Factor-Risk-Decomposition/notebooks/04_backtest_and_performance.ipynb

{
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  {
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    "# Backtest and Performance\n",
    "\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",
    "\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",
    "\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",
    "\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",
    "\n",
    "## Outputs\n",
    "\n",
    "Equity curve, drawdown chart, walk-forward performance table, Fama-French regression with alpha, survivorship sensitivity table.\n",
    "\n",
    "## Notebook Structure\n",
    "1. [Setup and Imports](#setup-and-imports)\n",
    "2. [Load Data and Benchmark Factors](#load-data-and-benchmark-factors)\n",
    "3. [Portfolio Formation](#portfolio-formation)\n",
    "4. [Turnover and Transaction Costs](#turnover-and-transaction-costs)\n",
    "5. [Performance Metrics](#performance-metrics)\n",
    "6. [Walk-Forward Analysis](#walk-forward-analysis)\n",
    "7. [Fama–French Alpha](#famafrench-alpha)\n",
    "8. [Survivorship Bias Sensitivity](#survivorship-bias-sensitivity)\n",
    "9. [Conclusion](#conclusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b2db11de",
   "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/04_backtest', exist_ok=True)\n",
    "\n",
    "RANDOM_STATE = 3\n",
    "TRANSACTION_COST_BPS = 5  # 5 bps round-trip\n",
    "REBALANCE_FREQ = 'ME'     # Monthly"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "293597e5",
   "metadata": {},
   "source": [
    "## Load Data and Benchmark Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9ffbee22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Momentum: (251, 501)\n",
      "Returns:   (251, 501)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Load momentum signal and returns\n",
    "==================================\n",
    "\"\"\"\n",
    "df_momentum = pd.read_csv('../data/processed/momentum_signal.csv', index_col=0, parse_dates=True)\n",
    "df_returns = pd.read_csv('../data/processed/returns_monthly.csv', index_col=0, parse_dates=True)\n",
    "\n",
    "print(f\"Momentum: {df_momentum.shape}\")\n",
    "print(f\"Returns:   {df_returns.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05c2fc00",
   "metadata": {},
   "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",
    "\n",
    "### What are the Fama–French factors?\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",
    "\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",
    "\n",
    "### Why do we care?\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)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5bfbd826",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FF factors: (257, 5)\n",
      "Columns: ['Mkt-RF', 'SMB', 'HML', 'RF', 'Mom']\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mkt-RF</th>\n",
       "      <th>SMB</th>\n",
       "      <th>HML</th>\n",
       "      <th>RF</th>\n",
       "      <th>Mom</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>-0.0275</td>\n",
       "      <td>-0.0166</td>\n",
       "      <td>0.0206</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-01</th>\n",
       "      <td>0.0188</td>\n",
       "      <td>-0.0057</td>\n",
       "      <td>0.0141</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-01</th>\n",
       "      <td>-0.0194</td>\n",
       "      <td>-0.0141</td>\n",
       "      <td>0.0207</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.0043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-01</th>\n",
       "      <td>-0.0261</td>\n",
       "      <td>-0.0393</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>-0.0070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-05-01</th>\n",
       "      <td>0.0365</td>\n",
       "      <td>0.0286</td>\n",
       "      <td>-0.0058</td>\n",
       "      <td>0.0024</td>\n",
       "      <td>0.0037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Mkt-RF     SMB     HML      RF     Mom\n",
       "Date                                              \n",
       "2005-01-01 -0.0275 -0.0166  0.0206  0.0016  0.0312\n",
       "2005-02-01  0.0188 -0.0057  0.0141  0.0016  0.0343\n",
       "2005-03-01 -0.0194 -0.0141  0.0207  0.0021  0.0043\n",
       "2005-04-01 -0.0261 -0.0393  0.0005  0.0021 -0.0070\n",
       "2005-05-01  0.0365  0.0286 -0.0058  0.0024  0.0037"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "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",
    "    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",
    "    df_ff3.index = df_ff3.index.to_timestamp()\n",
    "    df_ff3 = df_ff3 / 100  # Convert from percent to decimal\n",
    "    \n",
    "    # Momentum monthly\n",
    "    df_mom = pdr.famafrench.FamaFrenchReader('F-F_Momentum_Factor', start='2005-01-01').read()[0]\n",
    "    df_mom.index = df_mom.index.to_timestamp()\n",
    "    df_mom = df_mom / 100\n",
    "    \n",
    "    df_ff = df_ff3.join(df_mom)\n",
    "    df_ff.to_csv(ff_path)\n",
    "    print(f\"Saved to {ff_path}\")\n",
    "\n",
    "print(f\"FF factors: {df_ff.shape}\")\n",
    "print(f\"Columns: {list(df_ff.columns)}\")\n",
    "df_ff.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcbaf1c1",
   "metadata": {},
   "source": [
    "## Portfolio Formation\n",
    "\n",
    "At each rebalance date, we rank stocks by the momentum signal (the vector $c_t$) and form two portfolios:\n",
    "- **Long-only**: equal-weight the top decile (top 10% of stocks by momentum score). This is a sparse weight vector with $w_i = 1/k$ for the top $k$ stocks and $w_i = 0$ for the rest.\n",
    "- **Long-short (comparison)**: long the top decile, short the bottom decile, equal-weighted on each side with $\\sum w_i = 0$ (dollar-neutral).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8691aa8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Backtest period: 2006-02-28 00:00:00 to 2025-12-31 00:00:00\n",
      "Number of rebalances: 239\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>long</th>\n",
       "      <th>short</th>\n",
       "      <th>ls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>239.0000</td>\n",
       "      <td>239.0000</td>\n",
       "      <td>239.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.0168</td>\n",
       "      <td>-0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0560</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.0479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.1796</td>\n",
       "      <td>-0.2209</td>\n",
       "      <td>-0.4085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.0116</td>\n",
       "      <td>-0.0211</td>\n",
       "      <td>-0.0214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0176</td>\n",
       "      <td>0.0142</td>\n",
       "      <td>0.0023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0497</td>\n",
       "      <td>0.0486</td>\n",
       "      <td>0.0250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.1727</td>\n",
       "      <td>0.4796</td>\n",
       "      <td>0.1008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           long     short        ls\n",
       "count  239.0000  239.0000  239.0000\n",
       "mean     0.0164    0.0168   -0.0004\n",
       "std      0.0560    0.0700    0.0479\n",
       "min     -0.1796   -0.2209   -0.4085\n",
       "25%     -0.0116   -0.0211   -0.0214\n",
       "50%      0.0176    0.0142    0.0023\n",
       "75%      0.0497    0.0486    0.0250\n",
       "max      0.1727    0.4796    0.1008"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Form top-decile long and long-short portfolios\n",
    "==================================\n",
    "\n",
    "At each rebalance date:\n",
    "  - rank stocks by momentum score\n",
    "  - long the top decile, equal-weighted\n",
    "  - short the bottom decile, equal-weighted (for L/S portfolio)\n",
    "\n",
    "We trade on t+1 to avoid look-ahead bias: signal computed at month-end t,\n",
    "returns realized over month t+1.\n",
    "\"\"\"\n",
    "def form_decile_portfolios(signal_df, return_df, decile=0.1):\n",
    "    \"\"\"Form long (top decile) and short (bottom decile) portfolios.\"\"\"\n",
    "    common_dates = signal_df.index.intersection(return_df.index)\n",
    "    common_tickers = signal_df.columns.intersection(return_df.columns)\n",
    "    \n",
    "    signal_df = signal_df.loc[common_dates, common_tickers]\n",
    "    return_df = return_df.loc[common_dates, common_tickers]\n",
    "    \n",
    "    long_returns = []\n",
    "    short_returns = []\n",
    "    ls_returns = []\n",
    "    long_holdings = []\n",
    "    short_holdings = []\n",
    "    rebalance_dates = []\n",
    "    \n",
    "    for i in range(len(common_dates) - 1):\n",
    "        date = common_dates[i]\n",
    "        next_date = common_dates[i + 1]\n",
    "        \n",
    "        scores = signal_df.loc[date].dropna()\n",
    "        if len(scores) < 50:\n",
    "            continue\n",
    "        \n",
    "        n_long = max(int(len(scores) * decile), 1)\n",
    "        n_short = max(int(len(scores) * decile), 1)\n",
    "        \n",
    "        ranked = scores.sort_values(ascending=False)\n",
    "        long_tickers = ranked.head(n_long).index.tolist()\n",
    "        short_tickers = ranked.tail(n_short).index.tolist()\n",
    "        \n",
    "        # Returns realized over next month\n",
    "        next_rets = return_df.loc[next_date]\n",
    "        \n",
    "        long_ret = next_rets[long_tickers].mean()\n",
    "        short_ret = next_rets[short_tickers].mean()\n",
    "        ls_ret = long_ret - short_ret\n",
    "        \n",
    "        long_returns.append(long_ret)\n",
    "        short_returns.append(short_ret)\n",
    "        ls_returns.append(ls_ret)\n",
    "        long_holdings.append(long_tickers)\n",
    "        short_holdings.append(short_tickers)\n",
    "        rebalance_dates.append(next_date)\n",
    "    \n",
    "    df_port = pd.DataFrame({\n",
    "        'long': long_returns,\n",
    "        'short': short_returns,\n",
    "        'ls': ls_returns,\n",
    "        'long_holdings': long_holdings,\n",
    "        'short_holdings': short_holdings\n",
    "    }, index=pd.DatetimeIndex(rebalance_dates))\n",
    "    \n",
    "    return df_port\n",
    "\n",
    "df_port = form_decile_portfolios(df_momentum, df_returns)\n",
    "print(f\"Backtest period: {df_port.index.min()} to {df_port.index.max()}\")\n",
    "print(f\"Number of rebalances: {len(df_port)}\")\n",
    "df_port[['long', 'short', 'ls']].describe().round(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a438dcfb",
   "metadata": {},
   "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",
    "\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. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7f3b7fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average long turnover (one-way): 0.286\n",
      "Average L/S turnover (one-way):   0.295\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Compute one-way turnover at each rebalance\n",
    "==================================\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",
    "    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",
    "\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",
    "ax.plot(df_port.index, df_port['long_turnover'].rolling(12).mean(), color='coral', linewidth=2, label='12m MA')\n",
    "ax.set_ylabel('One-Way Turnover')\n",
    "ax.set_title('Long Portfolio Turnover')\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/04_backtest/turnover.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "print(f\"Average long turnover (one-way): {df_port['long_turnover'].mean():.3f}\")\n",
    "print(f\"Average L/S turnover (one-way):   {df_port['ls_turnover'].mean():.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d3cfa6a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net return summary:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>long_net</th>\n",
       "      <th>short_net</th>\n",
       "      <th>ls_net</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>238.0000</td>\n",
       "      <td>238.0000</td>\n",
       "      <td>238.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0163</td>\n",
       "      <td>0.0169</td>\n",
       "      <td>-0.0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0561</td>\n",
       "      <td>0.0702</td>\n",
       "      <td>0.0479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.1797</td>\n",
       "      <td>-0.2208</td>\n",
       "      <td>-0.4088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.0126</td>\n",
       "      <td>-0.0213</td>\n",
       "      <td>-0.0220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0177</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.0024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0496</td>\n",
       "      <td>0.0488</td>\n",
       "      <td>0.0248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.1725</td>\n",
       "      <td>0.4797</td>\n",
       "      <td>0.1005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "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",
       "max      0.1725     0.4797    0.1005"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "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",
    "\"\"\"\n",
    "tc = TRANSACTION_COST_BPS / 10000  # 5 bps = 0.0005\n",
    "\n",
    "# Net returns = gross - cost\n",
    "df_port['long_net'] = df_port['long'] - df_port['long_turnover'] * tc\n",
    "df_port['short_net'] = df_port['short'] + df_port['short_turnover'] * tc  # Short pays cost too\n",
    "df_port['ls_net'] = df_port['long_net'] - df_port['short_net']\n",
    "\n",
    "# Excess returns (subtract risk-free)\n",
    "# Align FF data (month-start) to portfolio index (month-end) via period index\n",
    "df_port_pm = df_port.copy()\n",
    "df_port_pm.index = df_port_pm.index.to_period('M')\n",
    "ff_pm = df_ff[['RF', 'Mkt-RF']].copy()\n",
    "ff_pm.index = ff_pm.index.to_period('M')\n",
    "df_port['RF'] = ff_pm['RF'].reindex(df_port_pm.index).values\n",
    "df_port['Mkt-RF'] = ff_pm['Mkt-RF'].reindex(df_port_pm.index).values\n",
    "df_port['long_excess'] = df_port['long_net'] - df_port['RF']\n",
    "df_port['short_excess'] = df_port['short_net'] - df_port['RF']\n",
    "df_port['ls_excess'] = df_port['ls_net']  # L/S is dollar-neutral, RF cancels\n",
    "\n",
    "print(\"Net return summary:\")\n",
    "display(df_port[['long_net', 'short_net', 'ls_net']].describe().round(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb3baab4",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ab72d21",
   "metadata": {},
   "source": [
    "## Sortino Ratio\n",
    "\n",
    "### The Problem with Sharpe\n",
    "\n",
    "Sharpe uses **total** standard deviation in the denominator:\n",
    "\n",
    "$$\\text{Sharpe} = \\frac{\\bar{r}_p}{\\text{std}(r_p)} \\times \\sqrt{12}$$\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",
    "\n",
    "### Sortino's Fix\n",
    "\n",
    "Sortino only penalizes returns that fall **below a target** (usually 0, meaning only actual losses count):\n",
    "\n",
    "$$\\text{Sortino} = \\frac{\\bar{r}_p}{\\sigma_D} \\times \\sqrt{12}$$\n",
    "\n",
    "where the downside deviation is:\n",
    "\n",
    "$$\\sigma_D = \\sqrt{\\frac{1}{T}\\sum_{t=1}^{T} \\min(0,\\; r_t - r_{\\text{target}})^2}$$\n",
    "\n",
    "### How $\\sigma_D$ Works Step by Step\n",
    "\n",
    "Say your monthly returns are: $[0.03,\\; -0.02,\\; 0.08,\\; -0.01,\\; 0.04]$ and $r_{\\text{target}} = 0$.\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",
    "\n",
    "The +8% month contributes **zero** to $\\sigma_D$ — Sortino doesn't care about it. Only the two negative months matter.\n",
    "\n",
    "$$\\sigma_D = \\sqrt{\\frac{0.0004 + 0.0001}{5}} = \\sqrt{0.0001} = 0.01$$\n",
    "\n",
    "Compare to regular std which would be inflated by that +8% outlier.\n",
    "\n",
    "### When Sortino > Sharpe\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",
    "\n",
    "---\n",
    "\n",
    "## Max Drawdown\n",
    "\n",
    "### The Formula, Deconstructed\n",
    "\n",
    "$$\\text{Max DD} = \\max_t \\left(\\max_{s \\leq t} V_s - V_t\\right)$$\n",
    "\n",
    "where:\n",
    "\n",
    "$$V_t = \\prod_{s=1}^{t}(1 + r_{p,s})$$\n",
    "\n",
    "There are three nested pieces here. Let's go from the inside out.\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)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2886153",
   "metadata": {},
   "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",
    "\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)**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4845c9f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance Summary\n",
      "\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ann_return</th>\n",
       "      <th>ann_vol</th>\n",
       "      <th>sharpe</th>\n",
       "      <th>sortino</th>\n",
       "      <th>max_drawdown</th>\n",
       "      <th>calmar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>long_net</th>\n",
       "      <td>0.1955</td>\n",
       "      <td>0.1945</td>\n",
       "      <td>1.0054</td>\n",
       "      <td>1.4074</td>\n",
       "      <td>-0.5713</td>\n",
       "      <td>0.3422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ls_net</th>\n",
       "      <td>-0.0072</td>\n",
       "      <td>0.1661</td>\n",
       "      <td>-0.0433</td>\n",
       "      <td>-0.0437</td>\n",
       "      <td>-0.7029</td>\n",
       "      <td>-0.0102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ew_universe</th>\n",
       "      <td>0.1594</td>\n",
       "      <td>0.1683</td>\n",
       "      <td>0.9471</td>\n",
       "      <td>1.2657</td>\n",
       "      <td>-0.4747</td>\n",
       "      <td>0.3359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Compute performance metrics\n",
    "==================================\n",
    "\"\"\"\n",
    "def performance_metrics(returns, freq=12, rf=0):\n",
    "    \"\"\"Compute standard performance metrics.\"\"\"\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",
    "    sortino = ann_return / downside_vol if downside_vol and downside_vol > 0 else np.nan\n",
    "    \n",
    "    cum = (1 + returns).cumprod()\n",
    "    running_max = cum.cummax()\n",
    "    drawdown = (cum - running_max) / running_max\n",
    "    max_dd = drawdown.min()\n",
    "    \n",
    "    calmar = ann_return / abs(max_dd) if max_dd < 0 else np.nan\n",
    "    \n",
    "    return {\n",
    "        'ann_return': ann_return,\n",
    "        'ann_vol': ann_vol,\n",
    "        'sharpe': sharpe,\n",
    "        'sortino': sortino,\n",
    "        'max_drawdown': max_dd,\n",
    "        'calmar': calmar\n",
    "    }\n",
    "\n",
    "# Equal-weight universe benchmark — align via period index\n",
    "ew_monthly = df_returns.mean(axis=1)\n",
    "ew_monthly.index = ew_monthly.index.to_period('M')\n",
    "df_port['ew_universe'] = ew_monthly.reindex(df_port.index.to_period('M')).values\n",
    "\n",
    "# Compute metrics for all portfolios\n",
    "metrics = {}\n",
    "for col, label in [('long_net', 'Long-Only (net)'), ('ls_net', 'Long-Short (net)'), ('ew_universe', 'EW Universe')]:\n",
    "    metrics[col] = performance_metrics(df_port[col])\n",
    "\n",
    "df_metrics = pd.DataFrame(metrics).T.round(4)\n",
    "print(\"Performance Summary\\n\")\n",
    "display(df_metrics)\n",
    "\n",
    "# Active return vs equal-weight universe\n",
    "df_port['active'] = df_port['long_net'] - df_port['ew_universe']\n",
    "active = df_port['active'].dropna()\n",
    "t_stat = active.mean() / (active.std() / np.sqrt(len(active)))\n",
    "ir = active.mean() / active.std() * np.sqrt(12)\n",
    "print(f\"\\nActive Return (Long-Only minus EW Universe)\")\n",
    "print(f\"  Monthly active return: {active.mean():.5f} (t = {t_stat:.2f})\")\n",
    "print(f\"  Annualized:            {active.mean()*12:.4f}\")\n",
    "print(f\"  Tracking error:        {active.std()*np.sqrt(12):.4f}\")\n",
    "print(f\"  Information ratio:     {ir:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e9999e92",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1400x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Equity curve and drawdown\n",
    "==================================\n",
    "\"\"\"\n",
    "fig, axes = plt.subplots(2, 1, figsize=(14, 10), sharex=True)\n",
    "\n",
    "# Cumulative returns\n",
    "ax = axes[0]\n",
    "for col, color, label in [('long_net', 'steelblue', 'Long-Only'), ('ls_net', 'coral', 'Long-Short')]:\n",
    "    cum = (1 + df_port[col]).cumprod()\n",
    "    ax.plot(cum.index, cum.values, color=color, linewidth=2, label=label)\n",
    "ax.set_ylabel('Cumulative Return')\n",
    "ax.set_title('Portfolio Equity Curves (Net of Costs)')\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "# Drawdown\n",
    "ax = axes[1]\n",
    "for col, color, label in [('long_net', 'steelblue', 'Long-Only'), ('ls_net', 'coral', 'Long-Short')]:\n",
    "    cum = (1 + df_port[col]).cumprod()\n",
    "    dd = (cum - cum.cummax()) / cum.cummax()\n",
    "    ax.fill_between(dd.index, dd.values, 0, color=color, alpha=0.3, label=label)\n",
    "ax.set_ylabel('Drawdown')\n",
    "ax.set_title('Portfolio Drawdowns')\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/04_backtest/equity_and_drawdown.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54006d1a",
   "metadata": {},
   "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",
    "\n",
    "This is pure consistency checking; we're not optimizing any parameters or anything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a0a6cf4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Walk-Forward Performance (5-Year Windows)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lo_sharpe</th>\n",
       "      <th>lo_ann_ret</th>\n",
       "      <th>ls_sharpe</th>\n",
       "      <th>ew_sharpe</th>\n",
       "      <th>active_ret</th>\n",
       "      <th>info_ratio</th>\n",
       "      <th>active_t</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-2011</th>\n",
       "      <td>0.3117</td>\n",
       "      <td>0.0712</td>\n",
       "      <td>-0.5382</td>\n",
       "      <td>0.5729</td>\n",
       "      <td>-0.0482</td>\n",
       "      <td>-0.5058</td>\n",
       "      <td>-1.1120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-2016</th>\n",
       "      <td>1.4347</td>\n",
       "      <td>0.2105</td>\n",
       "      <td>0.6602</td>\n",
       "      <td>1.3470</td>\n",
       "      <td>0.0420</td>\n",
       "      <td>0.7802</td>\n",
       "      <td>1.7445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-2021</th>\n",
       "      <td>1.2810</td>\n",
       "      <td>0.2515</td>\n",
       "      <td>-0.0018</td>\n",
       "      <td>1.1172</td>\n",
       "      <td>0.0592</td>\n",
       "      <td>0.7994</td>\n",
       "      <td>1.7874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-2026</th>\n",
       "      <td>1.2279</td>\n",
       "      <td>0.2447</td>\n",
       "      <td>0.2942</td>\n",
       "      <td>0.9738</td>\n",
       "      <td>0.0883</td>\n",
       "      <td>0.8852</td>\n",
       "      <td>1.9795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "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",
       "2011-2016      0.7802    1.7445  \n",
       "2016-2021      0.7994    1.7874  \n",
       "2021-2026      0.8852    1.9795  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Long-Only Sharpe positive in 4/4 windows\n",
      "Active return positive in 3/4 windows\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Walk-forward analysis: 5-year windows\n",
    "==================================\n",
    "\"\"\"\n",
    "windows = [(2006, 2011), (2011, 2016), (2016, 2021), (2021, 2026)]\n",
    "\n",
    "wf_results = []\n",
    "for start, end in windows:\n",
    "    mask = (df_port.index.year >= start) & (df_port.index.year < end)\n",
    "    if mask.sum() < 12:\n",
    "        continue\n",
    "    \n",
    "    m_lo = performance_metrics(df_port.loc[mask, 'long_net'])\n",
    "    m_ls = performance_metrics(df_port.loc[mask, 'ls_net'])\n",
    "    m_ew = performance_metrics(df_port.loc[mask, 'ew_universe'])\n",
    "    \n",
    "    active_sub = df_port.loc[mask, 'active'].dropna()\n",
    "    ir = active_sub.mean() / active_sub.std() * np.sqrt(12) if len(active_sub) > 12 and active_sub.std() > 0 else np.nan\n",
    "    t_act = active_sub.mean() / (active_sub.std() / np.sqrt(len(active_sub))) if len(active_sub) > 12 else np.nan\n",
    "    \n",
    "    wf_results.append({\n",
    "        'period': f'{start}-{end}',\n",
    "        'lo_sharpe': m_lo['sharpe'],\n",
    "        'lo_ann_ret': m_lo['ann_return'],\n",
    "        'ls_sharpe': m_ls['sharpe'],\n",
    "        'ew_sharpe': m_ew['sharpe'],\n",
    "        'active_ret': active_sub.mean() * 12,\n",
    "        'info_ratio': ir,\n",
    "        'active_t': t_act\n",
    "    })\n",
    "\n",
    "df_wf = pd.DataFrame(wf_results).set_index('period')\n",
    "print(\"Walk-Forward Performance (5-Year Windows)\\n\")\n",
    "display(df_wf.round(4))\n",
    "\n",
    "# Plot walk-forward Sharpe ratios\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "x = np.arange(len(df_wf))\n",
    "width = 0.25\n",
    "ax.bar(x - width, df_wf['lo_sharpe'], width, label='Long-Only', color='steelblue')\n",
    "ax.bar(x, df_wf['ew_sharpe'], width, label='EW Universe', color='coral')\n",
    "ax.bar(x + width, df_wf['ls_sharpe'], width, label='Long-Short', color='seagreen')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(df_wf.index)\n",
    "ax.set_ylabel('Sharpe Ratio')\n",
    "ax.set_title('Walk-Forward Sharpe 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/04_backtest/walk_forward.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "print(f\"\\nLong-Only Sharpe positive in {(df_wf['lo_sharpe'] > 0).sum()}/{len(df_wf)} windows\")\n",
    "print(f\"Active return positive in {(df_wf['active_ret'] > 0).sum()}/{len(df_wf)} windows\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79a4d8c5",
   "metadata": {},
   "source": [
    "## Fama–French Alpha\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",
    "\n",
    "$$\\hat{\\beta} = (\\mathbf{F}^\\top \\mathbf{F})^{-1} \\mathbf{F}^\\top r_p,$$\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "10c3bb8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression data points: 238\n",
      "============================================================\n",
      "LONG-ONLY: Fama-French 4-Factor Alpha (HEADLINE)\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",
      "\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",
      "\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",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Fama-French 4-factor regression: Long-Only and Long-Short\n",
    "==================================\n",
    "\"\"\"\n",
    "import statsmodels.api as sm\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.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",
    "    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",
    "\n",
    "print(f\"Regression data points: {len(df_reg)}\")\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",
    "\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",
    "\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(\"=\" * 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8020d29",
   "metadata": {},
   "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",
    "\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5a4bcd3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Survivorship Bias Sensitivity\n",
      "\n",
      "How much annual return drag from missing delisted stocks would erase the alpha?\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>annual_drag</th>\n",
       "      <th>alpha_monthly</th>\n",
       "      <th>alpha_annualized</th>\n",
       "      <th>t_stat</th>\n",
       "      <th>significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0%</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0595</td>\n",
       "      <td>3.9511</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5%</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>0.0545</td>\n",
       "      <td>3.6193</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0%</td>\n",
       "      <td>0.0041</td>\n",
       "      <td>0.0495</td>\n",
       "      <td>3.2875</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0%</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>2.6238</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0%</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>0.0295</td>\n",
       "      <td>1.9602</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0%</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0195</td>\n",
       "      <td>1.2966</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.0%</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.6330</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Survivorship bias sensitivity\n",
    "==================================\n",
    "\"\"\"\n",
    "drags = [0, 0.005, 0.01, 0.02, 0.03, 0.04, 0.05]\n",
    "sensitivity = []\n",
    "\n",
    "# Reuse the aligned FF regression frame built in the alpha cell;\n",
    "# rebuild it if this cell is run standalone.\n",
    "if 'df_reg' not in globals():\n",
    "    _pm = df_port.copy(); _pm.index = _pm.index.to_period('M')\n",
    "    _ff = df_ff[['Mkt-RF', 'SMB', 'HML', 'Mom']].copy(); _ff.index = _ff.index.to_period('M')\n",
    "    df_reg = _pm.join(_ff, how='inner', lsuffix='_port', rsuffix='_ff')\n",
    "    for _c in ['Mkt-RF', 'SMB', 'HML', 'Mom']:\n",
    "        if _c + '_ff' in df_reg.columns:\n",
    "            df_reg[_c] = df_reg[_c + '_ff']\n",
    "    df_reg = df_reg[['long_excess', 'Mkt-RF', 'SMB', 'HML', 'Mom']].dropna().astype(float)\n",
    "\n",
    "X_factors = sm.add_constant(df_reg[['Mkt-RF', 'SMB', 'HML', 'Mom']])\n",
    "\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",
    "    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",
    "    })\n",
    "\n",
    "df_sens = pd.DataFrame(sensitivity)\n",
    "print(\"### Survivorship Bias Sensitivity\\n\")\n",
    "print(\"How much annual return drag from missing delisted stocks would erase the alpha?\\n\")\n",
    "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.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",
    "ax.set_ylabel('FF 4-Factor Alpha t-Statistic')\n",
    "ax.set_title('Survivorship Bias Sensitivity: When Does Alpha Disappear?')\n",
    "ax.legend()\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('../images/04_backtest/survivorship_sensitivity.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "93b4cf77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved backtest returns to ../data/processed/backtest_returns.csv\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "==================================\n",
    "Save backtest returns for risk decomposition\n",
    "==================================\n",
    "\"\"\"\n",
    "df_port[['long_net', 'short_net', 'ls_net', 'long_excess', 'ls_excess']].to_csv(\n",
    "    '../data/processed/backtest_returns.csv'\n",
    ")\n",
    "\n",
    "print(\"Saved backtest returns to ../data/processed/backtest_returns.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75addbfc",
   "metadata": {},
   "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",
    "\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",
    "\n",
    "The next notebook decomposes the portfolio's **risk** (the quadratic form $w^\\top \\Sigma w$) into systematic and idiosyncratic components via PCA."
   ]
  }
 ],
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