diff --git a/notebooks/02_factor_analysis_and_diagnostics.ipynb b/notebooks/02_factor_analysis_and_diagnostics.ipynb new file mode 100644 index 0000000..a151336 --- /dev/null +++ b/notebooks/02_factor_analysis_and_diagnostics.ipynb @@ -0,0 +1,1211 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a36f5610", + "metadata": {}, + "source": [ + "# Factor Analysis and Diagnostics" + ] + }, + { + "cell_type": "markdown", + "id": "d09530ec", + "metadata": {}, + "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", + "\n", + "> does the direction of $f_t$ predict the direction of next month's return vector $r_{t+1}$?\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", + "\n", + "We test four standard cross-sectional factors, most of which we compute as priced-based proxies since we are using pretty basic data. They are momentum, value, quality, and low volatility.\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", + "\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", + "\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", + "\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", + "\n", + "## Notebook Structure\n", + "1. [Setup and Imports](#setup-and-imports)\n", + "2. [Load Data](#load-data)\n", + "3. [Factor Definitions](#factor-definitions)\n", + "4. [Information Coefficient Analysis](#information-coefficient-analysis)\n", + "5. [Subperiod IC Stability (Walk-Forward)](#subperiod-ic-stability-walk-forward)\n", + "6. [Factor Decay](#factor-decay)\n", + "7. [Turnover via Rank Autocorrelation](#turnover-via-rank-autocorrelation)\n", + "8. [Cross-Factor Correlations](#cross-factor-correlations)\n", + "9. [Conclusion](#conclusion)" + ] + }, + { + "cell_type": "markdown", + "id": "ff97ddb4", + "metadata": {}, + "source": [ + "## Setup and Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b6109fc9", + "metadata": {}, + "outputs": [], + "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", + "\n", + "pd.set_option('display.max_columns', None)\n", + "pd.set_option('display.max_rows', 100)\n", + "\n", + "palette = ['steelblue', 'coral', 'seagreen']\n", + "\n", + "os.makedirs('../data/processed', exist_ok=True)\n", + "os.makedirs('../images/02_factor_diagnostics', exist_ok=True)\n", + "\n", + "RANDOM_STATE = 3" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "aebef262", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Returns: (251, 501)\n", + "Prices: (252, 503)\n", + "Sectors: 503\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Load return panel and sector mapping\n", + "==================================\n", + "\"\"\"\n", + "df_returns = pd.read_csv('../data/processed/returns_monthly.csv', index_col=0, parse_dates=True)\n", + "df_prices = pd.read_csv('../data/processed/prices_monthly.csv', index_col=0, parse_dates=True)\n", + "df_sector = pd.read_csv('../data/processed/sector_mapping.csv')\n", + "\n", + "print(f\"Returns: {df_returns.shape}\")\n", + "print(f\"Prices: {df_prices.shape}\")\n", + "print(f\"Sectors: {len(df_sector)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ac67fd55", + "metadata": {}, + "source": [ + "## Factor definitions\n", + "We'll now compute are 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", + "\n", + "- **Momemtum**: Trailing 12-month return skipping the most recent month ($t - 12$ to $t - 2$). The intuition behind 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", + "\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", + "\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." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "dbc1fa9f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "momentum: (251, 501), non-null months: 240\n", + "value: (251, 501), non-null months: 191\n", + "quality: (251, 501), non-null months: 239\n", + "lowvol: (251, 501), non-null months: 191\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Compute factor exposures\n", + "==================================\n", + "\"\"\"\n", + "def cross_sectional_rank(series):\n", + " \"\"\"Rank cross-sectionally, scaled to [-0.5,0.5]\"\"\"\n", + " return series.rank(pct=True) - 0.5\n", + "\n", + "# Momentum (12-1): sum of returns from t-12 to t-2\n", + "mom_12_1 = df_returns.rolling(11).sum().shift(1)\n", + "df_momentum = mom_12_1.apply(cross_sectional_rank, axis=1)\n", + "\n", + "# Value (proxy): inverse 60-month momentum\n", + "mom_60 = df_returns.rolling(60).sum().shift(1)\n", + "df_value = (-mom_60).apply(cross_sectional_rank, axis=1)\n", + "\n", + "# Quality (proxy): 12m sharpe-like\n", + "mean_12 = df_returns.rolling(12).mean().shift(1)\n", + "std_12 = df_returns.rolling(12).std().shift(1)\n", + "quality_raw = mean_12 / std_12.replace(0, np.nan)\n", + "df_quality = quality_raw.apply(cross_sectional_rank, axis=1)\n", + "\n", + "# Low volatility: inverse 60m vol\n", + "vol_60 = df_returns.rolling(60).std().shift(1)\n", + "df_lowvol = (-vol_60).apply(cross_sectional_rank, axis=1)\n", + "\n", + "\n", + "factor_dict = {\n", + " 'momentum': df_momentum,\n", + " 'value': df_value,\n", + " 'quality': df_quality,\n", + " 'lowvol': df_lowvol,\n", + "}\n", + "\n", + "for name, df_f in factor_dict.items():\n", + " print(f\"{name}: {df_f.shape}, non-null months: {df_f.notna().any(axis=1).sum()}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e9ea22e1", + "metadata": {}, + "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", + "\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 ration of signal and noise, which tells us whether the factor reliably predicts returns or is just noise." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7146b0c", + "metadata": {}, + "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", + "\n", + "IC Summary\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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meanstdminmaxic_ir
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" + ], + "text/plain": [ + " mean std min max ic_ir\n", + "momentum 0.0060 0.1905 -0.6011 0.4032 0.1091\n", + "value -0.0216 0.1576 -0.3878 0.6297 -0.4750\n", + "quality -0.0025 0.1945 -0.5120 0.4798 -0.0448\n", + "lowvol -0.0259 0.2360 -0.5288 0.5478 -0.3808" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Compute monthly IC (Spearman rank correlation)\n", + "==================================\n", + "\"\"\"\n", + "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", + " common_dates = factor_df.index.intersection(return_df.index)\n", + " common_tickers = factor_df.columns.intersection(return_df.columns)\n", + "\n", + " ic_series = []\n", + " for date in common_dates:\n", + " f = factor_df.loc[date, common_tickers]\n", + " r = return_df.loc[date, common_tickers]\n", + "\n", + " mask = f.notna() & r.notna()\n", + " if mask.sum() < 20: # sample size filter\n", + " ic_series.append(np.nan)\n", + " continue\n", + " \n", + " ic, _ = spearmanr(f[mask], r[mask])\n", + " ic_series.append(ic)\n", + " return pd.Series(ic_series, index=common_dates)\n", + "\n", + "ic_results = {}\n", + "for name, df_f in factor_dict.items():\n", + " print(f\"Computing IC for {name}... \")\n", + " ic_results[name] = compute_monthly_ic(df_f,df_returns)\n", + "\n", + "df_ic = pd.DataFrame(ic_results)\n", + "df_ic = df_ic.dropna(how='all')\n", + "\n", + "print(\"\\nIC Summary\\n\")\n", + "ic_summary = df_ic.describe().T[['mean', 'std', 'min', 'max']]\n", + "ic_summary['ic_ir'] = ic_summary['mean']/ic_summary['std'] * np.sqrt(12)\n", + "display(ic_summary.round(4))" + ] + }, + { + "cell_type": "markdown", + "id": "5659b941", + "metadata": {}, + "source": [ + "Note that 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 ±1). If you don't gate this, early-history months, mass delistings, or data gaps inject garbage ±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", + "\n", + "We do this sort of masking throughout." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d0fe3ee3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Plot IC time series\n", + "==================================\n", + "\"\"\"\n", + "fig, axes = plt.subplots(4,1, figsize=(14,12), sharex = True)\n", + "\n", + "for ax, (name, ic_series) in zip(axes, ic_results.items()):\n", + " ax.bar(ic_series.index, ic_series.values, color='steelblue', alpha=0.6, width=20)\n", + " ax.plot(ic_series.index, ic_series.rolling(12).mean(), color='coral', linewidth=2, label='12m MA')\n", + " ax.axhline(y=0, color='black', linestyle='-', linewidth=0.5)\n", + " ax.set_ylabel('IC')\n", + " ax.set_title(f'{name.capitalize()} Factor IC')\n", + " ax.legend(loc='upper right')\n", + " ax.grid(alpha=0.3)\n", + "\n", + "axes[-1].set_xlabel('Date')\n", + "plt.tight_layout()\n", + "plt.savefig('../images/02_factor_diagnostics/ic_time_series.png', dpi=150, bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2cce2714", + "metadata": {}, + "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", + "\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." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "fba556cd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean IC by Subperiod\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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period2006-20112011-20162016-20212021-2026
factor
lowvolNaN-0.0029-0.0361-0.0295
momentum-0.01310.0276-0.00790.0174
quality-0.02870.0258-0.02580.0183
valueNaN-0.0179-0.0331-0.0095
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" + ], + "text/plain": [ + "period 2006-2011 2011-2016 2016-2021 2021-2026\n", + "factor \n", + "lowvol NaN -0.0029 -0.0361 -0.0295\n", + "momentum -0.0131 0.0276 -0.0079 0.0174\n", + "quality -0.0287 0.0258 -0.0258 0.0183\n", + "value NaN -0.0179 -0.0331 -0.0095" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Information Ration by Subperiod\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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period2006-20112011-20162016-20212021-2026
factor
lowvolNaN-0.043-0.560-0.420
momentum-0.2370.547-0.1300.325
quality-0.5930.461-0.3900.353
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" + ], + "text/plain": [ + "period 2006-2011 2011-2016 2016-2021 2021-2026\n", + "factor \n", + "lowvol NaN -0.043 -0.560 -0.420\n", + "momentum -0.237 0.547 -0.130 0.325\n", + "quality -0.593 0.461 -0.390 0.353\n", + "value NaN -0.576 -0.676 -0.170" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " % Months with Positive IC\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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period2006-20112011-20162016-20212021-2026
factor
lowvolNaN51.743.341.7
momentum48.361.745.058.3
quality47.563.343.360.0
valueNaN40.046.748.3
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" + ], + "text/plain": [ + "period 2006-2011 2011-2016 2016-2021 2021-2026\n", + "factor \n", + "lowvol NaN 51.7 43.3 41.7\n", + "momentum 48.3 61.7 45.0 58.3\n", + "quality 47.5 63.3 43.3 60.0\n", + "value NaN 40.0 46.7 48.3" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Subperiod IC stability (walk-forward)\n", + "==================================\n", + "\"\"\"\n", + "windows = [(2006,2011), (2011,2016),(2016,2021),(2021,2026)]\n", + "subperiod_results = []\n", + "\n", + "for name in factor_dict:\n", + " ic = ic_results[name]\n", + " for start, end in windows:\n", + " mask = (ic.index.year >= start) & (ic.index.year < end)\n", + " sub = ic[mask].dropna()\n", + " if len(sub) < 12:\n", + " continue\n", + " subperiod_results.append({\n", + " 'factor': name,\n", + " 'period': f'{start}-{end}',\n", + " 'mean_ic': sub.mean(),\n", + " 'std_ic': sub.std(),\n", + " 'ir': sub.mean() / sub.std() * np.sqrt(12) if sub.std() > 0 else np.nan,\n", + " 'n': len(sub),\n", + " 'pct_positive': (sub > 0).mean()\n", + " })\n", + "\n", + "df_subperiod = pd.DataFrame(subperiod_results)\n", + "\n", + "# Pivot for display\n", + "pivot_ic = df_subperiod.pivot(index='factor', columns='period', values='mean_ic')\n", + "pivot_ir = df_subperiod.pivot(index='factor', columns='period', values='ir')\n", + "pivot_pct = df_subperiod.pivot(index='factor', columns='period', values='pct_positive')\n", + "\n", + "print(\"Mean IC by Subperiod\\n\")\n", + "display(pivot_ic.round(4))\n", + "\n", + "print(\"\\n Information Ration by Subperiod\\n\")\n", + "display(pivot_ir.round(3))\n", + "\n", + "print(\"\\n % Months with Positive IC\\n\")\n", + "display((pivot_pct * 100).round(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "7b0bc01a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot subperiod IC\n", + "fig,ax = plt.subplots(figsize=(12,6))\n", + "x = np.arange(len(windows))\n", + "width = 0.2\n", + "colors = ['steelblue', 'coral', 'seagreen', 'goldenrod']\n", + "for i, name in enumerate(factor_dict):\n", + " vals = [pivot_ic.loc[name, f'{s}-{e}'] if f'{s}-{e}' in pivot_ic.columns else 0 for s, e in windows]\n", + " ax.bar(x + i * width, vals, width, label=name, color=colors[i], alpha=0.8)\n", + "\n", + "ax.set_xticks(x + width * 1.5)\n", + "ax.set_xticklabels([f'{s} - {e}' for s,e in windows])\n", + "ax.set_ylabel('Mean monthly IC')\n", + "ax.set_title('Factor IC by Subperiod')\n", + "ax.axhline(y=0, color='black', linewidth=0.5)\n", + "ax.legend()\n", + "ax.grid(alpha=0.3, axis='y')\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('../images/02_factor_diagnostics/ic_subperiod.png', dpi=150, bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6c6cc2c9", + "metadata": {}, + "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", + "\n", + "Momentum is typically slow. Short-term reversal is fast. Low-vol is very slow. The decay curve tells you which is which." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "29d1c1d8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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1m3m6m12m
momentum0.0015-0.0023-0.0032-0.0048
value-0.0229-0.0400-0.0463-0.0458
quality-0.0041-0.0128-0.0167-0.0251
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" + ], + "text/plain": [ + " 1m 3m 6m 12m\n", + "momentum 0.0015 -0.0023 -0.0032 -0.0048\n", + "value -0.0229 -0.0400 -0.0463 -0.0458\n", + "quality -0.0041 -0.0128 -0.0167 -0.0251\n", + "lowvol -0.0251 -0.0621 -0.0873 -0.1108" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "IC decay at horizons 1, 3, 6, 12 months\n", + "==================================\n", + "\"\"\"\n", + "horizons = [1,3,6,12]\n", + "decay_results = {}\n", + "\n", + "for name, df_f in factor_dict.items():\n", + " decay_results[name] = []\n", + " for h in horizons:\n", + " # Forward returns at horizon h\n", + " fwd_returns = df_returns.rolling(h).sum().shift(-h)\n", + " ic_series = compute_monthly_ic(df_f, fwd_returns)\n", + " decay_results[name].append(ic_series.mean())\n", + "\n", + "df_decay = pd.DataFrame(decay_results, index=[f'{h}m' for h in horizons]).T\n", + "\n", + "fig,ax = plt.subplots(figsize=(10,6))\n", + "df_decay.T.plot(ax=ax, marker='o')\n", + "ax.set_xlabel('Horizon')\n", + "ax.set_ylabel('Mean IC')\n", + "ax.set_title('Factor IC Decay by Horizon')\n", + "ax.axhline(y=0, color='black', linestyle='--', linewidth=0.5)\n", + "ax.grid(alpha=0.3)\n", + "ax.legend(title='Factor')\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('../images/02_factor_diagnostics/ic_decay.png', dpi=150, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "display(df_decay.round(4))" + ] + }, + { + "cell_type": "markdown", + "id": "d0205e12", + "metadata": {}, + "source": [ + "## Turnover via Rank Autocorrelation\n", + "\n", + "A factor that re-ranks the universe every month will produce high turnover, and this contributes to lowering net returns after transaction costs.\n", + "\n", + "**Rank autocorrelation** is the correlation between this month's factor rank vector and last month's. If the rank vector barely changes (autocorrelation $\\approx$ 1), the portfolio barely trades. If it changes a lot (autocorrelation $\\approx$ 0), turnover is high. In linear algebraic terms, the rank vector at $t$ is nearly parallel to the rank vector at $t-1$ for stable factors, and nearly orthogonal for unstable ones." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "65a97db6", + "metadata": {}, + "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", + "lowvol: rank autocorr = 0.997, turnover proxy = 0.003\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Rank autocorrelation as turnover proxy\n", + "==================================\n", + "\"\"\"\n", + "def rank_autocorrelation(factor_df, lag=1):\n", + " \"\"\"Averages cross-sectional rank correlation between t and t-lag.\"\"\"\n", + " common_dates = factor_df.index[:-lag]\n", + " autocorr = []\n", + " for date in common_dates:\n", + " f_now = factor_df.loc[date]\n", + " f_lag = factor_df.loc[factor_df.index[factor_df.index.get_loc(date) + lag]]\n", + " mask = f_now.notna() & f_lag.notna()\n", + " if mask.sum() < 20:\n", + " continue\n", + " rho,_ = spearmanr(f_now[mask], f_lag[mask])\n", + " autocorr.append(rho)\n", + " return np.mean(autocorr)\n", + "\n", + "turnover_proxy = {}\n", + "for name, df_f in factor_dict.items():\n", + " rho = rank_autocorrelation(df_f, lag=1)\n", + " turnover_proxy[name] = rho\n", + " # Rough turnover estimate: 1 - rho\n", + " print(f\"{name}: rank autocorr = {rho:.3f}, turnover proxy = {1 - rho:.3f}\")\n", + "\n", + "fig,ax = plt.subplots(figsize=(8,5))\n", + "ax.bar(turnover_proxy.keys(), [1-v for v in turnover_proxy.values()], color='coral')\n", + "ax.set_ylabel('Turnover proxy (1 - Rank Autocorr)')\n", + "ax.set_title('Factor Turnover Proxy')\n", + "ax.grid(alpha=0.3, axis='y')\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('../images/02_factor_diagnostics/turnover_proxy.png', dpi=150, bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6ac614b4", + "metadata": {}, + "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", + "\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." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "2d488e92", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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WLVuA54u/ACAmJgbW1tYwNzdX63Xi4uJgbW0NmUxW4Jizs3OBPT5ffk9fJ3/1fW5uLm7cuIE2bdpg1qxZuH79ulK96dOn47PPPsMHH3yAe/fuITc3F0II9O7dW3GuL3JxcVF6np8MFvaf06pVq+Dk5IThw4erFLORkRFcXV3x4MEDleqnp6cjMzPztd+rFxX1PhZWHh0djby8PFhbW8PIyAiGhoYwMDCAgYEBdu3apTSH82VnzpxBhw4d4OzsjJMnTyq+R/lzgQt7b1URFxcHOzu7QhP8wr43L39eeP6ZvS6ZsLGxAZ5vB6dt9vb2ivm8+SwtLWFrawupVFqg/MVYo6OjAQDffvut4jPJ/1zyt6F61ecCQJF4NmrUSClpXbduHc6dO4eQkBBF3ffeew/x8fGKnTl27dqFJ0+eYNSoUQViqlOnToHvSf4OBNnZ2Yr6hX3XHj58iJYtWyI1NRX79u1DSkoKhBCKuZ/535f8c3s5AS2sTNW48vt0cnIq0GdhZQCQlpaGvLw8xbx5ovKKSakeyR+1+Oeff4rV7uX/cPIXShQ2qplflv8XefXq1bF9+3YkJCTg1KlT6NmzJ77++muMGTNG0eZ1dTp37lxgFLRu3boqxb5u3Trg+X9cvr6+iv8kw8PDleo5OjoiKSlJ7UTYzs4OSUlJyMzMLPQ9eXmE4uX3VFWGhoaoXr06tm7dCiMjIwwbNkxpodOqVavQv39/vPvuu3ByclIsLnr5fPO9OOr2Ovv27YOlpSVatGiB0NBQldp07twZ9+7de+ViqHxmZmaQyWQqfa/yFfU+Flbu4OAAU1NTZGZmIjc3F3l5eZDL5ZDL5RBCvDJ5XrduHQwNDbFkyRL4+PgU+T0qLjs7O8THxxea1Bb2vSnO5/WiChUqQCaTKRIbbSoqJlVizT+/b7/9VvGZ5H8u+T/r77zzDt56660CvwM8PT2RnJyMTZs2Yc2aNQWOCyHQpEkTpe2h2rVrh8qVK2P58uUAgOXLl8Pd3V1ppDc/prCwsEK/J0IIpT8iCvuubdu2DSkpKVi8eDHq1KkDU1NToJDvi729PfD8j+KXvVymalz5fb7q5+hl+YuvPDw8Cj1OVF4wKdUjgwYNgq+vL+bMmVPk6MNff/2lNLJQGEdHR9SuXRs7d+4ssIXI1q1bYWFhUeDSqVQqRbNmzTB37lx06NABx44dK9CvKnXUkT+qkE8IgTVr1ijV6d69O/BCElsYMzMzSCQSxaW/F7Vr1w5CCGzfvl2p/NSpU4iOjka7du20cCb/sbOzw5dffolLly4VOJeXR6dCQkJe+5mqolKlSjhx4gQcHR3RunVrlVbqTps2Daamppg6dWqhN2UQQmD27NnA8yQmICAABw8eLLDzwtatW2FgYKC4/KmOHj16ICMjAzt27FCrvSrfo/yR9sK+I4Vp164d5HJ5gZiOHTuG2NhYrX1vjI2N0aRJkwKrtHXNy8tL8UeWOtsRrV27FhkZGQWmD+Tr0qUL1q5dq/hjUSKRYOTIkTh48CDOnj2Lf/75ByNGjFD6XPN/F7y8k4g6Xv5ZXLVqldLzgIAA4Pn0nBedP3++QFKqalxt27YFnk9XedGpU6eKvCvbuXPnAACtWrV6Zd9EZR2TUj1ibGyM7du3w8jICE2bNsXGjRsRGxuLrKwshISEYNy4cejfv7/SpamizJs3D+Hh4RgxYgQiIiIQExODWbNmYefOnQgMDISlpSWuXr2K3r17Y9++fXjy5AkyMjJw6NAhnD9/XvHLWJU6mujRowcyMzPxySefIDExEeHh4Rg+fDiqVq1aoF6fPn3w8ccfY8mSJXjy5AliYmKwbt06xWbnJiYmqFq1Ko4ePYqEhASl9gMGDECTJk3w4YcfYs+ePUhJScGJEycwbNgweHt7K81305YxY8agUqVKmDVrluIz69mzJzZt2oR9+/YhLS0Nx48fx+jRo9GmTRutvKajoyOCg4Ph6+uLgICA1/7h4OPjg/Xr1+PQoUPo1KkTgoODkZqaitTUVMX80RfvfjRnzhxkZGRgwIABuH37NuLj47Fw4UIsWbIEH330kUYjOX369EHfvn3x/vvvY/ny5YiKikJqaiouX76MmTNn4ttvvy2ybc+ePZGeno5PP/0UiYmJuHfvHoYOHYrq1asr1TM1NUWVKlUQHBys0vy8t956Cw0bNsTEiRMVl3mPHTuGESNGwMfHB2PHjlX7fF/Wo0cPXL58GfHx8VrrUxv++OMPXLt2DQMHDsTly5eRkZGBBw8e4K+//kKbNm1emeAvXboUDRo0KHKLuy5duiAhIUExXQcARo4cCTz/mc3Ly1M8z1enTh1MmzYNX375Jb7//ns8ePAAGRkZuH79OubPn19gHndhOnXqBBMTE0ydOhUxMTF4/Pgxpk+fXiBJbdmyJbp164bPP/8cO3bsQEpKCs6ePYuvv/66wBZVqsbVokULdO/eHV988QX+/vtvpKSk4PTp05gzZ06BPvMdPnwYbm5uqF+//mvPjahM0/VKKyooKSlJfPPNN6Jhw4bC0tJSyGQyUbVqVdG/f3+xZ88exarw/NX3mzdvLrSfw4cPi4CAAGFubi5kMplo0KCBWLt2reJ4Xl6e2L59u+jatatwcnIS5ubmonr16iIwMFBkZGSoXOdVVNkSauXKlcLX11fIZDJRrVo18ccff4idO3cWaJebmyu+//57UbNmTSGVSoWrq6t45513xP379xV1goODRd26dYWJiYkAIIYPH644lpycLCZPniwqVaokjIyMhLOzs3jvvffEkydPFHXyV9//9NNPrz23fIVtCZUvf/V3/krb5ORkMWbMGMV72bZtWxESEiIGDRokPDw8FO1e3uYpX2GfeWF109PTRbdu3YSpqWmBldeFCQsLExMnThTVqlUTMplMWFlZidq1a4tx48aJK1euKNW9ePGi6Natm7C2thYmJiaiZs2a4tdff1VadV5U/OL56vsmTZoUGkdeXp749ddfRcOGDYWZmZmwsrISDRo0EEFBQYrthIpafb98+XLh4+MjZDKZ8PHxEcuWLVOsVj99+rSi3qFDh4Sfn5/iOzJq1KhX9puUlCQmTZokKlasKIyMjISLi4sYPXq0YjW4eGH1fWG7LXh5eSltb1aU2NhYIZPJxG+//VZkHXVW3zs5ORWoO2rUKGFvb1+gfMyYMcLa2rpA+Y0bN8Tbb78tXFxchLGxsfD09BQDBw4Ux44dKzLWf//9VwAQM2fOLLJOXl6ecHR0FK1bt1Yq79KliwAg2rZtW2TbDRs2iNatWwtLS0thZmYmatWqJT799FPx6NEjRR2pVKrYteFle/bsEfXq1ROmpqaiUqVK4ptvvhEhISEFdoBIS0sTH3zwgXB0dBRmZmaiU6dOIjw8XNSqVUt07dpVrbjS09PFhx9+qOizQ4cOIiwsTDRo0KDA6vvMzExhbW0tZs+eXeR7QVReSERxl+ASEVGJ+Oijj3Dw4EGEhIQUa/cHKn22trbo27cvli1bVqKvs2zZMnz88ce4c+dOkSPOROUFf+sREemJL7/8Eo8fPy72HcSodB0+fBiJiYlam3ZTlOzsbHz99deYOXMmE1J6I3CklIiIqAgrVqxAeno6unfvDltbW5w8eRJjx46FpaUlLl68WGAeKhGpjyOlRERERejduzdu376N9u3bw9HREe+99x46duyII0eOMCGlMuX27dtYtmxZgZvbvMr58+exevVqBAcHq7UDR3FxpJSIiIionAoNDcXEiRPx4MEDPH78GJ9++ilmzpz5yjZyuRxDhw7F/v370aZNG5w/fx6VKlXC3r17YWFhUWKxcqSUiIiIqJzKysrC9OnTcevWrSJvZfuyNWvW4O+//8apU6ewdetWXLp0CREREQgKCirRWJmUEhEREZVTderUQYcOHYp1x7n169ejU6dOqFatGvD87mZDhgzB+vXrSzBSJqVEREREZUpWVhaSk5OVHqreqU4V165dQ40aNZTKatasifDwcKSnp2vtdV5mVGI9F9NYiaeuQyA94bp/n65DID3hPLCXrkMgPdI1TL9uw0q6425XcvMaNVFauYzzrBH46quvlMpmzZqluC20ppKTk2Fra6tUZmdnpzhmZmamldd5md4kpURERET0ejNmzMCUKVOUyrS5G4SpqSlSUlKUyvKfm5qaau11XsaklIiIiKgMkUqlJbolmbe3NyIiIpTKwsPD4eDgAGtr6xJ7Xc4pJSIiInqD3blzB0uXLkV2djYAoEePHtizZw+Sk5MBALm5udi0aRN69OhRonFwpJSIiIhICwxVX+BeapKTk7Fp0yYAQGpqKi5evIilS5fCzc0NXbp0AQCcPHkS77//Pvr37w8TExNMmDABa9asQbt27TBw4EAcPHgQT5480dqc1aJwpJSIiIionMrKysKZM2dw5swZ9O7dG/b29jhz5gxu3rypqFOtWjWMGjVKMSXAwsICp0+fxrBhwxAREQF/f39cuXIFlSpVKtFY9eaOTlx9T/m4+p7ycfU9vYir7ymfvq6+/9Cwcqm8zi954aXyOqWNI6VEREREpHNMSomIiIhI55iUEhEREZHOMSklIiIiIp3jllBEREREWqCPW0KVJRwpJSIiIiKd40gpERERkRYYSjhUqgmOlBIRERGRznGklIiIiEgLOKdUMxwpJSIiIiKdY1JKRERERDrHpJSIiIiIdI5zSomIiIi0gKvvNcORUiIiIiLSOY6UEhEREWkBV99rhiOlRERERKRzTEqJiIiISOeYlBIRERGRzmk0p1QulyM9Pb1AuYWFhSbdEhEREZU5XH2vGbVGSm/duoWAgACYmprC0tKywIOIiIiIqDjUGikdPnw4nJ2dsX37dtjY2Gg/KiIiIqIyhnMiNaNWUnrlyhXs3buXCSkRERERaYVaSamnpydiYmKYlBIRERE9xzmlmlErKQ0MDMTIkSMxd+5ceHl5QfLSh+Ds7Kyt+IiIiIjoDaBWUmpvb49r166hVatWhR4XQmgaFxERERG9QdRKSsePH49u3bph/PjxvIRPRERERBpTKyl98OABzp07BysrK+1HRERERFQGGXJKqUbU2r2gatWqePTokfajISIiIqI3klojpUOHDsXQoUMxd+5ceHt7F1jo5Onpqa34iIiIiMoErr7XjFpJ6SeffAIA6NixY6HHudCJiIiIiIpDraT0zp072o+EiIiIqAzjnFLNqJWUent7az8SIiIiInpjqZWUXrhw4ZXHGzZsqG48RERERPQGUispbdSo0SuPc04pERERERWHWklpQkKC0nO5XI47d+7ggw8+wIQJE7QVGxEREVGZwdX3mlFrn1IbGxulh52dHZo0aYKVK1diwYIF2o+SiIiIiMo1tUZKi+Lu7s6V+URERPRG4up7zaiVlKamphYoS0hIUGymT0RERERUHGolpZaWloWWu7m5YcOGDZrGRERERERvGLWS0tOnTxcos7W1RZUqVWBsbKyNuIiIiIjoDaJWUvrpp58iODi40GP+/v5FHiMiIiIqr7j6XjNqrb4/evRooeVyuRwnTpzQNCYiIiIiesMUa6Q0NDS00H/jeUJ66tQpuLm5aS86IiIiInojFCsprV69eqH/zmdmZoZff/1VO5ERERERlSHcEkozxUpKHz58CACoWLGi4t/5jI2N4ejoCAMDtWYEEBEREdEbrFhJqbu7OwAgIyMDMpmspGIiIiIiKnM4UqoZtVbfy2QypKam4saNG4iPjy9wvHPnztqIjYiIiIjeEGolpXv37sWQIUOQkJAAQ0PDAsdzc3O1ERsRERERvSHUmgA6efJkjB8/HsnJycjNzS3wICIiIiIqDrVGSh8+fIhPP/0UFhYW2o+IiIiIqAzi5vmaUWuktHr16oiIiNB+NERERET0RlJrpHT06NEYNmwY5s2bB29vb0he+svA09NTW/ERERERlQlcfa8ZtZNSAOjYsWOhx4UQmkVVTng1b4Bq/k1x5/h53D1+7rX1JRIJanUNgHN1byQ8isaV7fuRk5FZ7DqkvzJTk/Eo5AxyMtJg7+mDCl41VG4bGx6KqFtX4OJTBw6VfdWuQ7plX8sXrq2aIDcrCw/2BSMtMvq1bdz8m8PWxwt5OTmIuRCCuJAbBeoYSk1QsX1rWHq4IzbkJqJOnC2hMyBty8vLw+kTxxD58CGcnJ3RvLU/TExMXtvu8r8XcD3kCuo1bIwatWprrV8iXVErKb1z5472IylH3Gr7YuSan5CdlgEnXy8YGhu/Nik1MDTEhF3L4VzdC1d3HUbjt3uj+6xJmN9qAFJj41WuQ/or8fF9HPz5M9i4esLS0QXX9m5C5SZt0bD/+69tm5GcgOPLv0dmSgIMjUwKTThVqUO6VXv8CDT87EPc274PUhsrNA38BPuGjMfjY2cKrW9gYoyuW5fBqnIlhO/cD2NzMzT9ahpCV2/B6c+CFPWsvTzRZfMfyE5OQeSxs/Br3RTe/bri+ORZpXh2pI7s7GxMnzQBT58+QaMmzbFvz06sXbkcPy1eAgsLy0LbhN64jrlfz4K1jQ3u3AqFsbFxgaRUnX5Jc5xTqhm1klJvb2/tR1KO5GRkYsXQyYi8GorAO8EqtWk+cgC8WjbEbN92SIyMhrGpDDMv70H3WZOw4YNZKtch/XV+0//gUNkXbcbMhEQigWfDNjj4y+fwbNDqlQmkEAKnVv2E6m174drejWrXId0yd3dB41lTcPSDz3F3004AQMsfZ6P1z19jQ70OhbZxa90Uri0bY0P9jkgOfwAAiDp5Hv6/BeHfeb8hKzEJEgMDdFj9C+Jv3sH+oROB51eq7Gvxj5KyYMfWzQgPu4sVG7bC1s4OaWmpeO/tgVi/agXeH/9hoW1kpqaY9e08eFaugv7dCr9iqU6/RLqm9j1BU1JSsHXrVnz//feKsps3b/LSPYCYuxGIvBparDb1+nfFjX3HkPj8Ul5ORibOrd2Oev27FqsO6afMlETE3L0Or2btFXOwnarVhqWjCx5cOvXKtjcObIVEIoGPfw+N6pBueXZph7zMbNzbtldRFrpyM6w8K8Khbq1C2+SkpQMA5Ll5ijJ5Xh7yMrMgf779npt/c9hVr4bzcxYoElIAiLtWvN9BpBvHjhxC05atYGtnBwAwN7eAf7uOOHb4UJFtPCtXgWflKlrvl0jX1EpKQ0NDUaNGDXz44Yf45JNPFOXfffcd1q9fr8343hjOvlUQcztcqezJ7XBYOzvC1NpK5Tqkn5KfRAIALCu4KZVbVnBD0pNHRbaLDQ9FaPBONBs6qcCCwuLUId2zqVYZqY8eQ56ToyhLCotQHCtM9OmLuBC0EB1X/4LGs6ag+dyZqPvR+zgydjpyUtMAABUa1kFmXALSIqPhM7Qfar4/FE5N65fSWZGmHtyPQMVKHkpl7pUqIepxJHJzc4psp6t+iUqS2pvnDxs2DI8eKf9n+uGHH2L+/Pnaiu2NIrUwR0ZSilJZRmLy82NmKtch/ZST9WwxmolM+XMyMTVHblbhC9WyM9Jw4s/5aDxoHEyt7dSuQ/rB2Nwc2cnKP785qWmQ5+bC2Lzwn1+JgQGk1pYwsbKEkZkZjM1MYWJlAROb//4IldpYQZ6bi55718G5SX3Y+FRB5/WLEfD7vBI/J9JcZkY6zM2V9/y2sLCEEAKZGixiLal+6dUMJaXzKK/UmlN6+vRprF+/vsCojK+vL65fv/7a9llZWcjKylIqy4OAIcrxO/0a2WnpkFkp/wIxtbZUHFO1Dule9O0QPL13U/G8WssuMJJKAQA5mcqfU3ZGGoykskL7uRW8E0IuR2LUfSRG3QcA5OZkI/r2FRgYGsA3oJdKdUg/5KSnw9hS+efXyNwMBkZGyE3PKLRN1bd6o/qIQdjQsBPSo2IAABXbt0KnDf/Dk7P/IvH2PeSmZ8DMyREnp32N8J37AQC3Vm9F3+CtuL1uGyKPni6Fs6PXeRoTg327dyie12vQCDX96kAmM0VaWppS3dS0VEgkEkhlhf9uUEVJ9UtUktQaKRVCKJLKFxPTe/fuwdra+rXtg4KCYG1trfS4hCR1Qik3nty6hwreypdaKlT1RHJMLNKfj4aqUod0T+TlIS8nR/EQAKyeX7ZPeRqlVDc1Nkpx7GUOnj6o3LitUl8Q4ln/z+cTqlKH9EPSnXBYVnSFxOi/sQDrKs9+nhPvhBfaxr62L5LCHygSUgCIOnUBBoaGsK9d/Xnbe8/KT19Q1Im9ch256RmwqfbqeYdUmgSys7MVjzy5HADg7uGBx48eKtWMfPgQzi6uMDY2VvvVSqpfejVDiaRUHuWVWklpx44dMW/es0tD+UlpTEwMJk6ciC5dury2/YwZM5CUlKT0qIfXJ7PliWvNaujy+UQYy56NoF36ax9qdG4DKydHAICRVIpGQ3rh8l/7FG1UqUO651K9Hur2GKp4yCysYGplC4cqvgg7898ig5iwG0h+EomKdZspyiIuHEXEhaOF9lO3x1AYmUjhUr0+anbop3Id0g/3/zkMI1MZKvf4b6W9z9B+SHkYiaeXrgHPt4Cq9/FY2NWsBjyfc2rl4Q5TR3tFG6fG9Z4du/tsPuqD/ceQk5oGp0Z1FXXsavnAyMwUCbfCSu386NUcKzjh3THjFQ+/us8+x1Zt2uLUiaNISkoEAGRkZODooQNo2SZA0fb2rZtYs2Ip8vLyiuz/Zar0S6Rv1Lp8/+OPP8Lf3x87duyAXC5Hq1atcOnSJTg7O2PDhg2vbS+VSiF9fjkzX3m6dC+zskTAB8MBAGa21vBu3RhdPp+I+PuROLtmGwDAvW4N9JozFcf+txY5mVk4uWwjGgzshqknNuPK9gOo2qoRjEyMsXPWT4p+ValD+qvRgDE4+MtMHP5tNqwquCHiwlF4t+iktIH+vbOHAQCeDdvoMFIqCSkPInHhu1/R5pc5cG3ZGFIba3h0bYf9QycoVs0byWRoNPMjpD1+gvjrt3FrzVZUG9wHvQ9twr2/98LIzBRVB/ZC6KrNiL3ybKpUVkIiTn36DQL+Nxd3Nu9EXmY2qg7qidsb/i5y/1PSH737D8Tx4EOYNPpdNGnREpcvXICJiQmGDH9XUefW9etY8cdiDBwyDIaGhkhMSMCuv7cCADIzM/DvhfPIzs6GW8VKCGjfUeV+6c3x+PFj/PDDDwgNDUXFihXxwQcfoGbNmq9sc/LkSaxevRqPHj2Co6MjevfujV69SnZKmESouYdTWloa1q1bhwsXLkAul6N+/foYNmwYLCwsVGhd0FhJ+bk1qam1FTpMLbghemz4Q5xavgkA4OZXHQ0GdMU/3y5S3JHJwNAQdft0grOvFxIjo3Fx8x5kpSrPCVKlTlnnur/8jvxmJMXjweVTyM5Ig4NHNbhUr6d0PD8prdKkbaHtr+3bjAreNV95JyhV6pQVzgPL35xYxwZ+cGvdFHlZ2YjYfRAp9/9bMGooNUG9qeMQvmM/4q4+n5cskaBSJ3/YVquCvOwcPP33Kp6cu1SgX1tfb1Ts0BpCLvD00lVEn7pQoE5Z1zWs/J0TAOTm5uDYkcOIfPgAFZyc0aZde8hkporjoTeu49Txoxg+ajQMjYyQEB+PbZsLDgBV9PBEh85dVe63LHO3Uy/XKGlbnF6d6GlL/yevX7+TLzY2FvXq1UOdOnUwfPhw/PPPP9i8eTPOnDlTZGK6a9cu9OrVC9OnT0fz5s1x8+ZNfPnll/jmm28wZcoULZ6JMrWTUm0rT0kpaaY8J6VUPOUxKSX1ldeklIpPX5PSbc6lk5T2iVY9KZ05cyZWrlyJsLAwxW1mW7RoAVdXV2zevLnQNsOHD0dERASOHj2qKBszZgwuX76Ms2dL7hbGam+eDwByuRypqakFHkRERESke/v370eXLl0UCSkA9OnTB/v37y+yTe3atREeHo7k5GeLqHNycnDt2jXUqVOnRGNVa07prVu3MHbsWJw6dQrZ2dkFjuvJ4CsRERFRqSmtlfGFba1Z2HodAIiIiED37t2Vytzd3ZGcnIyEhATY2toWaDNlyhSkp6fD29sbPj4+CAsLQ/fu3fHzzz+XwNn8R62kdPjw4XB2dsb27dthY2Oj/aiIiIiIqFBBQUH46quvlMpmzZqF2bNnF6ibnZ0NU1PlucRmZmaKY4U5efIkFixYgPfffx8tWrTAjRs3MG/ePLRt2xZvvfWWVs/lRWolpVeuXMHevXuZkBIRERE9Z1BKI6UzZswosOCosFFSALC1tUV8fLxSWVxcHCQSSZF7y0+bNg1du3bF3LlzAQA9e/ZETk4OJkyYgEGDBpXYLa3VmlPq6emJmJgYFWoSERERkTZJpVJYWVkpPYpKSuvWrYt///1Xqezff/+Fr68vZEXc3SsmJgaensoL0D09PZGYmFhg2oA2qZWUBgYGYuTIkThx4gSioqIQHR2t9CAiIiIi3RsxYgSOHDmCM2ee7VscHh6OdevWYcSIEYo6+/fvR/v27RW3pm3evDm2bNmChIQE4Pkc1tWrV6NevXpFJrLaoNble3t7e1y7dg2tWrUq9DgXOhERERHpXq9evfDxxx8jICAANWrUwK1bt9C9e3dMnjxZUefx48c4dOgQcnJyAAA//PADBgwYgMqVK6NmzZoICwuDg4ODSjdI0oRa+5T6+vqifv36GD9+fKHzSmvVqlXsQLhPKeXjPqWUj/uU0ou4Tynl09d9SndX9CuV1+n2MKTYbaKjo3Hnzh24u7ujcuXKSseioqJw/fp1+Pv7w8jov/HK+/fv49GjR6hQoQK8vLxgYKDRTqKvpdZI6YMHD3Du3DlYWVlpPyIiIiIi0ipnZ2c4OzsXeszFxQUuLi4Fyj08PODh4VEK0T2jVlJatWpVPHr0CDVqlP3bGBIRERFpg4Fh6ay+L6/USkqHDh2KoUOHYu7cufD29i6wNcDLK7aIiIiIiF5FraT0k08+AQB07Nix0ONc6ERERERExaFWUnrnzh3tR0JEREREbyy1klJvb2/tR0JERERUhkkMS3Z1enmnVlKaLzQ0FDdv3oQQAjVq1ICvr6/2IiMiIiKiN4ZaSWlCQgJGjBiBHTt2wNDQEBKJBLm5uejZsydWrlxZ6N6lREREROWZhKvvNaLWOPNHH32Ehw8f4vTp08jKykJmZiZOnz6Nhw8fYtKkSdqPkoiIiIjKNbVGSnfs2IEzZ87Ax8dHUda0aVOsW7cOzZs312Z8RERERGUC9ynVjFojpZmZmbCzsytQbmdnh4yMDG3ERURERERvELWS0ubNm2PGjBnIzMxUlGVmZuLTTz9FixYttBkfEREREb0B1Lp8v2DBAnTq1Anu7u7w8/MDAISEhMDExAT79u3TdoxEREREVM6plZTWrl0bd+7cwerVq3H9+nVIJBIMHDgQw4YNg7m5ufajJCIiIqJyTe19Ss3NzTF27FjtRkNERERURkkMuHm+JjTaPF8ulyM9Pb1AuYWFhSbdEhEREdEbRq2k9NatWxg7dixOnTqF7OzsAseFENqIjYiIiKjM4JZQmlErKR0+fDicnZ2xfft23r2JiIiIiDSmVlJ65coV7N27lwkpERER0XO8zahm1JqR6+npiZiYGO1HQ0RERERvJLVGSgMDAzFy5EjMnTsXXl5ekEiU/zJwdnbWVnxERERE9AZQKym1t7fHtWvX0KpVq0KPc6ETERERERWHWknp+PHj0a1bN4wfP57zSomIiIgASAy5T6km1EpKHzx4gHPnzsHKykr7ERERERHRG0etpLRq1ap49OgRatSoof2IiIiIiMog7lOqGbWS0qFDh2Lo0KGYO3cuvL29Cyx08vT01FZ8RERERPQGUCsp/eSTTwAAHTt2LPQ4FzoRERERUXGolZTeuXNH+5EQERER0RtLraTU29tb+5EQERERlWESA84p1QT3LiAiIiIinVNrpJSIiIiIlBlwn1KN8N0jIiIiIp3jSCkRERGRFki4T6lGOFJKRERERDrHpJSIiIiIdI5JKRERERHpHOeUEhEREWkB55RqhiOlRERERKRzHCklIiIi0gLuU6oZvntEREREpHMcKSUiIiLSAs4p1QxHSomIiIhI55iUEhEREZHOMSklIiIiIp3jnFIiIiIiLTAw4JxSTXCklIiIiIh0jiOlRERERFog4T6lGuG7R0REREQ6x6SUiIiIiHSOSSkRERER6RznlBIRERFpgQHv6KQRjpQSERERkc7pzUip6/59ug6B9MTjjp10HQLpCdPgg7oOgfSIS8wlXYdA+sKula4jKJSEI6Ua4UgpEREREekck1IiIiIi0jm9uXxPREREVJZx83zN8N0jIiIiIp1jUkpEREREOseklIiIiIh0jnNKiYiIiLSAm+drhiOlRERERKRzHCklIiIi0gKJgf6OlAohkJiYCCsrKxgaGqrcLicnB9nZ2TA3Ny/R+MCRUiIiIqLybc2aNXBxcYGrqyvs7OzwzTffvLbN48eP0b9/f1hZWaFSpUro2LEj7t+/X6JxcqSUiIiISAsM9HCf0jNnzmDEiBFYtmwZhg0bhuDgYHTr1g0VK1bEO++8U2ibtLQ0BAQEwNvbGw8ePICjoyOOHj2KEydOwMPDo8RiZVJKREREVE4tWrQIjRo1wvDhwwEAbdu2Rf/+/fHrr78WmZQuXrwYjx8/xpkzZ2BrawsAaNOmTYnHqn8pPRERERFpxblz59CyZUulstatW+PSpUvIyckptM3u3bvRrl072NraIiUlBXl5eaUSK5NSIiIiojIkKysLycnJSo+srKxC6z59+hQODg5KZY6OjsjNzUVCQkKhbR48eABra2u0a9cOLi4uMDc3R9euXUt8TimTUiIiIiItkBhKSuURFBQEa2trpUdQUFDhMUkkyM3NVSrLf17UKnyJRILVq1djzJgxSE1NRWRkJFJTUzFw4MASeNf+wzmlRERERGXIjBkzMGXKFKUyqVRaaF03Nzc8efJEqezJkyeQyWSK+aKFtbG2tlYkofb29pg2bRp69uyJ2NjYAiOv2sKklIiIiEgLJKW0+l4qlRaZhL6sdevWOHDggFLZvn370LJlSxgYPIs3IyMDSUlJcHJygkQiQUBAADZs2KDUJjU1FRKJBDKZTItnooyX74mIiIjKqcmTJ+Phw4f4+OOPcfPmTfz444/Ys2cPPvvsM0WdjRs3wsXFBUlJSQCA8ePHIz4+Hp999hnu3r2LI0eO4Msvv8TgwYNhYWFRYrEyKSUiIiIqp7y8vHDo0CFcvXoVnTt3xtatW/HXX38hICBAUcfU1BROTk6KkdMKFSrg2LFjCA0NRfv27TFt2jQMHToUS5cuLdFYefmeiIiIqBxr0qQJ9u/fX+TxQYMGYdCgQUplvr6++Ouvv0ohuv8wKSUiIiLSAokBL0Brgu8eEREREekcR0qJiIiItMCglFbfl1d894iIiIhI5zhSSkRERKQFpbVPaXnFd4+IiIiIdI5JKRERERHpHJNSIiIiItI5ziklIiIi0gLOKdUM3z0iIiIi0jmOlBIRERFpAe/opBm+e0RERESkc0xKiYiIiEjnmJQSERERkc5xTikRERGRFkgMDXUdQpnGkVIiIiIi0jmOlBIRERFpAfcp1QzfPSIiIiLSOY6UEhEREWmBAfcp1QjfPSIiIiLSOSalRERERKRzTEqJiIiISOeYlBIRERGRznGhExEREZEWcEsozfDdIyIiIiKd40gpERERkRZwpFQzar97crkc169fx86dOxVleXl52oqLiIiIiN4gaiWl0dHRaNmyJWrXro2ePXsqyrt3744DBw5oMz4iIiKiMkFiYFAqj/JKrTObMmUKPD09kZCQoFQ+Y8YMfPPNN9qKjYiIiIjeEGrNKT148CCuXr0Ka2trpfK6devi7Nmz2oqNiIiIiN4Qao2UpqWlQSqVAgAkEomiPDY2VlFORERERKQqtZLS5s2bY9WqVcALSWlOTg5mzpyJNm3aaDdCIiIiojJAYmhQKo/ySq3L999//z0CAgKwf/9+CCHw/vvv4/Dhw4iPj8fJkye1HyURERERlWtqpdt169bFpUuX4OPjg1atWiE0NBTdu3fHpUuXUKNGDe1HSURERKTnOFKqGbU3z/f09MQPP/yg3WiIiIiI6I2kVlKampr6yuMWFhbqxkNEREREbyC1klJLS8tXHhdCqBsPEREREb2B1EpKT58+rfRcLpfjzp07mDVrFiZPnqyt2IiIiIjKDINyPN+zNKiVlDZt2rRAWfPmzVGtWjVMmzYNkyZN0kZs5UZeTjaehodCnpsDe08fSM1Un96QEBmBlJhIOFT2hZmNvdp1SLdkVpbwCWiGxMho3L8QolIbG1cnuNetgbT4JEScuwwhl6tVh/RX7P27SE+Kh7WzO6wruKrcLjMlCVG3r8LSwQkOHlULHE+IeoDExw/g5F0DZtZ2Wo6atCUjKwuXQ+8hLy8PdX29YGFm+to2j5/GIexhFOysLOBbuRIMC0mC5HI5zl69hbSMTLRvWq+EoifSPrUXOhXGz88PISGq/Yf7pkiIDMeRxYEwlpnBWGaKpOiHaP7OZFT0K5jYvyw9MQ5HFs1GRnI8Wr//WaEJpyp1SHfMbKzQd94M1OrWFkYmxri+9yhWDHv91YR2k0eh15ypiDgfAocqFZEWl4hfOg5DytO4YtUh/ZSblYn9iwIRHxkBW1cPPI24Dd+WndB00OjXthVC4Miy+Yi6FQLvZm3R+p3/BgGi797Axb9XITX+KVJio9Fx4ixU8mtcwmdD6gi5fQ8T5iyEvY0VTIyNcT/qCX6cNhYt6tUstH7kk1h8tXg17kVGo4q7CyIio2FsZIQF08ehqoebot7K7fuxdvdhyIVAQnIqLm5cVIpnReX5vvSlQWvvnhACf/zxB5ycnLTVZblwauWPcKzsi+6f/4rOU+ejRrs+OL36Z2Slv3qxmJDLcfLPH+AT0EOjOqRbUksLRJy7gi+r+uP+hasqtXGvUwP95n+OZYM/xI/+gzDLpy0AYMBPXxSrDumvS3s2IunJY/SbvRjdPg5C1ynf4vqRnbh/5fW3aQ7ZtwUGRkZw9KxW4Fh2eirqdR+M3jN/LqHISRvkcjmm/bAErRrUxt+/fIVNP8zEgI6tMf2npUjPzCq0TUp6Bob36oiDS+bij1kfYc/ib+DhWgEzfl6mVM/AwAArvp6K9/p2KaWzIdIetZJSd3f3Ag9LS0vMmDED33zzjfajLKMSHoUj8fF9VG/bS3HnK5823ZGXnYXIq+de2fbq3o0wlpmhaotOGtUh3Up4+Bgnlm5AdnqGym0av90Lsfce4MqOAwCAnIxMHP1tNer16wJjU5nKdUh/3T1zGFWbtYWppTUAoEJlHzhXrYW7Zw6/sl3MvVDcOLJLaXT0RZX8GsPVt06JxEzac/lWGB5GP8XI3v/97h7eswMSU1Jx8tK1Qtv4Vq6oNIpqZGiIdk3r43ZEJOQvTNsZ1qM93JwcSvgMqCjcp1Qzal2+nzNnToEyW1tbNGrUCK6uqs+LKu8SHkcAAGzcKivKTMwsYGbrgMTIiCLbxdy9jrun9qPr9AUa1aGyyc2vOiJDQpXKIkNCYSyTwqlaFTy6ckOlOqSfstJSkJYQCzv3ykrldu6V8ejaxSLbZaen4cjSeWg5dCJMrWxKIVIqKbcjHsHQwADelf77/9LRzga2Vpa4FfEIHZo1UKmfsyE3UdndGQa8ZEzlhFpJ6YgRI7QfSTmUk5EGAyMjGJlIlcql5lbIzkgrtE1WWgpOrvwBTQZPhMzSGtmFXOZXpQ6VXabWlkh8FKVUlhaXADyfo6pqHdJP2RnpwPM/UF8kM7dEdkbRP8vH1yxExdqNULF2oxKPkUpWSloGLMxNFVfQ8tlYWiAlTbWrKntPXsDuY+fw62cTSihKotKnclKam5ureqdGr+42KysLWVnK82Zys7NhZGKi8mvom9jwUKQn/rfApGLd5jA0MoY8NxfyvDwYGBoqjuVmZcDQ2LjQfq7sXA1TK1vkZWfiwaWTyM3OUvRvYmoOp2q1VapDZVduVjZMXlqFK7UwBwDkPJ9vpkod0r2UuBjERtxWPK/gVR2GRs9+9vOyM5Xq5mRlwNC48N+BD0LO4eHV82gxZALCL54AAGSmJcMw9gnCL56AR52mMHjN713SHybGRsjMyi5Qnp6ZCanx6z/Hk5eu49OfluKTkQMQ0LhuCUVJVPpU/i1mXEQSVZjXbZ4fFBSEr776SqnMf+gEBLzzgcqvoW+e3ruJp+G3FM8r1mkGCwdnAEB6wlPFv+XyPKQnxcPC3rnQfiwdXZCZmoyIi8cBACLv2R8DMWE3YGBkBKdqtVWqQ2VX7L0HcKr20qXd55f5YsMfqlyHdC8lNhph548pnlvYV4BDJW8YmUiRGv9UqW5q/FNYOhT+e8FIKoN7zQa4f+WMoiwzJRl5OTkIO38MFWs1ZFJahrg7OyIrOwfxSSmws352M5rsnGfP3Z0dX9n21OXr+ODbRfhgSG8M79WxlCImVZXn+Z6lQeXfYkeOHNHai86YMQNTpkxRKpt//L7W+teF6u36oPpLZQ6VfWEsM8ODy6dQo31fAEDUzcvIyUiDa436inqPb1yEzMoWdu5VCvSTnZ6KzZ8MQY32fVGxTtNCX6uwOlR2mNlaw7dtc4QePoX0hCRc/ycYTYb1ga27CxKeX6JvMLAb7l+8ipSYWABQqQ7pnquPH1x9/AqUu9Wsj/CLJ1GzbU8AQHZmOh5dv4i6XQYp6sQ9vIf0xHhUrN2w0H52zp0Gaxf3Ihc9kf5qXMsHMhMT7D91AW91CQAAHD53BTm5uWhRr5aiXvD5K3Cr4KDY8un0lZuY+M0iTBjcE6P6dtZZ/EQlReWk1N/fX2svKpVKIZUqz7Msy5fui2JkIkXdXsNxcetS5GZnwcTUDNf3b0HVlp1h7VJJUe/8pt/hWqM+7AaO1Wm8VDLq9ukEAwMDWDo5wMRMhvr9uiA7PQPX/gkGADhVq4LRWxZjbtM+CD97Cf9u2YM2E97BxH/+RPCvq+BW2wf1B3TFL52GK/pUpQ7pr4a93sGO7z7GkaXz4OLjh9unDsLUyhbV23RV1Ll96iDuXz6Nt4JWqNxvWkIsYu6FKqb0PLl7A3k52bB2coedu2eJnAsVn6W5GSYO6YXvV2xGUmo6TIyN8Pvm3RjesyPcKvy31/QXv65En7bNMWV4f9y49wATv1mIOj5V4O7kgH0nLyjqBTSuA5PnVzOv3ApDdGwCboY/gFwuV9RrWqc6rJ9P8aGSw31KNcPrPSWsWqsusHRwxoNLJ5EaG416fd5F5UbKCb5rjQawfWklbj4DI2NUrNv8lZviq1KHdKfhoB4wMDRAbNizqwEN3+qB1LhERVKaFpeAf7fsUSxUEkLgl07voPXYt+HdsiHS4hMxt0kfPLx8XdGnKnVIf9m6VkLvmT8j9Ng/iLp1FRVrNUSNgO4wlv03T9i+YmXIc3OK7MPF1w/mL/3MpyXEKqYLeNZvgaSYx0iKeQyPOk2YlOqZd/t0QhV3Zxw6cwl5cjm+HDMUXVsr3+ggoFEdVPVwBwCkpqWjVf1nU7P+OX5eqV6zOjUUSem/N+/iSug9AECbBn6KujW8PJiUkt6TiNdNAC3CP//8g82bN+PBgwcFFkEFBwcXu7/AA7dUqEVvgscdue8qPeMVfFDXIZAemeIUpUItehMY+rbSdQiFSlj8aam8ju2470rldUqbWuPMv//+OwYPHgwzMzMcOnQIDRs2RF5eHo4ePYrKlQsf8SMiIiIiKopaSemCBQuwefNm/PrrrwCA+fPn4/jx4wgMDERSUpK2YyQiIiKick6tOaVhYWFo3bo1AMDExARpaWkwNzfHhAkTOFJKRERERMWm1khpTk6OYvW8u7s7rly5AgCIi4t7TUsiIiKi8qk07ntfnvdC1Xj1/ZAhQzB48GD06NED+/fvR7du3bQTGRERERG9MdRKSm/evKn49+zZs2FjY4PTp09j4MCBmD59ujbjIyIiIioTyvMoZmlQKyk1eWGje0NDQ3z88cfajImIiIiI3jBqpfReXl5o0aIFFi9ezHmkRERERKQxtZLSs2fPomHDhvjqq6/g4uKCXr16YfPmzcjMzNR+hERERERU7qmVlDZu3Bg///wzIiMjsXPnTlhbW+Pdd9+Fs7MzRo0apf0oiYiIiPScxMCgVB7llUZnZmhoiE6dOmHVqlU4ceIEPD09sXz5cu1FR0RERERvBI22hHry5Ak2btyItWvX4ty5c6hduzbmzp2rveiIiIiIygh9XX0vhEBwcDBCQ0NRsWJFdOrUCcbGxiq1TUtLw9KlS1GhQgUMHjy4RONUKyldvXo11q5di4MHD8LV1RWDBw/GsmXLUKtWLe1HSERERERqycvLQ9++fXH+/Hl06NABP/74I6ysrHD48GFYW1u/tv2ECROwZcsW+Pr6lnhSqlZK/+GHH6JixYo4ePAg7t+/j7lz5zIhJSIiItIzK1aswKFDh3Dq1CmsXLkSFy5cQFxcHObMmfPatmvXrsX169fx9ttvl0qsao2URkdHK24zSkRERET6efl+06ZN6Ny5Mzw9PQEA1tbWGDJkCNauXYvvv/++yHZ3797F1KlTcfToUfzyyy+lEqta7x4TUiIiIiLdyMrKQnJystIjKyur0Lo3btyAr6+vUpmvry8ePHiAtLS0QttkZ2fjrbfeQmBgIKpVq1Yi51AY/UvpiYiIiKhIQUFBsLa2VnoEBQUVWjclJQU2NjZKZba2topjhZk+fToqVqyI999/vwSiL5pGq++JiIiIqHTNmDEDU6ZMUSor6iq2mZlZgeQzOTlZcexld+/exc8//4zPPvsMCxYsAABcuXIFMTExWLBgAfr37w93d3ctns1/mJQSERERaUFpbWwvlUpVnkpZrVo1hIWFKZWFhYWhQoUKsLKyKlDf1NQUH374IVJTU5Gamgo8H1HNyspCREREid69k0kpERERUTnVq1cvzJkzB/Hx8bCzs0N2djY2btyI3r17K+rcuHED+/fvx7hx4+Dm5qYYIc03ceJEnDlzpkC5tnFOKREREZEWSAwMS+VRHOPGjYOXlxfatGmDWbNmoV27dkhJScGsWbMUdc6dO4fJkycjIyOjBN4V1XGklIiIiKicMjU1xfHjx7Fu3TrcunULAwcOxNChQxWLnQCgRo0amDRpUpFTAgICAlClSpUSj5VJKREREZE2FHMUs7TIZDK8++67RR5v3LgxGjduXOTxfv36lVBkynj5noiIiIh0jkkpEREREekck1IiIiIi0jnOKSUiIiLShlLap7S84rtHRERERDrHkVIiIiIiLZAY6ufq+7KCI6VEREREpHNMSomIiIhI55iUEhEREZHOcU4pERERkTbo6R2dygqOlBIRERGRznGklIiIiEgbOFKqEY6UEhEREZHOcaSUiIiISAskvKOTRvjuEREREZHOMSklIiIiIp1jUkpEREREOsc5pURERETawNX3GuFIKRERERHpHEdKiYiIiLSBI6Ua4UgpEREREekck1IiIiIi0jkmpURERESkc5xTSkRERKQFvKOTZvjuEREREZHOcaSUiIiISBu4+l4jHCklIiIiIp3jSCkRERGRNnCkVCMcKSUiIiIinWNSSkREREQ6x6SUiIiIiHSOc0qJiIiItEBiyDmlmuBIKRERERHpHJNSIiIiItI5Xr4nIiIi0gbeZlQjfPeIiIiISOc4UkpERESkDdw8XyMcKSUiIiIindObkVLngb10HQLpCdPgg7oOgfREmH97XYdAekQedUjXIZCe4Hhk+cSRUiIiIiLSOb0ZKSUiIiIqyyScU6oRjpQSERERkc5xpJSIiIhIG7hPqUb47hERERGRzjEpJSIiIiKdY1JKRERERDrHOaVEREREWsDV95rhSCkRERER6RxHSomIiIi0gSOlGuFIKRERERHpHEdKiYiIiLSB+5RqhO8eEREREekck1IiIiIi0jkmpURERESkc5xTSkRERKQFEkOuvtcER0qJiIiISOc4UkpERESkDdynVCMcKSUiIiIineNIKREREZE2cKRUIxwpJSIiIiKdY1JKRERERDrHpJSIiIioHAsPD8eYMWPQpk0bDB06FBcuXHhl/ezsbCxduhQDBw5Ep06dMG3aNDx+/LjE42RSSkRERKQFEgODUnkUR3R0NJo1a4aEhAR8+umnsLOzQ6tWrXDp0qUi2/Tt2xfnzp3DgAEDMGXKFISGhqJevXqIjIzUwrtUNC50IiIiIiqnfvrpJ8hkMqxfvx6Ghobo0qULQkJCEBgYiG3bthXaZsOGDbCwsFA8DwgIgKurK9auXYtPPvmkxGLlSCkRERGRNhgYls6jGA4dOoQuXbrA8IW7TfXq1QsHDx6EEKLQNi8mpABgbGwMqVSK7OxsNd8Y1TApJSIiIipDsrKykJycrPTIysoqtO79+/fh6uqqVObq6orU1FQkJCSo9Hp//vknoqOj0aNHD63EXxQmpURERERlSFBQEKytrZUeQUFBhdbNycmBVCpVKjM1NVUce52TJ09iwoQJmDNnDurUqaOlMygc55QSERERlSEzZszAlClTlMpeTjzz2dnZIT4+XqksLi4OBgYGsLGxeeXrnD17Fl27dsWkSZMwY8YMLUT+akxKiYiIiLRBUjoXoKVSaZFJ6Mvq16+P8+fPK5WdPXsWNWrUeGUf586dQ6dOnTB27NgiR2G1jZfviYiIiMqpUaNG4ejRowgODgYA3Lp1Cxs2bMCoUaMUdXbv3o2mTZsiNTUVAHDhwgV07NgRY8eOxdy5c0stVo6UEhEREWlDKY2UFkeXLl3w1VdfoXPnzvDw8EBERASGDRuGDz74QFHn6dOnOHv2LHJzcwEA7777LtLS0hAcHIymTZsq6nXr1g1ffPFFicXKpJSIiIioHPv8888xYcIEhIeHw9XVFU5OTkrHu3XrhtOnT8PS0hIAsHr1amRkZBTox9HRsUTjZFJKREREpAVCD0dK89nY2KBevXqFHnN0dFRKOEt6lX1R9PfdIyIiIqI3BpNSIiIiItI5JqVEREREpHNMSomIiIhI57jQiYiIiEgb9HihU1nAd4+IiIiIdE6tpHTevHmIjo7WfjREREREZZVEUjqPckqtpHTx4sWoWLEievbsib///ltxBwAiIiIiInWolZTeu3cPe/fuhaWlJYYMGQJ3d3dMmzYNN2/e1H6ERERERGWBgUHpPMoptc5MIpGgXbt2WLt2LaKiojBr1iwEBwejRo0aaNasGZYuXVro7amIiIiIiAqjcbptZWWF6tWro3r16jAxMUFsbCxmzJgBDw8P7N27VztREhEREVG5pnZS+vDhQ8yZMwfe3t7o0qUL5HI59u3bhzt37uDRo0eYOnUq3n//fe1GS0RERETlklr7lHbu3BkHDhxArVq18NFHH2Ho0KGwtbVVHJdKpZg6dSqmT5+uzViJiIiI9JbgPqUaUSsprVSpEk6fPo3GjRsXWcfAwAAPHz7UJDYiIiIiekOoldJv2rSpyITUxsZG8W93d3f1IyMiIiIqSyQGpfMop9Q6s6SkpELLc3JyuOqeiIiIiIqtWJfvN2zYUOi/AUAul+Ps2bPw9vbWXnRERERE9EYoVlI6ceLEQv8NAMbGxvD09MSiRYu0Fx0RERERvRGKlZTGxsYCAHx9fREaGlpSMRERERGVPeV4vmdpUOvdY0JKRERERNqk8kjpmjVrAABDhw5V/LsoQ4cO1TwyIiIiorKEI6UakQghhCoVnZ2dAQDR0dGKfxclOjq62IH8Yetb7DZUPiX9vUvXIZCeCPNvr+sQSI8sjDqk6xBITxg7e+k6hELlRt0pldcxcqlaKq9T2lQeKX0x0VQn6SQiIiIqz3hHJ83w3StBhlITWFXxgLGlucptjMxMYVW5EkysLF9ZT2prA3O3V49Yk37KTElCckwU5Hl5xWqXnZ6G+MgIZKYmF9lvfGQE8nJztBQplRSHKpXgUKVSsdrYe7rDytlR4zqkX548jcXj6CfFahMdE4uwiAca1yHSN8WeU6oKzikFarw3BE1mfYzs5FTI7G1x88+NOPXpN0XWN5RJ0ebXb+HZtR3Sop7AzMkRTy9fx5Ex05AW+d/ItKWHO1r/MgeO9Woj42ksclJScWTsdCSE3i2lMyN15WRmIHjZfDy8fgEmphaQSIDWwz9CxdqNXttWLs/D/kVfIfrOdTTu/y78OvZTHIu5F4prB7fj0fWLyM5Iw4A5S2BdwbWEz4bU0fzdgWgzbihcalRF1I07CGrU87VtPBvVwaj1v8DUxgompjLcvxCCJQMnIvnJ02LVIf0SGfUEU2Z9i3sPHsLI0BCO9nb4YfYMVK3iWWSbIyfPYO3WHbhy/SYgkeD83r/UqkOkr1ROSqdOnapyp296UurcvCGaf/c5DgybiPv/HIFtjWro9c9aJIXdx/UlhSf3dSe9D7c2zbCxcRekPYqCsaU5uu9Y9byfDwAAxlYW6L5zJWLOX8Ea35bITc+Ara83bKpWYVJaBpzZvAQJUQ8weO4qmFpa4/KejTj4vyAMnPMHzG0dXtn20s51MLO2h9TcqsCxx7dC4FG3Car7d8Pu+dNL8AxIE4bGxvBq3gDrx3+BRkN6wbtlw9e2MTEzxdi//8Dlbfuw8YNZMDaV4aODazF85Q9Y2PkdleuQ/pn6VRCsrSxxcsdGGBoa4NM58/HhzK+xY9XvMDYq/L/m0xcuY+Tg/ngYGYUf/rdM7TpE+krly/fR0dEqP9501UcMQsz5y7j/zxEAQMKN27i7dTeqjxxYZBtzVyck3ApD2qMoAEBOShqenP0XFm4uijo13h0MqbU1jk36Arnpz27nmhB6F+E795f4OZFmcrMycfdMMGp36ANTS2sAgF/HfjAyNsbdM4df2fbxrRDcOXMELYZOKPR43S4D4dXYH4ZF/EdG+iEvJwer35uOiPNXVG7j17MDLCvYY+esnyCEQHZ6Bv755lfU7NQa9h7uKtch/XLzdhiu3ryNCSOHwsTEGIaGhvhg1DA8jIzC2YuXi2z32aSxaNGoPiSSovtWpQ6VoNK47305nrdafs9Mhxzr1ETMhRClsidnL8HWxxuGMmmhba4vWw9bHy/UHD0MDnVqwqt/d1Tp3RmXFyxR1HEPaIHHx88gJzUNlpXcILW1KfFzIe1IiHqIvJwsOFb2UZQZGBnBwaMqYu8XPcqdmZKEo8t/ROsRH0FqZlFK0ZK+8GhQC0/D7iMtLkFRFnbqIgCgUoNaKtch/XL99h1IJBLU8v1vBXUld1fY2Vjj+m1e9aI3l8ZDK7GxscjNzVUqe92WUeWd1M4GmQmJSmWZCYmQGBhAamuN9KiYAm3iQm7g2v9Wosnsj5H+5CnMKjjg7tbdeHDgqKKOuasz4q+Fos+RrZDZ20BqbY2ksAgEj/+Ul+/1XFbas8VJMnPlBWxSC0tkJCUU0Qo4+udP8GrSBq4+fiUeI+kfc3tbpMUp/y5JT0iCPC8PFg52Ktch/ZKUnAJLC3MYGhoqlVtbWSExqfCFjFRGcIhaI2qNlCYmJmL48OEwNzeHo6MjXFxclB6vk5WVheTkZKVHjpCrE4peErm5MDQ2ViozNDEBAMhzcgtt02jmR6g+cjA2NemGDfU6YE311rCpWhntl/34X795efDs0QHnv/4J6/3aYbVPC6Q/eYr2f/5cwmdExZGenID4yAjFIy83BxKDZ//55OUpf/55uTkwMCz8b8NbJ/Yh/lEEqjRsrehLiDxkJCUgMfpRqZwL6ZY8NxdGJi/9LjE2hoGhIfJyclSuQ/rF0NAQObkF/y/IycmB0UuJKtGbRK2kdNq0aXj48CH27dsHADh//jwWLVoER0dHzJ0797Xtg4KCYG1trfTYmxmvTih6KTUyGmYvbcti5uyI3IxMZMYVPipWuVdnhP21G6kPIwEA2ckpuLF8PSp18oehqexZv4+iEH8tFI8OnwAA5GVm4fqStbD18YKZS4USPy9Szb1zR3FkyTzFIz0xDhZ2z74PGUnK3/OMpASY2xW+yCknMwMmpmY4uvwHRV85mRm4ezYYJ9b8WirnQroV/zAK1i/9bOc/T3gYpXId0i/Ojg7IyMhEWnq6oiwvLw9xiYlwrsAtvejNpdbl+927d+PYsWPw9vYGANSvXx8NGzaEt7c3Pv30U3zyySevbD9jxgxMmTJFqWx1pdevRC0rHp84C68+XZ8N4z+/YValjm0QdfK84rmxlQUs3FyQeCsMQi5HdmISZHa2Sv3I7O2Ql5mFvMwsAEDk0VOo+f7bSv3K7G2etU9OLfXzpMLVat8btdr3VioTQsDc1gEPr16Ac9Vn8/wykhPxNOI2qvt3U9RLiXs2tcPSvkKh/ayePBi1O/ZR2hKKyg+JRAKXGlWR8CgaGUnJuH3kNHoGToGbX3VEhtwEANTuFoDsjEzcO3MJAFSqQ/qlYZ1aMDQ0wLEz59GlbRsAwIUrV5GRkYnG9f6bqhP+4BEsLczh8NL/DUTllVojpVFRUfDyenaLLysrK8THPxv9admyJW7cuPHa9lKpFFZWVkoP43K0muza/1bD2NwM/r8FwblZAzSY8QHc2jTDv9//pqjj0TkAA07thInNsy1+bq7chKoDe6D2hJFwrFcLVQf1Qv2p4xC6ZqsiAb3550YIuUDrBYGo0LAOKvfoiMZfTkHo6i3ITUsvMh7SPYlEgvo93sa1g3/j5tE9eBx6BYf++A42zhVRpWErRb2Taxbi5JqFxeo74/mm+clPn+18kfzkMeIjI5CVlqL18yDNOHp5wLVmNZjb28BIJoVrzWpwrVkNkufz0GRWlvjy2n7U7d0RAHD3xHlc33cMI1f/iBodW6PhWz3R85tp2D/vd2Slpqlch/SLg70dBvXqju8W/oEDx07i6OlzmPX9L+gU0Eppn9IRk6Zj9ea/Fc+jYp7izr0IPI2Nh5AL3LkXgTv3IpCVlV2sOlSCuPpeI2ovdMr/JVq9enVs3rwZ48aNw+7du+HoyEsP6dEx2N55COp/Mh7Nvv0M6VFPsKfPKDw599+oRXZSCuJv3obIfXZXn1trtiIzPhHVBveGV9+uyIyLx/lvf0boys2KNjkpadjRZQgafDIBLeZ9gcz4RFz5ZTluLF+vk/Ok4vFp2RFGJia4dWI/sjPS4FjZB/V7vA1Do//mA1rYO72yDxuXipBZKO9Vev/SKVw/vBMAYOvqgXNblwMA6vd8G5XrtyiRcyH19Pv+MzhW/S/pGLXh2R8g3zXqiZzMLMjz8hB57RbSE5IUdf7oNxZdPp+IHoFTkJuVhR0z5+Pob6uV+lWlDumX6RPfh4uTI1Zt2oa8vDz06NgW7w1R3jawciV3pVHS1Zu24dSFZ/+PuLs6Y1rgs+ly82d9Cu/KHirXIdJXEiGeD8MVQ926dXH58rO91Hbt2oV+/frB0tISCQkJWLhwIcaPH1/sQP6w9S12Gyqfkv7epesQSE+E+bfXdQikRxZGHdJ1CKQnjJ29dB1CobJjS2cRqolD+dyDWK2R0vyEFAC6d++O0NBQXLx4ET4+Pqhdu7Y24yMiIiKiN4BWbgFTuXJlVK5cWRtdEREREZVNBuV3vmdpUCspXbp06SuPv/fee+rGQ0RERERvILWS0jlz5ig9l8vliIqKQm5uLipVqsSklIiIiIiKRa2kNCIiokBZamoqRo0ahWbNmmkjLiIiIiJ6g2ht8oOFhQV+/PFHLF68WFtdEhEREZUd3KdUI1o9MxMTE0RGRmqzSyIiIiJ6A6h1+f7EiRMFyhISEvDLL7+gcePG2oiLiIiIiN4gaiWlrVq1KlBmamqKli1bvnZlPhEREVG5VI4vrZcGtZLSjIyMAmUymUwb8RARERHRG0itpJQJKBEREdFLOFKqkRLZPP9F3LOUiIiIiF5HraQ0KCgI9+7dAwA4ODgAAGJjYwEAVapUgUQiUdRlUkpEREREr6PWOPOoUaPQrl073Lt3D0+fPsXTp09x7949tGvXDu+99x7u3r2reBARERERvY5aSemyZcuwYsUKVK5cWVFWuXJlrFixAsuWLdNmfERERERlgpAYlMqjvFLrzB4/flzkMW6eT0RERETFpVZS2rp1a7z33nu4f/++ouz+/fsYNWoU2rRpo834iIiIiMoG3mZUI2qd2ZIlS5CUlITKlSvDyckJFSpUgKenJ1JTU7l5PhEREREVm1qr7ytVqoQzZ87g9OnTuHHjBgCgRo0aaNasmbbjIyIiIqI3gFpJab5mzZoxESUiIiIijWmUlBIRERHRcy/s007FV35nyxIRERFRmcGRUiIiIiJtKMcr40sD3z0iIiIi0jmOlBIRERFpQXm+21Jp4LtHREREVM7l5OTg4cOHyMjIKNE2mmBSSkRERFSO/f7773B0dETdunVhZ2eHGTNmlEgbTTEpJSIiIiqnjh07hvHjx2PZsmWIi4vD0aNHsXDhQixZskSrbbSBSSkRERGRNpTGfe+LOW/1f//7H5o3b45+/foBABo3bowBAwZg8eLFWm2jDUxKiYiIiMqp8+fPF7j7ZsuWLRESEoLs7GyttdEGrr4nIiIi0gJRSnd0ysrKQlZWllKZVCqFVCotUDc2Nhb29vZKZQ4ODsjLy0NiYiIqVKiglTbawJFSIiIiojIkKCgI1tbWSo+goKBC6xoYGCAnJ0epLH+009DQUGtttIEjpURERERaIETpvM6MGTMwZcoUpbLCRkkBoGLFioiOjlYqi46OhpmZGWxtbbXWRhuYlBIRERGVIUVdqi+Mv78/9uzZAyEEJM+nF+zZswetW7eGgcGzC+YpKSl48uQJqlSpAgMDA5XalAReviciIiIqpyZPnoyYmBiMHTsW58+fR2BgIA4ePIiZM2cq6mzduhVVq1ZFcnKyym1KApNSIiIionLKw8MDx44dw9OnTzFixAicPHkS//zzD1q0aKGoY2VlBS8vL8V8UVXalASJEKU1A+LV/rD11XUIpCeS/t6l6xBIT4T5t9d1CKRHFkYd0nUIpCeMnb10HUKhUtNL53acFmampfI6pY0jpURERESkc1zoRERERKQFenHpuQzjSCkRERER6RyTUiIiIiLSOSalRERERKRznFNKREREpAVyTirVCEdKiYiIiEjnOFJKREREpAV6svV7mcWRUiIiIiLSOY6UEhEREWkB55RqhiOlRERERKRzTEqJiIiISOeYlBIRERGRzjEpJSIiIiKd40InIiIiIi3gOifNcKSUiIiIiHSOI6VEREREWsAtoTTDkVIiIiIi0jmOlBIRERFpAW8zqhmOlBIRERGRzjEpJSIiIiKdY1JKRERERDrHOaVEREREWiDXdQBlHEdKiYiIiEjnJEJPloo9ik/VdQikJ1xiLuk6BNITchtXXYdAeuQDl3a6DoH0xP9EhK5DKFRUYlqpvI6LjXmpvE5p40gpEREREekck1IiIiIi0jkmpURERESkc1x9T0RERKQFcr1YpVN2caSUiIiIiHSOI6VEREREWqAnGxqVWRwpJSIiIiKd40gpERERkRbwjk6a4UgpEREREekck1IiIiIi0jkmpURERESkc5xTSkRERKQFXHyvGY6UEhEREZHOcaSUiIiISAvkHCrVCEdKiYiIiEjnmJQSERERkc4xKSUiIiIineOcUiIiIiIt4IxSzXCklIiIiIh0jiOlRERERFog51CpRjhSSkREREQ6x5FSIiIiIi3gNqWa4UgpEREREekck1IiIiIi0jkmpURERESkc5xTSkRERKQFcu5UqhGOlBIRERGRznGklIiIiEgLuPpeMxwpJSIiIiKdY1JKRERERDrHy/dEREREWsDbjGqGI6VEREREpHNMSomIiIhI55iUEhEREZHOcU4pERERkRZwSyjNcKSUiIiIiHSOI6VEREREWlBebjMql8uxe/duhIaGomLFiujVqxdMTU1f2ebhw4c4cuQIkpKS4OfnhzZt2hT7dTlSSkREREQAgJycHHTq1AkffvghIiIi8O2336Jhw4aIi4srss1nn32GgIAAHDp0CLdv38agQYPQpUsX5OTkFOu1mZQSEREREQBg6dKlOHv2LE6ePIlFixbh9OnTyMrKwtdff11kmw4dOiA0NBQrV67EwoULcf78eQQHB2PVqlXFem0mpUREREQEANiyZQu6dOkCV1dXAIC5uTkGDx6MLVu2FNkmICAARkb/zQitWLEi3N3dce/evWK9NpNSIiIiIi0QonQeWVlZSE5OVnpkZWVp5RxCQ0NRrVo1pbJq1aohMjISqampKvXx77//IiwsDE2bNi3Wa3OhExEREVEZEhQUhK+++kqpbNasWZg9e3aBupcvX8auXbte2V/Pnj3h5+cHAEhNTYW1tbXScRsbG8UxCwuLV/YVFxenmFPavXt3lc8JTEqJiIiItENeShuVzpgxA1OmTFEqk0qlhdbNy8tDZmbmK/vLy8tT/Nvc3BzJyclKx5OSkhTHXiUxMREdO3ZEhQoVsHHjRkgkkteey4tUTko7d+6scqd79+4tVhBEREREpBqpVFpkEvqyBg0aoEGDBir37ePjg7t37yqV3blzBy4uLrC0tCyyXWJiIjp06ACpVIq9e/e+dkS1MConpbVq1Sp250RERERvijy5riPQXN++ffHFF1/gyZMncHJyQkZGBjZs2IC+ffsq6oSEhGDHjh2YNm0apFIpkpKS0LFjRxgbG2Pv3r2vTF5fRSKEftwU61G8apNnqfxzibmk6xBIT8htXHUdAumRD1za6ToE0hP/ExG6DqFQFx8mlsrrNKhoU2J9Z2VloUOHDoiKikLPnj1x9OhRJCUl4eTJk6hQoQIA4M8//8TIkSORkJAAGxsbdOnSBQcPHsSUKVOUEtI6deqgR48eKr8255QSEREREfB8asDhw4fx119/4datW5gwYQIGDBigdDm+Tp06+PzzzyGTyYDnUzzzpwi8OH+1uJvnqz1SeuzYMcybNw83b96EEAI1atTAJ598gtatW6vTHUdKSYEjpZSPI6X0Io6UUj6OlJbcSKkuqbVP6fr169G2bVvIZDKMGzcOEyZMgEwmQ9u2bbF+/XrtR0lERESk5+RClMqjvFLr8n1gYCB+//13jBo1Sql86dKlCAwMxODBg7UVHxERERG9AdQaKQ0LC8OAAQMKlA8YMABhYWHaiIuIiIioTMkTolQe5ZVaSamLiwtOnTpVoPzkyZOKe6USEREREalKrcv3EydOxODBgzFp0iQ0btwYAHD27FksWLAAM2fO1HaMRERERHqvPM/3LA1qJaXTpk2DtbU15s2bh8DAQABAlSpV8P3332P06NHajpGIiIiIyjm19ykdPXo0Ro8erdiPKn+vKiIiIiKi4lJrTmnNmjURFBSEBw8eQCaTMSElIiIiIo2olZT269cPy5Ytg6enJ9q0aYMlS5YgMbF0NowlIiIi0kd58tJ5lFdqJaWBgYG4e/cuTp06BT8/P3z++edwdnZGv379sG3bNu1HWU4U9+ZZeXl5yM7K0nq/pBtyefF/k6jy2Wbn5CInN1fNqEgX1PmZzcnNfe0t+1SpQ/pFYmAAIxMTXYdBpBfUnlMKAE2bNkXTpk3x008/4cCBA5gxYwb69u3LJOkF6WlpWPjjPBw7fBC5ubmo16ARJk//HE4uLkW2iQi/h13btmD/P7uRlpqKQ6cvaqVf0o2Q2/cw5/d1uHnvASzMTTGgY2t8NLQPDAwK/5sw6mk8lm3bi8NnLiEuKQUVnR3wbp/O6Nu+paJOXp4cwReuYOM/wTh5+Qa6tW6MeVPeL8WzInU8iorGV/MX4vzlqzA2NkIn/1b4bNI4mJkWPQXq/OWr2Lh9Nw4dP4WqVTyx6Y9f1KpD+sWtti9ajxuKJkN7AxIJPrKs+do2ZjZWGPzbHNTp3RESAwlu7j+OtWM+Q1JUTLHqUMnh6nvNqDVS+qKwsDAEBQVh8uTJCAkJQatWrbQTWTnxw3dzEHrjOpas3oCNO/ZCYmCAz6ZOQt4rRrZWLf0dLm7ueHf0eK32S6UvPikFo2cvQL3q3ji99mcsnvkhNu8/hj+27CmyzZb9x1DFzRlr587AhY2/YtygHpi1aBX+OX5OUefeoyj8dfAkhvZoj2Z1qpfS2ZAmcnJzMe6TWZBJpTiydTW2LF2IS1ev46sfFhbZJjs7B4uWr0ZA8ybo27WT2nVI//T8+mM8vhqK3YGq/wExYvVPcPKpgtm+7fC5R0tILcwx7u8/il2HSF+plZQ+ffoUv/76K5o1awZvb29s3LgRw4cPR3h4OI4dO6b9KMuopzExOHroAEaNnQBX94qwsbXFB1OmIeJeGC6cP1tkuy+/mYt+g4bA3NJSq/1S6dt26AQAYOqI/rAwM0VdXy+83a0t1uw6VOTl/A/e7o0h3drCxdEOxkZG6Na6CZr6VceB0/8q6lT1cMOizyeidYPaMJBISu18SH0nzl5A+IOH+OyjcbC1sYaHuxsmvDsM/xw6iti4+ELbmJgY489f5qFbhwAYGxd+YUuVOqR/Fvd+H0cXr0FmSppK9StUrQy/7u3w1ydBiH8QieQnT7F58tfwbFwX3i0bqVyHSJ+plZS6urriu+++Q4sWLXDp0iVcu3YNM2bMgIeHh/YjLMNuXr8KIQTq1GugKHN1rwjHCk64cTVE7/ol7bsceg91fb1gbPRfstCkti/ik1LwoBiX02ITk2BlYV5CUVJpuHz9JtxdneFSwVFR1rieH+RyOa6G3tZpbKT/qjSrDwC4c+y/KyaPrtxAWnyi4pgqdYj0mVp/Vu/btw/+/v5FzomjZxLi42BoaAiLl0Y8bWxtkRBf+MiILvsl7YtLTIanm5NSmZ31s88tNjEZnm7Or+1jwz/BCHsYhTkfjCixOKnkxcUnws7GWqnM1toKEokEcfHcvYRezcrJAemJych7aSFbytM4WDo5qFyHSlZ5vi99aVArKR01ahT8/f0Vj+KOkGZlZSHrpVXlWVk5kEql6oSjF3JzcyB/YZ8Gk+fnUtjXU8gFJBpeci2pfkn75HLlTyt/Irwqn9XRCyEIWroBn73/Fmp6e5ZYjFQ6Xv4uKP7/4o8tqUBiUPCLYmBg8MIXSbU6RPpKraHOWbNmQS6XY9asWfD09ETlypUxcuRIrFy5Evfv339t+6CgIFhbWys9Fi34QZ1Q9MbcwFno2dFf8cjLy4O9vQPkeXlITlIeBUlIiIedvb3ar1VS/ZL2OdpZIz4pWaksPjEFAOBga/XKtsf/vYaP5i7Gx8P74a0uASUaJ5U8R3s7xL+0n3NCUhKEEHCws9VZXFQ2JEXFwNTKEkYvDd5YONojOfqpynWoZMlF6TzKK7WS0hEjRmDlypWIiIhAeHg4vvzyS2RkZGDUqFHw9Hz9aM6MGTOQlJSk9Jjw0cfqhKI3Pg/8FnuPnlY8DA0NUaO2HwwMDHD54gVFvQcR4YiLfYqafnUUZTk5OcVaNa9qv6R79at741JoGLJfuJx2JuQmHGytUdHp2dxCIQSysnOUtlI78e81fBi0CJOG9sE7PTvoJHbSrvq1a+BxdAwePo5SlJ25eBmGhgaoU8NXUZaVlY28vDwdRUn6xMjEBAaGhgCAsJPPft/7BDRTHK/UoDbMbKxw9+RFlesQ6TO1J4VmZ2fj+PHjWL16NVavXo3t27fDw8MD77777mvbSqVSWFlZKT3K8qX7otjZO6Bjl+5Y8ttC3A69iciHD/HT3G9Rzbc66jdsrKj37uD+SiPFubk5yM7KUiSq2VlZyM7KUqzWVrVf0r3e7VpAZmKMrxavQXRsPI5dvIrVOw/i3T6dFHOyQ27fQ70B4xBy+x4A4PSVm/ggaBHGDeqBwV0CkJWdg6zsHGTnKP/hkp3zrFwuhCKxzebG6XqreaP68PWugq/mL8SDyChcvXkbvyxdiT5dOsL2+VzT5JRUNOjYGzv3H1a0y8nJQVZW9rPpQeJZ0pqVla3Utyp1SL8YGBnBSCqFodGzpNNIKi0wwvnN/ZPoOWcqACA2/CEubtqF/j/OhFttXzhVq4LBi77G7aNnEX7mX5XrUMnKk4tSeZRXas0pbd++PU6dOgUXFxf4+/tj5MiRWLVqFdzd3bUfYRn34dTpWLJ4IWZO+wjZ2Tlo0KgxJkyeqrRIzNjEBEYvrM7++fvvcGDvHsWxnh39AQDzFiyCX736KvdLumdtYY7lX09F0NIN6PXhLFhbmGPMgG4Y0aujoo5EIoGJsZFijummfUchhMBvG3bgtw07FPX8qlXBqm8/UTxvNvQjpRG1A6f/hZO9Lfb9HlRq50eqMzQ0xG9zv8J3v/yOwWM/gomxMTq3bY3Jo0cq6kgkEpiYGCv9HL89/mPcjfhvWlSz7gMAACd2bFRsuq9KHdIvI1b+gHp9Oyue/5T4bOeUec374uGl6wCA3MwspUVLq979BP3mf44P96+GoZEhru89ik0fBSr1q0odIn0lEWrcfkkmk8HS0hK9e/dGQEAA/P394erqqlEgj+JTNWpP5YdLzCVdh0B6Qm6j2e8VKl8+cGmn6xBIT/xPROg6hELtu1U6d87q5FOhVF6ntKk1rJaYmIhNmzbB1dUVv//+O7y8vODj44OxY8di48aN2o+SiIiIiMo1tUZKX3br1i0EBQVhzZo1yMvLgzpdcqSU8nGklPJxpJRexJFSyseR0vI5UqrWnNJHjx4hODgYR44cQXBwMO7duwc3NzcMGjQI/v7+2o+SiIiISM/JuR+sRtRKSitWrAg3Nze0adMGn376Kfz9/VG1alXtR0dEREREbwS1ktI7d+7A29tb+9EQERERlVF5HCjViFoLnZiQEhEREZE2qb2p5bFjx9C9e3d4eXmhSpUq6N69O44dO6bd6IiIiIjKCLkQpfIor9RKStevX4+2bdtCJpNh3LhxmDBhAmQyGdq2bYv169drP0oiIiIiKtfUmlMaGBiI33//HaNGjVIqX7p0KQIDAzF48GBtxUdEREREbwC1RkrDwsIwYMCAAuUDBgxAWFiYNuIiIiIiojeIWkmpi4sLTp06VaD85MmTGt9ulIiIiKgsypOLUnmUV2pdvp84cSIGDx6MSZMmoXHjxgCAs2fPYsGCBZg5c6a2YyQiIiKick6tpHTatGmwtrbGvHnzEBgYCACoUqUKvv/+e4wePVrbMRIRERHpvfK8Mr40qJWUAsDo0aMxevRoZGZmAgBkMpk24yIiIiKiN4jaSWk+JqNEREREpCmVk9LOnTur3OnevXvVjYeIiIiI3kAqJ6W1atUq2UiIiIiIyrA8TinViMpJ6fz580s2EiIiIiJ6Y2k8p5SIiIiIuPpeU2ptnk9EREREpE0cKSUiIiLSAnk5vttSaeBIKRERERHpHJNSIiIiItI5JqVEREREpHOcU0pERESkBdynVDMcKSUiIiIineNIKREREZEWcJ9SzXCklIiIiIh0jkkpEREREekck1IiIiIi0jnOKSUiIiLSgjzOKdUIR0qJiIiISOc4UkpERESkBXI5R0o1wZFSIiIiItI5jpQSERERaQHv6KQZjpQSERERkc4xKSUiIiIinWNSSkREREQ6xzmlRERERFog5z6lGuFIKRERERHpHJNSIiIiIi3IE6JUHiUtNDQUb7/9Nho0aIDevXvj+PHjKre9ePEiateujbfeeqvYr8uklIiIiIgAAI8ePUKLFi1gbGyMBQsWoEaNGmjfvj3Onj372rYpKSkYMmQIjIyMcPfu3WK/NueUEhEREWlBXjm4o9NPP/0EGxsbLF++HAYGBmjVqhUuXLiAr7/+Grt27Xpl27Fjx6J3795IS0vDmTNniv3aHCklIiIiIgDAkSNH0LlzZxgY/JciduvWDcHBwRCvmDqwYsUK3Lx5E19//bXar82klIiIiKgMycrKQnJystIjKytLK30/fPgQLi4uSmUuLi5IS0tDQkJCoW1u3bqF6dOnY926dTAxMVH7tZmUEhEREZUhQUFBsLa2VnoEBQUVWvevv/5CrVq1XvnYvn27on5ubm6BxFIqlQIAcnJyCvSflZWFQYMG4auvvoKvr69G58U5pURERERlyIwZMzBlyhSlsvzE8WUBAQHYsGHDK/tzd3dX/Nve3h5xcXFKx+Pi4mBoaAhbW9sCbW/evIkrV65g0aJFWLRoEQAgKioK6enpqFWrFlasWIFGjRqpdF5MSomIiIi0oLQWOkml0iKT0JfZ2toWmkwWpUGDBgVW2p86dQq1atUq9NK8r68vrl69qlT27bffIiQkBBs2bEDlypVVfm1eviciIiIiAMDo0aNx4sQJ7NmzBwBw5coVbNq0CWPGjFHU2b59O2rVqoWUlBTIZLIC0wHs7OwU5ebm5iq/NkdKiYiIiLSgPGwJ1a5dO/zwww8YOHAgrK2tERsbi3HjxmHs2LGKOgkJCbh+/Try8vK0+toS8ar1/aXoUXyqrkMgPeESc0nXIZCekNu46joE0iMfuLTTdQikJ/4nInQdQqG+P1r8DePVMa2Nd4m/RkZGBh49egQnJydYWVkpHUtMTMSjR49Qo0YNpa2j8uXPKfXy8irWa3KklIiIiIiUmJqaomrVqoUes7GxgY2NTZFtX95SSlWcU0pEREREOseRUiIiIiItKA9zSnWJI6VEREREpHMcKSUiIiLSAo6UaoYjpURERESkcxwpJSIiItICjpRqhiOlRERERKRzTEqJiIiISOeYlBIRERGRzunNbUYJyMrKQlBQEGbMmAGpVKrrcEiH+F2gfPwuUD5+F6i8Y1KqR5KTk2FtbY2kpKQC95mlNwu/C5SP3wXKx+8ClXe8fE9EREREOseklIiIiIh0jkkpEREREekck1I9IpVKMWvWLE5gJ34XSIHfBcrH7wKVd1zoREREREQ6x5FSIiIiItI5JqVEREREpHNMSon0TFhYGHbv3q3rMEiPHT58GFevXi3yOeleZmYmNmzYgKSkJF2HUqSyECO9WZiUlhMLFizA1KlTdR0GacGBAwcwadIkXYdBeiwwMBAbN24s8vmhQ4dw7do1HUVHAJCYmIjBgwfj4cOHug6lSGUhRnqzMCktJyIiIhAaGqrrMIhIB9q2bQs/Pz/F81mzZmHLli06jYmIqLiMdB1AaZo3bx4SEhJQuXJlHDhwAAkJCejbty/Gjh2LJUuW4O+//4ahoSHeffdd9O3bV6ltcHAw/ve//+Hx48fw8vLCxx9/jFq1amml7+vXr2PBggW4c+cO3NzcMHLkSLRv316p78TERPj5+WHbtm1ISkpC165dMXHiRBgYGODPP//Exo0bkZmZiaZNmwIAfvjhB+zevRs2Njb45JNPFH39/vvvuHLlCn777TeN4yZlsbGxOHjwIHr37g2ZTKYoj4mJweHDh9G7d2/ExcXh+PHjAAAzMzP4+vqiWrVqr+w3JCQE8fHx8Pf3V5SFh4fj6tWr6Nmzp1Ld8PBwhISEwMHBAfXr14epqanWz5OKlpSUhOPHj8PW1hb16tXDxYsXYWlpibp16wIAdu7ciZo1a6JKlSqKNkePHoW1tbWizunTp3H//n1IJBI4ODigbt26sLe3f+XrtmzZEo6OjgCAEydOIDY2FteuXcOGDRsAAB4eHsjIyEDbtm2V2l24cAEpKSkICAjQ+ntBhYuKisK5c+dgamqK5s2bw8LCQnHs0KFDcHFxQY0aNQAAN2/exJUrV9CrVy/Fz/KePXvg4+ODkJAQ+Pn5wcvLS6n/v/76C3Xq1FGUv+r1iPSOeIOMGTNGmJiYiH79+om9e/eKhQsXCkNDQ9GwYUMxePBgsW/fPvHDDz8IAwMDceHCBUW7AwcOCGNjY/HFF1+IvXv3ilGjRglzc3Nx584djfs+ffq0sLW1FYGBgeLQoUNi8eLFwtbWVmzYsEGpb5lMJnr16iX++ecf8eeffwpra2uxcOFCIYQQjx49EoMGDRItWrQQp0+fFqdPnxYJCQmiX79+YsyYMUrvweeffy7atGmjcdxUUFpamjA3NxebN29WKg8MDBTVqlUTQghx/vx5MWjQIDFo0CDRrVs3YWNjI0aOHKlUf/HixcLLy0vxfPr06aJdu3ZKdVasWCHc3NwUz/Py8sTYsWOFvb296Natm2jYsKHw9PQUly5dKqGzpZedP39e2Nvbi9q1a4u2bduKqlWriurVq4uPP/5YUcfDw0MsWbJEqV2nTp2U6vz888+K70jLli2FlZWV2LJli1KbNm3aiM8//7zQ5/PnzxcODg6iZs2ain7WrFkjzM3NRXJyslI/VatWFYGBgVp/L0iIqKgoAUBcvXpVUfbTTz8JU1NTERAQIOrWrSvs7e3FsWPHFMffe+89MXjwYMXzoUOHCgBi3759Qggh0tPThYmJiTh27Jjo3bu3ePvtt5VeMyQkRAAQ169fV+n1CouRSJfeuKTUw8ND5OTkKMo6dOggqlWrJvLy8hRlzZo1E7Nnz1Y8b9SokRg9erRSXy1bthTDhg3TuO/mzZuLr776SqnvhQsXipo1axboOzs7W1E2ZcoU0b59e8XzSZMmiW7duin1o2pSqk7cVLi3335b9O7dW6nM19e3wGecLyoqSjg6Oopdu3YpytRJSn/55Rfh6+srEhISFGUzZ84UderU0cp50es1aNBAjBgxQsjlciGEEJs3bxYAip2Uvmzt2rXCwcFBZGZmKspelZQKIUSLFi3ErFmzFM9zcnKEi4uL0msfO3ZMGBgYiAcPHmh03lS4lxO+0NBQYWRkpPQHxrhx44SXl5fIysoSQgixZs0a4eLiojhesWJF0bBhQzFjxgwhhBAHDx4UpqamIisrS2zevFmYm5uL1NRURf3p06eLevXqqfx6TEpJ37xxc0rr1KkDI6P/Zi24uLigbt26MDAwUCqLjo4GAOTl5eHy5cvo3LmzUj9dunTBhQsXNOo7MzMTZ86cwcaNG9GyZUu0aNECzZs3x2+//Ybbt2/jxfsa1KlTB8bGxorn7u7uin5K+z2hog0dOhR79uxBQkICAODixYsIDQ3F22+/raiTnZ2NU6dOYevWrQgODoa7uzvOnTun0euuWLECderUwcGDB7F582Zs2rQJ1tbWuHLliiIWKjl3797FxYsXMXXqVEgkEgBA//79lS7TF8eTJ09w6NAhbNy4EdnZ2YiNjcW9e/fUjs/IyAjDhw/H8uXLFWXLly9Hhw4dULFiRbX7JdVt2bIFVapUQb9+/RRln3/+OcLCwhT/lwQEBCAqKgq3bt1CWFgYEhISMHXqVBw5cgR4Po2sWbNmMDExQY8ePWBkZITt27cDz656Yv369Rg6dKjKr0ekb96oOaUAlBI7AJBIJIWW5SeEGRkZyMnJKTAPx8LCAikpKRr1nZ6eDrlcjvfeew/NmjUrEGv+f25F9a2tm3EVN24qWocOHWBra4stW7bg/fffx9q1a9GsWTPF/K6LFy+iZ8+esLS0RNWqVWFubo6nT58iJiZGo9eNiIiAkZFRgcUtgwYNQkZGBmxtbTXqn17twYMHAABPT0+l8sqVKxe7r7lz5yIwMBD16tWDk5OT4g/GmJgYVK9eXe0Y33vvPcydOxehoaFwd3fH5s2blZJUKln3798v8EeKm5sbpFIp7t+/j+bNm8PV1RXe3t44cuQIjI2N0aJFC7Rr1w7Dhg1DamoqgoOD0bFjR+D5LUf79euHtWvXYsiQITh+/DgiIyMxePBglV+PSN+8cUlpcVlYWKBChQq4efMmOnTooCi/efNmgQnmxWVrawt7e3skJycrFiipy8DAoEDSaG5ujvT0dKUyjnaWLENDQwwaNAhr167FqFGjsGHDBsycOVNxfMaMGejevTt+//13RZm/v/8rE34DAwPI5XKlsszMTKXnVlZW6Ny5MwIDA7V6PqSa/IVICQkJMDc3V5S/PEr9us8yLi4OM2bMwKFDhxSLj6Kjo7Fp0yaN/yj08vKCv78/li9fDh8fH0ilUvTq1UujPkl1Dg4OuHLlilJZeno6srKy4ODgoCjz9/dHcHAwjI2NERAQAAcHB/j4+ODAgQM4d+4cvv32W0XdoUOHomPHjnj69CnWrl2Ltm3bwsXFpVivR6RP3rjL9+oYNWoUfv75Z0RFRQHPV0OvWbMGo0aN0qhfiUSC8ePH46effsLZs2cV5ffu3cOiRYuK1ZeTkxMiIyOVymrXro0jR44gMTEReL7Kf/PmzRrFTK/39ttv49ixY/jzzz8RGxuLQYMGKY5FR0fDx8dH8fzhw4evvXTv5uaGe/fuKSUl+Zfz8nXu3BmrVq0q8EfIy98JKhk+Pj5wcHDA33//rSgLCwsrkBS4ubnh7t27iucJCQlKdZ48eQIhhNJ3RJ2tnSwsLAr84YLno6WrV6/GkiVLMHToUEil0mL3Tepp2bIl/v33X0RERCjKNm/eDDMzM9SrV09Rlp+UHj16VLHjhr+/P7799lsYGhqiSZMmirpt2rSBs7Mz1qxZg82bNysu3Rfn9Yj0CUdKVTBz5kzcunULXl5e8PT0RHh4OEaPHq30C0Bds2bNQnp6OgICAuDm5oacnByYmJjgxx9/LFY/gwYNwoIFC1C1alXY29vjhx9+wNixY7Ft2zZUrlwZbm5uyMvLQ6tWrZCamqpx3FS0xo0bw9vbG5MmTULnzp2VtvPp3bs3vvvuO8V83V9++eW1iUHfvn3x6aefYtiwYWjbti2OHj2qGEnJ9/XXXyM4OBiNGzfGu+++C5lMhtOnTyM6OhoHDhwowbMlAJDJZPj666/x0UcfISYmBhUqVMAvv/wCKysrpXrvvPMOJk+eDFtbW1hbW2PFihVK03SqVauG6tWrY/DgwRg+fDhu3ryJVatWFTuehg0bYsOGDfDx8YGpqalim7J+/frhgw8+wNmzZ5VG66nkde3aFR07dkT79u0xadIkxMfHY968eZg9e7bSyGVAQACePHkCS0tLNGjQAHielP76669o27YtTExMFHUNDAwwePBgfPnll5DL5Urb9qn6ekT65I1KSqdPn47c3Fylsi+++KJAvaCgIKWFP2ZmZti6dSuioqIQFRUFT09P2NnZaaVvQ0NDzJ8/H1999RXu3LkDe3v7AgsPCuv7rbfeUtpz0NPTExEREQgLC0NycjJ8fX1hYWGBEydOIDw8HHg+vy0yMhLJyckax02v9sUXX2D37t0YPXq0Uvns2bPh6emJkydPQiaT4X//+x9u3LgBS0tLRR1vb290795d8dzFxQXnzp3DsmXLcP78ebRr1w4jR45UGvV2dHTEv//+i9WrV+PcuXMwMzND165dMXDgwFI6Yxo7dixcXV2xfft2pKenY/ny5fjuu++U6rz//vuwtbXFgQMHYGNjg99++w2HDh1S/MwbGRnh6NGj+PXXXxEcHIxKlSrh9OnT+Oyzz1ChQgVFP23btoWvr2+Rzz/77DPY2dnh2LFjyMjIQJcuXSCTySCVStG5c2eEhoaiTp06pfK+vKlMTU0xaNAg2NjYKMq2bduGlStX4tSpU5DJZNiyZQu6dOmi1M7V1RXjxo2Ds7Oz4neuv78/Bg0aVOh0i5EjR+LBgweoV69egbUPr3u9wmIk0iWJ4OoVIqIS0b17d/j6+mL+/Pm6DgUAkJOTg4oVK2L27NkYO3asrsMhIlLCoS8iojfAli1bsGPHDhgbG2P48OG6DoeIqAAmpUREJaR169Zwc3PTdRgAgF27dsHCwgL79+/n7WeJSC/x8j0RERER6Ry3hCIiIiIinWNSSkREREQ6x6SUiIiIiHSOSSkRERER6RyTUiIiIiLSOSalRERERKRzTEqJiIiISOeYlBIRERGRzjEpJSIiIiKd+z/T9u3X4qXQ4AAAAABJRU5ErkJggg==", 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lowvol-0.1040.2100.1091.000
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" + ], + "text/plain": [ + " momentum value quality lowvol\n", + "momentum 1.000 -0.444 0.860 -0.104\n", + "value -0.444 1.000 -0.412 0.210\n", + "quality 0.860 -0.412 1.000 0.109\n", + "lowvol -0.104 0.210 0.109 1.000" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Cross-factor rank correlation (time-averaged)\n", + "==================================\n", + "\"\"\"\n", + "factor_names = list(factor_dict.keys())\n", + "corr_matrix = np.zeros((len(factor_names), len(factor_names)))\n", + "\n", + "for i, name_i in enumerate(factor_names):\n", + " for j, name_j in enumerate(factor_names):\n", + " if i == j:\n", + " corr_matrix[i,j] = 1.0\n", + " continue\n", + " df_i = factor_dict[name_i]\n", + " df_j = factor_dict[name_j]\n", + " common_dates = df_i.index.intersection(df_j.index)\n", + "\n", + " corrs = []\n", + " for date in common_dates:\n", + " a = df_i.loc[date]\n", + " b = df_j.loc[date]\n", + " mask = a.notna() & b.notna()\n", + " if mask.sum() < 20:\n", + " continue\n", + " rho, _ = spearmanr(a[mask], b[mask])\n", + " corrs.append(rho)\n", + " corr_matrix[i,j] = np.mean(corrs)\n", + "\n", + "df_corr = pd.DataFrame(corr_matrix, index=factor_names, columns=factor_names)\n", + "\n", + "fig,ax = plt.subplots(figsize=(8,6))\n", + "sns.heatmap(df_corr, annot=True, fmt='.2f', cmap='RdBu_r', center=0, ax=ax, square=True)\n", + "ax.set_title('Cross-Factor Rank Correlation (Time-Averaged)')\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig('../images/02_factor_diagnostics/factor_correlation.png', dpi=150, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "display(df_corr.round(3))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "f5fea3fa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved factor exposures, IC series, decay, and correlation matrix.\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "==================================\n", + "Save factor exposures and IC results\n", + "==================================\n", + "\"\"\"\n", + "for name, df_f in factor_dict.items():\n", + " df_f.to_csv(f'../data/processed/factor_{name}.csv')\n", + "\n", + "df_ic.to_csv('../data/processed/ic_monthly.csv')\n", + "df_decay.to_csv('../data/processed/ic_decay.csv')\n", + "df_corr.to_csv('../data/processed/factor_correlation.csv')\n", + "\n", + "print(\"Saved factor exposures, IC series, decay, and correlation matrix.\")" + ] + }, + { + "cell_type": "markdown", + "id": "a76a4424", + "metadata": {}, + "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", + "\n", + "The other three factors fail, and the **why** is more instructive than the failure:\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", + "\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." + ] + } + ], + "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 +}