\n", + "for drag in drags:\n", + " monthly_drag = drag / 12\n", + " y_adj = df_reg['long_excess'].copy()\n", + " # ~80% of the total drag falls in the worst quartile of months, 20% spread over the rest\n", + " n_crash = is_crash_month.sum()\n", + " n_other = (~is_crash_month).sum()\n", + " if n_crash > 0:\n", + " y_adj[is_crash_month] -= monthly_drag * len(y_adj) * 0.8 / n_crash\n", + " if n_other > 0:\n", + " y_adj[~is_crash_month] -= monthly_drag * len(y_adj) * 0.2 / n_other\n", + "\n", + " model = sm.OLS(y_adj, X_factors).fit()\n", + " model_hac = sm.OLS(y_adj, X_factors).fit(cov_type='HAC', cov_kwds={'maxlags': HAC_LAGS})\n", + " alpha_m = model.params['const']\n", + " concentrated_sensitivity.append({\n", + " 'annual_drag': f'{drag*100:.1f}%',\n", + " 'alpha_annualized': alpha_m * 12,\n", + " 'alpha_t_ols': model.tvalues['const'],\n", + " 'alpha_t_hac': model_hac.tvalues['const'],\n", + " 'significant_hac_t_gt_2': abs(model_hac.tvalues['const']) > 2,\n", + " })\n", + "\n", + "df_concentrated_sensitivity = pd.DataFrame(concentrated_sensitivity)\n", + "print(\"Concentrated (crash-weighted) drag sensitivity:\\n\")\n", + "display(df_concentrated_sensitivity.round(4))\n", + "print(\n", + " \"\\nCompare the drag level at which significance is lost here against the flat-drag table above. \"\n", + " \"A meaningfully lower breakeven drag under concentration would mean the flat-drag result \"\n", + " \"overstates how robust the alpha is to realistic (crash-clustered) survivorship bias.\"\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "93b4cf77", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T12:20:24.016580Z", + "iopub.status.busy": "2026-07-31T12:20:24.015953Z", + "iopub.status.idle": "2026-07-31T12:20:24.025025Z", + "shell.execute_reply": "2026-07-31T12:20:24.024306Z" + } + }, "outputs": [ { "name": "stdout", @@ -1639,6 +2162,537 @@ "print(\"Saved backtest returns to ../data/processed/backtest_returns.csv\")" ] }, + { + "cell_type": "markdown", + "id": "8d50b216", + "metadata": {}, + "source": [ + "## Robustness: Decile Size and Rebalance Timing\n", + "\n", + "The headline portfolio uses one specific set of choices: top 10%, monthly rebalance, equal-weight holdings. Before leaning on the alpha, we should vary the nearby choices that do not require new data.\n", + "\n", + "This grid varies:\n", + "\n", + "- **Decile cutoff:** 5%, 10%, 15%, 20%.\n", + "- **Rebalance interval:** every 1, 2, or 3 months.\n", + "\n", + "For each combination, we recompute net returns, Sharpe, max drawdown, Fama-French alpha, ordinary OLS t-stat, HAC/Newey-West t-stat, and market beta.\n", + "\n", + "This still does not solve universe-construction robustness. The current-constituent universe remains a limitation. But it does answer whether the result depends on exactly one decile/rebalance setting." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "970cd763", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T12:20:24.027280Z", + "iopub.status.busy": "2026-07-31T12:20:24.027058Z", + "iopub.status.idle": "2026-07-31T12:20:27.934145Z", + "shell.execute_reply": "2026-07-31T12:20:27.933511Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Long-only robustness across decile size and rebalance interval:\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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05%12390.21961.0230-0.59370.07883.81364.05751.24600.8271
15%22390.19840.9023-0.64310.05472.64982.88791.28140.8355
25%32390.21651.0064-0.59460.07263.65773.74321.27240.8410
310%12390.19481.0036-0.57130.05973.97774.16561.18890.8885
410%22390.18270.9256-0.61370.04602.92782.92441.20770.8818
510%32390.18720.9568-0.59740.04893.38113.10631.21600.8979
615%12390.18250.9848-0.56640.04973.88163.89001.16500.9109
715%22390.18020.9652-0.56500.04733.75513.91901.16930.9150
815%32390.18080.9730-0.55300.04773.88943.89091.16820.9187
920%12390.17450.9718-0.55070.04583.93474.09601.13020.9217
1020%22390.17430.9658-0.54540.04484.05954.28991.13830.9302
1120%32390.17190.9553-0.53710.04223.80933.84341.13660.9294
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" + ], + "text/plain": [ + " decile rebalance_every_months n_observations ann_return sharpe \\\n", + "0 5% 1 239 0.2196 1.0230 \n", + "1 5% 2 239 0.1984 0.9023 \n", + "2 5% 3 239 0.2165 1.0064 \n", + "3 10% 1 239 0.1948 1.0036 \n", + "4 10% 2 239 0.1827 0.9256 \n", + "5 10% 3 239 0.1872 0.9568 \n", + "6 15% 1 239 0.1825 0.9848 \n", + "7 15% 2 239 0.1802 0.9652 \n", + "8 15% 3 239 0.1808 0.9730 \n", + "9 20% 1 239 0.1745 0.9718 \n", + "10 20% 2 239 0.1743 0.9658 \n", + "11 20% 3 239 0.1719 0.9553 \n", + "\n", + " max_drawdown alpha_annualized alpha_t_ols alpha_t_hac mkt_beta \\\n", + "0 -0.5937 0.0788 3.8136 4.0575 1.2460 \n", + "1 -0.6431 0.0547 2.6498 2.8879 1.2814 \n", + "2 -0.5946 0.0726 3.6577 3.7432 1.2724 \n", + "3 -0.5713 0.0597 3.9777 4.1656 1.1889 \n", + "4 -0.6137 0.0460 2.9278 2.9244 1.2077 \n", + "5 -0.5974 0.0489 3.3811 3.1063 1.2160 \n", + "6 -0.5664 0.0497 3.8816 3.8900 1.1650 \n", + "7 -0.5650 0.0473 3.7551 3.9190 1.1693 \n", + "8 -0.5530 0.0477 3.8894 3.8909 1.1682 \n", + "9 -0.5507 0.0458 3.9347 4.0960 1.1302 \n", + "10 -0.5454 0.0448 4.0595 4.2899 1.1383 \n", + "11 -0.5371 0.0422 3.8093 3.8434 1.1366 \n", + "\n", + " r_squared \n", + "0 0.8271 \n", + "1 0.8355 \n", + "2 0.8410 \n", + "3 0.8885 \n", + "4 0.8818 \n", + "5 0.8979 \n", + "6 0.9109 \n", + "7 0.9150 \n", + "8 0.9187 \n", + "9 0.9217 \n", + "10 0.9302 \n", + "11 0.9294 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " decile rebalance_every_months n_observations ann_return sharpe \\\n", + "3 10% 1 239 0.1948 1.0036 \n", + "\n", + " max_drawdown alpha_annualized alpha_t_ols alpha_t_hac mkt_beta \\\n", + "3 -0.5713 0.0597 3.9777 4.1656 1.1889 \n", + "\n", + " r_squared \n", + "3 0.8885 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Interpretation: robust results should not require exactly one cutoff or one rebalance calendar. This grid still uses the same current-constituent universe, so universe robustness remains unresolved.\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Robustness sweep: decile size × rebalance interval, with FF alpha\n", + "==================================\n", + "\"\"\"\n", + "decile_grid = [0.05, 0.10, 0.15, 0.20]\n", + "rebalance_grid = [1, 2, 3]\n", + "tc_rate = TRANSACTION_COST_BPS / 10000\n", + "\n", + "\n", + "def form_decile_portfolios_interval(signal_df, return_df, decile=0.1, rebalance_every=1):\n", + " \"\"\"Long-only top-decile portfolio that rebalances every N months and holds between rebalances.\"\"\"\n", + " common_dates = signal_df.index.intersection(return_df.index)\n", + " common_tickers = signal_df.columns.intersection(return_df.columns)\n", + " signal_df = signal_df.loc[common_dates, common_tickers]\n", + " return_df = return_df.loc[common_dates, common_tickers]\n", + "\n", + " rows = []\n", + " rebalance_dates = []\n", + " current_holdings = None\n", + " prev_w = None\n", + "\n", + " for i in range(len(common_dates) - 1):\n", + " date = common_dates[i]\n", + " next_date = common_dates[i + 1]\n", + " should_rebalance = current_holdings is None or (i % rebalance_every == 0)\n", + "\n", + " if should_rebalance:\n", + " scores = signal_df.loc[date].dropna()\n", + " if len(scores) < 50:\n", + " continue\n", + " n_long = max(int(len(scores) * decile), 1)\n", + " current_holdings = scores.sort_values(ascending=False).head(n_long).index.tolist()\n", + " curr_w = _equal_weight_vector(current_holdings)\n", + " if prev_w is None or prev_w.empty:\n", + " turnover = curr_w.abs().sum()\n", + " else:\n", + " names = prev_w.index.union(curr_w.index)\n", + " turnover = (curr_w.reindex(names, fill_value=0.0) - prev_w.reindex(names, fill_value=0.0)).abs().sum() / 2.0\n", + " prev_w = curr_w\n", + " else:\n", + " turnover = 0.0\n", + "\n", + " next_rets = return_df.loc[next_date, current_holdings].dropna()\n", + " if len(next_rets) == 0:\n", + " continue\n", + " long_ret = next_rets.mean()\n", + " rows.append({\n", + " 'long': long_ret,\n", + " 'long_holdings': list(current_holdings),\n", + " 'long_turnover': float(turnover),\n", + " })\n", + " rebalance_dates.append(next_date)\n", + "\n", + " out = pd.DataFrame(rows, index=pd.DatetimeIndex(rebalance_dates))\n", + " out['long_net'] = out['long'] - out['long_turnover'] * tc_rate\n", + " return out\n", + "\n", + "\n", + "def ff_alpha_for_returns(return_series):\n", + " \"\"\"FF alpha diagnostics for a long-only net return series.\"\"\"\n", + " ret = return_series.rename('long_net').dropna().to_frame()\n", + " ret.index = ret.index.to_period('M')\n", + " ff_local = df_ff[['RF'] + FF_FACTOR_COLS].copy()\n", + " ff_local.index = ff_local.index.to_period('M')\n", + " reg = ret.join(ff_local, how='inner').dropna().astype(float)\n", + " if len(reg) < 24:\n", + " return pd.Series({\n", + " 'alpha_annualized': np.nan,\n", + " 'alpha_t_ols': np.nan,\n", + " 'alpha_t_hac': np.nan,\n", + " 'mkt_beta': np.nan,\n", + " 'r_squared': np.nan,\n", + " })\n", + " y = reg['long_net'] - reg['RF']\n", + " X = sm.add_constant(reg[FF_FACTOR_COLS], has_constant='add')\n", + " model = sm.OLS(y, X).fit()\n", + " model_hac = sm.OLS(y, X).fit(cov_type='HAC', cov_kwds={'maxlags': HAC_LAGS})\n", + " return pd.Series({\n", + " 'alpha_annualized': model.params['const'] * 12,\n", + " 'alpha_t_ols': model.tvalues['const'],\n", + " 'alpha_t_hac': model_hac.tvalues['const'],\n", + " 'mkt_beta': model.params['Mkt-RF'],\n", + " 'r_squared': model.rsquared,\n", + " })\n", + "\n", + "robustness_rows = []\n", + "for d in decile_grid:\n", + " for rebalance_every in rebalance_grid:\n", + " df_port_d = form_decile_portfolios_interval(\n", + " df_momentum, df_returns, decile=d, rebalance_every=rebalance_every\n", + " )\n", + " m = performance_metrics(df_port_d['long_net'])\n", + " alpha_diag = ff_alpha_for_returns(df_port_d['long_net'])\n", + " robustness_rows.append({\n", + " 'decile': f'{int(d*100)}%',\n", + " 'rebalance_every_months': rebalance_every,\n", + " 'n_observations': len(df_port_d),\n", + " 'ann_return': m['ann_return'],\n", + " 'sharpe': m['sharpe'],\n", + " 'max_drawdown': m['max_drawdown'],\n", + " **alpha_diag.to_dict(),\n", + " })\n", + "\n", + "df_robustness = pd.DataFrame(robustness_rows)\n", + "print(\"Long-only robustness across decile size and rebalance interval:\\n\")\n", + "display(df_robustness.round(4))\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n", + "for rebalance_every in rebalance_grid:\n", + " sub = df_robustness[df_robustness['rebalance_every_months'] == rebalance_every]\n", + " axes[0].plot(sub['decile'], sub['sharpe'], marker='o', label=f'every {rebalance_every}m')\n", + " axes[1].plot(sub['decile'], sub['alpha_t_hac'], marker='o', label=f'every {rebalance_every}m')\n", + "\n", + "axes[0].set_ylabel('Sharpe ratio')\n", + "axes[0].set_title('Sharpe Across Robustness Grid')\n", + "axes[0].grid(alpha=0.3)\n", + "axes[1].axhline(y=2, color='coral', linestyle='--', linewidth=1, label='t = 2')\n", + "axes[1].set_ylabel('HAC alpha t-stat')\n", + "axes[1].set_title('FF Alpha Significance Across Grid')\n", + "axes[1].grid(alpha=0.3)\n", + "for ax in axes:\n", + " ax.set_xlabel('Long cutoff')\n", + " ax.legend(fontsize=8)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('../images/04_backtest/robustness_grid.png', dpi=150, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "headline_row = df_robustness[\n", + " (df_robustness['decile'] == '10%') &\n", + " (df_robustness['rebalance_every_months'] == 1)\n", + "]\n", + "if not headline_row.empty:\n", + " print(\"Headline setting from grid:\")\n", + " display(headline_row.round(4))\n", + "\n", + "print(\n", + " \"\\nInterpretation: robust results should not require exactly one cutoff or one rebalance calendar. \"\n", + " \"This grid still uses the same current-constituent universe, so universe robustness remains unresolved.\"\n", + ")" + ] + }, { "cell_type": "markdown", "id": "75addbfc", @@ -1646,14 +2700,16 @@ "source": [ "## Conclusion\n", "\n", - "And, we're done. The headline result: a sector-neutralized momentum signal, traded long-only, generates a statistically significant alpha that holds up across most subperiods and survives plausible survivorship bias.\n", + "The main result is that a sector-neutralized momentum signal, traded long-only, produces a positive Fama-French alpha in this dataset. The result is more nuanced than the raw headline number:\n", "\n", - "- **Long-only momentum** earns a Fama–French 4-factor alpha of **5.95% annualized (t = 3.95)**, highly significant. Annualized return is 19.6% (Sharpe 1.01, max drawdown -57%) versus 15.9% (Sharpe 0.95) for the equal-weight universe — an active return of 3.6% per year, though that raw active edge is only marginally significant (IR 0.43, t = 1.92); the factor-adjusted alpha is the stronger result.\n", - "- **Walk-forward**: the long-only Sharpe is positive in all four 5-year windows, but the active return (vs EW universe) is positive in only **3 of 4**. The exception is 2006-2011 (active -4.8%, IR -0.51), which spans the 2008-09 momentum crash — a well-documented regime where momentum reverses. Per-window information ratios are -0.51, 0.71, 0.82, and 1.08, improving over the sample.\n", - "- **Survivorship bias**: the alpha remains significant (t > 2) even with up to roughly **3%** annual return drag from missing delisted stocks — well beyond the plausible bias for US large-caps. The result is robust.\n", - "- **Long-short**: the L/S alpha is not statistically significant (-2.95%, t = -1.41). The short side adds noise without value — the momentum factor's predictive power is concentrated on the long side in this universe.\n", + "- **Long-only momentum:** Fama-French 4-factor alpha is about **5.97% annualized**. The ordinary OLS t-stat is **3.98**, and the HAC/Newey-West t-stat is **4.17**, so the result is not weakened by this simple autocorrelation/heteroskedasticity check.\n", + "- **Market beta:** the long-only book has market beta around **1.19**, so much of the raw active return versus the equal-weight universe is market exposure. The FF alpha is the cleaner result because it controls for that beta.\n", + "- **Walk-forward:** long-only Sharpe is positive in all four 5-year windows, but active return versus the equal-weight universe is positive in only three of four. The weak window is 2006-2011, which includes the 2008-09 momentum crash.\n", + "- **Survivorship sensitivity:** the alpha survives **2%** annual synthetic drag under both flat and crash-concentrated assumptions. At **3%**, the flat drag is borderline, while the crash-concentrated drag loses significance. This is a breakeven stress test, not a true replacement for survivorship-free data.\n", + "- **Robustness grid:** nearby decile and rebalance choices are checked with Sharpe and FF alpha. Alpha remains positive and HAC-significant across the tested 5-20% cutoffs and 1/2/3-month rebalance intervals, though universe-construction robustness is still unresolved.\n", + "- **Long-short:** the long-short portfolio does not produce a significant alpha. In this universe, the useful part of the signal is concentrated on the long side; the short side adds noise.\n", "\n", - "The next notebook decomposes the portfolio's **risk** (the quadratic form $w^\\top \\Sigma w$) into systematic and idiosyncratic components via PCA." + "The next notebook decomposes the portfolio's risk, the quadratic form $w^\\top\\Sigma w$, into systematic and idiosyncratic components with PCA." ] } ], diff --git a/notebooks/05_risk_decomposition_via_PCA.ipynb b/notebooks/05_risk_decomposition_via_PCA.ipynb index f7cb060..1a661c8 100644 --- a/notebooks/05_risk_decomposition_via_PCA.ipynb +++ b/notebooks/05_risk_decomposition_via_PCA.ipynb @@ -9,45 +9,46 @@ "\n", "## Purpose\n", "\n", - "The backtest in the previous notebook tells us whether the strategy makes money. This notebook will tell us where its risk comes from. \n", + "Notebook 04 tells us how the strategy performed. This notebook asks where the portfolio's risk comes from.\n", "\n", - "Portfolio risk is the quadratic form\n", - "$$ \\langle \\Sigma w, w \\rangle = w^T \\Sigma w $$\n", - "where $\\Sigma$ is the $N \\times N$ covariance matrix and $w$ is our vector of weights. We use the eigendecomposition of $\\Sigma$ to split the quadratic form into systematic (factor-driven) and idiosyncratic (stock-specific) components.\n", + "Portfolio variance is the quadratic form\n", "\n", - "The main goals are the following.\n", - "1. To compute the sample covariance matrix $\\Sigma = \\frac{1}{T-1}X_c^T X_c$.\n", - "2. To use PCA on $\\Sigma$ using `sklearn.decomposition.PCA` (which is really an eigendecomposition $\\Sigma = V\\Lambda V^T$ under the hood).\n", - "3. To apply the Marchenko-Pastur cutoff from random matrix theory to separate signal eigenvalues from noise.\n", - "4. To implement Ledoit-Wolf shrinkage (a convex combination of sample and structured covariance) and show it improves out-of-sample estimation.\n", - "5. To decompose the portfolio's variance $\\langle \\Sigma w, w \\rangle$ into the factor subspace and its orthogonal complement. \n", + "$$w^\\top\\Sigma w,$$\n", + "\n", + "where $w$ is the portfolio weight vector and $\\Sigma$ is the stock-return covariance matrix. PCA gives an orthogonal basis for this covariance matrix, so we can split variance into common-factor directions and the remaining residual directions.\n", + "\n", + "The goals are:\n", + "1. Estimate the sample covariance matrix $\\Sigma=\\frac{1}{T-1}X_c^\\top X_c$.\n", + "2. Eigendecompose $\\Sigma$ with PCA.\n", + "3. Use the Marchenko-Pastur cutoff to separate large signal-like eigenvalues from the noise bulk.\n", + "4. Compare sample covariance to Ledoit-Wolf shrinkage.\n", + "5. Decompose the current long-only portfolio variance into top-$k$ PCA risk and the orthogonal remainder.\n", "\n", "### Terms used in this notebook\n", "\n", "| Term | Meaning |\n", "|------|---------|\n", - "| **Covariance matrix** $\\Sigma$ | $N \\times N$ symmetric PSD matrix; $\\Sigma_{ij} = \\mathrm{Cov}(r_i, r_j)$ |\n", - "| **PCA (principal component analysis)** | Eigendecomposition of $\\Sigma$; eigenvectors = principal components, eigenvalues = explained variances |\n", - "| **Scree plot** | Eigenvalues in decreasing order — visualizes the spectrum |\n", - "| **Marchenko–Pastur distribution** | Limiting eigenvalue law of a random covariance matrix; gives an objective noise cutoff |\n", - "| **Shrinkage estimator** | $\\hat\\Sigma = \\delta \\mathbf{F} + (1-\\delta)\\mathbf{S}$ — convex combination of sample $\\mathbf{S}$ and structured target $\\mathbf{F}$ |\n", - "| **Risk model** | Low-rank + diagonal split $\\Sigma \\approx B\\Sigma_f B^\\top + D$ |\n", - "| **Systematic risk** | Variance explained by common factors (the low-rank part $B\\Sigma_f B^\\top$) |\n", - "| **Idiosyncratic risk** | Stock-specific variance (the diagonal part $D$) |\n", - "| **Portfolio variance** | The quadratic form $w^\\top \\Sigma w$ being decomposed |\n", - "| **Beta** ↻ | Factor loadings reappear as the coordinates $B$ |\n", - "| **Portfolio weights** ↻ | The weight vector $w$ whose risk $w^\\top \\Sigma w$ we decompose |\n", + "| **Covariance matrix** $\\Sigma$ | $N\\times N$ matrix with $\\Sigma_{ij}=\\mathrm{Cov}(r_i,r_j)$ |\n", + "| **PCA** | Eigendecomposition of $\\Sigma$ |\n", + "| **Eigenvector** | A portfolio-like direction in stock space |\n", + "| **Eigenvalue** | Variance explained along that eigenvector |\n", + "| **Scree plot** | Eigenvalues plotted from largest to smallest |\n", + "| **Marchenko-Pastur distribution** | Random-matrix benchmark for the eigenvalue noise bulk |\n", + "| **Shrinkage estimator** | A weighted average of noisy sample covariance and a structured target |\n", + "| **Systematic risk** | Variance in common PCA directions |\n", + "| **Idiosyncratic risk** | Variance outside the retained PCA directions |\n", + "| **Portfolio variance** | The quantity $w^\\top\\Sigma w$ being decomposed |\n", "\n", "## Outputs\n", "\n", - "Eigenvalue spectrum with Marchenko–Pastur overlay, Ledoit–Wolf shrinkage comparison, and a portfolio variance decomposition table.\n", + "Eigenvalue spectrum, Marchenko-Pastur cutoff, Ledoit-Wolf comparison, and a portfolio variance decomposition table.\n", "\n", "## Notebook Structure\n", "1. [Setup and Imports](#setup-and-imports)\n", "2. [Load Data](#load-data)\n", "3. [Sample Covariance and PCA](#sample-covariance-and-pca)\n", - "4. [Marchenko–Pastur Noise Separation](#marchenkopastur-noise-separation)\n", - "5. [Ledoit–Wolf Shrinkage](#ledoitwolf-shrinkage)\n", + "4. [Marchenko-Pastur Noise Separation](#marchenkopastur-noise-separation)\n", + "5. [Ledoit-Wolf Shrinkage](#ledoitwolf-shrinkage)\n", "6. [Portfolio Variance Decomposition](#portfolio-variance-decomposition)\n", "7. [Conclusion](#conclusion)" ] @@ -64,7 +65,14 @@ "cell_type": "code", "execution_count": 1, "id": "4f9e782b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:09:08.396698Z", + "iopub.status.busy": "2026-07-31T11:09:08.395783Z", + "iopub.status.idle": "2026-07-31T11:09:09.597947Z", + "shell.execute_reply": "2026-07-31T11:09:09.597320Z" + } + }, "outputs": [], "source": [ "\"\"\"\n", @@ -94,7 +102,14 @@ "cell_type": "code", "execution_count": 2, "id": "e1c87a97", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:09:09.599975Z", + "iopub.status.busy": "2026-07-31T11:09:09.599687Z", + "iopub.status.idle": "2026-07-31T11:09:09.643387Z", + "shell.execute_reply": "2026-07-31T11:09:09.642776Z" + } + }, "outputs": [ { "name": "stdout", @@ -125,24 +140,41 @@ "source": [ "## Sample Covariance and PCA\n", "\n", - "Given the centered return matrix $\\mathbf{X}_c \\in \\mathbb{R}^{T \\times N}$ (each row demeaned), the **sample covariance matrix** is:\n", + "Let $X_c\\in\\mathbb{R}^{T\\times N}$ be the centered return matrix. Each column has its time-series mean subtracted. The sample covariance matrix is\n", "\n", - "$$\\Sigma = \\frac{1}{T-1} \\mathbf{X}_c^\\top \\mathbf{X}_c.$$\n", + "$$\\Sigma=\\frac{1}{T-1}X_c^\\top X_c.$$\n", "\n", - "This is an $N \\times N$ symmetric positive-semidefinite matrix. **PCA** is its eigendecomposition:\n", + "PCA eigendecomposes this matrix:\n", "\n", - "$$\\Sigma = \\mathbf{V} \\Lambda \\mathbf{V}^\\top, \\quad \\Lambda = \\text{diag}(\\lambda_1 \\geq \\lambda_2 \\geq \\dots \\geq \\lambda_N).$$\n", + "$$\\Sigma=V\\Lambda V^\\top,$$\n", "\n", - "The columns of $\\mathbf{V}$ are the **principal components** (directions of maximum variance, in decreasing order); the eigenvalues $\\lambda_i$ are the **explained variances**.\n", + "where\n", "\n", - "Under the hood, `sklearn.decomposition.PCA` computes this via SVD of $\\mathbf{X}_c = \\mathbf{U}\\mathbf{S}\\mathbf{V}^\\top$, which gives $\\Sigma = \\frac{1}{T-1}\\mathbf{V}\\mathbf{S}^2\\mathbf{V}^\\top$ with eigenvalues $\\lambda_i = s_i^2/(T-1)$. We delegate the numerics to sklearn and keep the eigenvalues/eigenvectors for the Marchenko–Pastur and variance decomposition analyses below." + "$$\\Lambda=\\mathrm{diag}(\\lambda_1,\\lambda_2,\\ldots,\\lambda_N), \\quad \\lambda_1\\ge\\lambda_2\\ge\\cdots.$$\n", + "\n", + "The columns of $V$ are principal component directions in stock space. The eigenvalues are the variance along those directions.\n", + "\n", + "`sklearn.decomposition.PCA` computes this through an SVD of $X_c$:\n", + "\n", + "$$X_c=USV^\\top,$$\n", + "\n", + "which implies\n", + "\n", + "$$\\Sigma=\\frac{1}{T-1}VS^2V^\\top.$$" ] }, { "cell_type": "code", "execution_count": 3, "id": "1600d228", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:09:09.645176Z", + "iopub.status.busy": "2026-07-31T11:09:09.645004Z", + "iopub.status.idle": "2026-07-31T11:09:09.675512Z", + "shell.execute_reply": "2026-07-31T11:09:09.675049Z" + } + }, "outputs": [ { "name": "stdout", @@ -211,11 +243,18 @@ "cell_type": "code", "execution_count": 4, "id": "337d202b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:09:09.679231Z", + "iopub.status.busy": "2026-07-31T11:09:09.679033Z", + "iopub.status.idle": "2026-07-31T11:09:10.218423Z", + "shell.execute_reply": "2026-07-31T11:09:10.217819Z" + } + }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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" ] @@ -273,22 +312,31 @@ "id": "183c2b9c", "metadata": {}, "source": [ - "## Marchenko–Pastur Noise Separation\n", + "## Marchenko-Pastur Noise Separation\n", "\n", - "**Random matrix theory** gives us an objective cutoff for how many principal components are signal vs. noise.\n", + "Random matrix theory gives a useful benchmark for deciding how many principal components are larger than we would expect from noise.\n", "\n", - "For an $N \\times T$ matrix $\\mathbf{W}$ of iid random variables with variance $\\sigma^2$, the eigenvalues of the sample covariance $\\frac{1}{T}\\mathbf{W}^\\top \\mathbf{W}$ converge (as $N, T \\to \\infty$ with ratio $q = T/N$ fixed) to the **Marchenko–Pastur distribution** with support:\n", + "For a random matrix with iid entries and variance $\\sigma^2$, the eigenvalues of its sample covariance concentrate inside the Marchenko-Pastur interval:\n", "\n", - "$$\\lambda_{\\pm} = \\sigma^2 \\left(1 + \\frac{1}{q} \\pm 2\\sqrt{\\frac{1}{q}}\\right), \\quad q = T/N.$$\n", + "$$\\lambda_{\\pm}=\\sigma^2\\left(1+\\frac{1}{q}\\pm2\\sqrt{\\frac{1}{q}}\\right), \\quad q=\\frac{T}{N}.$$\n", "\n", - "Eigenvalues falling inside $[\\lambda_-, \\lambda_+]$ are **consistent with noise** — they're what you'd get from a random matrix with no true factor structure. Eigenvalues **above** $\\lambda_+$ are statistically significant factors. This gives us a distribution-theoretic cutoff for how many components to retain, rather than an ad-hoc scree-plot eyeball." + "Eigenvalues inside $[\\lambda_-,\\lambda_+]$ are consistent with a no-factor random matrix. Eigenvalues above $\\lambda_+$ are candidates for real common factors.\n", + "\n", + "This is not magic; it is a rule of thumb with assumptions. Returns are not iid normal draws. But it is better than choosing $k$ by eye from a scree plot." ] }, { "cell_type": "code", "execution_count": 5, "id": "c67bfa0d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:09:10.220398Z", + "iopub.status.busy": "2026-07-31T11:09:10.220105Z", + "iopub.status.idle": "2026-07-31T11:09:10.721157Z", + "shell.execute_reply": "2026-07-31T11:09:10.720503Z" + } + }, "outputs": [ { "name": "stdout", @@ -302,7 +350,7 @@ }, { "data": { - "image/png": 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", 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" ] @@ -386,22 +434,31 @@ "id": "2c6a3e3e", "metadata": {}, "source": [ - "## Ledoit–Wolf Shrinkage\n", + "## Ledoit-Wolf Shrinkage\n", "\n", - "The sample covariance $\\mathbf{S} = \\frac{1}{T-1}\\mathbf{X}_c^\\top \\mathbf{X}_c$ is a noisy estimator when $N$ is large relative to $T$ — many eigenvalues are inflated or deflated by sampling noise (as the Marchenko–Pastur analysis shows).\n", + "The sample covariance matrix is noisy when the number of stocks is large relative to the number of months. Small estimation errors in covariance can create large errors in portfolio risk.\n", "\n", - "**Ledoit–Wolf shrinkage** forms a convex combination:\n", + "Ledoit-Wolf shrinkage estimates\n", "\n", - "$$\\hat\\Sigma = \\delta \\mathbf{F} + (1-\\delta) \\mathbf{S}, \\quad \\delta \\in [0, 1],$$\n", + "$$\\hat\\Sigma=\\delta F+(1-\\delta)S,$$\n", "\n", - "where $\\mathbf{F}$ is a structured target (here, the constant-correlation matrix) and $\\delta$ is chosen to minimize $\\mathbb{E}\\|\\hat\\Sigma - \\Sigma\\|_F^2$ (expected Frobenius-norm squared error). This is **ridge-like regularization** on the covariance: we trade bias for variance, pulling noisy eigenvalues toward a more structured pattern. The result is a biased but lower-variance estimator that performs better out of sample." + "where $S$ is the sample covariance matrix, $F$ is a structured target, and $\\delta\\in[0,1]$ is the shrinkage intensity. The idea is to accept a little bias in exchange for lower estimation variance.\n", + "\n", + "In plain terms: do not trust every noisy sample covariance equally. Pull the estimate toward a simpler structure unless the data are strong enough to justify the complexity." ] }, { "cell_type": "code", "execution_count": 6, "id": "c96abc5d", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:09:10.723075Z", + "iopub.status.busy": "2026-07-31T11:09:10.722900Z", + "iopub.status.idle": "2026-07-31T11:09:10.881731Z", + "shell.execute_reply": "2026-07-31T11:09:10.881112Z" + } + }, "outputs": [ { "name": "stdout", @@ -492,7 +549,14 @@ "cell_type": "code", "execution_count": 7, "id": "6dc7a3cc", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:09:10.884080Z", + "iopub.status.busy": "2026-07-31T11:09:10.883885Z", + "iopub.status.idle": "2026-07-31T11:09:13.088848Z", + "shell.execute_reply": "2026-07-31T11:09:13.088123Z" + } + }, "outputs": [ { "name": "stdout", @@ -506,7 +570,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -591,22 +655,39 @@ "source": [ "## Portfolio Variance Decomposition\n", "\n", - "Given a portfolio weight vector $w \\in \\mathbb{R}^N$, split $\\Sigma$ into its top-$k$ eigendirections and the orthogonal complement:\n", - "$$\\Sigma = \\underbrace{\\mathbf{B}\\Sigma_f \\mathbf{B}^\\top}_{\\text{factor subspace}} + \\underbrace{(\\Sigma - \\mathbf{B}\\Sigma_f \\mathbf{B}^\\top)}_{\\text{orthogonal complement}},$$\n", - "where $\\mathbf{B} \\in \\mathbb{R}^{N \\times k}$ holds the top-$k$ eigenvectors and $\\Sigma_f = \\text{diag}(\\lambda_1, \\dots, \\lambda_k)$ the top-$k$ eigenvalues. The portfolio variance decomposes **exactly**:\n", + "Let $B\\in\\mathbb{R}^{N\\times k}$ hold the top $k$ eigenvectors and let\n", "\n", - "$$w^\\top \\Sigma w = \\underbrace{w^\\top \\mathbf{B} \\Sigma_f \\mathbf{B}^\\top w}_{\\text{systematic}} + \\underbrace{w^\\top (\\Sigma - \\mathbf{B}\\Sigma_f \\mathbf{B}^\\top) w}_{\\text{idiosyncratic}}.$$\n", + "$$\\Sigma_f=\\mathrm{diag}(\\lambda_1,\\ldots,\\lambda_k).$$\n", "\n", - "**Geometrically:** the systematic term is $\\|\\Sigma_f^{1/2} \\mathbf{B}^\\top w\\|^2$ — the squared norm of the weight vector *projected into the factor subspace* (spanned by the top-$k$ eigenvectors), scaled by the eigenvalues. The idiosyncratic term is the variance living in the orthogonal complement — risk not captured by the top-$k$ common factors. (A strict *factor model* would assume this residual is diagonal, i.e. uncorrelated idiosyncratic risk; we keep the full residual so the split sums to 100% of total variance.)\n", + "The top-$k$ approximation to the covariance matrix is\n", "\n", - "The number of factors $k$ comes from the Marchenko–Pastur analysis above." + "$$\\Sigma_{\\text{factor}}=B\\Sigma_fB^\\top.$$\n", + "\n", + "The residual covariance is\n", + "\n", + "$$\\Sigma_{\\text{resid}}=\\Sigma-B\\Sigma_fB^\\top.$$\n", + "\n", + "For a portfolio $w$, variance splits exactly under this construction:\n", + "\n", + "$$w^\\top\\Sigma w = w^\\top\\Sigma_{\\text{factor}}w + w^\\top\\Sigma_{\\text{resid}}w.$$\n", + "\n", + "Geometrically, $B^\\top w$ is the portfolio's exposure to the top PCA directions. The systematic term is the variance from those common directions. The residual term is everything left outside that retained subspace.\n", + "\n", + "This is a PCA decomposition, not a full economic factor model. The first few PCs often resemble broad market or style factors, but the math itself only knows about covariance structure." ] }, { "cell_type": "code", "execution_count": 8, "id": "226662f6", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:09:13.090599Z", + "iopub.status.busy": "2026-07-31T11:09:13.090421Z", + "iopub.status.idle": "2026-07-31T11:09:13.125912Z", + "shell.execute_reply": "2026-07-31T11:09:13.125261Z" + } + }, "outputs": [ { "name": "stdout", @@ -645,7 +726,14 @@ "cell_type": "code", "execution_count": 9, "id": "7351ba96", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:09:13.127569Z", + "iopub.status.busy": "2026-07-31T11:09:13.127371Z", + "iopub.status.idle": "2026-07-31T11:09:13.339158Z", + "shell.execute_reply": "2026-07-31T11:09:13.338629Z" + } + }, "outputs": [ { "name": "stdout", @@ -662,7 +750,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -727,11 +815,18 @@ "cell_type": "code", "execution_count": 10, "id": "3b1f2340", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:09:13.340816Z", + "iopub.status.busy": "2026-07-31T11:09:13.340640Z", + "iopub.status.idle": "2026-07-31T11:09:13.685130Z", + "shell.execute_reply": "2026-07-31T11:09:13.684539Z" + } + }, "outputs": [ { "data": { - "image/png": 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", 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RSIRWrVrlKE/Ryxfu7u452jt9+nQAwKNHj9CtWzfY2tpK/+/q1KkDfLjTBwDu37+Phw8fIiAgAAYGBgq95qeUERcqVqwIPT09DBgwAOvWrcOjR4++uB7fI4Wn8S0tLWFkZITHjx8rlD9rmjBrCudTxYsXz/HmlJcvt31Z12VHjx6N0aNHyz0mJiYm1/K6deuGo0eP4ueff0bVqlVhYmICkUiE5s2bf/WHYWxsLHR0dKTTuVlEIhFsbW1zTJtaWFjkKENfX/+bP4w9PT2lH46CIGDhwoUYOXIkfv7558+uZ7hw4QIaN26MunXrYtWqVXBwcICenh727t2LmTNnfnXdtm3bhhkzZmD16tX4+eefUaRIEbRt2xZz5syBra3tF5cXHR392dX5u3fvRqdOndCxY0eMGTMGtra20NHRwbJly75pbUTNmjWxYMECiEQiGBkZwd3dHXp6etL9WV/gFD1PgoKCYGxsjI0bN2L58uXQ1tZG7dq1MXv27DxvAdPR0YG/vz8WL16MhIQEFCtWDGvXroWdnR2aNGkizffbb7/BwcEB27Ztw+zZs2FgYIAmTZpg7ty58PDw+Ko+sLKyklu3L33/mJuby/yd1Y+5paempkrTFD2Hs98lEBYWJhNoHBwc8uzn7J89sbGxyMjIwOLFi7F48WK5x2T/7Ml+rmdd8sqqZ3R0tLQun2NkZJQjyOrr68v0TV4MDAzktvfNmzeoVasWDAwMMGPGDJQsWRJGRkZ4/vw52rVr91V1lUcZccHd3R1HjhzBnDlzMGTIELx9+xZubm4YNmxYobpDqrBRONhra2ujQYMGOHjwIF68ePHZ/+ysN3hkZGSOvBERETmuqeX1jTb7vqxjg4KC0K5dO7nHlCpVSm56YmIi9u/fj8mTJ2P8+PHS9KxrvF/LwsICGRkZiI6Olgn4giAgKioKVatW/eqyv5ZIJMKIESMwbdo0he6j3bp1K3R1dbF//36ZD5S9e/d+Uz0sLS2xcOFCLFy4EM+ePcO+ffswfvx4vH79+qvu3rCyssqxmCe7jRs3wtXVFdu2bZN5/3zrQiZTU9M8g0O9evWgq6uLvXv3IjAw8LPl6ejoYOTIkRg5ciQSEhJw5MgRTJgwAU2aNMHz58/zvP7Yp08fzJ07V7pWZd++fRg+fLj0OjAAGBsbY+rUqZg6dSpevXolHeW3atUKd+/e/YoeyF1+vX+y+5Jz+OLFizJ/u7q6ftFrZf/sMTMzg7a2Nvz9/TFkyBC5x3zpa2R9XnzuPZ2fjh07hoiICJw4cUI6mgeQY+3It9ZVWXGhVq1aqFWrFiQSCS5duoTFixdj+PDhsLGxQZcuXb6qbprui6bxg4KCIAgC+vfvj/T09Bz7xWIx/vjjD+DD9Bc+fOh+6uLFi7hz5w4aNGjw1ZUuVaoUPDw8cP36dVSpUkXu9untZ58SiUQQBCHHgrLVq1dDIpHIpGX/Bp6XrPZkb++uXbvw9u3bb2qvIiIjI+WmR0REICkpCcWLF5em5TaDIBKJoKOjIxMs3r17hw0bNuTI+7WzEE5OThg6dCgaNWqEK1eufFV5zZo1w/Hjx/O8NCESiaCnpyfzYREVFaX01fjZ2draol+/fggPD5f7sB0AePjwIW7cuJEjvVixYujQoQOGDBmCuLi4zy7K9PT0RLVq1RAWFobNmzcjLS0Nffr0yTW/jY0Nevfuja5du+LevXtKX7n8Je+fb30dRc/h7J8L8mbUvoSRkRHq1auHq1evonz58nI/e770Nfz8/GBqaorly5cX2C2y2WWdJ9n7dMWKFTJ/lyxZEu7u7lizZk2eX5xz++xUdlzQ1tZGtWrVpHdCfPqZQrK+6D57X19fLFu2DIMHD0blypUxaNAglC1bFmKxGFevXsXKlSvh5eWFVq1aoVSpUhgwYAAWL14MLS0tNGvWTLrq0tHRESNGjPimiq9YsQLNmjVDkyZN0Lt3b9jb2yMuLg537tzBlStXsGPHDrnHmZiYoHbt2pg7dy4sLS3h4uKCkydPIjQ0NMfDH7y8vAAAK1euRNGiRWFgYABXV1e5J3OjRo3QpEkTjBs3DklJSahRo4Z0Nb63tzf8/f2/qp0lSpQAPly7z8uAAQOQkJCA9u3bw8vLC9ra2rh79y4WLFgALS0tjBs3Tpq3XLly2L17N5YtW4bKlStDS0sLVapUQYsWLTB//nx069YNAwYMQGxsLH799Ve5K+3LlSuHrVu3Ytu2bdIn9pUrVy5HvsTERNSrVw/dunVD6dKlUbRoUVy8eBGHDh2SmZXJrU7yTJs2DQcPHkTt2rUxYcIElCtXDgkJCTh06BBGjhyJ0qVLo2XLlti9ezcGDx6MDh064Pnz55g+fTrs7Ozw4MGDL/o/+FLz58/Ho0eP0Lt3b4SHh6Nt27awsbFBTEwMDh8+jLCwMGzduhXly5dHq1at4OXlhSpVqsDKygpPnz7FwoUL4ezsrNA0e9++fTFw4EBERETAz88vx4xWtWrV0LJlS5QvXx5mZma4c+cONmzYAF9fX+mswfr169G3b1+sWbPmm+5S+JL3z7f4knM4PyxatAg1a9ZErVq1MGjQILi4uCA5ORn//fcf/vjjD5mV64ooUqQI5s2bh379+qFhw4bo378/bGxs8N9//+H69etYsmRJvrUli5+fH8zMzBAYGIjJkydDV1cXmzZtwvXr13PkXbp0KVq1aoXq1atjxIgRcHJywrNnzxAeHi59YE/WZ8GiRYvQq1cv6OrqolSpUkqJC8uXL8exY8fQokULODk5ITU1VXpprmHDhkrvG43xNav6rl27JvTq1UtwcnIS9PT0BGNjY8Hb21uYNGmSzMpviUQizJ49WyhZsqSgq6srWFpaCj169BCeP38uU16dOnWEsmXL5nidrBWzc+fOlVuP69evC506dRKsra0FXV1dwdbWVqhfv76wfPlyaR55q/FfvHghtG/fXjAzMxOKFi0qNG3aVLh586bcVdkLFy4UXF1dBW1tbZlV5tlX4wsfVtSPGzdOcHZ2FnR1dQU7Ozth0KBBQnx8vEw+Z2dnoUWLFjnaU6dOnRwr3J2dnRVaOR8eHi707dtXKFOmjGBqairo6OgIdnZ2Qrt27aSrfrPExcUJHTp0EIoVKyaIRCLh07fBmjVrhFKlSgn6+vqCm5ubEBwcLISGhuZYWfvkyROhcePGQtGiRQUA0jpmX42fmpoqBAYGCuXLlxdMTEwEQ0NDoVSpUsLkyZNlVlznVafsq/EFQRCeP38u9O3bV7C1tRV0dXWF4sWLC506dRJevXolzTNr1izBxcVF0NfXFzw9PYVVq1YJkydPFrK/7b9kNb68/zd5MjIyhHXr1gn169cXzM3NBR0dHcHKykpo1qyZsHnzZundIvPmzRP8/PwES0tLQU9PT3BychICAgKEJ0+eKPQ6iYmJgqGhYa4r4sePHy9UqVJFMDMzk/6fjhgxQoiJiZHmCQsLy3EHRW4ACEOGDMl1v6Lvn9z6Ul758j4HvuQcludzny2fa+vjx4+Fvn37Cvb29oKurq5gZWUl+Pn5CTNmzJDmyfrs2bFjh9zXzt7fBw4cEOrUqSMYGxsLRkZGQpkyZYTZs2dL9/fq1UswNjbOURd572l5cvuczXLmzBnB19dXMDIyEqysrIR+/foJV65ckVvXs2fPCs2aNRNMTU0FfX19wd3dXRgxYoRMnqCgIKF48eKClpaWzGfwt8aFs2fPCm3bthWcnZ0FfX19wcLCQqhTp06ed2uQIIgEVc0bERERUYHgr94RERFpOAZ7IiIiDcdgT0REpOEY7ImIiDQcgz0REZGGY7AnIiLScAz2REREGu6LnqCnLgJFLqqugsaZvbqHqqugkQxcS6q6Chrnx5jyqq6CxlnZqaKqq5Cr/Pi8Xy7k/ahqdcSRPRERkYZjsCciItJwDPZEREQaTiOv2RMR0fdBW/5P3lM2DPZERKS2tEWM9orgND4REZGG48ieiIjUFqfxFcORPRERkYZjsCciItJwnMYnIiK1xQV6iuHInoiISMNxZE9ERGqLC/QUw5E9ERGRhuPInoiI1Bav2SuGI3siIiINx2BPRESk4TiNT0REaosL9BTDkT0REZGG48ieiIjUFhfoKYbBnoiI1BanpxXDfiIiItJwDPZEREQajsGeiIhIw/GaPRERqS0u0FMMgz0REakt3mevGE7jExERaTiO7ImISG1xGl8xHNkTERFpOAZ7IiIiDcdpfCIiUltcoKcYjuyJiIg0HEf2RESktrhATzGFemR/584duLm5qboaREREaq1Qj+zT09Px9OlTVVeDiIgKKV6zV4xKg/3IkSPz3B8dHV1gdSEiItJUKg32ixYtQsWKFWFiYiJ3/5s3bwq8TkRERJpGpcHew8MDI0aMQI8ePeTuv3btGipXrlzg9fpadQb1QKMxA2FqZ42IW/exY/g0/Pf3xdzzD/ZH3aG9YOHigLhnL3Fw5lKc37BbJo+hqQl+mDka3u2awsjMFDGPn2PXqBm4efBEAbRI9XZe/Q8bLtxD7JtUuFmaYET9ivB2tPrscddfxCBwywm4WZlgU+/G0vSHMYlY+fct3I2KR2RSCkbUr4CuVUrmcysKn60nL2Ht4XOISXwDdzsrjO3YCJU9nOTmPXL1LrafuoJ7L14hPSMD7nZWGNSyFmqUcZfmEUskCD10BvvO3cDrhGS42FhgeNv6qFnWXW6ZmqpVWVvUcrOAka42HselYPOVF4hMSlXo2KqOxdDf1wXXXiYi5J/H0nR9HS384GUHb3tTFNXXwfOEd9h69QWexr/Lx5aoDy7QU4xKF+hVrlwZly9fznW/SCSCIAgFWqevVblTS3RcOAkHZy7BTO/m+O/0RQw9uBZmjsXl5q8d2ANtgsdi/5SFmFa2EfZPXoiuS6ehXMsG0jzaurr46fAGWLg4YGWHQZhcqj429h+P+JevCrBlqnP4znPMP3oNfap7YkPvRqjoYIXhO08jKiklz+PepIkx5cAFVHG2zrEvTSyBvakxhtQpBwtjg3ysfeF16NJtzNlxGP2b1sD2Cf1QqYQjBi/disi4RLn5Lz94huqerlg6pDO2BgWgakln/BiyHXeeR0nzLNl3EjtPX0FQ5ybYO2kgOtaqhBErdsrk0XRNSlujYUkrbLnyAr8cuY+kVDFG1HGHvs7nP2bNjXTRoUJx3I/OOZvZs4ojytgUwZrzTzH1r7u4/SoZI+uUQDFD3XxqCWkilQb7efPmYfjw4bnur1ChAjIzMwu0Tl+r4ch++Cd0O/4J3Yaouw+xY8Q0xD+PRJ1B8mctqvm3xekVm3F5+37EPH6OS9v+wD+h29FkXKA0j1/fTjA2L4ZlbQbg4ZnLiHv2Eg//uYSXN+4UYMtUZ/Ol+2hd3hVtKrjB1cIEIxtUhE1RI+y6+jDP44LDL6OJpxPKFbfIsa+MnTmG1auAxp5O0NMu1Dej5Jv1R8+jrV9FtK/pDTc7S4zr1Bi2ZibYfuqK3PzjOjVG38a+8HIpDmdrc/zUph6crc1x8sYDaZ795/9Fv6Y1UMurBByszNC5TmX4lXHD+iPnC7BlqtXQwwoH7rzC1ZeJiEhKRdiFZ9DT1kI1J7M8jxOJgH7VnLHvVhRi3qTL7NPVFqGSQzHsuhGJBzFvEf0mHX/cikLM23TUcc/5/v4eaYuUv2kilX7a2drawtnZWZVVUAptXV04VfbCnb9Oy6Tf+es03PzkX4bQ0deDODVNJk38LhUuPhWgpfP+6kqF1g3x6OwVdF06DXOiLuLnf8PRNGgwRFqaH6TEkkzcjYpHNRdbmfRqrja48TIm1+P++PcxXiS8Qb8aZQqglupHnCHBnWeR8CvjKpPu6+mGa49eKFRGZqaAt6npMP1kZiQ9QwI9Xdmrgvq6Orj633Ml1bxwszTWg6mhLm5HJUvTMjIF3I9+AzdL4zyPbVnGFslpGfjncVyOfVoiEbS1RBBLZAc96ZJMlLAsosQWqC8Ge8WoNGrEx8dj8eLFSEpKyrEvMTEx132FTRFLM2jr6CDplezdA0mvomFiayn3mNvhp1CzXxc4VfICADhVLge/vh2ho6eHIpbvRwKWbk6o1KE5tLS1saR5HxycsQQNR/VHs4lDC6BVqpWQkgaJIMDCWF8m3dzIALFv5V8DfRaXjKUn/8W0ltWg8x18Ifoa8W9SIMkUYFFUNlBYFDVGTKJiC2LXHTmHd+liNK708QuVn6cbNhw9j6ev45CZKeDsnUc4cf0+opO+j0W2Jgbvv+gkpYpl0pNSxTA1yH1plLuFMWq6mmPDJflfitIyMvEw5i1alLGFqYEORCKgmpMZXC2M8iyXKDuVfiIuWbIEp06dkrsa39TUFKdPn8bixYvzLCMtLQ1JSUkymwSquc6ffXmBSCRCblU5MP033Dx4AuPO7cFS8X8Y/PsqnF276305H77Fi7RESH4dg40DgvDsyk1c2vYHDs5ckuulAc0k+zVbyOrXbCSZAn7efx79a5SFs3nRAqyfesrehQIEuf2a3YGLt7Dsz9OYG9AWFiYfR6zjOjWCk7U5fpiyHJV/DMYvW8Pxg28FjV085eNkht/alpNuOlry2/l+3ZH8MvR1tBBQzQkbLj3Hm3RJrq+15vxTiADMbe2FkPYVUN/DEheexSNTPZYz5TttkUjp25cKCQmBq6srDAwMULlyZZw+fTrP/Js2bUKFChVgZGQEOzs79OnTB7Gxsd/QC5+n0q+Gu3btwrx583LdP3DgQIwePRoTJ07MNU9wcDCmTp0qk1YZpqiCYkqta17exMRDkpEBU1vZVeJFrS2R9Er+lLM4NQ0bAsZi08AJMLGxRGLka9Qa0A3vkpLxJub9dF5iZDQkYjGET9YtRN15CFM7a2jr6kIiFsstWxMUM9KHtkiUYxQfn5IKcyP9HPlT0sW4ExWP+68S8OuRqwCATEGAAMB37k781qk2qspZsPe9MStiBG0tEWKyjbjjklNkgrc8hy7dxpQN+/Fr/3ao7il7GcC8qDEWBXZEmjgDCW9TYG1aFAv3Hoe9ZcGdhwXpekQiHse9lf6dNZNkYqCLxNQMaXpRfR0kpWXILcOqiD4si+hjSM2PTwnNijPLOlTApIN3EP02HdFv0/Hrif+gp60FQ10tJKZmoH91Z8S+TZNbLhWsbdu2Yfjw4QgJCUGNGjWwYsUKNGvWDLdv34aTU847XP7++2/07NkTCxYsQKtWrfDy5UsEBgaiX79+2LNnT77VU6XB/uHDh/Dw8Mh1v4eHBx4+zHsxVlBQUI6H84wyLae0OipCIhbj2eWb8GxUE9f2hkvTPRvVxPXfD+d5bGZGBhJevl+xXKVLK/y7/5j0DoSH/1yCT7cfZO5KsCnpioSIVxod6AFAV1sLpW3NcOHJK9QraS9Nv/DkFWqXsM+R31hfF1v6NJZJ23n1IS49e41ZP/iiuGnegex7oaujDU8nO5y98xgNKpaWpp+78xj1KuR+C+KBi7cwecN+zO7bBrXL5X7O6uvqwKaYCcQSCY5cvYvGlTyV3obCIC0jE9HZFtMlvhOjjE1RPE94f0uctpYIJa2KYPeNCLllRCWlYsqhuzJpbcrZQV9HC9uuvkTcO9lzPF2SiXRJJox0tVHW1gS7cimXCtb8+fMREBCAfv36AQAWLlyI8PBwLFu2DMHBwTnynzt3Di4uLhg2bBgAwNXVFQMHDsScOXPytZ4qDfba2tqIiIiQ++0HACIiIqD1mWuv+vr60NeXHelpo+CnDo/MX40+G+bj6aUbeHT2CmoN6AYzp+I4tXwTAKDNL2NRzN4Ga3uNAgBYe7jCxacCnpy/BiMzUzQY2Q/FvUpi3Yf9AHBq2UbU+7EXOi2ajOOL18HawwVNJwzG8d/WFnj7VKFblZKY/Od5eNqaoZy9BfZce4SopBS0q/h+JLT05L94/eYdprbwgZZIBHcrU5njzYz0oaejJZMulmTicUyS9N/Rye9w/1UCDPV04Gj2fSx46tmgGias/R1lne1QwdUBO/++isj4RHSsVQkAsGjvcbxKSMYvvVsDHwL9/9buw9hOjVDe1V56bV9fTwdFDd8v0rvx+CVeJySjtIMNXiUkY9mfp5GZKaBPY18VtrRgHXkQjWaeNnj1Jg2vk9PQzNMG6ZJMnH8WL83Tx8cJCe/E2PNvJDIyBURkuwc/5cN0/qfpZWyKQiQCopLTYF1EDx3K2+NVcirOPM7faV91kR8L6tLS0pCWJjtzIi/WpKen4/Llyxg/frxMeuPGjXHmzBm5Zfv5+WHixIk4cOAAmjVrhtevX2Pnzp1o0aKF8hvyCZUGe29vb+zduxfVq1eXu3/Pnj3w9vYu8Hp9jcvb96OIRTG0mPQTTOysEHHzPpY074O4Zy8BAKZ21jB3+jgi1dLWQsNR/WFbyg0SsRj3jp/DXL/2iH36cUV0/ItILGrcEx0X/IyfbxxCwssoHFsUhvDZy1XSxoLWyNMRialpCD1zGzFvU+FuaYIFHWrB7sMoPebtO7z6zD332UW/eYce6z7Otmy8eB8bL95HJUcrLO9aV+ltKIyaVimDhLcpWPHn34hOeoMSdlZYOqQLilu8/1IUnfgGUZ/cc7/z9BVkZGbil63h+GXrx5mr1tXLY0avVgCAdHEGluw7iRcx8TDS10NNrxL4pXdrmBh9P88yCL/7GnraWuheyQFGetp4HJuChScfIi3j42U4cyO9XK/h58ZQVxvtytuhmKEuUtIluPIiAXtvRkLCa/b5Rt7l4cmTJ2PKlCkyaTExMZBIJLCxsZFJt7GxQVSU/GdM+Pn5YdOmTejcuTNSU1ORkZGB1q1bf3Z92rcSCSp8as2uXbvQpUsXLFiwAIMGDYK2tjYAQCKRICQkBKNGjcLmzZvRoUOHLyo3UOSSTzX+fs1e/T0tCiw4Bq7f39P78tuPMeVVXQWNs7JTRVVXIVebrZR/m237F1cVGtlHRETA3t4eZ86cga/vx1msmTNnYsOGDbh7V/YyDQDcvn0bDRs2xIgRI9CkSRNERkZizJgxqFq1KkJDQ5XeliwqHdm3b98eY8eOxbBhwzBx4kS4ublBJBLh4cOHePPmDcaMGfPFgZ6IiOhbyAvs8lhaWkJbWzvHKP7169c5RvtZgoODUaNGDYwZMwYAUL58eRgbG6NWrVqYMWMG7OzslNQKWSq/GXnmzJk4f/48+vTpg+LFi8PW1hZ9+vTB2bNnMWvWLFVXj4iICjFVPlRHT08PlStXxuHDsguxDx8+DD8/P7nHpKSk5FiLljWrnZ8T7Sod2aekpGDMmDHYu3cvxGIxGjRogMWLF8PSUv6DaIiIiAqTkSNHwt/fH1WqVIGvry9WrlyJZ8+eITDw/aPPg4KC8PLlS6xfvx4A0KpVK/Tv3x/Lli2TTuMPHz4cPj4+KF5c/m+pKINKg/3kyZOxdu1adO/eHYaGhti8eTMGDRqEHTt2qLJaRERECuncuTNiY2Mxbdo0REZGwsvLCwcOHJA+Cj4yMhLPnj2T5u/duzeSk5OxZMkSjBo1CsWKFUP9+vUxe/bsfK2nShfoubu7Y+bMmejSpQsA4MKFC6hRowZSU1Ol0xpfgwv0lI8L9PIHF+gpHxfoKV9hXqC306as0svs8OqW0stUNZVes3/+/Dlq1aol/dvHxwc6OjqIiODDIoiIiJRFpdP4EokEenp6Mmk6OjrIyJD/eEkiIqJPaeqv1CmbSoO9IAjo3bu3zC0OqampCAwMhLHxx8eb7t69W0U1JCKiwkxTf2xJ2VQa7Hv16pUjrUcPXhsmIiJSJpUG+7CwMFW+PBER0XdB5Q/VISIiovyl0pE9ERHRt9DiNXuFMNgTEZHaEnE5vkI4jU9ERKThOLInIiK1pcWRvUI4siciItJwDPZEREQajtP4RESktkTaHLMqgr1ERESk4TiyJyIitcVb7xTDkT0REZGG48ieiIjUFm+9UwxH9kRERBqOwZ6IiEjDcRqfiIjUlkiLY1ZFsJeIiIg0HEf2RESktrhATzEM9kREpLZ4n71iOI1PRESk4RjsiYiINByDPRERkYbjNXsiIlJb/NU7xTDYExGR2uJqfMXwKxEREZGG48ieiIjUlkiLI3tFcGRPRESk4TiyJyIitaXFBXoKYS8RERFpOI0c2Q+PuqHqKmgc7aK6qq6CRrr2Q3NVV0HjeE8PU3UViAodjQz2RET0feCz8RXDaXwiIiINx5E9ERGpLY7sFcNgT0REaour8RXDXiIiItJwDPZEREQajsGeiIhIw/GaPRERqS0u0FMMgz0REaktLf4QjkI4jU9ERKThOLInIiK1JeKtdwphLxEREWk4BnsiIiINx2l8IiJSW1pcja8QjuyJiIi+QUhICFxdXWFgYIDKlSvj9OnTeeZPS0vDxIkT4ezsDH19fbi7u2PNmjX5WkeO7ImISG2p+j77bdu2Yfjw4QgJCUGNGjWwYsUKNGvWDLdv34aTk5PcYzp16oRXr14hNDQUJUqUwOvXr5GRkZGv9WSwJyIitaXq1fjz589HQEAA+vXrBwBYuHAhwsPDsWzZMgQHB+fIf+jQIZw8eRKPHj2Cubk5AMDFxSXf68lpfCIiok+kpaUhKSlJZktLS8uRLz09HZcvX0bjxo1l0hs3bowzZ87ILXvfvn2oUqUK5syZA3t7e5QsWRKjR4/Gu3fv8q09YLAnIiKSFRwcDFNTU5lN3ig9JiYGEokENjY2Muk2NjaIioqSW/ajR4/w999/4+bNm9izZw8WLlyInTt3YsiQIfnWHnAan4iISFZQUBBGjhwpk6avr59rfpFIdt2AIAg50rJkZmZCJBJh06ZNMDU1BT5cCujQoQOWLl0KQ0NDpbQhOwZ7IiJSW/lx652+vn6ewT2LpaUltLW1c4ziX79+nWO0n8XOzg729vbSQA8Anp6eEAQBL168gIeHhxJakBOn8YmISG2JtERK3xSlp6eHypUr4/DhwzLphw8fhp+fn9xjatSogYiICLx580aadv/+fWhpacHBweEbeiJvDPZERERfaeTIkVi9ejXWrFmDO3fuYMSIEXj27BkCAwOBD5cEevbsKc3frVs3WFhYoE+fPrh9+zZOnTqFMWPGoG/fvvk2hQ9O4xMRkTrTUvGtd507d0ZsbCymTZuGyMhIeHl54cCBA3B2dgYAREZG4tmzZ9L8RYoUweHDh/Hjjz+iSpUqsLCwQKdOnTBjxox8radIEAQhX19BBe6+SlJ1FTSOQ1FdVVdBI936obmqq6BxrkwPU3UVNM6g6vl/H/jXut2zldLLLLP+D6WXqWqcxiciItJwnMYnIiK1perH5aoLjuyJiIg0HEf2RESktlT9bHx1wV4iIiLScBzZExGR2hJpccyqCPYSERGRhlN5sL9+/TpmzJiBkJAQxMTEyOxLSkpC3759VVY3IiIiTaDSYP/XX3/Bx8cHW7duxezZs+Hp6Ynjx49L97979w7r1q1TZRWJiKgQ09LWUvqmiVTaqilTpmD06NG4efMmnjx5grFjx6J169Y4dOiQKqtFRESkUVS6QO/WrVvYsGED8OH3gMeMGQMHBwd06NABW7ZsgY+Pjyqr91UEQcDWsFUI/2MP3iYno2SZshg4YiycXN1zPebZ44fYHLoCD+/fxeuoSAQMHYHWnbrJ5NmyZiW2rl0lk1bM3Bzr9obnW1sKC0EQsHLFcuzetRvJyUnw8vLCuKAguLuXyPO4o0eOYFlICF68eA4HB0cMHjoU9evXl+5fExqK48eO4smTJ9DX10f5ChUw7KfhcHEpvI8GVRbrVj/ArmNn6FlY4N2TJ3i6bAmSb/6ba36Rri7se/SEZYOG0DUzR3pMNCI2b0J0+EEAgGXjJnAfMz7HcReaN4YgFudrWwoLQRBwbu9G3DxxAKlv38DWvTTq+w+BhUPu76f/Lv2NC39sRcLrCGRmZKCYrT0qN20PzxoNpXlCR/VEcsyrHMeWb9AK9XsOzbf2qAveeqcYlQZ7fX19JCQkyKR17doVWlpa6NKlC+bNm6eyun2t3ZvX4/ftm/FT0CQUd3TC9vVrMGnkUIRs2gkjI2O5x6SlpsKmuD386jXEmsXzcy3bydUN0+Yvlf6tpa2dL20obNatXYtNGzdiytRpcHJ2RuiqVRgcOAi79+6FsbH8Pr1x/TqCxo9D4KDBqFe/Po4fO4bx48YidE0YypUrBwC4cuUyOnbujLJly0KSIcHSpUswZNAg7Ny9O19/fUrVzOvUg/OgIXiyeCGSb92EdYtWKPXLbNwI6I306Ndyj/H432Tompnh0by5SI14Cd1iZhBle/9lvH2DG316yqR9L4EeAC4d2I6rh3ajcf9RKGbrgAv7NmP33CD0mhUKPUMjucfoGxeFT6uuMC/uCC1tHTy+fh5/rZ4HQ5NicClXBQDQdfJvEDIzpcfEvnyC3XOC4FG1VoG1rTBjsFeMSnupYsWKMtfos3Tu3BmrV6/GsGHDVFKvryUIAv7YsQUd/fvAt059OLuVwPAJU5CelopTh3MfgXt4lkWfwT+hdoPG0NXTyzWftrY2zCwspZtpMbN8aknhIQgCNm/ehL4B/VC/QQOUKFECU6dPR2rqOxw6eDDX4zZv3oRq1aqjb0AAXF1d0TcgAD4+PtiyaZM0z5KlIWjd+ge4u5dAyVKlMGXKVERFReLO7dsF1DrVsGvfEdGHDiD64AGkPnuGZ8uWIj36NWxatZab37RKVRQtXwF3J45H0tUrSH/1Cm/v3cWb27dkMwqAOD5eZvteCIKAq+F7UbV1F5SoUhOWDi5o3H80xOlpuHsu52dcFkfPCihRpQbMizuhmE1xeDduC0tHN0Tc/9i3RibFYFzMXLo9unYeptZ2cChdvoBaR5pApcF+0KBBePnypdx9Xbt2xbp161C7du0Cr9fXehX5EvFxsfCuWl2apqunh7IVKuHuzRvfXH7Ei+fo3bYZ+nf6AXOnTEBUxItvLrOwe/nyJWJjYlDd11eapqenh8qVq+D69Wu5Hnfjxg1U960uk+br64vr16/nesybN28AACampkqpe2Ek0tGBccmSSLx8SSY98fIlFCnrJfcYM98aeHv/Hop36gLvLdtRPmw9nAYEQpTti6m2oSEqbtwC783bUXL6LzD6zGUWTZIUHYWUxDg4e1WWpuno6sGhVDlEPlDsy6MgCHh26yriI5/DvpT8/wtJhhh3zxxD2dpNIBLxmfD4cJ+9sjdNpNJp/LZt26Jt27a57u/atSu6du1aoHX6FvGxsQAAU3NzmfRi5uZ4HRX1TWWXLFMWwydMRXFHJyTEx2LH+jUYNzgAi9dtg4lpsW8quzCL/XA7pkW2PjW3MEdkZGSex5lbWGQ7xgKxsTFy8wuCgPnz5qGitzdKlNDcIKVjagqRtnaOUbc4Ph66ZvJnivTt7FDUqxwy09Nxf8ok6JiawvXH4dAuaoLH8+YAAFKfP8PDubPw7vFjaBsZwbZte5RZuBj/BvZDWi5f6DXJ28Q4AICRiWwfGpmYISlW/qWRLGkpb7F6eDdIMsQQaWmhfs8fZb40fOrh5TNIS3mDMjUbK7H29D1QabCPj4/Hxo0b0atXL5iYmMjsS0xMxPr16+Xu+1RaWhrS0tJk0tLT0qCnr59v9c5y4q+DWDYvWPr3z7MXAABEkP3GLQgCvvVLeOXqNT75qwRKly2PgV3b4PihP/FD5+7fVnghcuDAn/hlxgzp34t+W/z+HyJ5fZp3p8r/f5B/zOxZwXjw4D5Cw9Z+feXViSDI/i16Pw0vj0hLBAgCHgbPhCTlLQDg6YoQePw8BU8WL4SQno43d+7gzZ070mOSb92E17KVsP2hHZ6GLM7XpqjC3TPHcHTtIunfP4ycDuR8mwL4/LmvZ2CI7tNDkJ6aiue3r+LklhUwsbKFo2eFHHlvngqHS/mqKGJmIbcsotyoNNgvWbIEN27cwI8//phjn6mpKU6fPo2kpCRMnDgx1zKCg4MxdepUmbQho8Zj6JigfKnzp3xq1kapMh+n28TidABAQlwszC0tpemJ8fEopuST08DQEM5uJRDx4rlSy1W1OnXqopxXOenf6R/6NDY2FlZWVtL0+Lh4mGcb7X/KwtIyxyg+Pi4O5uY5/x/mzJqFUydPYlXoGtjY2CipJYVTRmIiBIkEutn6TreYGcQJ8q+xp8fGIT0mRhroAeDds6cQaWlBz8pK/shdEPD23l0Y2NsrvxGFgJt3ddi6l5L+LfmwEPFtYjyMi318j6UkJeQY7Wcn0tJCMZv3/WTt7I64iOe4uH9bjmCfFPMKz29dRcthPyu5Neot+0JRkk+lFyd27dqFwMDAXPcPHDgQO3fuzLOMoKAgJCYmymwDho3Mh9rmZGRkDDsHR+nm6OIGM3MLXLt0XppHLBbj1vUrKO2l3MU04vR0vHj6BGYWmvUN39jYGI5OTtLNzc0dFpaWOH/urDSPWCzG5cuXUKFCxVzLKV++PM6fOyeTdu7sOVSo8PEDVBAEzJ4VjGPHjmL5ipWw19DA9CkhIwNv79+HaaUqMummlSrjza2bco9JvnUTuhYW0DIwkKYZ2jtCkEiQHh2d62sZuZdAelycEmtfeOgZGqGYjb10M7d3hpGpOZ7dvCLNI8kQ48W9f2HnUeYLSxcgych5F8Ot03/B0KQYXCtUU0IL6Huj0pH9w4cP4eHhket+Dw8PPHz4MM8y9PX1oZ9tyl7vXZLS6vglRCIRWnXsip0bw2Dn4IjiDo7YuXEt9PQNULtRE2m+BTMnw8LSCj0Hvr9HViwW4/mTR9J/x8ZE49GDezA0NIKdgyMAIGzpQlStUQtW1rZISIjHjvWhSHn7FvWbtlRJWwuKSCRCt27dsSY0FI5OznBycsKa0NUwMDBE02bNpPkm/e9/sLK2xo8f7uDo2rUb+vcLwNqwMNSpWxcnT5zA+QvnEbomTHrMrOBfcOjgQcxfsBBGxsbSxzUXKVIEBp8ENk0TuWsH3McF4e39e0i+cwvWzVtCz9oGr/b/AQBw7NsPupZWeDTn/SWq2GNHYN/dH25jxuHlurXQMTWF44CBiA4/CCH9/cyLfY+eeHP3DlJfvIC2sRFs2rSHkXsJPFm8KM+6aAqRSATvJm1wYf/W918AbO1x8Y8t0NXTR+nq9aT5wlfMgbGZJWp2ev8Y8At/bIWNqweKWReHJEOMJzcu4s4/R1C/p+xsp5CZidun/0KZmg2/m1tuFcVb7xSj0mCvra2NiIgIODk5yd0fEREBLTVbGdmuW0+kp6VhxfzZePMmGSU9y2LqvMUy99jHvIqC1icX8uJiojEioIf0771bN2Lv1o3wqlgJM39b8f6Y6Nf4der/kJyYAJNiZihVxgtzlq+Bta1dAbew4PXq3RtpaamYFfwLkpOS4OVVDkuXLZO5xz4qKvL9teUPKlSsiF+CZyEkZCmWhSyFg6MjZs2aLb3HHgB27tgBABjQv5/M602eOhWtW/9QIG1ThbiTx6FjYgL7Hj2ha26Od0+e4N7E8Uh//f7BLboWFtC3tpbmz0xNxd3xo+EyZBjKLl2OjKQkxJ06gedhodI82kWKwHX4SOiamUPy9i3ePvwPd0b+hLf37qqkjapQpXknZKSn49j6JUhLSYatW2m0HRMsc499Ulw08MlnWkZaKo6vX4LkuBjo6OnB3M4RTQaORalqdWXKfnbrKpJjX6Ns7SYg+hoiQci+Uqfg1KtXD9WqVcOsWbPk7h83bhwuXLgg9178vNx9pZqRvSZzKKqr6ipopFs/NFd1FTTOlelhCuSiLzGoeuF9quSzoD5KL9MpWPPeQyod2Q8dOhRdunSBg4MDBg0aBO0P01MSiQQhISFYsGABNm/erMoqEhERqT2VBvv27dtj7NixGDZsGCZOnAg3NzeIRCI8fPgQb968wZgxY9ChQwdVVpGIiEjtqTTYA8DMmTPRpk0bbNq0CQ8ePIAgCKhduza6deumlj+EQ0REBYcL9BSj0mCfkpKCMWPGYO/evRCLxWjQoAEWL14My0/uUSciIqJvo9KvRJMnT8batWvRokULdO3aFUeOHMGgQYNUWSUiIlIjIm0tpW+aSKUj+927dyM0NBRdunQBAHTv3h01atSARCKRLtYjIiLKjab+cI2yqbSXnj9/jlq1Pv4ms4+PD3R0dBAREaHKahEREWkUlQZ7iUQCvWw/k6mjo4OMjAyV1YmIiEjTqHQaXxAE9O7dW+Zxt6mpqQgMDJR5Otru3btVVEMiIiL1p9Jg36tXrxxpPXr0kJuXiIgoO01dUKdsKg32YWGa90hCIiIqOAz2imEvERERaTiVP0GPiIjoa2lxZK8Q9hIREZGGY7AnIiLScJzGJyIitcUn6CmGvURERKThOLInIiK1xVvvFMNeIiIi0nAc2RMRkdriyF4x7CUiIiINx2BPRESk4TiNT0REaou33imGvURERKThOLInIiK1paWtreoqqAUGeyIiUltcja8Y9hIREdE3CAkJgaurKwwMDFC5cmWcPn1aoeP++ecf6OjooGLFivleRwZ7IiJSWyJtLaVvX2Lbtm0YPnw4Jk6ciKtXr6JWrVpo1qwZnj17ludxiYmJ6NmzJxo0aPCNPaAYBnsiIqKvNH/+fAQEBKBfv37w9PTEwoUL4ejoiGXLluV53MCBA9GtWzf4+voWSD0Z7ImIiL5Ceno6Ll++jMaNG8ukN27cGGfOnMn1uLCwMDx8+BCTJ08ugFq+xwV6RESktvLjPvu0tDSkpaXJpOnr60NfX18mLSYmBhKJBDY2NjLpNjY2iIqKklv2gwcPMH78eJw+fRo6OgUXgjmyJyIi+kRwcDBMTU1ltuDg4Fzzi0Qimb8FQciRBgASiQTdunXD1KlTUbJkyXype244siciIrWVH7feBQUFYeTIkTJp2Uf1AGBpaQltbe0co/jXr1/nGO0DQHJyMi5duoSrV69i6NChAIDMzEwIggAdHR389ddfqF+/vtLbAwZ7IiIiWfKm7OXR09ND5cqVcfjwYbRt21aafvjwYfzwww858puYmODff/+VSQsJCcGxY8ewc+dOuLq6KqkFOTHYExGR2lL1Q3VGjhwJf39/VKlSBb6+vli5ciWePXuGwMBA4MMswcuXL7F+/XpoaWnBy8tL5nhra2sYGBjkSFc2BnsiIqKv1LlzZ8TGxmLatGmIjIyEl5cXDhw4AGdnZwBAZGTkZ++5LwgiQRAEVVdC2e6+SlJ1FTSOQ1FdVVdBI936obmqq6BxrkwPU3UVNM6g6i6qrkKu3myapvQyi3SfpPQyVY0jeyIiUlv8iVvFaGSwdzXKVHUVNI5Wsvx7RunbeG/ZoOoqaJwKxhaqrgJRoaORwZ6IiL4PIi3+xK0iGOyJiEh9MdgrhBc7iIiINByDPRERkYZjsCciItJwvGZPRETqi7feKYTBnoiI1JZImwv0FMGvRERERBqOI3siIlJfvPVOIRzZExERaTgGeyIiIg3HaXwiIlJfnMZXCEf2REREGo4jeyIiUlv8iVvFsJeIiIg0HEf2RESkvnjNXiEc2RMREWk4BnsiIiINx2l8IiJSX5zGVwhH9kRERBqOI3siIlJbvPVOMQz2RESkvjiNrxB+JSIiItJwDPZEREQajsGeiIhIw/GaPRERqS9es1cIgz0REaktkTaDvSI4jU9ERKThOLInIiL1xfvsFcJeIiIi0nAc2RMRkfriAj2FcGRPRESk4VQe7FevXo1evXohLCwMALBt2zZ4enrCzc0NkydPVnX1iIiI1J5Kp/EXLlyI//3vf2jSpAkmTpyIiIgILFiwACNGjEBmZibmzZsHe3t7DBgwQJXVJCKiQkrEaXyFqDTYr1ixAitXrkS3bt1w9epV+Pj4YPny5QgICAAAODg4YOnSpQz2RERE30Cl0/hPnz5FzZo1AQDe3t7Q1tZG9erVpftr1aqFhw8fqrCGX2brjp1o2roNqvjVQucePXH56tU881+6fAWde/REFb9aaPZDW2zfuVtm/5Fjx9HFvxdq1G0An5p10LFbD/zx54F8bkXhsnXPH2jSqRcqNWyFTv2G4vL1m7nmjY6Jxdhps9CyewDK1WmGWb8tl5vv8Im/0dp/ALwbtEJr/wE4cuqffGxB4bR19+9o2qE7Ktdrik59A3H52o0881+8eh2d+gaicr2maNqxB7bv+SNHng3bdqFVl16oUq8ZGrbtgtmLQpCWlp6PrShctm3bhmbNm6Oqjw+6dO2KK1eu5Jn/0qVL6NK1K6r6+KB5ixbYvmNHjjxHjhxB23btUKVqVbRt1w5Hjx3LxxaoKS0t5W8aSKWtMjIywtu3b6V/W1lZoUiRIjJ5MjIyVFCzL3for8OYM28B+vftg+2b1qOSd0UMHjYCkVFRcvO/eBmBwT+NQCXviti+aT369emNWb/Ow+GjH09mUxMT9O/bBxvCVmPX1k34oVVLTJo2A/+cPVeALVOdg0dPYtbiFejfswt2rF6KSuW9EDj2f4h89Vpu/nSxGGampujv3xWlSrjJzXPt5m2MnvoLWjWpj11rQtCqSX2MnvwLbty+m8+tKTwOHTmO2YtC0L9nN+wIW4HK5cth0OggREa9kpv/RUQkhoyegMrly2FH2Ar09++K4IVLcPj4KWme/eFHsHD5KgT27YnfN4dh2vjRCD96AguXry7AlqnOofBwzJk7F/379cO2rVtRydsbg4cMQWRkpNz8L16+xJChQ1HJ2xvbtm5Fv4AAzJ49G0eOHJHmuX79OsaOG4eWLVpgx/btaNmiBcaOHYsb//5bgC0r/ERa2krfNJFKg33p0qVx48bHEcXz58/h7Ows/fvu3btwcXFRUe2+zPpNW9D2h9Zo3+YHuLm6YtyokbC1scH2nbvk5t+xazfsbG0xbtRIuLm6on2bH9C2dSus27hJmqdqlcpoUK8u3Fxd4ejggB5du8CjRAlcvXatAFumOuu370a7Fk3QoWUzuLs4YfywQNhaWWHr3v1y89vb2SLop0H4oWlDFDE2kptnw4698K1SCf17dIGbsyP69+iCapUrYsOOPfncmsJj/badaNeyGdq3bgE3F2eMGz4EttbW2CZntA4A2/f+AVsba4wbPgRuLs5o37oF2rZoirVbtkvzXL95G97lvNCicQPY29nCr1oVNGtUD7fv3ivAlqnOhg0b0LZtW7Rr1w5ubm4YO3YsbG1t5Y7WAWDHjh2ws7PD2LFj4ebmhnbt2qFNmzZYt369NM/GTZtQvXp1BAQEwNXVFQEBAfDx8cGmTZvklkmUF5UG+9mzZ6NUqVK57n/27BkGDhxYoHX6GmKxGHfu3oVf9Woy6b7VfXDthvxv4df//Re+1X1k0vx8q+P27TsQy5nNEAQB5y5cxJOnT1HZ21vJLSh8xGIxbt9/AL+qlWTS/apWwvWbd7663Ou37uQos4ZPZVz7hjLViVgsxu179+HnU0Um3c+nMq7dvCX3mOs3b8PPp7JMWo1qVXH77n3pe7VSBS/cvncf/36YIXn+MgKnz15ALb/qcsvUJGKxGHfu3IGvr69Mum/16rh+/brcY27cuAHf6rJ94+fnh9u3b0MsFueex9c31zKJ8qLSBXo1atTIc//gwYMLrC7fIj4hARKJBBbm5jLpFuYWiImRP+UeGxsLC3OLbPnNkSGRICEhAVaWlgCA5Ddv0LBZS4jT06GlrY2J48bAN9uXCk0Un5gEiSQTFmZmMukW5maIiYv76nJj4uJzlmlmhpi4+K8uU53EJyS+71fznH0QGyu/X2Pj4uT+P7x/rybCytICzRrWR1x8InoO+gkQBGRIJOjctjX6+XfN1/YUBvHx8fLPfwsLxMTEyD0mJiYGFn5+svnNzZGRkfH+/Leyep/HIttnRB5lEuVFpcE+Pj4eGzduRK9evWBiYiKzLzExEevXr5e771NpaWlIS0uTTUxPg76+fn5VO1cikUjmb0EQcqTJ5pf9WxCE9+n4uMPYyAg7Nm9ASso7nL94Eb8uWAQHe3tUrVI5e3EaSV4f5dWnX1/mNxWpfrL3AeR0zKfZ5by3P02/eOUaVq3fhP+NGoZyZT3x/EUEZi1aCsswcwT28c+HBhQ+X37+592nX1Pmd0lDr7Erm0qn8ZcsWYJTp07JDeampqY4ffo0Fi9enGcZwcHBMDU1ldnmzFuQj7XOyaxYMWhrayMmNlYmPS4+DhYW5nKPsbCwkJM/Hjra2jAtZipN09LSgpOjI0qXKolePbqjYYP6CF27Lp9aUniYmZpAW1srx4g7Lj4hxyjzS1ia5xzFxyV8W5nqxKyYKbS1tRAbm71f43OM9rNYmJvnmE2Ji094/141fX/uLlkVhlZNGqF96xYo6e6GBnVqYtjAvgjdsAWZmZn52CLVMzMzk3/+x8XlGJlnsbS0zDFCj4uPh46ODkxNTXPPk0eZ3y2uxleISlu1a9cuBAYG5rp/4MCB2LlzZ55lBAUFITExUWYbO2pEPtQ2d7q6uvAsXRpnz1+QST93/gIqli8n95gK5crhXLb8Z86dR5kyntDVyWPCRRCQni5WTsULMV1dXZQp6YGzl2RvXzx76SoqeHl+dbkVynri7EXZMs9cvIKK31CmOtHV1UWZUiVx9uJlmfSzFy+joldZucdU8CqTI/+ZC5dQpnRJ6Xv1XVoaRFqyI05tLW0IgiAdsWoqXV1deHp64tzZszLp586fR4UKFeQeU758eZw7f14m7ezZsyhTpgx0dXU/5jknexnw7LlzuZZJlBeVBvuHDx/Cw8Mj1/0eHh6fvc9eX18fJiYmMpsqpvB7du+K3Xt/x57f9+HR48eYM28BIqNeoWP7dgCARUuWYsKkKdL8Hdu3Q0RkFObOX4hHjx9jz+/7sOf3fejVo7s0z+qwtTh77jxevHiJx0+eYP3GzfjjzwNo0bxpgbdPFXp2aodd+w9h95/hePjkGWYvXoHI16/R+YcWAIAFK9YgaOZcmWPuPniIuw8eIuVdKuITEnH3wUM8fPJUur9HhzY4c+kyQjdtx6OnzxG6aTvOXboK/45tC7x9qtKzcwfs+uMA9uw/iEdPnmL2ohBEvnqNTm1bAQAWLluNCdNnSfN3atMKkVGvMee3EDx68hR79h/E7v0H0btrJ2meujV8sX3PHzh45BheRETizIVLWLIqDHVr+kFbW/OnWf39/bF7zx7s2bsXjx49wty5cxEZGYmOHToAABb99hsm/u9/0vwdO3ZEREQE5v76Kx49eoQ9e/diz5496NWzpzRP927dcPbcOawJC8Pjx4+xJiwM58+fR/fu3eXW4Xsl0tZW+qaJVHrNXltbGxEREXBycpK7PyIiAlpqMqXStHEjJCQmYsXqNYiOiUEJdzcsXbQAxe3sgA8PfIn65D5mB/viCFm0AHPmL8TWHTthZWWJ8aNHoVGD+tI8796lYubsOXj1Ohr6+vpwdXHGL9OnomnjRippY0Fr1qAOEpOSsHzdJkTHxsPD1RnLZk9HcVsbAEBMbFyOe+47BAyR/vv2vQf488hxFLe1xl/b39/S5F2uDOZODsLi1euwOHQ9HIvbYe6UIJQvU7qAW6c6TRvWQ0JSEpaHbUB0bBxKuLkg5Ndgab9Gx8bK9KtDcTss/fUXzP0tBFt374O1pQWChg9Fo3q1pXkG9OoBkUiExSvD8Do6BmZmxVCnRnUMGxCgkjYWtKZNmiAxIQErV6x4f/6XKIGlS5agePHiAICY6GhEfXLPvYO9PZYuWYK5v/6Kbdu2wcrKCuPGjUPDhg2leSpWrIjZs2ZhydKlWLp0KRwdHTF79myULyd/tpAoLyJBhXNs9erVQ7Vq1TBr1iy5+8eNG4cLFy7g+PHjX1RuWnKCkmpIWbRSvo/V6gVN0NZVdRU0TqYxr2krm4GhoaqrkCvJzaNKL1Pbq4HSy1Q1lQ6bhw4dinnz5mHJkiWQSCTSdIlEgsWLF2PBggUYMmRInmUQEdF3TEtb+dsXCgkJgaurKwwMDFC5cmWcPn0617y7d+9Go0aNYGVlBRMTE/j6+iI8PPwbO+HzVBrs27dvj7Fjx2LYsGEwNzeHt7c3KlWqBHNzcwwfPhwjR45Ehw/XvIiIiAqbbdu2Yfjw4Zg4cSKuXr2KWrVqoVmzZnj27Jnc/KdOnUKjRo1w4MABXL58GfXq1UOrVq1w9TO/pfKtVDqNn+XixYvYtGkTHjx4AEEQULJkSXTr1g0+Pj4KHJ0Tp/GVj9P4+YPT+MrHaXzlK9TT+HdzH0V/Le3StRTOW61aNVSqVAnLli2Tpnl6eqJNmzYIDg5WqIyyZcuic+fOmDRp0lfVVxEqXaCXkpKCMWPGYO/evRCLxWjQoAEWL14Myw9PjyMiIiqs0tPTcfnyZYwfP14mvXHjxjhz5oxCZWRmZiI5ORnm5vKfyaIsKg32kydPxtq1a9G9e3cYGhpi8+bNGDRoEHbk8uMRREREnxLlwx1b8p7Mqq+vn+O27piYGEgkEtjY2Mik29jYICqXXzzNbt68eXj79i06deqkQO6vp9Jr9rt370ZoaChWrlyJRYsW4c8//8TevXtlFusREREVJHlPZs1rSv5rH2u8ZcsWTJkyBdu2bYO1tbVS6p4blY7snz9/jlq1Pl4b8fHxgY6ODiIiIuDo6KjKqhER0XcqKCgII0eOlEmT97A2S0tLaGtr5xjFv379OsdoP7tt27YhICAAO3bskHm+Qn5R6cheIpFAT09PJk1HRwcZcn7ilYiIKId8uPVO0Sez6unpoXLlyjh8+LBM+uHDh+GX7VcNP7Vlyxb07t0bmzdvRosWLfKlW7JT6cheEAT07t1bphNTU1MRGBgIY2Njadru3btVVEMiIqLcjRw5Ev7+/qhSpQp8fX2xcuVKPHv2TPq7L0FBQXj58iXWr3//FM8tW7agZ8+eWLRoEapXry6dFTA0NJT+CFJ+UGmw79WrV460Hj16qKQuRESkhkSqfaR6586dERsbi2nTpiEyMhJeXl44cOAAnJ2dAQCRkZEy99yvWLECGRkZGDJkiMxD43r16oW1a9fmWz0LxX32ysb77JWP99nnD95nr3y8z175CvN99pmPLim9TC23KkovU9XU41dmiIiI6Ksx2BMREWk4BnsiIiINp9IFekRERN9CUPECPXXBYE9EROqLwV4h7CUiIiINx5E9ERGpLwWeQU8c2RMREWk8BnsiIiINx2l8IiJSX/nwe/aaiL1ERESk4TiyJyIitcX77BXDXiIiItJwHNkTEZH64sheIQz2RESkvhjsFcJeIiIi0nAM9kRERBqOwZ6IiEjD8Zo9ERGpL16zVwiDPRERqS3eZ68Y9hIREZGG48ieiIjUF0f2CmEvERERaTgGeyIiIg3HaXwiIlJfIpGqa6AWOLInIiLScBzZExGR+uICPYVoZLBPEvRUXQWNYx79SNVV0EiPbXxUXQWNc/txoqqroHFalzFUdRXoG2lksCciou8DH6qjGPYSERGRhmOwJyIi0nCcxiciIvWlxTGrIthLREREGo4jeyIiUl9coKcQBnsiIlJfDPYKYS8RERFpOAZ7IiIiDcdgT0REpOF4zZ6IiNQXr9krhMGeiIjUFh+Xqxj2EhERkYbjyJ6IiNQXR/YKYS8RERFpOAZ7IiIiDcdpfCIiUl8ikaproBY4siciItJwHNkTEZH64gI9hbCXiIiINByDPRERqS1BpKX07UuFhITA1dUVBgYGqFy5Mk6fPp1n/pMnT6Jy5cowMDCAm5sbli9f/g09oBgGeyIiUl8iLeVvX2Dbtm0YPnw4Jk6ciKtXr6JWrVpo1qwZnj17Jjf/48eP0bx5c9SqVQtXr17FhAkTMGzYMOzatUtJHSKfSBAEIV9fQQWik1JUXQWNY/7srKqroJEe2/iougoa53b0W1VXQeO0LmOr6irkKvXdO6WXaWBoqHDeatWqoVKlSli2bJk0zdPTE23atEFwcHCO/OPGjcO+fftw584daVpgYCCuX7+Os2fz73OWI3siIqJPpKWlISkpSWZLS0vLkS89PR2XL19G48aNZdIbN26MM2fOyC377NmzOfI3adIEly5dglgsVnJLPmKwJyIi+kRwcDBMTU1lNnmj9JiYGEgkEtjY2Mik29jYICoqSm7ZUVFRcvNnZGQgJiZGyS35iLfeERGR2hLy4aE6QUFBGDlypEyavr5+rvlF2eogCEKOtM/ll5euTAz2RESktvJj1Zm+gX6ewT2LpaUltLW1c4ziX79+nWP0nsXW1lZufh0dHVhYWHxjzXPHaXwiIqKvoKenh8qVK+Pw4cMy6YcPH4afn5/cY3x9fXPk/+uvv1ClShXo6urmW10Z7ImISG1lCoLSty8xcuRIrF69GmvWrMGdO3cwYsQIPHv2DIGBgcCHSwI9e/aU5g8MDMTTp08xcuRI3LlzB2vWrEFoaChGjx6t9L75FKfxiYiIvlLnzp0RGxuLadOmITIyEl5eXjhw4ACcnZ0BAJGRkTL33Lu6uuLAgQMYMWIEli5diuLFi+O3335D+/bt87WevM+eFML77PMH77NXPt5nr3yF+T77NynKv8++iJHi99mri0I5jX/ixAm8y4cHJRARkWYR8mHTRIUy2Ddu3BhPnjxRdTWIiIg0gkqv2VeqVEluekZGBtq3bw8DAwMAwJUrVwq4ZkREpA4yNXUormQqDfb//vsvGjZsiOrVq0vTBEHA9evXUa9ePVhbW6uyekRERBpBpcH+xIkT6NWrF3x8fDB58mRoab2/qjBz5kwMGTIEZcqUUWX1voogCFizagX27dmF5ORklCnrhZFjg+Dm7p7ncSeOHcHq5SF4+eIF7B0c0H/QUNSpV18mT/Tr11i2eBHOnf0HaalpcHRywvifJ6O0p/r1k6K2HDqJNb8fQXR8Iko42mF8n46oUqaE3LyHz13F1vDTuPvkBdLFGSjhaIchnVqgpvfH/tlx+G/8fvI8/nsWAQAo4+aE4d1/QHkPlwJrU2EgCAI2r1mJQ/v24E1yMkqVKYtBI8fB2S339+nTRw+xMXQ5/rt3F6+jItF/2Ei06dRNJs+fe3biwN6deBUZCQBwdnVD1979UMW3Rr63SdUEQcDhbWtx/q8/kPI2GU4eZdB2wHDYOrnmesz5v/7A5RPhiHr2GABg714Kzbr3h1NJT2me1HcpCN8cipvnT+NNYjzsXT3wQ8CPcPTwzLXc74kGrjHPFyq9Zl+jRg1cuXIF9+/fh6+vLx4+fKjK6ijFpvVrsW3zRowcMx6r126EhYUFRgwNRMrb3FcI37xxHZMnjEeTZi2wdvM2NGnWApOCxuHWzX+leZKSkjCoX2/o6Ojg10VLsHH7LgwdPhJFixYtoJYVvIP/XEJw2E4MbN8Uu34NQmXPEhg4cykiouPk5r90+z/4VSiN5RMHY8ec8fDxKonBs5bh9qPn0jwXbj1Ai5pVEDZ1ODb/MgZ2VuboP20xXsUmFGDLVG/npnXYs20zAkeOxYLV62BmYYH/jRiClJTc36dpaamwLe6A3oFDYZbLk74srazRO3AoFq1ej0Wr16N8pSqYHjQKTx+p/7n9OSf2bMGpfdvRpv9w/DRnBYqamWPVlFFIfZf73UEPb11DxVoNMHD6QgydFQIzS2usmjoaibHR0jw7l87Bg+uX0PWniRi1MAwlK1bFyimjZPIQfY7KF+iZmJhgy5YtCAwMRM2aNbFy5cp8fT5wfhIEATu2bEbPPgGoU78B3EqUwMQp05GWmoq/wg/metz2LZtRxaca/PsEwNnFFf59AlC5qg+2b9kkzbNpXRisbWwxYfJUlCnrBbvixVHFpxrsHRwLqHUFb+0fx9C+vh86NKwBdwc7BPXtCDuLYtgafkpu/qC+HRHQpjHKlXCBS3FrjOj+A5xtrXHi0scvTXOH90HXpnXg6eoINwdbTAvsjkxBwLl/7xZgy1RLEAT8vmMLOvfsgxp16sPFrQRGTpyKtLRUnPzrUK7HlfQsi4AhP6FOwybQ1dWTm6dazdqo6lsT9k7OsHdyRq+BQ2BgaIS7t/+Vm19TCIKA0/t3oEEHf5TzrQ1bZzd0GRaE9LQ0XD11JNfjuo34GX7N2sLe1QPWDs7oMHgMBCETD25cBgCI09Lw79lTaNEzEG5lK8DSzgGNu/SBmbUdzh76vQBbSOpO5cE+S58+fXDq1CmsXr0aGRkZqq7OV4l4+RKxsTHwqe4rTdPT00PFSpVx88b1XI+7+e8NmWMAoJqvr8wx/5w+idKeZfC/8WPQsnF99OneBfv27M6nlqheujgDtx8+Q42KslOVfhU8ce3eI4XKyMzMxNvUVJgWMco1T2p6OjIkEpgWMf7mOquLqIiXiI+NRSWfj2tldPX04FWxEu7cvKG015FIJDh5JBypqe/gWba80sotjOJeRSI5Pg4lK1aRpuno6sGtbAU8vXtT4XLS09MgkWTAqIgJAECSKUFmpgQ6erJfrnT19PD4jmZ/gVJUpqD8TRMVqifoeXh44Ny5c0hOToaJiYmqq/PF4mLf/zyhubm5TLqZuQVeRUXmeZyZuUWOY+JiY6V/R7x8ib27dqBztx7o2ScAt2/dxMJ5c6Crp4tmLVopvS2qlpD8BpLMTFiYyl6msChmgpiEJIXKCNt3FO9S09G0RuVc88zfuBfW5sXgW770N9dZXcTHvX9fFcv2nitmZoHoV7m/TxX15OF/GBXYB+np6TA0NMT/fpkLJ1e3by63MEtOeH9pqUgx2XO/aDEzxEe/UricA+tXwNTcCh4V3r9nDQyN4FyqLI5sXw9rB2cUNTXD1dNH8fzBHVjaOSi5FepJQ2Oz0ql0ZB8fH4/FixcjKenjh7eWlhZMTU2RlJSUY588aWlpSEpKktnS0tIKoPbAXwcPoFFtP+kmnZHIfhlCEADkfWkix5WLbD+RmJmZiZKlSmPgkB9RslRptGnXAa3btMXeXTuU1p7CSO5PR36mLwHgz9MXEbL9T8wbGZDjC0OW0L1/4c+/L+G3Mf2hr5d/P0Chasf/Ooj2jWpJN8mH92nOfvz8+1QR9k7OWBy2GfNXhKF5mw6YP3MKnj1WbDZGXVw5eRgTuzaVbrn1qSAIck5u+Y7v2Yxrfx9Fz3HToav38RfXuvw0ERAEzAhoj6BOjfDPn7tQsVZDiLQKzcQsqQGVjuyXLFmCGzdu4Mcff8yxz9TUFKdPn0ZSUhImTpyYaxnBwcGYOnWqTNro8RMwNij3Y5SlZu06KOPlJf07PV0MAIiLjYWlpZU0PT4+DuYW5nLLAABzC0uZUXzWMWafzBBYWFrCxU12dOTs4ooTx44qpS2FTbGiRaCtpZVjFB+XmAyLYnkvSjz4zyX8HLIRC0b3g18F+SP2Nb8fxspd4QidPAylXDR7hFStZm2UKvPxfSpOTwcAxMfFwNzSUpqekO0997V0dXVR/MNaEo/SZXD/zm38vmMLfhyb/+dkQSnjU0NmxXyG+P25n5wQC5NPZkzeJCagqKnZZ8s7sXcrju3chAFT56G4i+wdEZZ29hg08zekp75DakoKTMwtsPHXKTC3sVNqm9SVpk67K5tKvxru2rVL+stA8gwcOBA7d+7Ms4ygoCAkJibKbD+NzN9fD8piZGwMB0cn6ebq5gYLC0tcPH9OmkcsFuPalcvwKl8h13K8ypWXOQYALpw7K3NMuQoV8ezpU5k8z589g62tZp7wero6KOPuhDPX78ikn7lxFxVL5T4l/Ofpi5iwZAPmDO+DOpXLyc0Tuvcwlu88iJU/D4VXCWel172wMTIyRnEHR+nm5OoGMwsLXL14XppHLBbj5rUr8PTKj2vrAsQfgqGmMDA0gqWdg3SzcXRBUTNz3L9+SZonQyzGo1vX4VzaK8+yTuzZgqM71qPfpDlwLJH75SQ9A0OYmFsg5U0y7l29iLI+mn87IymPSkf2Dx8+hIeHR677PTw8Pns7nr6+PvT19WXS0lT0QzgikQgdu3bDhrBQODg6wdHRCevXhkLfwACNmzST5ps++X+wsrJG4NBhAICOXbpi6MB+2LguDLXq1MXpkydw6cIFhKxeIz2mc9ceCAzojfVhoajfsBFu37qFfXt2YeyEn1XS1oLQu1V9jPttHcq6O6NiKVfsOPwPImPi0blxLeDD9fbXcQmYNaw38CHQBy1eh6C+HVGhpCui4xMBAAZ6eihq/P6HLUL3/oXftuzH3OF9UNzKXJrHyEAfxoYGKmtrQRKJRPihY1ds3xCG4g5OKO7oiO3rw6Cvb4A6jZtK882bPgkWH26lw4cvBM+evJ+OzxCLERsdjYcP7sHQ0Eg6kl+3YikqV/eDlbUN3qWk4OSRcPx79TKmzftNRa0tGCKRCLVadsSxnZtgaecAKzsHHN21EXr6+vCu3VCab8uimTA1t0Jz/wHAh6n78M1r0G3kzzCztkVS/PsZPn0DQ+gbvl9Yeu/qBQiCAGt7J8REvsD+dcthZe+IqvWbq6i1pI5UGuy1tbUREREBJycnufsjIiKkD9pRF9179kZaWhrmzw5GcnISypT1woLFy2Bk/HG196uoKGiJPrarXIWKmDIzGKuWhWD18hDYOzhi2i+zUNbr48jUs2xZ/DJ3HlYsXYy1q1fCrrg9ho0cg8bNNPeEb1ajChKS32LZjgOIjk+Ch5MdVkwYDHvr99OkMfFJiIyJl+bffvhvZEgyMX3VNkxftU2a3qZudfzy4/vfk95y6BTEGRkY/usqmdca3Kk5hnZuWWBtU7UO3XshPS0NIfNnfXiojhemL1gCI6OP79PoV1Ey14XjYqIxrE936d+7t2zA7i0bUK5iJcxashL4sPhv3vRJiIuNgbFxEbi4e2DavN/gXbU6NF3dtl0hTk/DnpUL8O7NGzh5eKL/5F9hYPjxbpCE6NcQfXLunz34OyQZYmyYM0mmrEade6Nxlz4AgNSUNziwYRUSY6NhVLQoylWvg6bd+0Fbp1Ctr1YZPlRHMSr9idt69eqhWrVqmDVrltz948aNw4ULF3D8+PEvKpc/cat8/Inb/MGfuFU+/sSt8hXmn7h9naj8/29rU827FVelXw2HDh2KLl26wMHBAYMGDYK2tjbw4f7ckJAQLFiwAJs3b1ZlFYmIqBDLVHUF1IRKg3379u0xduxYDBs2DBMnToSbmxtEIhEePnyIN2/eYMyYMejQoYMqq0hERKT2VH7RZ+bMmWjTpg02bdqEBw8eQBAE1K5dG926dYOPD6c4iYgod7xkrxiVBvuUlBSMGTMGe/fuhVgsRoMGDbB48WJYfnLvLxEREX0blS51nzx5MtauXYsWLVqga9euOHLkCAYNGqTKKhEREWkclY7sd+/ejdDQUHTp0gUA0L17d9SoUQMSiUS6WI+IiCg3fIKeYlQ6sn/+/Dlq1aol/dvHxwc6OjqIiIhQZbWIiIg0ikpH9hKJBHrZfrpRR0dHbX/iloiIChYfqqMYlQZ7QRDQu3dvmcfdpqamIjAwEMafPHFu927N/d12IiL6erzPXjEqDfa9evXKkdajRw+V1IWIiEhTqTTYh4WFqfLliYiIvgvq9SszRERE9MVU/gQ9IiKir8X1eYphsCciIrWVyWivEE7jExERaTiO7ImISG1xXK8YjuyJiIg0HEf2RESktvhsfMVwZE9ERKThGOyJiIg0HKfxiYhIbfHOO8VwZE9ERKThOLInIiK1lcmb7xTCYE9ERGqL0/iK4TQ+ERGRhmOwJyIi0nAM9kRERBqO1+yJiEht8Ql6imGwJyIitcUFeorhND4REZGG48ieiIjUFu+zVwxH9kRERBqOwZ6IiEjDMdgTEZHaEgTlb/klPj4e/v7+MDU1hampKfz9/ZGQkJBrfrFYjHHjxqFcuXIwNjZG8eLF0bNnT0RERHzxazPYExERFYBu3brh2rVrOHToEA4dOoRr167B398/1/wpKSm4cuUKfv75Z1y5cgW7d+/G/fv30bp16y9+bS7QIyIitZWpJvfe3blzB4cOHcK5c+dQrVo1AMCqVavg6+uLe/fuoVSpUjmOMTU1xeHDh2XSFi9eDB8fHzx79gxOTk4Kvz6DPRER0SfS0tKQlpYmk6avrw99ff2vLvPs2bMwNTWVBnoAqF69OkxNTXHmzBm5wV6exMREiEQiFCtW7Iten9P4RESktiSZyt+Cg4Ol19WztuDg4G+qZ1RUFKytrXOkW1tbIyoqSqEyUlNTMX78eHTr1g0mJiZf9PoaObLXEqm6BprnnZufqqugkW4/SVR1FTROTccv+xAkyi4oKAgjR46UScttVD9lyhRMnTo1z/IuXrwIABCJcgYnQRDkpmcnFovRpUsXZGZmIiQk5LP5s9PIYE9ERPS1vmTKfujQoejSpUueeVxcXHDjxg28evUqx77o6GjY2NjkebxYLEanTp3w+PFjHDt27ItH9WCwJyIidabqBXqWlpawtLT8bD5fX18kJibiwoUL8PHxAQCcP38eiYmJ8PPLfeY0K9A/ePAAx48fh4WFxVfVk9fsiYiI8pmnpyeaNm2K/v3749y5czh37hz69++Pli1byizOK126NPbs2QMAyMjIQIcOHXDp0iVs2rQJEokEUVFRiIqKQnp6+he9Pkf2RESktiRqcusdAGzatAnDhg1D48aNAQCtW7fGkiVLZPLcu3cPiYnv1/K8ePEC+/btAwBUrFhRJt/x48dRt25dhV+bwZ6IiNSWqqfxv4S5uTk2btyYZx7hk/a4uLjI/P0tOI1PRESk4RjsiYiINByDPRERkYbjNXsiIlJbkkxV10A9MNgTEZHaUqcFeqrEaXwiIiINx5E9ERGpLXW6z16VOLInIiLScAz2REREGo7T+EREpLYyOYuvEI7siYiINBxH9kREpLYkHNorhCN7IiIiDceRPRERqS0+VEcxDPZERKS2JIz1CuE0PhERkYZjsCciItJwDPZEREQajtfsiYhIbXGBnmIY7ImISG3xPnvFcBqfiIhIw3FkT0REaovT+IrhyJ6IiEjDMdgTERFpOE7jExGR2uIT9BTDkT0REZGG48ieiIjUFhfoKUalI/v79+9D+OQ/6u+//0abNm1QtmxZNGzYEL///rsqq0dERKQRVBrsPT09ER0dDQA4ceIE6tSpg8zMTHTv3h3FihVDu3btEB4ersoqEhFRIZaZKSh900Qqncb/dFQ/Y8YMBAYGYunSpdK0oKAg/PLLL2jSpImKakhERKT+Cs0Cvdu3b6Nnz54yaf7+/rh165bK6kRERKQJVL5ALzk5GQYGBjA0NIS+vr7MPj09Pbx7905ldfsagiAgdOUK7NuzC0nJyShb1gujxgXBzd09z+OOHz2CVctD8PLFC9g7OGDg4KGoU6++dP/qFcuxZtUKmWPMLSywP/xIvrWlsBAEAStXLMfuXbuRnJwELy8vjAsKgrt7iTyPO3rkCJaFhODFi+dwcHDE4KFDUb/+xz69cvky1q9fhzu37yAmJhq/zp+Pep/0uSYTBAGHt63F+b/+QMrbZDh5lEHbAcNh6+Sa6zHn//oDl0+EI+rZYwCAvXspNOveH04lPaV5Ut+lIHxzKG6eP403ifGwd/XADwE/wtHDM9dyNUXWuf/7J+f+aAXP/ZXZzv262c79UDnn/p/fwbmvCN56pxiVj+xLliwJMzMzPH78GJcvX5bZd+vWLdjb26usbl9j47q12Lp5I0aOHY/QdRthbmGB4UMC8fbt21yP+ffGdUyaMB5Nm7fAui3b0LR5C/xv/DjcuvmvTD5XN3f8ceiwdNuwdXsBtEj11q1di00bN2Lc+PFYv3ETLCwsMThwUJ59euP6dQSNH4fmLVpgy7btaN6iBcaPG4t///3Yp+/evUPJkiUxbvz4AmpJ4XFizxac2rcdbfoPx09zVqComTlWTRmF1HcpuR7z8NY1VKzVAAOnL8TQWSEws7TGqqmjkRgbLc2zc+kcPLh+CV1/mohRC8NQsmJVrJwySiaPptq4bi22bN6IUWPHY826jbCwsMBPCpz7P38499fnce67ublj/6HD0m3jd3Luk/KoNNgfP34cx44dw7Fjx3D8+HHUqlVLZv+TJ0/Qv39/ldXvSwmCgO1bNqNXnwDUrd8A7iVK4Oep05GamorDhw7metz2LZtRtVo19OwTABcXV/TsE4AqPj7YtnmTTD4dHW1YWFpKNzMz8wJolWoJgoDNmzehb0A/1G/QACVKlMDU6dORmvoOhw7m3qebN29CtWrV0TcgAK6urugbEAAfHx9s2fSxT2vUrInBQ4aifoMGBdSawkEQBJzevwMNOvijnG9t2Dq7ocuwIKSnpeHqqdxHi91G/Ay/Zm1h7+oBawdndBg8BoKQiQc33n9JF6el4d+zp9CiZyDcylaApZ0DGnfpAzNrO5w9pNl31giCgG1bNqO3nHP/rzzO/W0fzv1eH879Xrmc+9rf4bmvqExBUPqmiVQa7OvUqSOzlSxZUmb/Tz/9hDFjxqisfl8q4uVLxMbGwKe6rzRNT08PFStVxr83rud63M0bN+BTzVcmrVp13xzHPH/2DK2bNkL71i3wc9A4vHzxIh9aUbi8fPkSsTExqO4r26eVK1fB9evXcj3uxo0bqO5bXSbN19cX16/n/v/wvYh7FYnk+DiUrFhFmqajqwe3shXw9O5NhctJT0+DRJIBoyImAABJpgSZmRLo6OnJ5NPV08PjO//mUopmyO3c91biud+qaSO0+47OfUVJBEHpmyZSabCPj4/H4sWLkZSUlGNfYmJirvsKq7jYGACAuYXst25zCwvExsbmelxsbAzMLSxyHBP3yTFlvbzw89TpWLAkBOMn/oy42FgMDOiNxIQEpbejMImNed+nFubZ+9Q87z6Nkd+nsR/+j75nyQlxAIAixWT7tGgxM+k+RRxYvwKm5lbwqFAZAGBgaATnUmVxZPt6JMbFIFMiweUTf+H5gztIjs/9/0oTxOZx7sd9xbkfm+3cn/TJuR8bG4sB38G5T8ql0mC/ZMkSnDp1CiYmJjn2mZqa4vTp01i8eHGeZaSlpSEpKUlmS0tLy8dafxR+8AAa1PKTbhkZGQAAkUgkk08QhBxpn5P9GN8aNVGvQUO4l/BA1WrV8eui9/1yYP8fSmlLYXHgwJ+o6ecr3bL6FF/RpyJ8+/+DJrhy8jAmdm0q3SRZ71M5/ZO9n3NzfM9mXPv7KHqOmw5dvY8La7v8NBEQBMwIaI+gTo3wz5+7ULFWQ4i0VL48SKnCDx5A/Vp+0i2vc/9zfZpjby7nfokSHvCpVh3zNPTcp/yl0tX4u3btwrx583LdP3DgQIwePRoTJ07MNU9wcDCmTp0qkzZm/ASMm5D7McpSs3YdlPXykv6dni4GAMTGxMLS0kqaHh8XB3Pz3K+xWVhY5vj2Hx8XB7M8jjE0NIS7ewm8eP7sG1tRuNSpUxflvMpJ/04XpwMAYmNjYWX1aZ/G592nlpY5RvHv/x8scj1GU5XxqSGzYj5D/P59mpwQC5NP+uNNYgKKmpp9trwTe7fi2M5NGDB1Hoq7yK40t7Szx6CZvyE99R1SU1JgYm6Bjb9OgbmNnVLbpGo1a9dBmU/OffE3nPvZZ6jiPnNM1rn/XMPOfcpfKv26/fDhQ3h4eOS638PDAw8fPsyzjKCgICQmJspsw0eNzofa5mRsbAwHRyfp5urmBgsLS1w8f06aRywW49qVyyhXvkKu5XiVLy9zDABcOH82z2PS09Px5MljWFhaKqk1hYOxsTEcnZykm5ubOywsLXH+3FlpHrFYjMuXL6FChYq5llO+fHmcPyfbp+fOnkOFCrn3qaYyMDSCpZ2DdLNxdEFRM3Pcv35JmidDLMajW9fhXNorz7JO7NmCozvWo9+kOXAsUTrXfHoGhjAxt0DKm2Tcu3oRZX1qKLVNqmZsbAxHRyfpltu5f5Xnfr7jE/QUo9KRvba2NiIiIuDk5CR3f0REBLQ+M/2nr6+f4/58cXLutw/lJ5FIhE5du2F9WCgcnd5/AVgfFgoDAwM0atpMmm/apP/Bytoag4YOAwB06tIVgwf0w4a1YahVty5OnziBi+cvYHnoGukxixfOR81atWFja4f4+DisDV2Nt2/folnLVippa0ERiUTo1q071oSGwtHJGU5OTlgTuhoGBoZo2uxjn0763/s+/XHY+z7t2rUb+vcLwNqwMNSpWxcnT5zA+QvnEbomTHpMSkqKzOgo4uVL3Lt3FyYmprCz06yR6KdEIhFqteyIYzs3wdLOAVZ2Dji6ayP09PXhXbuhNN+WRTNham6F5v4DgA9T9+Gb16DbyJ9hZm2LpA/X4fUNDKFvaAQAuHf1AgRBgLW9E2IiX2D/uuWwsndE1frNVdTagiESidC5azesCwuFg9P7LwDrPpz7jT8596d+OPcHf+bcX/HJuf/bh3Pf9sO5H/bh3G+u4ee+onifvWJUGuy9vb2xd+9eVK9eXe7+PXv2wNvbu8Dr9S169OqNtLQ0/DorGMnJSSjj5YUFS5bB2NhYmudVVJTMl5hyFSpi6sxgrFwWglXLQ2Dv4IjpwbNQ9pPp7NevXmHyxCAkJCSgmJkZvLzKYVXYOtjZFS/wNha0Xr17Iy0tFbOCf0FyUhK8vMph6TLZPo2KioRI6+N1zgoVK+KX4FkICVmKZSFL4eDoiFmzZqNcuY99evv2LQz85NbO+R8uKbVs1QpTp00vsPapQt22XSFOT8OelQvw7s0bOHl4ov/kX2HwIWgDQEL0a4hEH9+nZw/+DkmGGBvmTJIpq1Hn3mjcpQ8AIDXlDQ5sWIXE2GgYFS2KctXroGn3ftDWUfnzu/KdvHN/4WfO/fIVKmLazGCsWBaClR/O/RnZzv1oOef+6u/k3CflEQmC6u4z2LVrF7p06YIFCxZg0KBB0NbWBgBIJBKEhIRg1KhR2Lx5Mzp06PBF5caqaGSvyfS1v7+FbQXh2JNEVVdB49R0zLngl76NeVEjBXKpxrJzT5Re5qDqLkovU9VU+nW7ffv2GDt2LIYNG4aJEyfCzc0NIpEIDx8+xJs3bzBmzJgvDvREREQkS+VzazNnzkSbNm2wadMmPHjwAIIgoHbt2ujWrRt8fHxUXT0iIiK1p9Jgn5KSgjFjxmDv3r0Qi8Vo0KABFi9eDEuuMiUiIgVo6hPvlE2lt95NnjwZa9euRYsWLdC1a1ccOXIEgwYNUmWViIiINI5KR/a7d+9GaGgounTpAgDo3r07atSoAYlEIl2sR0RElBuJht4Xr2wqHdk/f/5c5pfufHx8oKOjg4iICFVWi4iISOni4+Ph7+8PU1NTmJqawt/fHwlf8BsHAwcOhEgkwsKFC7/4tVU6spdIJNDL9gtZOjo6H5+HTkRElAd1Gtl369YNL168wKFDhwAAAwYMgL+/P/744/O/c7B3716cP38exYt/3fMVVBrsBUFA7969ZZ6Al5qaisDAQJkHUezevVtFNSQiIvp2d+7cwaFDh3Du3DlUq1YNALBq1Sr4+vri3r17KFWqVK7Hvnz5EkOHDkV4eDhatGjxVa+v0mDfq1evHGk9evRQSV2IiIjyy9mzZ2FqaioN9ABQvXp1mJqa4syZM7kG+8zMTPj7+2PMmDEoW7bsV7++SoN9WFiYArmIiIjky49p/LS0tBw/lS7vd1i+RFRUFKytrXOkW1tbIyoqKtfjZs+eDR0dHQz78LsfX0uzfmSaiIjoGwUHB0sX0WVtwcHBcvNOmTIFIpEoz+3Spfe/MCkS5XzsuCAIctMB4PLly1i0aBHWrl2bax5FqfwJekRERF8rP0b2QUFBGDlypExabqP6oUOHSm8fz42Liwtu3LiBV69e5dgXHR0NGxsbucedPn0ar1+/lvllWIlEglGjRmHhwoV48kTx3wVgsCciIrWVH8H+S6bsLS0tFXrqq6+vLxITE3HhwgXpo+DPnz+PxMRE+Pn5yT3G398fDRs2lElr0qQJ/P390adPH4Xql4XBnoiIKJ95enqiadOm6N+/P1asWAF8uPWuZcuWMovzSpcujeDgYLRt2xYWFhawsLCQKUdXVxe2trZ5rt6Xh9fsiYhIbUkyBaVv+WXTpk0oV64cGjdujMaNG6N8+fLYsGGDTJ579+4hMVH5P33NkT0REVEBMDc3x8aNG/PMI3zmh32+5Dr9pziyJyIi0nAc2RMRkdpSp8flqhJH9kRERBqOI3siIlJbHNkrhiN7IiIiDceRPRERqS2O7BXDkT0REZGGY7AnIiLScJzGJyIitcVpfMVwZE9ERKThOLInIiK1lcGRvUIY7ImISG1xGl8xnMYnIiLScAz2REREGo7BnoiISMPxmj0REaktXrNXDIM9ERGpLYnAYK8ITuMTERFpOI7siYhIbXEaXzEc2RMREWk4BnsiIiINJxIErm5QlbS0NAQHByMoKAj6+vqqro5GYJ8qH/tU+dinVNAY7FUoKSkJpqamSExMhImJiaqroxHYp8rHPlU+9ikVNE7jExERaTgGeyIiIg3HYE9ERKThGOxVSF9fH5MnT+YCHSVinyof+1T52KdU0LhAj4iISMNxZE9ERKThGOyJiIg0HIM9ERGRhmOwV7K6deti+PDhqq4GERGRFIO9Anr37g2RSITAwMAc+wYPHgyRSITevXsrXFabNm0+m+/JkycQiUTSzdTUFNWrV8cff/whk2/t2rUy+bK21atXf0ELC6/Xr19j4MCBcHJygr6+PmxtbdGkSROcPXsWAODi4gKRSIStW7fmOLZs2bIQiURYu3atNC0rv0gkgra2NooXL46AgADEx8cXaLtUKbf34IkTJyASiZCQkCD9t5mZGVJTU2XyXbhwQdqH8o79nijzs4EoPzHYK8jR0RFbt27Fu3fvpGmpqanYsmULnJyc8u11jxw5gsjISJw/fx4+Pj5o3749bt68KZPHxMQEkZGRMlv37t3zrU4FqX379rh+/TrWrVuH+/fvY9++fahbty7i4uKkeRwdHREWFiZz3Llz5xAVFQVjY+McZU6bNg2RkZF49uwZNm3ahFOnTmHYsGEF0h51U7RoUezZs0cmbc2aNfn6nlc3qvpsIPoSDPYKqlSpEpycnLB7925p2u7du+Ho6Ahvb+9cjzt06BBMTU2xfv16TJkyBevWrcPvv/8uHRmdOHEiz9e1sLCAra0tSpcujZkzZ0IsFuP48eMyeUQiEWxtbWU2Q0NDJbRatRISEvD3339j9uzZqFevHpydneHj44OgoCC0aNFCmq979+44efIknj9/Lk1bs2YNunfvDh0dnRzlFi1aFLa2trC3t0e9evXQs2dPXLlypcDapU569eqFNWvWSP9+9+4dtm7dil69eqm0XoWJop8NaWlpGDZsGKytrWFgYICaNWvi4sWL0v1ZsyPh4eHw9vaGoaEh6tevj9evX+PgwYPw9PSEiYkJunbtipSUlAJvJ6k3Bvsv0KdPH5kR5Jo1a9C3b99c82/duhWdOnXC+vXr0bNnT4wePRqdOnVC06ZNpSNwPz8/hV5bLBZj1apVAABdXV0ltKbwK1KkCIoUKYK9e/ciLS0t13w2NjZo0qQJ1q1bBwBISUnBtm3b8vy/yfLy5Uvs378f1apVU2rdNYW/vz9Onz6NZ8+eAQB27doFFxcXVKpUSdVVK1QU+WwYO3Ysdu3ahXXr1uHKlSsoUaIEmjRpIjNLBQBTpkzBkiVLcObMGTx//hydOnXCwoULsXnzZvz55584fPgwFi9eXGBtI83AYP8F/P398ffff+PJkyd4+vQp/vnnH/To0UNu3pCQEAQGBuL333/HDz/8AHwIXoaGhtJrz7a2ttDT08vzNf38/FCkSBEYGBhg1KhRcHFxQadOnWTyJCYmSgNjkSJFYGtrq8RWq46Ojg7Wrl2LdevWoVixYqhRowYmTJiAGzdu5Mjbt29frF27FoIgYOfOnXB3d0fFihXlljtu3Djp/4WDgwNEIhHmz59fAC0qPPbv3y/znilSpAiaNWuWI5+1tTWaNWsmXffwuS+436vPfTa8ffsWy5Ytw9y5c9GsWTOUKVMGq1atgqGhIUJDQ2XKmjFjBmrUqAFvb28EBATg5MmTWLZsGby9vVGrVi106NAhx+we0ecw2H8BS0tLtGjRAuvWrUNYWBhatGgBS0vLHPl27dqF4cOH46+//kK9evU+W26zZs2kH7hly5aV2bdt2zZcvXoV+/btQ4kSJbB69WqYm5vL5ClatCiuXbsm3c6cOaOE1hYO7du3R0REBPbt24cmTZrgxIkTqFSpksyiOwBo0aIF3rx5g1OnTn02II0ZMwbXrl3DjRs3cPToUenxEokk39tTWNSrV0/mPXPt2rVcF3VmfZF69OgRzp49qzHrQZTpc58NDx8+hFgsRo0aNaRpurq68PHxwZ07d2TKKl++vPTfNjY2MDIygpubm0za69ev871NpFlyXtCkPPXt2xdDhw4FACxdulRunooVK+LKlSsICwtD1apVZVYty7N69Wrp4p7sU/SOjo7w8PCAh4cHihQpgvbt2+P27duwtraW5tHS0kKJEiWU0LrCycDAAI0aNUKjRo0wadIk9OvXD5MnT5ZZ5ayjowN/f39MnjwZ58+fz7Go7FOWlpbS/vLw8MDChQvh6+uL48ePo2HDhgXSJlUzNjbO8Z558eKF3LzNmzfHwIEDERAQgFatWsHCwqKAaqle8vpsyHoqefbPAkEQcqR9+hkgEolyfCaIRCJkZmYqvf6k2Tiy/0JNmzZFeno60tPT0aRJE7l53N3dcfz4cfz+++/48ccfZfbp6enlGEHa29ujRIkSKFGiBJydnXN97Tp16sDLywszZ85UUmvUU5kyZfD27dsc6X379sXJkyfxww8/wMzMTOHytLW1gQ+LzygnbW1t+Pv748SJE5zCz0Nenw0lSpSAnp4e/v77b2maWCzGpUuX4OnpqYLa0veGI/svpK2tLZ12ywoS8pQsWRLHjx9H3bp1oaOjg4ULFwIf7vMODw/HvXv3YGFhAVNT0y9acDdq1Ch07NgRY8eOhb29vRJaVHjFxsaiY8eO6Nu3L8qXL4+iRYvi0qVLmDNnjnQdxKc8PT0RExMDIyOjPMtNTk5GVFQUBEHA8+fPMXbsWFhaWiq8WPJ7NH36dIwZM+azo/p///0XRYsWlUnLbe2Epsnrs8HY2BiDBg3CmDFjYG5uDicnJ8yZMwcpKSkICAhQUY3pe8Jg/xVMTEwUyleqVCkcO3YMdevWhba2NubNm4f+/fvjxIkTqFKlCt68eSP9QqColi1bwsXFBTNnzkRISMg3tKLwK1KkCKpVq4YFCxZIr3k6Ojqif//+mDBhgtxjFJlinjRpEiZNmgQAsLKyQtWqVXH48GFOT+dBT09P7vqU7GrXrp0j7Xv6Yc28PhtmzZqFzMxM+Pv7Izk5GVWqVEF4ePgXzUIRfS3+xC0REZGG4zV7IiIiDcdgT0REpOEY7ImIiDQcgz0REZGGY7AnIiLScAz2REREGo7BnoiISMMx2BMREWk4BnsiIiINx2BPRESk4RjsiYiINByDPRERkYb7P1+kzyT/5Xs2AAAAAElFTkSuQmCC", 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" ] @@ -871,7 +966,14 @@ "cell_type": "code", "execution_count": 11, "id": "2566bdea", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:09:13.686923Z", + "iopub.status.busy": "2026-07-31T11:09:13.686718Z", + "iopub.status.idle": "2026-07-31T11:09:13.693767Z", + "shell.execute_reply": "2026-07-31T11:09:13.693164Z" + } + }, "outputs": [ { "name": "stdout", @@ -913,15 +1015,15 @@ "source": [ "## Conclusion\n", "\n", - "We now have a complete picture of where the portfolio's risk comes from:\n", + "This notebook decomposes the risk of the long-only momentum portfolio.\n", "\n", - "- The **Marchenko–Pastur analysis** tells us how many eigenvalues of $\\Sigma$ exceed the random-matrix noise threshold — i.e., how many statistically significant factors drive the cross-section.\n", - "- The **Ledoit–Wolf shrinkage** shows that the sample covariance is noisy enough to benefit from regularization (a convex combination with a structured target).\n", - "- The **variance decomposition** splits the quadratic form $w^\\top \\Sigma w$ into the factor subspace (systematic risk) and its orthogonal complement (idiosyncratic risk).\n", + "- The Marchenko-Pastur cutoff gives an objective estimate of how many covariance eigenvalues are too large to dismiss as random-matrix noise.\n", + "- Ledoit-Wolf shrinkage shows why the raw sample covariance should be treated cautiously when $N$ is large relative to $T$.\n", + "- The portfolio variance decomposition shows that most of the long-only portfolio's risk lives in common PCA directions rather than purely stock-specific residual risk.\n", "\n", - "The momentum long-only portfolio's risk is overwhelmingly systematic (driven by common market and factor exposures), with a small idiosyncratic component — consistent with a diversified top-decile portfolio of ~50 stocks. The Fama–French alpha of 5.95% (t = 3.95) from notebook 04 is the component of return *orthogonal* to these systematic factors.\n", + "One wording caveat: the Fama-French alpha from notebook 04 is orthogonal to the Fama-French benchmark factors, not literally to these PCA directions. The two ideas are related because both are factor decompositions, but they are not the same basis.\n", "\n", - "The next notebook (06) uses the truncated-SVD factor structure to generate synthetic markets for stress testing — asking whether the alpha is genuine skill or luck." + "Notebook 06 uses the PCA factor structure to generate alternate market histories and ask whether the observed alpha looks unusually lucky." ] } ], diff --git a/notebooks/06_synthetic_market_generation.ipynb b/notebooks/06_synthetic_market_generation.ipynb new file mode 100644 index 0000000..df13f7d --- /dev/null +++ b/notebooks/06_synthetic_market_generation.ipynb @@ -0,0 +1,821 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c0965427", + "metadata": {}, + "source": [ + "# Synthetic Market Generation and Stress Testing\n", + "\n", + "## Purpose\n", + "\n", + "Notebook 04 found a Fama-French alpha of about 6% per year for the full momentum pipeline. Notebook 05 described the covariance structure of the stock universe. This notebook asks a different question:\n", + "\n", + "> If the same factor structure had appeared in a different order, would the strategy still look good?\n", + "\n", + "We model centered returns with a truncated PCA/SVD factor model:\n", + "\n", + "$$R \\approx FB^\\top+E.$$\n", + "\n", + "Here:\n", + "\n", + "- $R$ is the return panel.\n", + "- $B$ contains the top-$k$ PCA loading vectors.\n", + "- $F=R_cB$ contains the factor scores through time.\n", + "- $E=R_c-FB^\\top$ contains the residual stock-level shocks.\n", + "\n", + "Then we generate many alternate return panels, rerun a compact version of the momentum backtest on each one, and compare the generated alphas to the real-panel alpha.\n", + "\n", + "We use two generators:\n", + "1. **Block bootstrap:** resample time blocks from the observed factor/residual rows. This is the result we trust most.\n", + "2. **Conditional VAE:** a small neural generator for factor scores. This is included as a cautionary example because it can create artificial momentum if factor scores and residuals are stitched together poorly.\n", + "\n", + "### Terms used in this notebook\n", + "\n", + "| Term | Meaning |\n", + "|------|---------|\n", + "| **Truncated factor model** | $R\\approx FB^\\top+E$, a low-rank approximation plus residuals |\n", + "| **Loadings** $B$ | Stock exposures to the retained PCA directions |\n", + "| **Factor scores** $F$ | Time series of factor realizations, one row per month |\n", + "| **Residuals** $E$ | Return components not captured by the retained PCA factors |\n", + "| **Block bootstrap** | Resample neighboring time blocks rather than individual months |\n", + "| **Tail share** | Fraction of synthetic alphas greater than or equal to the real alpha |\n", + "| **Path dependence** | Dependence on the specific order of historical months |\n", + "\n", + "## Outputs\n", + "\n", + "- `synthetic_backtest_results.csv`\n", + "- Bootstrap and VAE alpha distribution plots\n", + "\n", + "## Notebook Structure\n", + "1. [Setup and Factor Model](#setup-and-factor-model)\n", + "2. [Backtest Baseline](#backtest-baseline)\n", + "3. [Block-Bootstrap Generator](#block-bootstrap-generator)\n", + "4. [Conditional VAE Generator](#conditional-vae-generator)\n", + "5. [Results and Interpretation](#results-and-interpretation)\n", + "6. [Conclusion](#conclusion)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "bd2a00cd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:11:27.660596Z", + "iopub.status.busy": "2026-07-31T11:11:27.659904Z", + "iopub.status.idle": "2026-07-31T11:11:28.975793Z", + "shell.execute_reply": "2026-07-31T11:11:28.975192Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returns: (251, 501) | FF: (257, 5)\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Setup and imports\n", + "==================================\n", + "\"\"\"\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import os\n", + "from sklearn.decomposition import PCA\n", + "import statsmodels.api as sm\n", + "\n", + "RANDOM_STATE = 3\n", + "np.random.seed(RANDOM_STATE)\n", + "\n", + "os.makedirs('../images/06_synthetic_markets', exist_ok=True)\n", + "os.makedirs('../data/processed', exist_ok=True)\n", + "\n", + "df_returns = pd.read_csv('../data/processed/returns_monthly.csv', index_col=0, parse_dates=True)\n", + "df_ff = pd.read_csv('../data/raw/ff_factors.csv', index_col=0, parse_dates=True)\n", + "\n", + "print(f\"Returns: {df_returns.shape} | FF: {df_ff.shape}\")" + ] + }, + { + "cell_type": "markdown", + "id": "262a6ed0", + "metadata": {}, + "source": [ + "## Setup and Factor Model\n", + "\n", + "We rebuild the notebook 05 factor model on a balanced panel. The steps are:\n", + "\n", + "1. Choose stocks with enough history and drop remaining missing rows.\n", + "2. Center each stock's return series by subtracting its time-series mean.\n", + "3. Estimate $k$ with the Marchenko-Pastur cutoff.\n", + "4. Keep the top-$k$ PCA loading vectors in $B$.\n", + "5. Compute factor scores $F=X_cB$.\n", + "6. Compute residuals $E=X_c-FB^\\top$.\n", + "7. Align the Fama-French factors to the same months.\n", + "\n", + "The centered panel is what gets decomposed. The mean vector is added back when we reconstruct synthetic returns." + ] + }, + { + "cell_type": "markdown", + "id": "b7ee87ef", + "metadata": {}, + "source": [ + "### Small Example: What Are $R$, $F$, $B$, and $E$?\n", + "\n", + "Suppose we only had $T=3$ months, $N=4$ stocks, and kept $k=2$ PCA factors.\n", + "\n", + "A return matrix might look like this:\n", + "\n", + "$$\n", + "R=\\begin{bmatrix}\n", + "0.042 & 0.009 & 0.031 & 0.038\\\\\n", + "-0.010 & -0.018 & -0.001 & 0.009\\\\\n", + "-0.006 & 0.025 & 0.047 & -0.006\n", + "\\end{bmatrix}.\n", + "$$\n", + "\n", + "Rows are months; columns are stocks. First subtract the column mean vector $\\bar r$ to get $X_c=R-\\mathbf{1}\\bar r^\\top$.\n", + "\n", + "Now imagine PCA gives two loading vectors. Put them into\n", + "\n", + "$$\n", + "B=\\begin{bmatrix}\n", + "0.5 & 0.5\\\\\n", + "0.5 & -0.5\\\\\n", + "0.5 & -0.5\\\\\n", + "0.5 & 0.5\n", + "\\end{bmatrix}.\n", + "$$\n", + "\n", + "The first column is a market-like direction: all four stocks move together. The second column is a spread direction: stocks 1 and 4 move opposite stocks 2 and 3.\n", + "\n", + "For three months of factor realizations, suppose\n", + "\n", + "$$\n", + "F=\\begin{bmatrix}\n", + "0.04 & 0.02\\\\\n", + "-0.03 & 0.01\\\\\n", + "0.01 & -0.04\n", + "\\end{bmatrix}.\n", + "$$\n", + "\n", + "The first month has a positive market shock and a positive spread shock. Multiplying $F$ by $B^\\top$ maps those factor shocks back into stock returns:\n", + "\n", + "$$\n", + "FB^\\top=\\begin{bmatrix}\n", + "0.03 & 0.01 & 0.01 & 0.03\\\\\n", + "-0.01 & -0.02 & -0.02 & -0.01\\\\\n", + "-0.015 & 0.025 & 0.025 & -0.015\n", + "\\end{bmatrix}.\n", + "$$\n", + "\n", + "That low-rank matrix will not match every stock return exactly, so the residual matrix stores the leftover stock-specific pieces:\n", + "\n", + "$$\n", + "E=X_c-FB^\\top.\n", + "$$\n", + "\n", + "The bootstrap generator resamples matching rows of $F$ and $E$. If it draws month indices $[3,1,2]$, the synthetic centered panel is\n", + "\n", + "$$\n", + "X_{\\text{synth}}=\n", + "\\begin{bmatrix}\n", + "F_3B^\\top+E_3\\\\\n", + "F_1B^\\top+E_1\\\\\n", + "F_2B^\\top+E_2\n", + "\\end{bmatrix},\n", + "$$\n", + "\n", + "and the final synthetic return panel is\n", + "\n", + "$$R_{\\text{synth}}=X_{\\text{synth}}+\\mathbf{1}\\bar r^\\top.$$\n", + "\n", + "The key rule is that the same time index is used for $F$, $E$, and the benchmark factors. That keeps each synthetic month internally consistent." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d334f4f7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:11:28.977654Z", + "iopub.status.busy": "2026-07-31T11:11:28.977462Z", + "iopub.status.idle": "2026-07-31T11:11:29.009004Z", + "shell.execute_reply": "2026-07-31T11:11:29.008602Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Panel (240, 394) | k=9 | F (240, 9) | E (240, 394) | FF (240, 5)\n", + "Top-9 PCs explain 53.0% of variance\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Truncated-SVD factor model: R ≈ F Bᵀ + E (mirrors NB05)\n", + "==================================\n", + "\"\"\"\n", + "panel = df_returns.dropna(thresh=240, axis=1).dropna() # balanced panel -> maximize T\n", + "tickers = panel.columns.tolist()\n", + "X = panel.values\n", + "T, N = X.shape\n", + "mean_r = X.mean(axis=0)\n", + "Xc = X - mean_r\n", + "\n", + "pca_full = PCA(n_components=min(T, N)).fit(Xc)\n", + "eigvals = pca_full.explained_variance_\n", + "q = T / N\n", + "sigma_sq = eigvals.sum() / N\n", + "lam_plus = sigma_sq * (1 + 1 / q + 2 * np.sqrt(1 / q))\n", + "k = max(int((eigvals > lam_plus).sum()), 1) # Marchenko–Pastur signal count\n", + "B = pca_full.components_[:k].T # (N, k) loadings\n", + "F = Xc @ B # (T, k) factor scores\n", + "E = Xc - F @ B.T # (T, N) residuals\n", + "\n", + "# Fama–French factors aligned to these T months (RangeIndex for positional work below)\n", + "ff = df_ff[['Mkt-RF', 'SMB', 'HML', 'Mom', 'RF']].copy()\n", + "ff.index = ff.index.to_period('M')\n", + "panel_pm = panel.copy(); panel_pm.index = panel_pm.index.to_period('M')\n", + "ff = ff.reindex(panel_pm.index).dropna().reset_index(drop=True)\n", + "\n", + "print(f\"Panel {X.shape} | k={k} | F {F.shape} | E {E.shape} | FF {ff.shape}\")\n", + "print(f\"Top-{k} PCs explain {pca_full.explained_variance_ratio_[:k].sum()*100:.1f}% of variance\")" + ] + }, + { + "cell_type": "markdown", + "id": "e2139982", + "metadata": {}, + "source": [ + "## Backtest Baseline\n", + "\n", + "For an apples-to-apples comparison we run a **compact version of the notebook 04 pipeline** on the *real* panel: re-derive the raw 12-1 momentum signal (rolling 11-month sum, shifted), form the top-decile equal-weight long-only book, and regress its excess returns on the Fama–French factors. (This simplified path — no winsorize/neutralize — is what we re-run inside every synthetic market, so real and synthetic share the exact same pipeline. Notebook 04's headline 5.97% used the full pipeline.)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6a3dd658", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:11:29.011598Z", + "iopub.status.busy": "2026-07-31T11:11:29.011414Z", + "iopub.status.idle": "2026-07-31T11:11:29.136737Z", + "shell.execute_reply": "2026-07-31T11:11:29.136054Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Real-panel raw-momentum alpha: 4.49%/yr (t = 2.59)\n", + "(Notebook 04 full-pipeline headline: 5.97%/yr, t = 3.98)\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Backtest primitives + real-panel baseline alpha\n", + "==================================\n", + "\"\"\"\n", + "def block_indices(T, L=15, rng=np.random):\n", + " \"\"\"Stationary block bootstrap: T time indices resampled in variable-length blocks.\"\"\"\n", + " idx = []\n", + " while len(idx) < T:\n", + " start = rng.randint(T)\n", + " blen = rng.geometric(1.0 / L)\n", + " idx.extend((start + np.arange(blen)) % T)\n", + " return np.array(idx[:T])\n", + "\n", + "def momentum_signal(ret_df):\n", + " \"\"\"12-1 momentum: rolling 11-month sum, shifted to avoid look-ahead.\"\"\"\n", + " return ret_df.rolling(11).sum().shift(1)\n", + "\n", + "def decile_long_returns(signal_df, ret_df, decile=0.1):\n", + " \"\"\"Top-decile equal-weight long-only monthly returns (signal at t, return at t+1).\"\"\"\n", + " out, ix = [], []\n", + " for i in range(len(signal_df) - 1):\n", + " s = signal_df.iloc[i].dropna()\n", + " if len(s) < 50:\n", + " continue\n", + " n = max(int(len(s) * decile), 1)\n", + " long = s.sort_values(ascending=False).head(n).index\n", + " out.append(ret_df.iloc[i + 1][long].mean())\n", + " ix.append(i + 1)\n", + " return pd.Series(out, index=ix, dtype=float)\n", + "\n", + "def ff_alpha(long_ret, ff_df):\n", + " \"\"\"Annualized FF 4-factor alpha (and t-stat) for a long-only return series.\"\"\"\n", + " a = pd.concat([long_ret.rename('r'), ff_df[['Mkt-RF', 'SMB', 'HML', 'Mom', 'RF']]], axis=1).dropna()\n", + " y = a['r'] - a['RF']\n", + " Xf = sm.add_constant(a[['Mkt-RF', 'SMB', 'HML', 'Mom']])\n", + " m = sm.OLS(y.values, Xf.values).fit()\n", + " return m.params[0] * 12.0, m.tvalues[0]\n", + "\n", + "# Real-panel baseline (apples-to-apples with the synthetic loop below)\n", + "ret_real = pd.DataFrame(X, columns=tickers)\n", + "real_alpha, real_t = ff_alpha(decile_long_returns(momentum_signal(ret_real), ret_real), ff)\n", + "print(f\"Real-panel raw-momentum alpha: {real_alpha*100:.2f}%/yr (t = {real_t:.2f})\")\n", + "print(f\"(Notebook 04 full-pipeline headline: 5.97%/yr, t = 3.98)\")" + ] + }, + { + "cell_type": "markdown", + "id": "cbdb1675", + "metadata": {}, + "source": [ + "## Block-Bootstrap Generator\n", + "\n", + "The block bootstrap resamples time indices in short runs rather than one month at a time. With $T\\approx240$, the average block length is about $\\sqrt{T}\\approx15$ months.\n", + "\n", + "For a generated index sequence `idx`, we reconstruct synthetic returns as\n", + "\n", + "$$R_{\\text{synth},t}=F_{\\mathrm{idx}_t}B^\\top+E_{\\mathrm{idx}_t}+\\bar r.$$\n", + "\n", + "We use the same `idx` for the Fama-French factor rows. This matters: if the stock return panel is pretending that month 37 came next, the benchmark factor panel should also use month 37.\n", + "\n", + "This generator tests **path dependence**. It asks whether the strategy needed the exact historical order of regimes, crashes, rebounds, and quiet periods." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2aa45300", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:11:29.138516Z", + "iopub.status.busy": "2026-07-31T11:11:29.138286Z", + "iopub.status.idle": "2026-07-31T11:12:06.129556Z", + "shell.execute_reply": "2026-07-31T11:12:06.128901Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 50/300 paths done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 100/300 paths done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 150/300 paths done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 200/300 paths done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 250/300 paths done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 300/300 paths done\n", + "\n", + "Bootstrap: mean synth alpha 4.85%/yr | P(>= real 4.49%) = 56.3%\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Block bootstrap: 300 synthetic markets\n", + "==================================\n", + "\"\"\"\n", + "N_PATHS = 300\n", + "L = 15\n", + "rng = np.random.RandomState(RANDOM_STATE)\n", + "boot_alphas = []\n", + "for p in range(N_PATHS):\n", + " idx = block_indices(T, L, rng)\n", + " R_synth = F[idx] @ B.T + E[idx] + mean_r\n", + " ret_s = pd.DataFrame(R_synth, columns=tickers)\n", + " long_s = decile_long_returns(momentum_signal(ret_s), ret_s)\n", + " a, _ = ff_alpha(long_s, ff.iloc[idx].reset_index(drop=True))\n", + " boot_alphas.append(a)\n", + " if (p + 1) % 50 == 0:\n", + " print(f\" {p+1}/{N_PATHS} paths done\")\n", + "boot_alphas = np.array(boot_alphas)\n", + "p_boot = np.mean(boot_alphas >= real_alpha)\n", + "print(f\"\\nBootstrap: mean synth alpha {boot_alphas.mean()*100:.2f}%/yr | \"\n", + " f\"P(>= real {real_alpha*100:.2f}%) = {p_boot*100:.1f}%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "eb0bb233", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:12:06.131528Z", + "iopub.status.busy": "2026-07-31T11:12:06.131300Z", + "iopub.status.idle": "2026-07-31T11:12:06.745422Z", + "shell.execute_reply": "2026-07-31T11:12:06.744889Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Bootstrap: one path + alpha distribution\n", + "==================================\n", + "\"\"\"\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "ax = axes[0]\n", + "ax.plot((1 + X.mean(axis=1)).cumprod() - 1, color='steelblue', label='real')\n", + "idx0 = block_indices(T, L, rng)\n", + "R0 = F[idx0] @ B.T + E[idx0] + mean_r\n", + "ax.plot((1 + R0.mean(axis=1)).cumprod() - 1, color='coral', alpha=0.85, label='one synthetic')\n", + "ax.set_title('Real vs one synthetic market (equal-weight cumulative)')\n", + "ax.set_xlabel('synthetic month'); ax.legend(); ax.grid(alpha=0.3)\n", + "\n", + "ax = axes[1]\n", + "ax.hist(boot_alphas * 100, bins=30, color='steelblue', alpha=0.7)\n", + "ax.axvline(real_alpha * 100, color='coral', linewidth=2, label=f'real {real_alpha*100:.2f}%')\n", + "ax.set_xlabel('Annualized FF alpha (%)'); ax.set_ylabel('# synthetic markets')\n", + "ax.set_title(f'Block-bootstrap alpha (p = {p_boot*100:.1f}%)')\n", + "ax.legend(); ax.grid(alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('../images/06_synthetic_markets/bootstrap_alpha.png', dpi=150, bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "17f709e9", + "metadata": {}, + "source": [ + "## Conditional VAE Generator\n", + "\n", + "The VAE section is intentionally more cautious. A small conditional variational autoencoder tries to model factor-score transitions:\n", + "\n", + "$$f_{t-1}\\rightarrow f_t.$$\n", + "\n", + "The encoder maps the previous and current factor score to latent parameters, samples a latent vector $z$, and the decoder tries to reconstruct the next factor score. After training, we sample new factor paths one step at a time.\n", + "\n", + "This is interesting, but it creates a serious validation problem. To reconstruct stock returns we still need residual rows $E_t$. If generated factor scores are paired with independently sampled residuals, we may create combinations that never existed in the real data. That can accidentally create cross-sectional persistence, which is exactly what a momentum strategy profits from.\n", + "\n", + "So the VAE is included as a warning: generative finance models can look sophisticated while quietly injecting the signal being tested." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "844c73c3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:12:06.747247Z", + "iopub.status.busy": "2026-07-31T11:12:06.747003Z", + "iopub.status.idle": "2026-07-31T11:12:09.315924Z", + "shell.execute_reply": "2026-07-31T11:12:09.315412Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "VAE trained | recon 0.0096 | kl 7.69\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Conditional VAE on the factor scores F (autoregressive)\n", + "==================================\n", + "\"\"\"\n", + "import torch\n", + "import torch.nn as nn\n", + "torch.manual_seed(RANDOM_STATE)\n", + "\n", + "Ft = torch.tensor(F, dtype=torch.float32) # (T, k)\n", + "cond, target = Ft[:-1], Ft[1:] # f_{t-1} -> f_t\n", + "d = 4 # latent dim\n", + "\n", + "class CVAE(nn.Module):\n", + " def __init__(self, k, d):\n", + " super().__init__()\n", + " self.enc = nn.Sequential(nn.Linear(2*k, 32), nn.ReLU(), nn.Linear(32, 2*d))\n", + " self.dec = nn.Sequential(nn.Linear(k+d, 32), nn.ReLU(), nn.Linear(32, k))\n", + " self.d = d\n", + " def encode(self, c, x):\n", + " h = self.enc(torch.cat([c, x], -1)); return h[..., :self.d], h[..., self.d:]\n", + " def decode(self, c, z):\n", + " return self.dec(torch.cat([c, z], -1))\n", + "\n", + "model = CVAE(k, d)\n", + "opt = torch.optim.Adam(model.parameters(), lr=1e-2)\n", + "for epoch in range(300):\n", + " mu, logvar = model.encode(cond, target)\n", + " z = mu + torch.randn_like(mu) * torch.exp(0.5 * logvar)\n", + " recon = model.decode(cond, z)\n", + " recon_loss = ((recon - target) ** 2).mean()\n", + " kl = -0.5 * (1 + logvar - mu**2 - torch.exp(logvar)).sum(-1).mean()\n", + " loss = recon_loss + 1e-3 * kl\n", + " opt.zero_grad(); loss.backward(); opt.step()\n", + "print(f\"VAE trained | recon {recon_loss.item():.4f} | kl {kl.item():.2f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bf811200", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:12:09.318171Z", + "iopub.status.busy": "2026-07-31T11:12:09.317809Z", + "iopub.status.idle": "2026-07-31T11:12:51.102586Z", + "shell.execute_reply": "2026-07-31T11:12:51.101852Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " VAE 50/300 paths done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " VAE 100/300 paths done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " VAE 150/300 paths done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " VAE 200/300 paths done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " VAE 250/300 paths done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " VAE 300/300 paths done\n", + "\n", + "VAE: mean synth alpha 14.46%/yr | P(>= real 4.49%) = 99.7%\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "VAE: ancestral-sample 300 synthetic markets, run the backtest\n", + "==================================\n", + "\"\"\"\n", + "def sample_path(model, T, k, d, f0):\n", + " f = [f0]\n", + " with torch.no_grad():\n", + " for _ in range(T - 1):\n", + " z = torch.randn(d)\n", + " f.append(model.decode(f[-1], z))\n", + " return torch.stack(f).numpy()\n", + "\n", + "vae_alphas = []\n", + "for p in range(N_PATHS):\n", + " F_synth = sample_path(model, T, k, d, Ft[0]) # (T, k)\n", + " idx = rng.randint(0, T, size=T) # iid residual draw\n", + " R_synth = F_synth @ B.T + E[idx] + mean_r\n", + " ret_s = pd.DataFrame(R_synth, columns=tickers)\n", + " long_s = decile_long_returns(momentum_signal(ret_s), ret_s)\n", + " a, _ = ff_alpha(long_s, ff.iloc[idx].reset_index(drop=True))\n", + " vae_alphas.append(a)\n", + " if (p + 1) % 50 == 0:\n", + " print(f\" VAE {p+1}/{N_PATHS} paths done\")\n", + "vae_alphas = np.array(vae_alphas)\n", + "p_vae = np.mean(vae_alphas >= real_alpha)\n", + "print(f\"\\nVAE: mean synth alpha {vae_alphas.mean()*100:.2f}%/yr | \"\n", + " f\"P(>= real {real_alpha*100:.2f}%) = {p_vae*100:.1f}%\")" + ] + }, + { + "cell_type": "markdown", + "id": "diag-vae", + "metadata": {}, + "source": [ + "### Why the VAE Overstates the Alpha\n", + "\n", + "The VAE synthetic markets produce alphas well above the real raw-momentum baseline. I would not treat that as evidence of a better stress test. It is more likely an artifact of how the synthetic return rows are assembled.\n", + "\n", + "| reconstruction of $R$ | mean synthetic alpha |\n", + "|---|---:|\n", + "| $F[\\mathrm{idx}]B^\\top+E[\\mathrm{idx}]$ with the **same** time index | about 5% |\n", + "| generated $\\hat F$ plus independently resampled $E$ | often 10-25% |\n", + "\n", + "The second construction breaks the observed relationship between common factor shocks and stock-specific residual shocks. That can manufacture momentum-like structure.\n", + "\n", + "The lesson is practical: when stress-testing a signal, preserving the joint distribution matters more than using a fancier generator. For this project, the row/block bootstrap is the result to rely on." + ] + }, + { + "cell_type": "markdown", + "id": "b3574655", + "metadata": {}, + "source": [ + "## Results and Interpretation\n", + "\n", + "The plot compares the real-panel alpha to two synthetic-alpha distributions.\n", + "\n", + "The number labeled as a bootstrap tail share is the fraction of synthetic markets with alpha greater than or equal to the real-panel alpha. A value near 50% means the real result is close to the middle of the synthetic distribution. A value near 5% would mean the real history was unusually favorable compared with the generated histories.\n", + "\n", + "Because the bootstrap keeps the same loadings $B$ and resamples observed factor/residual rows, it does **not** test whether momentum exists from first principles. It tests whether the observed alpha is unusually dependent on the order in which historical months happened." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "43719fe4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:12:51.104749Z", + "iopub.status.busy": "2026-07-31T11:12:51.104514Z", + "iopub.status.idle": "2026-07-31T11:12:51.528711Z", + "shell.execute_reply": "2026-07-31T11:12:51.528086Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Real alpha : 4.49%/yr (t = 2.59)\n", + "Block bootstrap (300) : mean 4.85% | median 4.81% | p = 56.3%\n", + "VAE (300) : mean 14.46% | median 14.12% | p = 99.7%\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Compare bootstrap vs VAE alpha distributions\n", + "==================================\n", + "\"\"\"\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "ax.hist(boot_alphas * 100, bins=30, alpha=0.6, color='steelblue',\n", + " label=f'block bootstrap (p = {p_boot*100:.1f}%)')\n", + "ax.hist(vae_alphas * 100, bins=30, alpha=0.6, color='coral',\n", + " label=f'VAE (p = {p_vae*100:.1f}%)')\n", + "ax.axvline(real_alpha * 100, color='black', linewidth=2, linestyle='--',\n", + " label=f'real {real_alpha*100:.2f}%')\n", + "ax.set_xlabel('Annualized FF alpha (%)'); ax.set_ylabel('# synthetic markets')\n", + "ax.set_title('Synthetic-market alpha: real vs generated histories')\n", + "ax.legend(); ax.grid(alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.savefig('../images/06_synthetic_markets/vae_vs_bootstrap.png', dpi=150, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print(f\"Real alpha : {real_alpha*100:.2f}%/yr (t = {real_t:.2f})\")\n", + "print(f\"Block bootstrap (300) : mean {boot_alphas.mean()*100:.2f}% | \"\n", + " f\"median {np.median(boot_alphas)*100:.2f}% | p = {p_boot*100:.1f}%\")\n", + "print(f\"VAE (300) : mean {vae_alphas.mean()*100:.2f}% | \"\n", + " f\"median {np.median(vae_alphas)*100:.2f}% | p = {p_vae*100:.1f}%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a0fe42d5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-31T11:12:51.530423Z", + "iopub.status.busy": "2026-07-31T11:12:51.530244Z", + "iopub.status.idle": "2026-07-31T11:12:51.536802Z", + "shell.execute_reply": "2026-07-31T11:12:51.536199Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved ../data/processed/synthetic_backtest_results.csv\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Save synthetic-market results\n", + "==================================\n", + "\"\"\"\n", + "pd.DataFrame({'bootstrap_alpha': boot_alphas, 'vae_alpha': vae_alphas}).to_csv(\n", + " '../data/processed/synthetic_backtest_results.csv', index=False)\n", + "print(\"Saved ../data/processed/synthetic_backtest_results.csv\")" + ] + }, + { + "cell_type": "markdown", + "id": "610b2bc9", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "The stress test asks whether the raw-momentum alpha is a path-dependent fluke. The block bootstrap result says it probably is not: the real-panel alpha sits near the middle of the synthetic distribution rather than in an extreme right tail.\n", + "\n", + "- **Block bootstrap:** trustworthy for this purpose because it resamples matched rows of factor scores, residuals, and benchmark factors. It preserves the joint structure of the return panel while changing the order of market history.\n", + "- **Conditional VAE:** useful as a cautionary example, not as the headline result. Generating factor paths and attaching independent residuals can create artificial momentum.\n", + "\n", + "The honest scope is narrower than “we proved momentum works.” The bootstrap keeps the same factor structure and loadings, so it asks whether the strategy is unusually lucky conditional on that structure. It does not test a null world where the momentum premium has been removed.\n", + "\n", + "The next steps beyond this project would be a survivorship-free universe, real fundamental data for value and quality, and a cleaner null model that explicitly removes momentum before running the stress test." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..f0e4c8a --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,3 @@ +[tool.pytest.ini_options] +pythonpath = ["."] +testpaths = ["tests"] diff --git a/requirements-dev.txt b/requirements-dev.txt new file mode 100644 index 0000000..fb55eaf --- /dev/null +++ b/requirements-dev.txt @@ -0,0 +1,2 @@ +-r requirements-webapp.txt +pytest==9.1.0 diff --git a/requirements-webapp.txt b/requirements-webapp.txt new file mode 100644 index 0000000..f3db933 --- /dev/null +++ b/requirements-webapp.txt @@ -0,0 +1,9 @@ +# Dashboard runtime only. Notebook/data-generation dependencies live in requirements.txt. +pandas==2.3.3 +numpy==2.4.6 +scipy==1.16.2 +scikit-learn==1.8.0 +statsmodels==0.14.6 +fastapi==0.141.1 +uvicorn[standard]==0.52.0 +pydantic==2.13.4 diff --git a/requirements.txt b/requirements.txt index a9814a5..8f54865 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,15 +1,22 @@ -pandas -numpy -scipy -scikit-learn -matplotlib -seaborn -yfinance -pandas-datareader -joblib -tqdm -Pillow -html5lib -statsmodels -requests -torch \ No newline at end of file +# Full notebook/research environment. +# For the dashboard-only runtime, use requirements-webapp.txt. +pandas==2.3.3 +numpy==2.4.6 +scipy==1.16.2 +scikit-learn==1.8.0 +matplotlib==3.10.8 +seaborn==0.13.2 +yfinance==0.2.65 +pandas-datareader==0.11.1 +joblib==1.5.2 +tqdm==4.67.3 +Pillow==12.3.0 +html5lib==1.1 +statsmodels==0.14.6 +requests==2.33.1 +torch==2.10.0 + +# Webapp dependencies are repeated here so a full install can run everything. +fastapi==0.141.1 +uvicorn[standard]==0.52.0 +pydantic==2.13.4 diff --git a/tests/test_research.py b/tests/test_research.py new file mode 100644 index 0000000..1161d61 --- /dev/null +++ b/tests/test_research.py @@ -0,0 +1,107 @@ +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest + +from frd.research import ( + ArtifactError, + ArtifactSpec, + block_indices, + fama_french_alpha, + form_decile_portfolios, + momentum_signal, + validate_artifacts, +) + + +def test_momentum_signal_uses_prior_eleven_months(): + dates = pd.date_range("2020-01-31", periods=13, freq="ME") + returns = pd.DataFrame({"AAA": np.ones(13) * 0.01}, index=dates) + + signal = momentum_signal(returns) + + assert pd.isna(signal.iloc[10, 0]) + assert signal.iloc[11, 0] == pytest.approx(0.11) + assert signal.iloc[12, 0] == pytest.approx(0.11) + + +def test_decile_portfolios_include_initial_cost_and_weight_turnover(): + dates = pd.date_range("2020-01-31", periods=3, freq="ME") + signal = pd.DataFrame( + { + "A": [4.0, 4.0, 4.0], + "B": [3.0, 1.0, 1.0], + "C": [2.0, 3.0, 3.0], + "D": [1.0, 2.0, 2.0], + }, + index=dates, + ) + returns = pd.DataFrame( + { + "A": [0.00, 0.10, 0.20], + "B": [0.00, 0.00, 0.00], + "C": [0.00, 0.04, 0.06], + "D": [0.00, 0.00, 0.00], + }, + index=dates, + ) + + port = form_decile_portfolios(signal, returns, decile=0.5, min_names=1) + + assert port.index.tolist() == list(dates[1:]) + assert port["long"].iloc[0] == pytest.approx(0.05) + assert port["long"].iloc[1] == pytest.approx(0.13) + assert port["long_turnover"].iloc[0] == pytest.approx(1.0) + assert port["long_turnover"].iloc[1] == pytest.approx(0.5) + assert port["ls_turnover"].iloc[0] == pytest.approx(2.0) + + +def test_fama_french_alpha_aligns_month_start_and_month_end(): + dates = pd.date_range("2020-01-31", periods=24, freq="ME") + ff_dates = pd.date_range("2020-01-01", periods=24, freq="MS") + returns = pd.Series(0.01 + np.tile([-0.001, 0.001], 12), index=dates) + ff = pd.DataFrame( + { + "Mkt-RF": np.zeros(24), + "SMB": np.zeros(24), + "HML": np.zeros(24), + "Mom": np.zeros(24), + "RF": np.zeros(24), + }, + index=ff_dates, + ) + + alpha, tstat, r2 = fama_french_alpha(returns, ff) + + assert alpha == pytest.approx(0.12) + assert np.isfinite(tstat) + assert r2 >= 0 + + +def test_block_indices_are_in_bounds_and_requested_length(): + rng = np.random.RandomState(3) + idx = block_indices(25, 5, rng) + + assert len(idx) == 25 + assert idx.min() >= 0 + assert idx.max() < 25 + + +def test_validate_artifacts_reports_missing_and_malformed(tmp_path: Path): + good = tmp_path / "good.csv" + bad = tmp_path / "bad.csv" + good.write_text("a,b\n1,2\n", encoding="utf-8") + bad.write_text("a\n1\n", encoding="utf-8") + + with pytest.raises(ArtifactError, match="Missing notebook-generated"): + validate_artifacts( + tmp_path, + { + "good": ArtifactSpec(good, ("a", "b")), + "missing": ArtifactSpec(tmp_path / "missing.csv", ("x",)), + }, + ) + + with pytest.raises(ArtifactError, match="Malformed notebook-generated"): + validate_artifacts(tmp_path, {"bad": ArtifactSpec(bad, ("a", "b"))}) diff --git a/webapp/Dockerfile b/webapp/Dockerfile new file mode 100644 index 0000000..d5c269a --- /dev/null +++ b/webapp/Dockerfile @@ -0,0 +1,17 @@ +FROM python:3.12-slim + +ENV PYTHONDONTWRITEBYTECODE=1 +ENV PYTHONUNBUFFERED=1 + +WORKDIR /app + +# Runtime-only deps (no torch / matplotlib / jupyter — the webapp doesn't need them). +COPY requirements-webapp.txt . +RUN pip install --no-cache-dir -r requirements-webapp.txt + +COPY frd ./frd +COPY webapp ./webapp + +EXPOSE 8055 + +CMD ["uvicorn", "webapp.app:app", "--host", "0.0.0.0", "--port", "8055"] diff --git a/webapp/app.py b/webapp/app.py new file mode 100644 index 0000000..df283e2 --- /dev/null +++ b/webapp/app.py @@ -0,0 +1,325 @@ +"""FastAPI web demo for the Factor-Risk-Decomposition project. + +Showcases the quant pipeline (factor analysis, backtest, PCA risk decomposition, +and a live synthetic-market stress test) as a single-page dashboard. + +The app consumes the precomputed CSVs that the notebooks write to data/processed/ +(and data/raw/ff_factors.csv), recomputes the light ML pieces once at startup +(PCA, Marchenko-Pastur cutoff, the Fama-French alpha, and the NB06 factor model +used by the live "generate a synthetic market" button), and serves JSON + static. + +Local run: + + uvicorn webapp.app:app --host 127.0.0.1 --port 8055 +""" +from __future__ import annotations + +import re +import time +from contextlib import asynccontextmanager +from pathlib import Path +from typing import Any + +import numpy as np +import pandas as pd +from fastapi import FastAPI, HTTPException +from fastapi.responses import HTMLResponse +from fastapi.staticfiles import StaticFiles +from sklearn.decomposition import PCA + +from frd.research import ( + FF_COLUMNS, + ArtifactError, + ArtifactSpec, + block_indices, + decile_long_returns, + fama_french_alpha, + fama_french_regression, + marchenko_pastur, + momentum_signal, + series_metrics, + validate_artifacts, +) + +REPO_ROOT = Path(__file__).resolve().parents[1] +PROCESSED = REPO_ROOT / "data" / "processed" +RAW = REPO_ROOT / "data" / "raw" +STATIC_DIR = REPO_ROOT / "webapp" / "static" +REQUIRED_ARTIFACTS = { + "returns": ArtifactSpec(PROCESSED / "returns_monthly.csv"), + "signal": ArtifactSpec(PROCESSED / "momentum_signal.csv"), + "backtest": ArtifactSpec(PROCESSED / "backtest_returns.csv", ("long_net", "short_net", "ls_net", "long_excess", "ls_excess")), + "ff": ArtifactSpec(RAW / "ff_factors.csv", tuple(FF_COLUMNS)), + "sectors": ArtifactSpec(PROCESSED / "sector_mapping.csv", ("ticker", "sector")), + "ic": ArtifactSpec(PROCESSED / "ic_monthly.csv"), + "factor_corr": ArtifactSpec(PROCESSED / "factor_correlation.csv"), + "variance": ArtifactSpec(PROCESSED / "variance_decomposition.csv", ("component", "variance", "pct")), + "synthetic": ArtifactSpec(PROCESSED / "synthetic_backtest_results.csv", ("bootstrap_alpha",)), +} + +# Headline numbers are recomputed from data at startup, not hardcoded, so the demo +# always matches whatever the notebooks last produced. + + +def _file_version(path: Path) -> str: + try: + st = path.stat() + return f"{int(st.st_mtime)}-{st.st_size}" + except FileNotFoundError: + return "missing" + + +# --------------------------------------------------------------------------- # +# Startup: load data, recompute the ML pieces once for the process lifetime. +# --------------------------------------------------------------------------- # +@asynccontextmanager +async def lifespan(app: FastAPI): + s = app.state + try: + validate_artifacts(REPO_ROOT, REQUIRED_ARTIFACTS) + except ArtifactError as exc: + raise RuntimeError(str(exc)) from exc + + # --- core panels --- + s.returns = pd.read_csv(PROCESSED / "returns_monthly.csv", index_col=0, parse_dates=True) + s.signal = pd.read_csv(PROCESSED / "momentum_signal.csv", index_col=0, parse_dates=True) + s.backtest = pd.read_csv(PROCESSED / "backtest_returns.csv", index_col=0, parse_dates=True) + s.ff = pd.read_csv(RAW / "ff_factors.csv", index_col=0, parse_dates=True) + s.sectors = pd.read_csv(PROCESSED / "sector_mapping.csv") + s.ic_monthly = pd.read_csv(PROCESSED / "ic_monthly.csv", index_col=0, parse_dates=True) + s.factor_corr = pd.read_csv(PROCESSED / "factor_correlation.csv", index_col=0) + s.variance_decomp = pd.read_csv(PROCESSED / "variance_decomposition.csv") + s.synthetic = pd.read_csv(PROCESSED / "synthetic_backtest_results.csv") + + # --- overview: equity curves, drawdowns, FF alpha, headline metrics --- + bt = s.backtest + bt_plot = bt.dropna(subset=["long_net", "ls_net"]) + s.equity = { + "dates": bt_plot.index.strftime("%Y-%m-%d").tolist(), + "long": ((1 + bt_plot["long_net"]).cumprod() - 1).round(4).tolist(), + "ls": ((1 + bt_plot["ls_net"]).cumprod() - 1).round(4).tolist(), + } + ew_monthly = s.returns.mean(axis=1).reindex(bt.index) + ew_plot = ew_monthly.reindex(bt_plot.index) + s.equity["ew"] = ((1 + ew_plot).cumprod() - 1).round(4).fillna(0).tolist() + long_wealth = (1 + bt_plot["long_net"]).cumprod() + long_dd = ((long_wealth - long_wealth.cummax()) / long_wealth.cummax()).round(4) + s.drawdown = {"dates": bt_plot.index.strftime("%Y-%m-%d").tolist(), "long": long_dd.tolist()} + + # FF 4-factor alpha (re-fitted from long_excess, period-aligned) + ff_pm = s.ff[FF_COLUMNS].copy() + ff_pm.index = ff_pm.index.to_period("M") + m = fama_french_regression(bt["long_net"], s.ff) + s.headline = { + "alpha_annual": float(m.params[0] * 12), + "alpha_t": float(m.tvalues[0]), + "alpha_p": float(m.pvalues[0]), + "mkbeta": float(m.params[1]), + "smb_beta": float(m.params[2]), + "hml_beta": float(m.params[3]), + "mom_beta": float(m.params[4]), + "r_squared": float(m.rsquared), + **series_metrics(bt["long_net"]), + } + s.headline_ew = series_metrics(ew_monthly) + s.headline_ls = series_metrics(bt["ls_net"]) + active = (bt["long_net"] - ew_monthly).dropna() + s.headline["active_annual"] = float(active.mean() * 12) + s.headline["active_ir"] = float(active.mean() / active.std() * np.sqrt(12)) if active.std() > 0 else float("nan") + + # --- factors: IC summary, walk-forward, correlation --- + ic = s.ic_monthly + s.factor_ic = { + f: {"mean": float(ic[f].mean()), "std": float(ic[f].std()), + "ir": float(ic[f].mean() / ic[f].std() * np.sqrt(12)) if ic[f].std() > 0 else float("nan")} + for f in ic.columns + } + # walk-forward: mean IC per 5y window (momentum + others), if enough dates + wf = {} + for start, end in [(2006, 2011), (2011, 2016), (2016, 2021), (2021, 2026)]: + mask = (ic.index.year >= start) & (ic.index.year < end) + sub = ic[mask] + if len(sub) >= 12: + wf[f"{start}-{end}"] = {f: float(sub[f].mean()) for f in ic.columns} + s.walkforward = wf + + # --- PCA / risk: recompute on the balanced panel (mirrors NB05) --- + panel = s.returns.dropna(thresh=240, axis=1).dropna() + s.panel_tickers = panel.columns.tolist() + Xc = panel.values + T, N = Xc.shape + mean_r = Xc.mean(axis=0) + Xc = Xc - mean_r + s.T, s.N = T, N + n_comp = min(T, N) + pca = PCA(n_components=n_comp).fit(Xc) + eigvals = pca.explained_variance_ # length n_comp + s.eigvals = eigvals + s.explained_pct = (pca.explained_variance_ratio_ * 100) + B_all = pca.components_.T # (N, n_comp) + s.mp = marchenko_pastur(eigvals, T, N) + s.k = max(int(s.mp["signal_count"]), 1) + s.B = B_all[:, : s.k] + s.F = Xc @ s.B + s.E = Xc - s.F @ s.B.T + s.mean_r = mean_r + # variance decomp (from the saved canonical table) + vd = {row["component"]: row["pct"] for _, row in s.variance_decomp.iterrows()} + s.var_decomp = { + "systematic": float(vd.get("systematic", float("nan"))), + "idiosyncratic": float(vd.get("idiosyncratic", float("nan"))), + } + # PC1/PC2 loadings + latest momentum score per ticker (for the explorer scatter) + last_scores = s.signal.iloc[-2].reindex(s.panel_tickers) # -2: trade t+1 + sec_map = dict(zip(s.sectors["ticker"], s.sectors["sector"])) + s.ticker_rows = [ + { + "ticker": t, + "sector": sec_map.get(t, "Unknown"), + "momentum": (None if pd.isna(last_scores[t]) else float(last_scores[t])), + "pc1": float(B_all[i, 0]), + "pc2": float(B_all[i, 1]), + } + for i, t in enumerate(s.panel_tickers) + ] + + # --- stress test: real baseline + precomputed synthetic distribution --- + ff_panel = ff_pm.reindex(panel.index.to_period("M")) + if ff_panel[FF_COLUMNS].isna().any().any(): + raise RuntimeError("Fama-French factors do not cover the full balanced PCA panel.") + ff_panel = ff_panel.reset_index(drop=True) + s.ff_panel = ff_panel + ret_real = pd.DataFrame(panel.values, columns=s.panel_tickers) + real_alpha, _, _ = fama_french_alpha(decile_long_returns(momentum_signal(ret_real), ret_real), ff_panel) + s.real_alpha = real_alpha + s.synth_alphas = s.synthetic["bootstrap_alpha"].dropna().to_numpy() + s.synth_mean = float(s.synth_alphas.mean()) + s.synth_p = float((s.synth_alphas >= real_alpha).mean()) + + print(f"[webapp] startup ok: T={T} N={N} k={s.k} signal={s.mp['signal_count']} " + f"alpha={s.headline['alpha_annual']*100:.2f}% p={s.synth_p:.2f}") + yield + + +app = FastAPI(title="Factor Risk Decomposition", version="0.1.0", lifespan=lifespan) +app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static") + + +def _json_safe(o: Any) -> Any: + """Recursively coerce numpy types / NaN for FastAPI JSON.""" + if isinstance(o, dict): + return {k: _json_safe(v) for k, v in o.items()} + if isinstance(o, (list, tuple)): + return [_json_safe(v) for v in o] + if isinstance(o, (np.integer,)): + return int(o) + if isinstance(o, (np.floating,)): + v = float(o) + return v if not np.isnan(v) else None + if isinstance(o, np.ndarray): + return [_json_safe(v) for v in o.tolist()] + if isinstance(o, float) and np.isnan(o): + return None + return o + + +@app.get("/") +def index(): + html = (STATIC_DIR / "index.html").read_text(encoding="utf-8") + css_v = _file_version(STATIC_DIR / "app.css") + js_v = _file_version(STATIC_DIR / "app.js") + html = re.sub(r"app\.css\?v=[^\s\"']+", f"app.css?v={css_v}", html) + html = re.sub(r"app\.js\?v=[^\s\"']+", f"app.js?v={js_v}", html) + return HTMLResponse(content=html) + + +@app.get("/api/health") +def health(): + s = app.state + return {"ok": True, "T": s.T, "N": s.N, "signal_factors": s.mp["signal_count"], + "alpha_annual": s.headline["alpha_annual"]} + + +@app.get("/api/overview") +def overview(): + s = app.state + return _json_safe({ + "headline": s.headline, + "ew": s.headline_ew, + "long_short": s.headline_ls, + "equity": s.equity, + "drawdown": s.drawdown, + }) + + +@app.get("/api/factors") +def factors(): + s = app.state + return _json_safe({ + "ic": s.factor_ic, + "walkforward": s.walkforward, + "correlation": {"labels": list(s.factor_corr.columns), + "matrix": s.factor_corr.values.tolist()}, + }) + + +@app.get("/api/risk") +def risk(): + s = app.state + top = 50 + return _json_safe({ + "spectrum": {"explained_pct": s.explained_pct[:top].tolist(), + "eigenvalue": s.eigvals[:top].tolist()}, + "mp": s.mp, + "variance_decomp": s.var_decomp, + "pc1_market_corr": None, # placeholder (kept for forward-compat) + }) + + +@app.get("/api/tickers") +def tickers(): + return _json_safe(app.state.ticker_rows) + + +@app.get("/api/ticker/{ticker}") +def ticker_detail(ticker: str): + ticker = ticker.upper() + for row in app.state.ticker_rows: + if row["ticker"] == ticker: + return _json_safe(row) + raise HTTPException(status_code=404, detail=f"Unknown ticker: {ticker}") + + +@app.get("/api/stress") +def stress_data(): + """Precomputed synthetic-market alpha distribution + the real baseline.""" + s = app.state + return _json_safe({ + "real_alpha": s.real_alpha, + "mean": s.synth_mean, + "p_value": s.synth_p, + "distribution": s.synth_alphas.tolist(), + }) + + +@app.post("/api/stress") +def stress_generate(): + """Generate ONE fresh synthetic market (block bootstrap) and re-run the backtest.""" + s = app.state + rng = np.random.RandomState(int(time.time() * 1e6) % (2**31)) + idx = block_indices(s.T, L=15, rng=rng) + R_synth = s.F[idx] @ s.B.T + s.E[idx] + s.mean_r + ret_df = pd.DataFrame(R_synth, columns=s.panel_tickers) + long_ret = decile_long_returns(momentum_signal(ret_df), ret_df) + ff_synth = s.ff_panel.iloc[idx].reset_index(drop=True) + alpha, tstat, _ = fama_french_alpha(long_ret, ff_synth) + wealth = (1 + long_ret).cumprod() + equity = (wealth - 1).round(4).tolist() + percentile = float((s.synth_alphas <= alpha).mean()) if not np.isnan(alpha) else None + return _json_safe({ + "alpha": alpha, + "t": tstat, + "equity": equity, + "percentile": percentile, + "real_alpha": s.real_alpha, + }) diff --git a/webapp/static/app.css b/webapp/static/app.css new file mode 100644 index 0000000..c3c76ac --- /dev/null +++ b/webapp/static/app.css @@ -0,0 +1,520 @@ +/* Theme tokens shared by the full single-page demo. */ +:root { + --base00: #1A1B26; + --base01: #16161E; + --base02: #2F3549; + --base03: #444B6A; + --base04: #787C99; + --base05: #A9B1D6; + --base07: #D5D6DB; + --base08: #F7768E; + --base0a: #0DB9D7; + --base0b: #9ECE6A; + --base0c: #B4F9F8; + --base0d: #2AC3DE; + --base0e: #BB9AF7; + --base0f: #F7768E; + --bg: var(--base00); + --off-bg: var(--base01); + --inner-bg: var(--base02); + --fg: var(--base05); + --off-fg: var(--base04); + --muted: var(--base03); + --link: var(--base0d); + --hover: var(--base0c); + --highlight: var(--base0a); + --logo: var(--base0b); + --danger: var(--base08); + --border: rgba(120, 124, 153, 0.3); + --sans: "Inter", sans-serif; + --mono: "Fira Mono", ui-monospace, SFMono-Regular, Menlo, Consolas, "Liberation Mono", monospace; +} + +* { box-sizing: border-box; } + +/* Page shell and header status. */ +body { + margin: 0; + font-family: var(--sans); + font-size: 16px; + line-height: 1.6rem; + background: var(--bg); + color: var(--fg); +} + +.site-header { + display: flex; + justify-content: space-between; + align-items: flex-start; + gap: 1.5rem; + max-width: 78rem; + margin: 1rem auto 0; + padding: 0 1rem; +} + +.eyebrow { + margin: 0; + color: var(--logo); + font-size: 1rem; +} + +.site-header h1 { + margin: 0; + font-size: 1rem; + font-weight: 600; +} + +.site-header h1::before { + content: none; +} + +.site-header p { + margin: 0; + color: var(--off-fg); +} + +.header-sub { + margin: 0; + color: var(--off-fg); + font-size: 0.85rem; +} + +.health { + flex-shrink: 0; + font-size: 0.78rem; + color: var(--highlight); + white-space: nowrap; + border: 1px solid var(--border); + padding: 0.25rem 0.5rem; + background: var(--inner-bg); +} + +label { + display: block; + margin: 0.7rem 0; + font-size: 0.82rem; + color: var(--off-fg); +} + +input, select, textarea { + display: block; + width: 100%; + margin-top: 0.28rem; + border: 1px solid var(--border); + padding: 0.6rem 0.7rem; + font: inherit; + color: var(--fg); + background: var(--inner-bg); +} + +input:focus, select:focus, textarea:focus { + outline: 2px solid rgba(137, 221, 255, 0.28); + border-color: var(--hover); +} + +textarea { + resize: vertical; + min-height: 96px; + font-size: 0.78rem; +} + +button { + width: 100%; + border: 1px solid var(--link); + padding: 0.68rem 0.9rem; + margin-top: 0.4rem; + font-weight: 700; + color: var(--bg); + background: var(--link); + cursor: pointer; + font-family: var(--sans); +} + +button:hover { + border-color: var(--hover); + background: var(--hover); +} +button:disabled { opacity: 0.55; cursor: not-allowed; } + +/* On narrower screens, stack the header. */ +@media (max-width: 900px) { + .site-header { flex-direction: column; } +} + + +/* Footer. */ +.site-footer { + display: flex; + flex-wrap: wrap; + gap: 0.5rem 1.5rem; + max-width: 78rem; + margin: 0 auto 1.5rem; + padding: 0 1rem; + color: var(--off-fg); + font-size: 0.82rem; +} + +.site-footer a { + color: var(--link); + transition: color 0.15s ease; +} + +.site-footer a:hover, +.link-list a:hover { + color: var(--hover); +} + + +/* ===== Factor-Risk-Decomposition dashboard additions (built on the shared theme) ===== */ + +.dashboard { + max-width: 78rem; + margin: 0 auto; + padding: 2rem 1rem 2rem; + display: grid; + grid-template-columns: minmax(0, 1fr); + gap: 1.25rem; + align-items: start; +} + +/* Strategy section — trading rule + realized alpha. */ +.hero-section { + margin-top: 0; +} +.hero-card { + display: flex; + flex-wrap: wrap; + gap: 1.25rem; + background: var(--off-bg); + border: 1px solid var(--border); + padding: 1rem; + align-items: flex-start; + margin-bottom: 0.75rem; +} +.hero-main { flex: 1 1 24rem; } +.hero-action { + flex: 0 0 auto; + display: flex; + flex-direction: column; + align-items: flex-end; + gap: 0.5rem; +} +.hero-action:empty { display: none; } +.hero-alpha { + font-family: var(--mono); + font-size: 2.4rem; + font-weight: 700; + line-height: 1.1; + color: var(--fg); +} +.hero-alpha.pos { color: var(--base0b); } +.hero-alpha.neg { color: var(--base08); } +.hero-label { + color: var(--off-fg); + font-size: 0.9rem; + margin-top: 0.15rem; +} +.hero-context { + color: var(--muted); + font-family: var(--mono); + font-size: 0.78rem; + margin-top: 0.3rem; +} +.hero-blurb { + margin: 0.65rem 0 0; + color: var(--off-fg); + font-size: 0.85rem; + line-height: 1.5; + max-width: 34rem; +} +.hero-blurb b { color: var(--highlight); } +.hero-btn { + width: auto; + padding: 0.75rem 1.4rem; + font-size: 0.95rem; +} +@media (max-width: 700px) { + .hero-card { flex-direction: column; align-items: flex-start; } + .hero-action { align-items: flex-start; } + .hero-alpha { font-size: 2.2rem; } +} + +section.dashboard-section { scroll-margin-top: 1rem; } +section.dashboard-section > h2 { + font-size: 1rem; + font-weight: 600; + margin: 0 0 0.35rem; +} +section.dashboard-section > h2::before { content: none; } +section.dashboard-section > p.section-blurb { + margin: 0 0 0.75rem; + color: var(--off-fg); + font-size: 0.85rem; + max-width: 60rem; +} + +@media (min-width: 980px) { + .dashboard { + grid-template-columns: 22rem minmax(0, 1fr); + gap: 2rem; + } + + #hero { + grid-column: 1; + grid-row: 1; + } + + #stress { + grid-column: 2; + grid-row: 1; + } + + #overview, + #factors, + #risk, + #explorer, + #process { + grid-column: 1 / -1; + } + + #hero .hero-card { + flex-direction: column; + } + + #hero .hero-main, + #hero .hero-action { + width: 100%; + flex: 1 1 auto; + } + + #hero .hero-action { + align-items: stretch; + } + + #hero .explain-details { + max-width: none; + } +} + +/* Explanation blocks. */ +.explain-details { + margin: 0 0 1rem; + background: var(--off-bg); + border: 1px solid var(--border); + padding: 1rem; + max-width: 60rem; +} +.explain-details h3 { + margin: 0 0 0.55rem; + color: var(--link); + font-size: 0.82rem; + font-weight: 600; +} +.explain-details ol { + margin: 0.6rem 0 0; + padding-left: 1.2rem; + color: var(--off-fg); + font-size: 0.82rem; + line-height: 1.6; +} +.explain-details ol li { margin-bottom: 0.35rem; } +.explain-details ol li b { color: var(--highlight); } +.explain-details p { + margin: 0.6rem 0 0; + color: var(--off-fg); + font-size: 0.78rem; + line-height: 1.5; +} +.strategy-list { + display: grid; + grid-template-columns: minmax(0, 1fr); + gap: 0.5rem; + margin-bottom: 0.75rem; +} +.strategy-list div { + background: var(--off-bg); + border: 1px solid var(--border); + padding: 0.65rem 0.75rem; +} +.strategy-list b { + display: block; + color: var(--fg); + font-size: 0.76rem; + margin-bottom: 0.12rem; +} +.strategy-list span { + display: block; + color: var(--off-fg); + font-size: 0.76rem; + line-height: 1.45; +} +.explain-grid { + display: grid; + grid-template-columns: minmax(0, 1fr); + gap: 1rem; + margin-top: 1rem; +} + +.links-card { + margin-top: 1rem; + background: var(--off-bg); + border: 1px solid var(--border); + padding: 1rem; + max-width: 60rem; +} +.links-card h3 { + margin: 0 0 0.75rem; + color: var(--fg); + font-size: 0.9rem; + font-weight: 600; +} +.link-list { + margin: 0; + padding-left: 1.1rem; + columns: 2; + column-gap: 2rem; +} +.link-list li { + break-inside: avoid; + margin: 0 0 0.35rem; + color: var(--off-fg); + font-size: 0.82rem; +} +.link-list li::marker { + color: var(--highlight); +} +.link-list a { + color: var(--link); + transition: color 0.15s ease; +} +@media (max-width: 600px) { + .link-list { + columns: 1; + } +} + +@media (min-width: 980px) { + .explain-grid { + grid-template-columns: repeat(2, minmax(0, 1fr)); + } + + #stress .explain-details { + max-width: none; + } +} + +/* Headline metric tiles. */ +.metric-grid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(11rem, 1fr)); + gap: 0.75rem; + margin-bottom: 1rem; +} +.metric { + background: var(--off-bg); + border: 1px solid var(--border); + padding: 0.9rem 1rem; +} +.metric .label { color: var(--off-fg); font-size: 0.74rem; text-transform: uppercase; } +.metric .value { color: var(--fg); font-size: 1.5rem; font-weight: 700; font-family: var(--mono); } +.metric .value.pos { color: var(--base0b); } +.metric .value.neg { color: var(--base08); } +.metric .sub { color: var(--muted); font-size: 0.72rem; margin-top: 0.15rem; } + +/* Chart wells. */ +.chart-grid { display: grid; grid-template-columns: minmax(0, 1fr); gap: 1rem; } +.chart-grid.two { grid-template-columns: repeat(auto-fit, minmax(0, 1fr)); } +@media (min-width: 980px) { .chart-grid.two { grid-template-columns: 1fr 1fr; } } +.chart { + background: var(--off-bg); + border: 1px solid var(--border); + padding: 1rem; +} +.chart h3 { margin: 0 0 0.5rem; font-size: 0.85rem; color: var(--fg); font-weight: 600; } +.chart .note { color: var(--muted); font-size: 0.72rem; margin: 0.4rem 0 0; } + +/* Chart-level explainer (smaller than section-level). */ +.chart-explain { + margin-top: 0.65rem; + font-size: 0.76rem; +} +.chart-explain h4 { + margin: 0 0 0.35rem; + color: var(--muted); + font-size: 0.74rem; + font-weight: 600; + user-select: none; +} +.chart-explain p { + margin: 0.4rem 0 0; + color: var(--off-fg); + line-height: 1.55; +} +.chart-explain p b { color: var(--highlight); } + +/* Stress-test control row. */ +.stress-controls { + display: flex; + align-items: center; + gap: 1rem; + flex-wrap: wrap; + background: var(--off-bg); + border: 1px solid var(--border); + padding: 0.75rem; + margin-bottom: 0.75rem; +} +.stress-note { + margin: 0 0 0.75rem; + color: var(--off-fg); + font-size: 0.82rem; + line-height: 1.45; + max-width: 60rem; +} +.stress-controls button { width: auto; padding: 0.55rem 1rem; } +.stress-readout { color: var(--highlight); font-family: var(--mono); font-size: 0.85rem; } + +/* Ticker explorer. */ +.ticker-controls { display: flex; gap: 0.75rem; flex-wrap: wrap; align-items: end; margin-bottom: 0.75rem; } +.ticker-controls label { margin: 0; flex: 0 0 12rem; } +.ticker-detail { + font-family: var(--mono); + font-size: 0.82rem; + color: var(--off-fg); + background: var(--inner-bg); + border: 1px solid var(--border); + padding: 0.6rem 0.8rem; + min-height: 2.2rem; +} +.ticker-detail b { color: var(--highlight); } + +/* Notebook process recap. */ +.process-grid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(15rem, 1fr)); + gap: 0.75rem; + margin: 1rem 0; +} +.process-step { + background: var(--off-bg); + border: 1px solid var(--border); + padding: 1rem; +} +.process-step > b { + display: inline-block; + font-family: var(--mono); + color: var(--highlight); + font-size: 0.8rem; + margin-bottom: 0.45rem; +} +.process-step h3 { + margin: 0 0 0.35rem; + color: var(--fg); + font-size: 0.88rem; + font-weight: 600; +} +.process-step p { + margin: 0; + color: var(--off-fg); + font-size: 0.78rem; + line-height: 1.5; +} diff --git a/webapp/static/app.js b/webapp/static/app.js new file mode 100644 index 0000000..cfcd547 --- /dev/null +++ b/webapp/static/app.js @@ -0,0 +1,286 @@ +/* + * Browser-side controller for the Factor-Risk-Decomposition demo. + * + * The server returns precomputed results + a live synthetic-market generator as + * JSON. This file renders the dashboard with Plotly.js, themed to the same Tokyo + * Night palette as the rest of the site (transparent paper, Inter font). + */ + +// Tokyo Night palette (matches app.css tokens). +const COLORS = { + long: "#9ECE6A", ew: "#7aa2f7", ls: "#F7768E", accent: "#BB9AF7", + real: "#E0AF68", muted: "#787C99", neg: "#F7768E", pos: "#9ECE6A", + bar: "#2AC3DE", grid: "rgba(120,124,153,0.18)", fg: "#A9B1D6", +}; +const FONT = { family: "Inter, sans-serif", size: 12, color: COLORS.fg }; +const baseLayout = (extra = {}) => Object.assign({ + paper_bgcolor: "rgba(0,0,0,0)", plot_bgcolor: "rgba(0,0,0,0)", font: FONT, + margin: { l: 52, r: 18, t: 16, b: 38 }, + xaxis: { gridcolor: COLORS.grid, zerolinecolor: COLORS.grid, linecolor: COLORS.grid, tickfont: { size: 10 } }, + yaxis: { gridcolor: COLORS.grid, zerolinecolor: COLORS.grid, linecolor: COLORS.grid, tickfont: { size: 10 } }, + legend: { font: { color: COLORS.fg }, orientation: "h", y: -0.2 }, + colorway: [COLORS.bar, COLORS.long, COLORS.accent, COLORS.ls, COLORS.real], +}, extra); +const CONFIG = { responsive: true, displayModeBar: false }; +const $ = (id) => document.getElementById(id); + +async function fetchJson(url, opts) { + const r = await fetch(url, opts); + const t = await r.text(); + let payload = {}; + try { payload = t ? JSON.parse(t) : {}; } + catch { + if (!r.ok) throw new Error(t || `HTTP ${r.status}`); + throw new Error(`Invalid JSON from ${url}`); + } + if (!r.ok) { + const detail = payload.detail || payload.message || t; + throw new Error(detail || `HTTP ${r.status}`); + } + return payload; +} + +const pct = (x, n = 2) => (x == null || isNaN(x)) ? "—" : (x * 100).toFixed(n) + "%"; +const fixed = (x, n = 2) => (x == null || isNaN(x)) ? "—" : Number(x).toFixed(n); +const cls = (x) => (x == null || isNaN(x)) ? "" : (x >= 0 ? "pos" : "neg"); + +function tile(label, value, sub, valueClass = "") { + return `
${label}
` + + `
${value}
` + + (sub ? `
${sub}
` : "") + `
`; +} + +let overviewCache = null; + +// --------------------------------------------------------------------------- // +async function init() { + try { + const h = await fetchJson("/api/health"); + $("health").textContent = `T=${h.T} · N=${h.N} · ${h.signal_factors} signal factors · α=${pct(h.alpha_annual)}`; + } catch (e) { $("health").textContent = "offline"; } + + renderHero(); + renderStress(); + renderOverview(); + renderFactors(); + renderRisk(); + renderExplorer(); +} + +// ------------------------------- hero ------------------------------------- // +async function renderHero() { + const d = await fetchJson("/api/overview"); + overviewCache = d; + const hl = d.headline; + const alphaPct = pct(hl.alpha_annual); + const tStat = fixed(hl.alpha_t); + + $("hero-main").innerHTML = + `
${alphaPct}
` + + `
Fama-French 4-factor alpha (annualized)
` + + `
t = ${tStat} · R² = ${fixed(hl.r_squared)} · net of 5 bps costs
` + + `

This is the average return left after controlling for market, size, value, and momentum benchmark factors. The stock-ranking IC is weak, so this alpha is treated as evidence to stress-test, not a victory lap.

`; + + $("hero-action").innerHTML = ""; +} + +// ----------------------------- overview ------------------------------------ // +async function renderOverview() { + const d = overviewCache || await fetchJson("/api/overview"); + const hl = d.headline; + $("overview-metrics").innerHTML = + tile("Sharpe (long-only)", fixed(hl.sharpe), `ann. return ${pct(hl.ann_return)}`) + + tile("Max drawdown", pct(hl.max_drawdown), `vol ${pct(hl.ann_vol)}`) + + tile("Active vs EW", pct(hl.active_annual), `IR ${fixed(hl.active_ir)}`, cls(hl.active_annual)) + + tile("EW universe Sharpe", fixed(d.ew.sharpe), `ann. return ${pct(d.ew.ann_return)}`); + + const dates = d.equity.dates; + Plotly.newPlot($("chart-equity"), [ + { x: dates, y: d.equity.long, name: "Long-only", mode: "lines", line: { color: COLORS.long, width: 2 } }, + { x: dates, y: d.equity.ew, name: "EW universe", mode: "lines", line: { color: COLORS.ew, width: 1.5 } }, + { x: dates, y: d.equity.ls, name: "Long-short", mode: "lines", line: { color: COLORS.ls, width: 1.5, dash: "dot" } }, + ], baseLayout({ yaxis: { title: "cumulative return", gridcolor: COLORS.grid }, legend: {} }), CONFIG); + + Plotly.newPlot($("chart-drawdown"), [ + { x: d.drawdown.dates, y: d.drawdown.long, name: "drawdown", type: "scatter", fill: "tozeroy", + line: { color: COLORS.ls, width: 1 }, fillcolor: "rgba(247,118,142,0.25)" }, + ], baseLayout({ yaxis: { title: "drawdown", gridcolor: COLORS.grid, tickformat: ".0%" } }), CONFIG); +} + +// ------------------------------ factors ------------------------------------ // +async function renderFactors() { + const d = await fetchJson("/api/factors"); + const names = Object.keys(d.ic); + Plotly.newPlot($("chart-ic"), [{ + x: names, y: names.map((n) => d.ic[n].mean), type: "bar", + marker: { color: names.map((n) => n === "momentum" ? COLORS.pos : COLORS.bar) }, + text: names.map((n) => `IR=${fixed(d.ic[n].ir)}`), textposition: "outside", + }], baseLayout({ + yaxis: { title: "mean monthly IC", gridcolor: COLORS.grid, zerolinecolor: COLORS.muted }, + shapes: [{ type: "line", x0: -0.5, x1: names.length - 0.5, y0: 0, y1: 0, line: { color: COLORS.muted, width: 1 } }], + }), CONFIG); + + Plotly.newPlot($("chart-corr"), [{ + z: d.correlation.matrix, x: d.correlation.labels, y: d.correlation.labels, + type: "heatmap", colorscale: [[0, "#F7768E"], [0.5, "#1A1B26"], [1, "#9ECE6A"]], + zmin: -1, zmax: 1, showscale: false, + text: d.correlation.matrix.map((r) => r.map((v) => v.toFixed(2))), + texttemplate: "%{text}", + }], baseLayout({ margin: { l: 64, r: 18, t: 16, b: 40 } }), CONFIG); + + const periods = Object.keys(d.walkforward); + Plotly.newPlot($("chart-walkforward"), names.map((n) => ({ + name: n, type: "bar", + x: periods, y: periods.map((p) => d.walkforward[p][n]), + })), baseLayout({ + barmode: "group", + yaxis: { title: "mean IC", gridcolor: COLORS.grid, zerolinecolor: COLORS.muted }, + shapes: [{ type: "line", x0: -0.5, x1: periods.length - 0.5, y0: 0, y1: 0, line: { color: COLORS.muted, width: 1 } }], + }), CONFIG); +} + +// ------------------------------- risk -------------------------------------- // +async function renderRisk() { + const d = await fetchJson("/api/risk"); + const mp = d.mp; + $("risk-metrics").innerHTML = + tile("Significant factors", mp.signal_count, `above MP λ+ = ${fixed(mp.lam_plus)}`) + + tile("q = T / N", fixed(mp.q), `σ² = ${fixed(mp.sigma2)}`) + + tile("Systematic risk", pct(d.variance_decomp.systematic / 100), "variance in top-k subspace", "pos") + + tile("Idiosyncratic risk", pct(d.variance_decomp.idiosyncratic / 100), "orthogonal complement", "neg"); + + const xs = d.spectrum.eigenvalue.map((_, i) => i + 1); + Plotly.newPlot($("chart-scree"), [ + { x: xs, y: d.spectrum.eigenvalue, type: "bar", name: "eigenvalue", marker: { color: COLORS.bar } }, + ], baseLayout({ + yaxis: { title: "eigenvalue", gridcolor: COLORS.grid }, + xaxis: { title: "principal component", gridcolor: COLORS.grid }, + shapes: [{ type: "line", x0: 0, x1: xs.length, y0: mp.lam_plus, y1: mp.lam_plus, + line: { color: COLORS.real, width: 2, dash: "dash" } }], + annotations: [{ x: xs.length, y: mp.lam_plus, xanchor: "right", yanchor: "bottom", + text: "λ₊ (MP cutoff)", font: { color: COLORS.real, size: 10 }, showarrow: false }], + }), CONFIG); + + Plotly.newPlot($("chart-vardecomp"), [{ + labels: ["Systematic", "Idiosyncratic"], + values: [d.variance_decomp.systematic, d.variance_decomp.idiosyncratic], + type: "pie", hole: 0.55, + marker: { colors: [COLORS.bar, COLORS.ls] }, + textinfo: "label+percent", textfont: { color: COLORS.fg, size: 11 }, + }], baseLayout({ margin: { l: 10, r: 10, t: 10, b: 10 } }), CONFIG); +} + +// ------------------------------ stress ------------------------------------- // +let stressDist = null; +async function renderStress() { + const d = await fetchJson("/api/stress"); + stressDist = d; + $("stress-metrics").innerHTML = + tile("Real alpha (baseline)", pct(d.real_alpha), "raw-momentum pipeline", cls(d.real_alpha)) + + tile("Bootstrap mean", pct(d.mean), "across 300 paths") + + tile("Share ≥ real", pct(d.p_value), "higher = more typical (not a lucky path)"); + drawStressDist(null); + $("stress-btn").addEventListener("click", generateStress); +} + +function drawStressDist(genAlpha) { + const shapes = [{ type: "line", x0: stressDist.real_alpha, x1: stressDist.real_alpha, + y0: 0, y1: 1, yref: "paper", line: { color: COLORS.real, width: 2 } }]; + if (genAlpha != null && !isNaN(genAlpha)) + shapes.push({ type: "line", x0: genAlpha, x1: genAlpha, y0: 0, y1: 1, yref: "paper", + line: { color: COLORS.ls, width: 2, dash: "dot" } }); + Plotly.newPlot($("chart-stress-dist"), [{ + x: stressDist.distribution, type: "histogram", name: "synthetic α", + marker: { color: COLORS.bar, opacity: 0.65 }, + xbins: { size: 0.01 }, + }], baseLayout({ + xaxis: { title: "annualized FF alpha", tickformat: ".0%", gridcolor: COLORS.grid }, + yaxis: { title: "# synthetic markets", gridcolor: COLORS.grid }, shapes, + annotations: [ + { x: stressDist.real_alpha, y: 1, yref: "paper", xanchor: "left", yanchor: "top", + text: "real", font: { color: COLORS.real, size: 10 }, showarrow: false }, + ...(genAlpha != null && !isNaN(genAlpha) ? [{ x: genAlpha, y: 0.9, yref: "paper", xanchor: "left", + text: "generated", font: { color: COLORS.ls, size: 10 }, showarrow: false }] : []), + ], + }), CONFIG); +} + +async function generateStress() { + const btn = $("stress-btn"); const ro = $("stress-readout"); + btn.disabled = true; ro.textContent = "generating…"; + try { + const r = await fetchJson("/api/stress", { method: "POST" }); + drawStressDist(r.alpha); + const xs = r.equity.map((_, i) => i); + Plotly.newPlot($("chart-stress-equity"), [{ + x: xs, y: r.equity, type: "scatter", mode: "lines", fill: "tozeroy", + line: { color: COLORS.ls, width: 1.5 }, fillcolor: "rgba(247,118,142,0.18)", + }], baseLayout({ + xaxis: { title: "synthetic month", gridcolor: COLORS.grid }, + yaxis: { title: "cumulative return", gridcolor: COLORS.grid }, + annotations: [{ x: 0.5, y: 1.08, yref: "paper", xref: "paper", text: + `α = ${pct(r.alpha)} · t = ${fixed(r.t)} · ${fixed((r.percentile || 0) * 100, 0)}th pctile`, + font: { color: COLORS.real, size: 11 }, showarrow: false }], + }), CONFIG); + ro.textContent = `α = ${pct(r.alpha)} (${fixed((r.percentile || 0) * 100, 0)}th percentile)`; + const heroRo = $("hero-stress-readout"); + if (heroRo) heroRo.textContent = `Last generated: α = ${pct(r.alpha)} (${fixed((r.percentile || 0) * 100, 0)}th percentile)`; + } catch (e) { ro.textContent = "error: " + e.message; } + btn.disabled = false; +} + +// ----------------------------- explorer ------------------------------------ // +async function renderExplorer() { + const rows = await fetchJson("/api/tickers"); + const sectors = [...new Set(rows.map((r) => r.sector))].sort(); + const palette = [COLORS.bar, COLORS.long, COLORS.accent, COLORS.ls, COLORS.real, + "#7aa2f7", "#B4F9F8", "#e0af68", "#f7768e", "#bb9af7", "#9ece6a"]; + const bySector = {}; + rows.forEach((r) => (bySector[r.sector] ||= []).push(r)); + const traces = sectors.map((sec, i) => ({ + name: sec, type: "scatter", mode: "markers", + x: bySector[sec].map((r) => r.pc1), y: bySector[sec].map((r) => r.pc2), + text: bySector[sec].map((r) => r.ticker), + marker: { color: palette[i % palette.length], size: bySector[sec].map((r) => 5 + 3 * Math.min(Math.abs(r.momentum || 0), 3)), + opacity: 0.75, line: { width: 0 } }, + })); + Plotly.newPlot($("chart-scatter"), traces, baseLayout({ + xaxis: { title: "PC1 (≈ market)", gridcolor: COLORS.grid }, + yaxis: { title: "PC2 (≈ value)", gridcolor: COLORS.grid }, + legend: { font: { color: COLORS.fg, size: 9 }, orientation: "v", x: 1.02 }, + margin: { l: 52, r: 120, t: 16, b: 38 }, + }), CONFIG); + + const sel = $("ticker-select"); + rows.map((r) => r.ticker).sort().forEach((t) => { + const o = document.createElement("option"); o.value = t; o.textContent = t; sel.appendChild(o); + }); + sel.addEventListener("change", () => showTicker(rows.find((r) => r.ticker === sel.value), sel.value)); + // click a point to select that ticker + $("chart-scatter").on("plotly_click", (ev) => { + const p = ev.points[0]; const t = p.text; + sel.value = t; showTicker(rows.find((r) => r.ticker === t), t); + }); + sel.value = "AAPL"; showTicker(rows.find((r) => r.ticker === "AAPL"), "AAPL"); +} + +function showTicker(r, t) { + const detail = $("ticker-detail"); + detail.replaceChildren(); + if (!r) { + detail.textContent = `${t}: not in the balanced panel.`; + return; + } + const ticker = document.createElement("b"); + ticker.textContent = r.ticker; + const momentum = document.createElement("b"); + momentum.textContent = fixed(r.momentum); + detail.append( + ticker, + document.createTextNode(` · sector: ${r.sector} · momentum z-score: `), + momentum, + document.createTextNode(` · PC1 (market): ${fixed(r.pc1)} · PC2 (value): ${fixed(r.pc2)}`), + ); +} + +init(); diff --git a/webapp/static/favicon.svg b/webapp/static/favicon.svg new file mode 100644 index 0000000..bc9209f --- /dev/null +++ b/webapp/static/favicon.svg @@ -0,0 +1,4 @@ + + + P + diff --git a/webapp/static/index.html b/webapp/static/index.html new file mode 100644 index 0000000..c97a5af --- /dev/null +++ b/webapp/static/index.html @@ -0,0 +1,167 @@ + + + + + + Factor Risk Decomposition + + + + + + + + +
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Factor Risk Decomposition

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Sector-neutralized momentum from return matrix to alpha and risk

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Interactive research dashboard for factor diagnostics, PCA risk, and synthetic-market stress tests.

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Trading strategy

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Rank large-cap stocks by trailing 12-1 momentum, remove sector tilts, hold the top decile equal-weight, and rebalance monthly net of turnover costs.

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Signal12-month momentum, skipping the most recent month.
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NeutralizationProject out sector dummy exposure, then re-rank stocks.
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PortfolioLong-only, equal-weight top decile, monthly rebalance.
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Costs5 bps per unit of one-way turnover, including initial buy.
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Alpha and t-stat

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Alpha is the regression intercept: the average return left after subtracting exposure to benchmark factors. The t-statistic is alpha divided by its standard error; larger absolute values mean the estimate is less likely to be noise.

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The four Fama-French factors used here are MKT (market excess return), SMB (Small Minus Big size factor), HML (High Minus Low value factor), and MOM (winner-minus-loser momentum factor).

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Generate a new alpha

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Draw an alternate market path from the factor model, re-run the momentum backtest, and see whether the generated alpha lands near the real one. A "Share ≥ real" near 50% is the ideal — it means the real alpha is typical, not a lucky outlier.

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Why is the baseline alpha here (~4.5%) lower than the headline 5.97% above? The stress test uses a simplified pipeline — raw 12-1 momentum with no sector neutralization and no transaction costs, run on the balanced PCA panel (~394 stocks) instead of the full universe. This keeps each bootstrap path fast enough to compute. The stress test answers the same question either way: is the alpha a lucky path? (No.)

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Synthetic-market alpha distribution

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Generated path — long-only equity curve

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Performance — how did the portfolio do?

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A top-decile long-only momentum portfolio, rebalanced monthly net of 5 bps per unit of one-way turnover. Benchmarked against the equal-weight universe.

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Equity curves (net of cost)

Long-only top decile vs the equal-weight universe (the fair benchmark) and the long-short book.

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Long-only drawdown

Peak-to-trough drop of the compounded wealth curve.

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Factor diagnostics — which signals predict next-month returns?

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Information coefficient (IC) = Spearman rank correlation between the factor vector and next-month returns — the cosine of the angle between the rank vectors. Momentum is the only factor with positive IC.

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Mean information coefficient by factor

Bars = mean monthly IC; momentum is the only positive-IC factor.

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Cross-factor rank correlation (Gram matrix)

Momentum vs quality ≈ 0.86 — nearly collinear, redundant information.

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Walk-forward IC (5-year windows)

Momentum IC by subperiod — regime-dependent; the 2006–11 window includes the 2008–09 crash.

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Risk decomposition — where does the portfolio's variance live?

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Eigendecompose the covariance Σ = VΛVT. Marchenko–Pastur (random matrix theory) separates signal eigenvalues from noise. Portfolio variance wTΣw splits into the top-k factor subspace (systematic) and its orthogonal complement (idiosyncratic).

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Eigenvalue scree + Marchenko–Pastur cutoff

Eigenvalues above λ+ (dashed) are statistically significant factors; the rest is noise.

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Systematic vs idiosyncratic risk

Variance in the top-k eigenspace vs its orthogonal complement.

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Ticker explorer — stocks in factor space

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Each stock's loadings on PC1 (≈ the market factor) and PC2 (≈ value), colored by sector. Marker size = latest momentum score. Pick a ticker for its details.

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PC1 vs PC2 loadings, colored by sector

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Select a ticker to see its momentum score, sector, and factor loadings.
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Notebook process — what this project did

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The app is a compact view of the notebook pipeline: build the return matrix, test factors, construct the signal, backtest it, decompose risk, then stress-test the alpha with synthetic markets.

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Build the return matrix

Use cached S&P 500 constituent and adjusted-price data to assemble monthly returns, sectors, breadth, dispersion, and an equal-weight baseline.

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Diagnose factors

Compute price-based momentum, value, quality, and low-volatility proxies; measure IC, decay, stability, turnover proxy, and cross-factor correlation.

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Construct the signal

Winsorize, z-score, sector-neutralize, and compare momentum-only against a four-factor composite. Momentum-only is the cleaner signal.

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Backtest and challenge it

Trade the top decile long-only, subtract turnover costs, run Fama-French alpha, HAC t-stats, beta checks, survivorship drag, and robustness grids.

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Decompose risk

Estimate covariance, run PCA, use Marchenko-Pastur to choose signal factors, and split portfolio variance into systematic and idiosyncratic pieces.

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Synthesize markets

Write R ≈ F BT + E, block-bootstrap matched rows of F and E, rebuild R, and rerun the strategy on alternate histories.

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Synthetic panel construction

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The return matrix R is decomposed into factor scores F, stock loadings B, and residuals E. A synthetic path resamples matched rows of F and E in short blocks, then reconstructs Rsynth = FbootBT + Eboot + mean(r). Matching the rows matters because it keeps market-wide shocks and stock-specific shocks internally consistent.

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Heuristic picture

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Think of historical months as index cards. Instead of shuffling single cards, the bootstrap shuffles small packets of neighboring cards, so short regimes like selloffs, rebounds, and momentum bursts mostly stay intact. Each shuffled deck is an alternate market history; the app reruns the same strategy and records the alpha.

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+ Educational project. Not investment advice. + Price data: Yahoo Finance / yfinance · Factor data: Kenneth French Data Library +
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