{
"cells": [
{
"cell_type": "markdown",
"id": "ba32eb4a",
"metadata": {},
"source": [
"# 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"
]
},
{
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Mkt-RF
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SMB
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HML
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RF
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Mom
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Date
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2005-01-01
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2005-04-01
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0.0005
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2005-05-01
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" 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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ls
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239.0000
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239.0000
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239.0000
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mean
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0.0164
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0.0560
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0.0700
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0.0479
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min
\n",
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-0.1796
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-0.2209
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-0.4085
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25%
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-0.0116
\n",
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-0.0211
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-0.0214
\n",
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50%
\n",
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0.0176
\n",
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0.0142
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0.0023
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75%
\n",
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0.0497
\n",
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0.0486
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0.0250
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max
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0.1727
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" 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": [
"
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],
"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)."
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"### 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)**."
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"Performance Summary\n",
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