Chapter 14. Backtest Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(palette='Set2')\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "from typing import Tuple\n",
    "\n",
    "from scipy.stats import norm, skew, kurtosis\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import nbimporter\n",
    "from Functions import get_dollar_bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Auxilary Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_bets_timing(tPos: pd.Series) -> pd.Index:\n",
    "    '''\n",
    "    Calculates the timestamps of flattening or flipping trades from target positions series.\n",
    "    \n",
    "    Parameters:\n",
    "        tPos (pd.Series): series with target positions\n",
    "        \n",
    "    Returns:\n",
    "        bets (pd.Index): bets timing\n",
    "    '''\n",
    "    df0 = tPos[tPos == 0].index\n",
    "    df1 = tPos.shift(1)\n",
    "    df1 = df1[df1 != 0].index\n",
    "    bets = df0.intersection(df1)    # flattening\n",
    "    df0 = tPos.iloc[1:] * tPos.iloc[:-1].values\n",
    "    bets = bets.union(df0[df0 < 0].index).sort_values()    # tPos flips\n",
    "    if tPos.index[-1] not in bets:\n",
    "        bets = bets.append(tPos.index[-1:])    # last bet\n",
    "    return bets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_holding_period(tPos: pd.Series) -> float:\n",
    "    '''\n",
    "    Derives average holding period (in days) using average entry time pairing algo.\n",
    "    \n",
    "    Parameters:\n",
    "        tPos (pd.Series): series with target positions\n",
    "        \n",
    "    Returns:\n",
    "        hp (float): holding period\n",
    "    '''\n",
    "    hp, tEntry = pd.DataFrame(columns=['dT', 'w']), 0.0\n",
    "    pDiff, tDiff = tPos.diff(), (tPos.index - tPos.index[0]) / np.timedelta64(1, 'D')\n",
    "    for i in range(1, tPos.shape[0]):\n",
    "        if pDiff.iloc[i] * tPos.iloc[i - 1] >= 0:    # increased or unchanged\n",
    "            if tPos.iloc[i] != 0:\n",
    "                tEntry = (tEntry * tPos.iloc[i - 1] + tDiff[i] * pDiff.iloc[i]) / tPos.iloc[i]\n",
    "        else:    # decreased\n",
    "            if tPos.iloc[i] * tPos.iloc[i-1] < 0:    # flip\n",
    "                hp.loc[tPos.index[i], ['dT', 'w']] = (tDiff[i] - tEntry, abs(tPos.iloc[i - 1]))\n",
    "                tEntry = tDiff[i]    # reset entry time\n",
    "            else:\n",
    "                hp.loc[tPos.index[i], ['dT', 'w']] = (tDiff[i] - tEntry, abs(pDiff.iloc[i]))\n",
    "    if hp['w'].sum() > 0:\n",
    "        hp = (hp['dT'] * hp['w']).sum() / hp['w'].sum()\n",
    "    else:\n",
    "        hp = np.nan\n",
    "    return hp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_HHI(betRet: pd.Series) -> float:\n",
    "    '''\n",
    "    Derives HHI concentration of returns (see p. 200 for definition). Returns can be divided into positive\n",
    "    and negative or you can calculate the concentration of bets across the months.\n",
    "    \n",
    "    Parameters:\n",
    "        betRet (pd.Series): series with bets returns\n",
    "        \n",
    "    Returns:\n",
    "        hhi (float): concentration\n",
    "    '''\n",
    "    if betRet.shape[0] <= 2:\n",
    "        return np.nan\n",
    "    wght = betRet / betRet.sum()\n",
    "    hhi = (wght ** 2).sum()\n",
    "    hhi = (hhi - betRet.shape[0] ** (-1)) / (1.0 - betRet.shape[0] ** (-1))\n",
    "    return hhi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_DD_TuW(series: pd.Series, dollars: bool = False) -> Tuple[pd.Series, pd.Series]:\n",
    "    '''\n",
    "     Computes series of drawdowns and the time under water associated with them.\n",
    "    \n",
    "    Parameters:\n",
    "        series (pd.Series): series with either returns (dollars=False) or dollar performance (dollar=True)\n",
    "        dollars (bool): indicator charachterizing series\n",
    "        \n",
    "    Returns:\n",
    "        dd (pd.Series): drawdown series\n",
    "        tuw (pd.Series): time under water series\n",
    "    '''\n",
    "    df0 = series.to_frame('pnl')\n",
    "    df0['hwm'] = series.expanding().max()\n",
    "    df1 = df0.groupby('hwm').min().reset_index()\n",
    "    df1.columns = ['hwm', 'min']\n",
    "    df1.index = df0['hwm'].drop_duplicates(keep='first').index    # time of hwm\n",
    "    df1 = df1[df1['hwm'] > df1['min']]    # hwm followed by a drawdown\n",
    "    if dollars:\n",
    "        dd = df1['hwm'] - df1['min']\n",
    "    else:\n",
    "        dd = 1 - df1['min'] / df1['hwm']\n",
    "    tuw = ((df1.index[1:] - df1.index[:-1]) / np.timedelta64(1, 'Y')).values    # in years\n",
    "    tuw = pd.Series(tuw, index=df1.index[:-1])\n",
    "    return dd, tuw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I use the clean dataset generated in Chapter 2. I transform it into dollar bars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('sp10-19.csv')\n",
    "# $50,000 per bar\n",
    "dollar_bars = get_dollar_bars(data['price'].values, data['volume'].values, data['datetime'].values, 50000)\n",
    "dollar_bars_df = pd.DataFrame(data=dollar_bars[:, 1:], index=dollar_bars[:, 0],\n",
    "                           columns=['open', 'high', 'low', 'close', 'volume'])\n",
    "dollar_bars_df = dollar_bars_df[~dollar_bars_df.index.duplicated(keep='first')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Compute Statistics, Long Only."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I assume that we simply long the contract for the whole period from 2010 to 2019. So the returns are simply the relative increase in bars close price:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2010-01-03 17:00:45.000            NaN\n",
       "2010-01-03 17:04:29.000    0.000179469\n",
       "2010-01-03 17:15:11.000     0.00053831\n",
       "2010-01-03 17:52:34.000      8.967e-05\n",
       "2010-01-03 18:47:00.000    0.000268986\n",
       "                              ...     \n",
       "2019-12-17 07:48:29.219    -0.00131324\n",
       "2019-12-18 08:11:34.083      0.0014402\n",
       "2019-12-20 08:11:47.600     0.00909773\n",
       "2019-12-24 07:26:30.124     0.00666109\n",
       "2019-12-30 08:08:53.765    -0.00763265\n",
       "Name: close, Length: 80799, dtype: object"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns = (dollar_bars_df['close'] - dollar_bars_df['close'].shift(1)) / dollar_bars_df['close'].shift(1)\n",
    "returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I also create a dataframe for storing all the statistics calculated further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stats(returns: pd.Series) -> pd.DataFrame:\n",
    "    returns = returns.dropna()\n",
    "    \n",
    "    dd, tuw = compute_DD_TuW(returns.dropna())\n",
    "    returns_per_year = returns.groupby(pd.Grouper(freq='Y')).count().mean()\n",
    "    skewness = skew(returns.dropna())\n",
    "    kurt = kurtosis(returns.dropna())\n",
    "    sr = returns.mean() / returns.std()\n",
    "    val = sr * np.sqrt(len(returns) - 1) / np.sqrt(1 - skewness * sr + (kurt - 1) / 4 * sr ** 2)\n",
    "    var, N, gamma = 0.5, 100, 0.5772\n",
    "    \n",
    "    hhi_pos = get_HHI(returns[returns >= 0])\n",
    "    hhi_pos = get_HHI(returns[returns >= 0])\n",
    "    hhi_time = get_HHI(returns.groupby(pd.Grouper(freq='M')).count())\n",
    "    dd_95 = np.quantile(dd, 0.95)\n",
    "    tuw_95 = np.quantile(tuw, 0.95)\n",
    "    ann_avr_ret = (returns + 1).prod() ** (0.1) - 1\n",
    "    avg_hit_ret = returns[returns >= 0].mean()\n",
    "    avg_miss_ret = returns[returns < 0].mean()\n",
    "    ann_sr = returns.mean() / returns.std() * returns_per_year ** 0.5\n",
    "    psr = norm.cdf(val)\n",
    "    dsr = np.sqrt(var) * ((1 - gamma) * norm.cdf(1 - 1 / N) ** (-1) +\n",
    "                          gamma * norm.cdf(1 - 1 / N * np.exp(1) ** (-1)) ** (-1))\n",
    "    \n",
    "    return pd.DataFrame(data={'statistics': ['HHI_pos', 'HHI_neg', 'HHI_time', 'DD_95th', 'TuW_95th',\n",
    "                                             'Annualized_Avg_Return', 'Avg_Hit_Return', 'Avg_Miss_Return',\n",
    "                                             'Ann_SR', 'PSR', 'DSR'],\n",
    "                              'value': [hhi_pos, hhi_neg, hhi_time, dd_95, tuw_95, ann_avr_ret,\n",
    "                                        avg_hit_ret, avg_miss_ret, ann_sr, psr, dsr]},\n",
    "                        dtype=object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "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>statistics</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HHI_pos</td>\n",
       "      <td>8.21257e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HHI_neg</td>\n",
       "      <td>9.7649e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HHI_time</td>\n",
       "      <td>0.00595024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DD_95th</td>\n",
       "      <td>2.95174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TuW_95th</td>\n",
       "      <td>4.06383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Annualized_Avg_Return</td>\n",
       "      <td>0.112092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Avg_Hit_Return</td>\n",
       "      <td>0.000721879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Avg_Miss_Return</td>\n",
       "      <td>-0.000863072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ann_SR</td>\n",
       "      <td>0.775973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PSR</td>\n",
       "      <td>0.992607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DSR</td>\n",
       "      <td>0.841993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               statistics        value\n",
       "0                 HHI_pos  8.21257e-05\n",
       "1                 HHI_neg   9.7649e-05\n",
       "2                HHI_time   0.00595024\n",
       "3                 DD_95th      2.95174\n",
       "4                TuW_95th      4.06383\n",
       "5   Annualized_Avg_Return     0.112092\n",
       "6          Avg_Hit_Return  0.000721879\n",
       "7         Avg_Miss_Return -0.000863072\n",
       "8                  Ann_SR     0.775973\n",
       "9                     PSR     0.992607\n",
       "10                    DSR     0.841993"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_stats(returns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Compute Statistics, Long on Even Years, Short on Odd Years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we only have to redefine returns and use the same functions to calculate all the statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2010-01-03 17:00:45.000            NaN\n",
       "2010-01-03 17:04:29.000    0.000179469\n",
       "2010-01-03 17:15:11.000     0.00053831\n",
       "2010-01-03 17:52:34.000      8.967e-05\n",
       "2010-01-03 18:47:00.000    0.000268986\n",
       "                              ...     \n",
       "2019-12-17 07:48:29.219     0.00131324\n",
       "2019-12-18 08:11:34.083     -0.0014402\n",
       "2019-12-20 08:11:47.600    -0.00909773\n",
       "2019-12-24 07:26:30.124    -0.00666109\n",
       "2019-12-30 08:08:53.765     0.00763265\n",
       "Name: close, Length: 80799, dtype: object"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns_custom = returns.copy(deep=True)\n",
    "for ind in returns_custom.index:\n",
    "    if ind.year % 2 == 1:\n",
    "        returns_custom[ind] *= -1\n",
    "returns_custom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "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>statistics</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HHI_pos</td>\n",
       "      <td>9.19837e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HHI_neg</td>\n",
       "      <td>9.7649e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HHI_time</td>\n",
       "      <td>0.00595024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DD_95th</td>\n",
       "      <td>2.95442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TuW_95th</td>\n",
       "      <td>0.756247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Annualized_Avg_Return</td>\n",
       "      <td>-0.0461461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Avg_Hit_Return</td>\n",
       "      <td>0.000713706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Avg_Miss_Return</td>\n",
       "      <td>-0.000870762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ann_SR</td>\n",
       "      <td>-0.235333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PSR</td>\n",
       "      <td>0.228376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DSR</td>\n",
       "      <td>0.841993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               statistics        value\n",
       "0                 HHI_pos  9.19837e-05\n",
       "1                 HHI_neg   9.7649e-05\n",
       "2                HHI_time   0.00595024\n",
       "3                 DD_95th      2.95442\n",
       "4                TuW_95th     0.756247\n",
       "5   Annualized_Avg_Return   -0.0461461\n",
       "6          Avg_Hit_Return  0.000713706\n",
       "7         Avg_Miss_Return -0.000870762\n",
       "8                  Ann_SR    -0.235333\n",
       "9                     PSR     0.228376\n",
       "10                    DSR     0.841993"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_stats(returns_custom)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I also plot the cumulative return of both strategies to see how they performed over the decade:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14, 8))\n",
    "ax.plot((1 + returns).cumprod(), color='blue', label='Strategy 1 (Long)')\n",
    "ax.plot((1 + returns_custom).cumprod(), color='red', label='Strategy 2 (Long + Short)')\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Cumulative return')\n",
    "ax.set_title('Strategies\\' dynamics')\n",
    "ax.legend(loc='best')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}