24_alpha_factor_library/05_alphalens_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance Analysis with Alphalens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports & Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-04T16:28:14.497736Z",
     "start_time": "2020-05-04T16:28:14.490972Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-04T17:25:07.747157Z",
     "start_time": "2020-05-04T17:25:07.732372Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from collections import defaultdict\n",
    "from time import time\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from alphalens.tears import (create_returns_tear_sheet,\n",
    "                             create_summary_tear_sheet,\n",
    "                             create_full_tear_sheet)\n",
    "\n",
    "from alphalens import plotting\n",
    "from alphalens import performance as perf\n",
    "from alphalens import utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-04T16:28:19.644878Z",
     "start_time": "2020-05-04T16:28:19.631944Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')\n",
    "np.random.seed(42)\n",
    "idx = pd.IndexSlice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-04T16:31:51.386448Z",
     "start_time": "2020-05-04T16:31:51.378404Z"
    }
   },
   "outputs": [],
   "source": [
    "DATA_STORE = Path('..', 'data', 'assets.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alphalens Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get trade prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-04T17:09:00.951289Z",
     "start_time": "2020-05-04T17:09:00.948871Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_trade_prices(tickers):\n",
    "    return (pd.read_hdf(DATA_STORE, 'quandl/wiki/prices')\n",
    "              .loc[idx['2006':'2017', tickers], 'adj_open']\n",
    "              .unstack('ticker')\n",
    "              .sort_index()\n",
    "            .shift(-1)\n",
    "            .tz_localize('UTC'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-04T17:09:09.806420Z",
     "start_time": "2020-05-04T17:09:01.221139Z"
    }
   },
   "outputs": [],
   "source": [
    "trade_prices = get_trade_prices(tickers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-04T17:09:09.821926Z",
     "start_time": "2020-05-04T17:09:09.807535Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 3020 entries, 2006-01-03 to 2017-12-29\n",
      "Columns: 500 entries, A to ZION\n",
      "dtypes: float64(500)\n",
      "memory usage: 11.5 MB\n"
     ]
    }
   ],
   "source": [
    "trade_prices.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-04T16:58:12.974876Z",
     "start_time": "2020-05-04T16:58:09.045524Z"
    }
   },
   "outputs": [],
   "source": [
    "factors = (pd.concat([pd.read_hdf('data.h5', 'factors/common'),\n",
    "                      pd.read_hdf('data.h5', 'factors/formulaic')\n",
    "                      .rename(columns=lambda x: f'alpha_{int(x):03}')],\n",
    "                     axis=1)\n",
    "           .dropna(axis=1, thresh=100000)\n",
    "           .sort_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-04T16:58:18.634604Z",
     "start_time": "2020-05-04T16:58:18.607204Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 1255093 entries, (A, 2007-01-04 00:00:00) to (ZION, 2016-12-29 00:00:00)\n",
      "Columns: 135 entries, sector to alpha_101\n",
      "dtypes: float64(124), int64(11)\n",
      "memory usage: 1.3+ GB\n"
     ]
    }
   ],
   "source": [
    "factors.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-04T17:08:42.933497Z",
     "start_time": "2020-05-04T17:08:42.905847Z"
    }
   },
   "outputs": [],
   "source": [
    "tickers = factors.index.get_level_values('ticker').unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-04T17:17:20.204024Z",
     "start_time": "2020-05-04T17:17:20.202014Z"
    }
   },
   "outputs": [],
   "source": [
    "alpha = 'alpha_054'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-04T17:17:21.474962Z",
     "start_time": "2020-05-04T17:17:20.504838Z"
    }
   },
   "outputs": [],
   "source": [
    "factor = (factors[alpha]\n",
    "          .unstack('ticker')\n",
    "          .stack()\n",
    "          .tz_localize('UTC', level='date')\n",
    "          .sort_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Alphalens input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-04T17:17:32.460062Z",
     "start_time": "2020-05-04T17:17:29.512626Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 0.5% entries from factor data: 0.0% in forward returns computation and 0.4% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 1249360 entries, (2007-01-04 00:00:00+00:00, A) to (2016-12-29 00:00:00+00:00, ZION)\n",
      "Data columns (total 5 columns):\n",
      "1D                 1249360 non-null float64\n",
      "5D                 1249360 non-null float64\n",
      "10D                1249360 non-null float64\n",
      "factor             1249360 non-null float64\n",
      "factor_quantile    1249360 non-null float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 52.4+ MB\n"
     ]
    }
   ],
   "source": [
    "factor_data = utils.get_clean_factor_and_forward_returns(factor=factor,\n",
    "                                                   prices=trade_prices,\n",
    "                                                   quantiles=5,\n",
    "                                                   max_loss=0.35,\n",
    "                                                   periods=(1, 5, 10)).sort_index()\n",
    "factor_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-04T17:42:08.001937Z",
     "start_time": "2020-05-04T17:42:03.161980Z"
    }
   },
   "outputs": [],
   "source": [
    "mean_quant_ret_bydate, std_quant_daily = perf.mean_return_by_quantile(\n",
    "    factor_data,\n",
    "    by_date=True,\n",
    "    by_group=False,\n",
    "    demeaned=True,\n",
    "    group_adjust=False,\n",
    ")\n",
    "\n",
    "mean_quant_rateret_bydate = mean_quant_ret_bydate.apply(\n",
    "    rate_of_return,\n",
    "    base_period=mean_quant_ret_bydate.columns[0],\n",
    ")\n",
    "\n",
    "compstd_quant_daily = std_quant_daily.apply(std_conversion,\n",
    "                                            base_period=std_quant_daily.columns[0])\n",
    "\n",
    "alpha_beta = perf.factor_alpha_beta(factor_data,\n",
    "                                    demeaned=True)\n",
    "\n",
    "mean_ret_spread_quant, std_spread_quant = perf.compute_mean_returns_spread(\n",
    "    mean_quant_rateret_bydate,\n",
    "    factor_data[\"factor_quantile\"].max(),\n",
    "    factor_data[\"factor_quantile\"].min(),\n",
    "    std_err=compstd_quant_daily,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-04T17:42:18.983778Z",
     "start_time": "2020-05-04T17:42:18.957604Z"
    }
   },
   "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>1D</th>\n",
       "      <th>5D</th>\n",
       "      <th>10D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean Period Wise Spread (bps)</th>\n",
       "      <td>1.616562</td>\n",
       "      <td>1.239560</td>\n",
       "      <td>0.685174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ann. alpha</th>\n",
       "      <td>-0.017286</td>\n",
       "      <td>0.011855</td>\n",
       "      <td>0.012242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>0.056012</td>\n",
       "      <td>0.053415</td>\n",
       "      <td>0.044731</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     1D        5D       10D\n",
       "Mean Period Wise Spread (bps)  1.616562  1.239560  0.685174\n",
       "Ann. alpha                    -0.017286  0.011855  0.012242\n",
       "beta                           0.056012  0.053415  0.044731"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ret_spread_quant.mean().mul(10000).to_frame('Mean Period Wise Spread (bps)').join(alpha_beta.T).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot spread and cumulative returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-04T17:40:00.671158Z",
     "start_time": "2020-05-04T17:37:51.270474Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=3, figsize=(20, 5))\n",
    "\n",
    "mean_quant_ret, std_quantile = mean_return_by_quantile(factor_data,\n",
    "                                                       by_group=False,\n",
    "                                                       demeaned=True)\n",
    "\n",
    "mean_quant_rateret = mean_quant_ret.apply(rate_of_return, axis=0,\n",
    "                                          base_period=mean_quant_ret.columns[0])\n",
    "\n",
    "plot_quantile_returns_bar(mean_quant_rateret, ax=axes[0])\n",
    "\n",
    "\n",
    "factor_returns = perf.factor_returns(factor_data)\n",
    "\n",
    "title = \"Factor Weighted Long/Short Portfolio Cumulative Return (1D Period)\"\n",
    "plotting.plot_cumulative_returns(factor_returns['1D'],\n",
    "                                 period='1D',\n",
    "                                 freq=pd.tseries.offsets.BDay(),\n",
    "                                 title=title,\n",
    "                                 ax=axes[1])\n",
    "\n",
    "plotting.plot_cumulative_returns_by_quantile(mean_quant_ret_bydate['1D'],\n",
    "                                             freq=pd.tseries.offsets.BDay(),\n",
    "                                             period='1D',\n",
    "                                             ax=axes[2])\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Tearsheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-04T17:18:05.949555Z",
     "start_time": "2020-05-04T17:17:43.234131Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantiles Statistics\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>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "      <th>count %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>factor_quantile</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>1.0</th>\n",
       "      <td>-564576.097386</td>\n",
       "      <td>-0.053881</td>\n",
       "      <td>-1558.250338</td>\n",
       "      <td>7272.525816</td>\n",
       "      <td>250286</td>\n",
       "      <td>20.033137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>-84.567946</td>\n",
       "      <td>-0.029206</td>\n",
       "      <td>-2.659084</td>\n",
       "      <td>2.541298</td>\n",
       "      <td>249845</td>\n",
       "      <td>19.997839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>-13.157136</td>\n",
       "      <td>-0.019221</td>\n",
       "      <td>-1.325346</td>\n",
       "      <td>1.035789</td>\n",
       "      <td>249408</td>\n",
       "      <td>19.962861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>-5.569446</td>\n",
       "      <td>-0.001684</td>\n",
       "      <td>-0.749801</td>\n",
       "      <td>0.586770</td>\n",
       "      <td>249775</td>\n",
       "      <td>19.992236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>-3.371885</td>\n",
       "      <td>12.567973</td>\n",
       "      <td>-0.323038</td>\n",
       "      <td>0.323306</td>\n",
       "      <td>250046</td>\n",
       "      <td>20.013927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           min        max         mean          std   count  \\\n",
       "factor_quantile                                                               \n",
       "1.0             -564576.097386  -0.053881 -1558.250338  7272.525816  250286   \n",
       "2.0                 -84.567946  -0.029206    -2.659084     2.541298  249845   \n",
       "3.0                 -13.157136  -0.019221    -1.325346     1.035789  249408   \n",
       "4.0                  -5.569446  -0.001684    -0.749801     0.586770  249775   \n",
       "5.0                  -3.371885  12.567973    -0.323038     0.323306  250046   \n",
       "\n",
       "                   count %  \n",
       "factor_quantile             \n",
       "1.0              20.033137  \n",
       "2.0              19.997839  \n",
       "3.0              19.962861  \n",
       "4.0              19.992236  \n",
       "5.0              20.013927  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returns Analysis\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1D</th>\n",
       "      <th>5D</th>\n",
       "      <th>10D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ann. alpha</th>\n",
       "      <td>-0.017</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>0.056</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Period Wise Return Top Quantile (bps)</th>\n",
       "      <td>1.187</td>\n",
       "      <td>0.651</td>\n",
       "      <td>0.224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Period Wise Return Bottom Quantile (bps)</th>\n",
       "      <td>-0.429</td>\n",
       "      <td>-0.599</td>\n",
       "      <td>-0.465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Period Wise Spread (bps)</th>\n",
       "      <td>1.617</td>\n",
       "      <td>1.240</td>\n",
       "      <td>0.685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  1D     5D    10D\n",
       "Ann. alpha                                    -0.017  0.012  0.012\n",
       "beta                                           0.056  0.053  0.045\n",
       "Mean Period Wise Return Top Quantile (bps)     1.187  0.651  0.224\n",
       "Mean Period Wise Return Bottom Quantile (bps) -0.429 -0.599 -0.465\n",
       "Mean Period Wise Spread (bps)                  1.617  1.240  0.685"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information Analysis\n"
     ]
    },
    {
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       "      <th>1D</th>\n",
       "      <th>5D</th>\n",
       "      <th>10D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IC Mean</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IC Std.</th>\n",
       "      <td>0.118</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Risk-Adjusted IC</th>\n",
       "      <td>0.018</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t-stat(IC)</th>\n",
       "      <td>0.911</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value(IC)</th>\n",
       "      <td>0.362</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IC Skew</th>\n",
       "      <td>0.010</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IC Kurtosis</th>\n",
       "      <td>0.446</td>\n",
       "      <td>0.559</td>\n",
       "      <td>0.684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1D     5D    10D\n",
       "IC Mean           0.002  0.002  0.002\n",
       "IC Std.           0.118  0.114  0.110\n",
       "Risk-Adjusted IC  0.018  0.018  0.018\n",
       "t-stat(IC)        0.911  0.894  0.915\n",
       "p-value(IC)       0.362  0.372  0.360\n",
       "IC Skew           0.010  0.036  0.077\n",
       "IC Kurtosis       0.446  0.559  0.684"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Turnover Analysis\n"
     ]
    },
    {
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       "      <th>5D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Quantile 1 Mean Turnover</th>\n",
       "      <td>0.795</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantile 2 Mean Turnover</th>\n",
       "      <td>0.798</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantile 3 Mean Turnover</th>\n",
       "      <td>0.797</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantile 4 Mean Turnover</th>\n",
       "      <td>0.800</td>\n",
       "      <td>0.797</td>\n",
       "      <td>0.798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantile 5 Mean Turnover</th>\n",
       "      <td>0.788</td>\n",
       "      <td>0.792</td>\n",
       "      <td>0.788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             10D     1D     5D\n",
       "Quantile 1 Mean Turnover   0.795  0.791  0.795\n",
       "Quantile 2 Mean Turnover   0.798  0.798  0.797\n",
       "Quantile 3 Mean Turnover   0.797  0.795  0.797\n",
       "Quantile 4 Mean Turnover   0.800  0.797  0.798\n",
       "Quantile 5 Mean Turnover   0.788  0.792  0.788"
      ]
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       "      <th>10D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean Factor Rank Autocorrelation</th>\n",
       "      <td>0.006</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                                     1D    5D   10D\n",
       "Mean Factor Rank Autocorrelation  0.006  0.01  0.01"
      ]
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    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
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     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x5544 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_summary_tear_sheet(factor_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "342.391px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}