07_linear_models/06_evaluating_signals_using_alphalens.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alphalens Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:14:34.737829Z",
     "start_time": "2021-04-15T20:14:34.735088Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:14:35.351188Z",
     "start_time": "2021-04-15T20:14:34.744658Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "from alphalens.tears import create_summary_tear_sheet\n",
    "from alphalens.utils import get_clean_factor_and_forward_returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:14:35.354076Z",
     "start_time": "2021-04-15T20:14:35.352214Z"
    }
   },
   "outputs": [],
   "source": [
    "idx = pd.IndexSlice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:14:35.680803Z",
     "start_time": "2021-04-15T20:14:35.355387Z"
    }
   },
   "outputs": [],
   "source": [
    "with pd.HDFStore('data.h5') as store:\n",
    "    lr_predictions = store['lr/predictions']\n",
    "    lasso_predictions = store['lasso/predictions']\n",
    "    lasso_scores = store['lasso/scores']\n",
    "    ridge_predictions = store['ridge/predictions']\n",
    "    ridge_scores = store['ridge/scores']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:14:35.683992Z",
     "start_time": "2021-04-15T20:14:35.681990Z"
    }
   },
   "outputs": [],
   "source": [
    "DATA_STORE = Path('..', 'data', 'assets.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:14:35.694127Z",
     "start_time": "2021-04-15T20:14:35.684902Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_trade_prices(tickers, start, stop):\n",
    "    prices = (pd.read_hdf(DATA_STORE, 'quandl/wiki/prices').swaplevel().sort_index())\n",
    "    prices.index.names = ['symbol', 'date']\n",
    "    prices = prices.loc[idx[tickers, str(start):str(stop)], 'adj_open']\n",
    "    return (prices\n",
    "            .unstack('symbol')\n",
    "            .sort_index()\n",
    "            .shift(-1)\n",
    "            .tz_localize('UTC'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:14:35.702148Z",
     "start_time": "2021-04-15T20:14:35.695116Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_best_alpha(scores):\n",
    "    return scores.groupby('alpha').ic.mean().idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:14:35.710457Z",
     "start_time": "2021-04-15T20:14:35.703034Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_factor(predictions):\n",
    "    return (predictions.unstack('symbol')\n",
    "            .dropna(how='all')\n",
    "            .stack()\n",
    "            .tz_localize('UTC', level='date')\n",
    "            .sort_index())    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:14:35.752043Z",
     "start_time": "2021-04-15T20:14:35.712768Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                       symbol\n",
       "2014-12-09 00:00:00+00:00  AAL       0.001836\n",
       "                           AAPL     -0.001626\n",
       "                           ABBV      0.001321\n",
       "                           AGN       0.002162\n",
       "                           AIG      -0.000331\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_factor = get_factor(lr_predictions.predicted.swaplevel())\n",
    "lr_factor.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:14:35.757851Z",
     "start_time": "2021-04-15T20:14:35.752977Z"
    }
   },
   "outputs": [],
   "source": [
    "tickers = lr_factor.index.get_level_values('symbol').unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:14:58.118829Z",
     "start_time": "2021-04-15T20:14:35.758731Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1007 entries, 2014-01-02 00:00:00+00:00 to 2017-12-29 00:00:00+00:00\n",
      "Columns: 257 entries, AAL to GWW\n",
      "dtypes: float64(257)\n",
      "memory usage: 2.0 MB\n"
     ]
    }
   ],
   "source": [
    "trade_prices = get_trade_prices(tickers, 2014, 2017)\n",
    "trade_prices.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:14:59.198679Z",
     "start_time": "2021-04-15T20:14:58.119863Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% 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: 74054 entries, (Timestamp('2014-12-09 00:00:00+0000', tz='UTC', freq='C'), 'AAL') to (Timestamp('2017-11-29 00:00:00+0000', tz='UTC', freq='C'), 'XOM')\n",
      "Data columns (total 6 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   1D               74054 non-null  float64\n",
      " 1   5D               74054 non-null  float64\n",
      " 2   10D              74054 non-null  float64\n",
      " 3   21D              74054 non-null  float64\n",
      " 4   factor           74054 non-null  float64\n",
      " 5   factor_quantile  74054 non-null  int64  \n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "lr_factor_data = get_clean_factor_and_forward_returns(factor=lr_factor,\n",
    "                                                      prices=trade_prices,\n",
    "                                                      quantiles=5,\n",
    "                                                      periods=(1, 5, 10, 21))\n",
    "lr_factor_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:15:26.545662Z",
     "start_time": "2021-04-15T20:14:59.199526Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantiles Statistics\n"
     ]
    },
    {
     "data": {
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       "  <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</th>\n",
       "      <td>-0.047411</td>\n",
       "      <td>0.008700</td>\n",
       "      <td>-0.003169</td>\n",
       "      <td>0.004062</td>\n",
       "      <td>14982</td>\n",
       "      <td>20.231183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.015560</td>\n",
       "      <td>0.010750</td>\n",
       "      <td>-0.000996</td>\n",
       "      <td>0.003360</td>\n",
       "      <td>14877</td>\n",
       "      <td>20.089394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.013523</td>\n",
       "      <td>0.012632</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.003351</td>\n",
       "      <td>14336</td>\n",
       "      <td>19.358846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.012081</td>\n",
       "      <td>0.014850</td>\n",
       "      <td>0.001213</td>\n",
       "      <td>0.003458</td>\n",
       "      <td>14877</td>\n",
       "      <td>20.089394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.010094</td>\n",
       "      <td>0.038492</td>\n",
       "      <td>0.003323</td>\n",
       "      <td>0.004279</td>\n",
       "      <td>14982</td>\n",
       "      <td>20.231183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      min       max      mean       std  count    count %\n",
       "factor_quantile                                                          \n",
       "1               -0.047411  0.008700 -0.003169  0.004062  14982  20.231183\n",
       "2               -0.015560  0.010750 -0.000996  0.003360  14877  20.089394\n",
       "3               -0.013523  0.012632  0.000134  0.003351  14336  19.358846\n",
       "4               -0.012081  0.014850  0.001213  0.003458  14877  20.089394\n",
       "5               -0.010094  0.038492  0.003323  0.004279  14982  20.231183"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returns Analysis\n"
     ]
    },
    {
     "data": {
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       "<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",
       "      <th>21D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ann. alpha</th>\n",
       "      <td>0.033</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>-0.010</td>\n",
       "      <td>-0.079</td>\n",
       "      <td>-0.060</td>\n",
       "      <td>0.012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Period Wise Return Top Quantile (bps)</th>\n",
       "      <td>1.108</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Period Wise Return Bottom Quantile (bps)</th>\n",
       "      <td>-2.869</td>\n",
       "      <td>-0.714</td>\n",
       "      <td>-0.524</td>\n",
       "      <td>-0.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Period Wise Spread (bps)</th>\n",
       "      <td>3.976</td>\n",
       "      <td>1.468</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  1D     5D    10D    21D\n",
       "Ann. alpha                                     0.033  0.021  0.010  0.007\n",
       "beta                                          -0.010 -0.079 -0.060  0.012\n",
       "Mean Period Wise Return Top Quantile (bps)     1.108  0.724  0.028  0.212\n",
       "Mean Period Wise Return Bottom Quantile (bps) -2.869 -0.714 -0.524 -0.740\n",
       "Mean Period Wise Spread (bps)                  3.976  1.468  0.575  0.944"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information Analysis\n"
     ]
    },
    {
     "data": {
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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",
       "      <th>21D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IC Mean</th>\n",
       "      <td>0.020</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IC Std.</th>\n",
       "      <td>0.181</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Risk-Adjusted IC</th>\n",
       "      <td>0.108</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t-stat(IC)</th>\n",
       "      <td>2.965</td>\n",
       "      <td>2.168</td>\n",
       "      <td>2.105</td>\n",
       "      <td>2.555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value(IC)</th>\n",
       "      <td>0.003</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IC Skew</th>\n",
       "      <td>0.005</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>-0.031</td>\n",
       "      <td>-0.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IC Kurtosis</th>\n",
       "      <td>-0.129</td>\n",
       "      <td>-0.178</td>\n",
       "      <td>-0.153</td>\n",
       "      <td>-0.291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1D     5D    10D    21D\n",
       "IC Mean           0.020  0.013  0.013  0.014\n",
       "IC Std.           0.181  0.167  0.167  0.155\n",
       "Risk-Adjusted IC  0.108  0.079  0.077  0.093\n",
       "t-stat(IC)        2.965  2.168  2.105  2.555\n",
       "p-value(IC)       0.003  0.030  0.036  0.011\n",
       "IC Skew           0.005 -0.018 -0.031 -0.022\n",
       "IC Kurtosis      -0.129 -0.178 -0.153 -0.291"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Turnover Analysis\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",
       "      <th>21D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Quantile 1 Mean Turnover</th>\n",
       "      <td>0.296</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantile 2 Mean Turnover</th>\n",
       "      <td>0.516</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantile 3 Mean Turnover</th>\n",
       "      <td>0.568</td>\n",
       "      <td>0.739</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantile 4 Mean Turnover</th>\n",
       "      <td>0.509</td>\n",
       "      <td>0.701</td>\n",
       "      <td>0.754</td>\n",
       "      <td>0.784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantile 5 Mean Turnover</th>\n",
       "      <td>0.298</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.738</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              1D     5D    10D    21D\n",
       "Quantile 1 Mean Turnover   0.296  0.520  0.621  0.728\n",
       "Quantile 2 Mean Turnover   0.516  0.710  0.757  0.791\n",
       "Quantile 3 Mean Turnover   0.568  0.739  0.777  0.804\n",
       "Quantile 4 Mean Turnover   0.509  0.701  0.754  0.784\n",
       "Quantile 5 Mean Turnover   0.298  0.529  0.637  0.738"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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",
       "      <th>21D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean Factor Rank Autocorrelation</th>\n",
       "      <td>0.821</td>\n",
       "      <td>0.547</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     1D     5D    10D    21D\n",
       "Mean Factor Rank Autocorrelation  0.821  0.547  0.392  0.242"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x7056 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_summary_tear_sheet(lr_factor_data);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:15:26.552660Z",
     "start_time": "2021-04-15T20:15:26.546553Z"
    }
   },
   "outputs": [],
   "source": [
    "best_ridge_alpha = get_best_alpha(ridge_scores)\n",
    "ridge_predictions = ridge_predictions[ridge_predictions.alpha==best_ridge_alpha].drop('alpha', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:15:26.585946Z",
     "start_time": "2021-04-15T20:15:26.553563Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                       symbol\n",
       "2014-12-09 00:00:00+00:00  AAL       0.001953\n",
       "                           AAPL     -0.001374\n",
       "                           ABBV      0.001091\n",
       "                           AGN       0.001934\n",
       "                           AIG      -0.000471\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_factor = get_factor(ridge_predictions.predicted.swaplevel())\n",
    "ridge_factor.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:15:27.676523Z",
     "start_time": "2021-04-15T20:15:26.586838Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% 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: 74054 entries, (Timestamp('2014-12-09 00:00:00+0000', tz='UTC', freq='C'), 'AAL') to (Timestamp('2017-11-29 00:00:00+0000', tz='UTC', freq='C'), 'XOM')\n",
      "Data columns (total 6 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   1D               74054 non-null  float64\n",
      " 1   5D               74054 non-null  float64\n",
      " 2   10D              74054 non-null  float64\n",
      " 3   21D              74054 non-null  float64\n",
      " 4   factor           74054 non-null  float64\n",
      " 5   factor_quantile  74054 non-null  int64  \n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "ridge_factor_data = get_clean_factor_and_forward_returns(factor=ridge_factor,\n",
    "                                                         prices=trade_prices,\n",
    "                                                         quantiles=5,\n",
    "                                                         periods=(1, 5, 10, 21))\n",
    "ridge_factor_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:15:54.846281Z",
     "start_time": "2021-04-15T20:15:27.677383Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantiles Statistics\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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</th>\n",
       "      <td>-0.036597</td>\n",
       "      <td>0.009164</td>\n",
       "      <td>-0.003368</td>\n",
       "      <td>0.003631</td>\n",
       "      <td>14982</td>\n",
       "      <td>20.231183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.012857</td>\n",
       "      <td>0.011247</td>\n",
       "      <td>-0.001338</td>\n",
       "      <td>0.003060</td>\n",
       "      <td>14877</td>\n",
       "      <td>20.089394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.010657</td>\n",
       "      <td>0.012705</td>\n",
       "      <td>-0.000268</td>\n",
       "      <td>0.003092</td>\n",
       "      <td>14336</td>\n",
       "      <td>19.358846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.009426</td>\n",
       "      <td>0.015165</td>\n",
       "      <td>0.000752</td>\n",
       "      <td>0.003225</td>\n",
       "      <td>14877</td>\n",
       "      <td>20.089394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.007759</td>\n",
       "      <td>0.038524</td>\n",
       "      <td>0.002717</td>\n",
       "      <td>0.004053</td>\n",
       "      <td>14982</td>\n",
       "      <td>20.231183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      min       max      mean       std  count    count %\n",
       "factor_quantile                                                          \n",
       "1               -0.036597  0.009164 -0.003368  0.003631  14982  20.231183\n",
       "2               -0.012857  0.011247 -0.001338  0.003060  14877  20.089394\n",
       "3               -0.010657  0.012705 -0.000268  0.003092  14336  19.358846\n",
       "4               -0.009426  0.015165  0.000752  0.003225  14877  20.089394\n",
       "5               -0.007759  0.038524  0.002717  0.004053  14982  20.231183"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Returns Analysis\n"
     ]
    },
    {
     "data": {
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       "<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",
       "      <th>21D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ann. alpha</th>\n",
       "      <td>0.033</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>-0.007</td>\n",
       "      <td>-0.081</td>\n",
       "      <td>-0.061</td>\n",
       "      <td>0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Period Wise Return Top Quantile (bps)</th>\n",
       "      <td>1.377</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Period Wise Return Bottom Quantile (bps)</th>\n",
       "      <td>-2.776</td>\n",
       "      <td>-1.345</td>\n",
       "      <td>-1.045</td>\n",
       "      <td>-1.153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Period Wise Spread (bps)</th>\n",
       "      <td>4.153</td>\n",
       "      <td>1.920</td>\n",
       "      <td>1.136</td>\n",
       "      <td>1.353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  1D     5D    10D    21D\n",
       "Ann. alpha                                     0.033  0.023  0.014  0.011\n",
       "beta                                          -0.007 -0.081 -0.061  0.015\n",
       "Mean Period Wise Return Top Quantile (bps)     1.377  0.549  0.071  0.210\n",
       "Mean Period Wise Return Bottom Quantile (bps) -2.776 -1.345 -1.045 -1.153\n",
       "Mean Period Wise Spread (bps)                  4.153  1.920  1.136  1.353"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information Analysis\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",
       "      <th>21D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IC Mean</th>\n",
       "      <td>0.020</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IC Std.</th>\n",
       "      <td>0.181</td>\n",
       "      <td>0.169</td>\n",
       "      <td>0.169</td>\n",
       "      <td>0.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Risk-Adjusted IC</th>\n",
       "      <td>0.110</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t-stat(IC)</th>\n",
       "      <td>3.011</td>\n",
       "      <td>2.278</td>\n",
       "      <td>2.182</td>\n",
       "      <td>2.452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value(IC)</th>\n",
       "      <td>0.003</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IC Skew</th>\n",
       "      <td>0.004</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>-0.042</td>\n",
       "      <td>-0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IC Kurtosis</th>\n",
       "      <td>-0.143</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>-0.146</td>\n",
       "      <td>-0.279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1D     5D    10D    21D\n",
       "IC Mean           0.020  0.014  0.013  0.014\n",
       "IC Std.           0.181  0.169  0.169  0.155\n",
       "Risk-Adjusted IC  0.110  0.083  0.080  0.090\n",
       "t-stat(IC)        3.011  2.278  2.182  2.452\n",
       "p-value(IC)       0.003  0.023  0.029  0.014\n",
       "IC Skew           0.004 -0.016 -0.042 -0.029\n",
       "IC Kurtosis      -0.143 -0.192 -0.146 -0.279"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Turnover Analysis\n"
     ]
    },
    {
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       "      <th>1D</th>\n",
       "      <th>5D</th>\n",
       "      <th>10D</th>\n",
       "      <th>21D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Quantile 1 Mean Turnover</th>\n",
       "      <td>0.293</td>\n",
       "      <td>0.512</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantile 2 Mean Turnover</th>\n",
       "      <td>0.508</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantile 3 Mean Turnover</th>\n",
       "      <td>0.562</td>\n",
       "      <td>0.729</td>\n",
       "      <td>0.776</td>\n",
       "      <td>0.806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantile 4 Mean Turnover</th>\n",
       "      <td>0.504</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0.749</td>\n",
       "      <td>0.786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantile 5 Mean Turnover</th>\n",
       "      <td>0.297</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.633</td>\n",
       "      <td>0.737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              1D     5D    10D    21D\n",
       "Quantile 1 Mean Turnover   0.293  0.512  0.617  0.725\n",
       "Quantile 2 Mean Turnover   0.508  0.703  0.753  0.789\n",
       "Quantile 3 Mean Turnover   0.562  0.729  0.776  0.806\n",
       "Quantile 4 Mean Turnover   0.504  0.694  0.749  0.786\n",
       "Quantile 5 Mean Turnover   0.297  0.521  0.633  0.737"
      ]
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       "      <th></th>\n",
       "      <th>1D</th>\n",
       "      <th>5D</th>\n",
       "      <th>10D</th>\n",
       "      <th>21D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean Factor Rank Autocorrelation</th>\n",
       "      <td>0.824</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.252</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                                     1D     5D    10D    21D\n",
       "Mean Factor Rank Autocorrelation  0.824  0.565  0.407  0.252"
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       "<Figure size 432x288 with 0 Axes>"
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     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x7056 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_summary_tear_sheet(ridge_factor_data);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:15:54.852776Z",
     "start_time": "2021-04-15T20:15:54.847107Z"
    }
   },
   "outputs": [],
   "source": [
    "best_lasso_alpha = get_best_alpha(lasso_scores)\n",
    "lasso_predictions = lasso_predictions[lasso_predictions.alpha==best_lasso_alpha].drop('alpha', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:15:54.896728Z",
     "start_time": "2021-04-15T20:15:54.853796Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                       symbol\n",
       "2014-12-09 00:00:00+00:00  AAL       0.001872\n",
       "                           AAPL     -0.001646\n",
       "                           ABBV      0.001163\n",
       "                           AGN       0.001919\n",
       "                           AIG      -0.000530\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_factor = get_factor(lasso_predictions.predicted.swaplevel())\n",
    "lasso_factor.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:15:56.002215Z",
     "start_time": "2021-04-15T20:15:54.897452Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% 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: 74054 entries, (Timestamp('2014-12-09 00:00:00+0000', tz='UTC', freq='C'), 'AAL') to (Timestamp('2017-11-29 00:00:00+0000', tz='UTC', freq='C'), 'XOM')\n",
      "Data columns (total 6 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   1D               74054 non-null  float64\n",
      " 1   5D               74054 non-null  float64\n",
      " 2   10D              74054 non-null  float64\n",
      " 3   21D              74054 non-null  float64\n",
      " 4   factor           74054 non-null  float64\n",
      " 5   factor_quantile  74054 non-null  int64  \n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "lasso_factor_data = get_clean_factor_and_forward_returns(factor=lasso_factor,\n",
    "                                                      prices=trade_prices,\n",
    "                                                      quantiles=5,\n",
    "                                                      periods=(1, 5, 10, 21))\n",
    "lasso_factor_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-15T20:16:22.806032Z",
     "start_time": "2021-04-15T20:15:56.003253Z"
    },
    "scrolled": false
   },
   "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</th>\n",
       "      <td>-0.044839</td>\n",
       "      <td>0.009398</td>\n",
       "      <td>-0.003459</td>\n",
       "      <td>0.003828</td>\n",
       "      <td>14982</td>\n",
       "      <td>20.231183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.014281</td>\n",
       "      <td>0.011321</td>\n",
       "      <td>-0.001359</td>\n",
       "      <td>0.003232</td>\n",
       "      <td>14877</td>\n",
       "      <td>20.089394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.012327</td>\n",
       "      <td>0.013100</td>\n",
       "      <td>-0.000258</td>\n",
       "      <td>0.003285</td>\n",
       "      <td>14336</td>\n",
       "      <td>19.358846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.010948</td>\n",
       "      <td>0.015352</td>\n",
       "      <td>0.000791</td>\n",
       "      <td>0.003434</td>\n",
       "      <td>14877</td>\n",
       "      <td>20.089394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.009000</td>\n",
       "      <td>0.038298</td>\n",
       "      <td>0.002835</td>\n",
       "      <td>0.004327</td>\n",
       "      <td>14982</td>\n",
       "      <td>20.231183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      min       max      mean       std  count    count %\n",
       "factor_quantile                                                          \n",
       "1               -0.044839  0.009398 -0.003459  0.003828  14982  20.231183\n",
       "2               -0.014281  0.011321 -0.001359  0.003232  14877  20.089394\n",
       "3               -0.012327  0.013100 -0.000258  0.003285  14336  19.358846\n",
       "4               -0.010948  0.015352  0.000791  0.003434  14877  20.089394\n",
       "5               -0.009000  0.038298  0.002835  0.004327  14982  20.231183"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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     "output_type": "stream",
     "text": [
      "Returns Analysis\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",
       "      <th>21D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ann. alpha</th>\n",
       "      <td>0.034</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>-0.009</td>\n",
       "      <td>-0.078</td>\n",
       "      <td>-0.058</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Period Wise Return Top Quantile (bps)</th>\n",
       "      <td>1.967</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Period Wise Return Bottom Quantile (bps)</th>\n",
       "      <td>-2.507</td>\n",
       "      <td>-0.772</td>\n",
       "      <td>-0.764</td>\n",
       "      <td>-0.906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Period Wise Spread (bps)</th>\n",
       "      <td>4.473</td>\n",
       "      <td>1.579</td>\n",
       "      <td>0.827</td>\n",
       "      <td>1.134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                                  1D     5D    10D    21D\n",
       "Ann. alpha                                     0.034  0.022  0.012  0.009\n",
       "beta                                          -0.009 -0.078 -0.058  0.014\n",
       "Mean Period Wise Return Top Quantile (bps)     1.967  0.778  0.044  0.238\n",
       "Mean Period Wise Return Bottom Quantile (bps) -2.507 -0.772 -0.764 -0.906\n",
       "Mean Period Wise Spread (bps)                  4.473  1.579  0.827  1.134"
      ]
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     "output_type": "stream",
     "text": [
      "Information Analysis\n"
     ]
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       "      <th>21D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IC Mean</th>\n",
       "      <td>0.020</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IC Std.</th>\n",
       "      <td>0.181</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Risk-Adjusted IC</th>\n",
       "      <td>0.111</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t-stat(IC)</th>\n",
       "      <td>3.037</td>\n",
       "      <td>2.239</td>\n",
       "      <td>2.058</td>\n",
       "      <td>2.551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value(IC)</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IC Skew</th>\n",
       "      <td>0.002</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>-0.035</td>\n",
       "      <td>-0.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IC Kurtosis</th>\n",
       "      <td>-0.152</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>-0.161</td>\n",
       "      <td>-0.307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                     1D     5D    10D    21D\n",
       "IC Mean           0.020  0.014  0.013  0.014\n",
       "IC Std.           0.181  0.168  0.168  0.155\n",
       "Risk-Adjusted IC  0.111  0.082  0.075  0.093\n",
       "t-stat(IC)        3.037  2.239  2.058  2.551\n",
       "p-value(IC)       0.002  0.025  0.040  0.011\n",
       "IC Skew           0.002 -0.011 -0.035 -0.022\n",
       "IC Kurtosis      -0.152 -0.192 -0.161 -0.307"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
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     "output_type": "stream",
     "text": [
      "Turnover Analysis\n"
     ]
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Quantile 1 Mean Turnover</th>\n",
       "      <td>0.296</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantile 2 Mean Turnover</th>\n",
       "      <td>0.513</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantile 3 Mean Turnover</th>\n",
       "      <td>0.566</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantile 4 Mean Turnover</th>\n",
       "      <td>0.504</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantile 5 Mean Turnover</th>\n",
       "      <td>0.297</td>\n",
       "      <td>0.527</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.739</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              1D     5D    10D    21D\n",
       "Quantile 1 Mean Turnover   0.296  0.517  0.619  0.728\n",
       "Quantile 2 Mean Turnover   0.513  0.705  0.752  0.789\n",
       "Quantile 3 Mean Turnover   0.566  0.737  0.778  0.806\n",
       "Quantile 4 Mean Turnover   0.504  0.698  0.752  0.783\n",
       "Quantile 5 Mean Turnover   0.297  0.527  0.636  0.739"
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean Factor Rank Autocorrelation</th>\n",
       "      <td>0.822</td>\n",
       "      <td>0.553</td>\n",
       "      <td>0.397</td>\n",
       "      <td>0.243</td>\n",
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       "                                     1D     5D    10D    21D\n",
       "Mean Factor Rank Autocorrelation  0.822  0.553  0.397  0.243"
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kd1Zp6dKlslqtdmexbmfIkCG2Y6b+69q1qyT793F8fLyuXLkis9ms6tWra//+/bZ9hw4d0tGjR9W5c2dVqlQpzTHM5tt//fjhhx+UkJCg3r172539DQgIUK9evZSQkKDt27fbPaZw4cIOL2SR+pmTOrYzkrr/n1Pssir1dbNarYqLi9Ply5cVHh6uwMBAu9ctVWRkpO1sb6p69erZFiFxpVWrVql8+fK677777N4/SUlJatCggX7++Wddu3bNpccE7iZM8wNg06VLFw0cOFC7d+/W0qVLVa1aNVWoUCHdtsePH5d0M6xk5OLFi7b/vnTpkiZMmKBNmzalew1QbGxsmulypUqVsvs5dSpMdHS0Q89HuvnFsFatWjKZTPL391f58uXtjnP8+HHFx8fbLchwq0uXLqlcuXK2n8uWLevQsS9fvqyEhIR0X8PChQsrJCTEdh3RpUuXlJCQoPLly6dpm9HvICN169bV7t279fPPP6t+/fras2ePBg0aZNtfq1Yt23VCqaGqTp06mfbZrl07rVq1StOmTdOcOXNUvXp1PfDAA2rXrp1t2t7ly5cVHR2tbdu2qX79+un248iX6FRvvPGGypUrp8TERO3cuVPz589XbGys3UX1WR2HrvbPcXGrkiVLptlWuHDhLI3fIkWKpBmbbdu2VatWrfT666/r22+/tV071a1bNy1fvlyrVq1S3759ZRiGli1bpsqVK6tKlSoOH9NisWT4fvjrr7/08ccfa9u2bYqNjbXb98/FGVIDY0REhMPHvVXqtT4VK1ZMt0ZJaa7DK1WqlDw8PBzqP/VzIC4uLtN2qaGraNGiDvWbnh07dmjKlCn65Zdf0vwxIiYmJk37Wz/7pJtjR8ra558jjh8/rmvXrmX4npWkK1eu6J577nHpcYG7BWEKgM0DDzyg4sWL65NPPtHOnTv173//O8O2hmFIkl566SVVrlw53TapF8obhqF+/frp+PHjtmWXAwMD5eHhoaVLl2rNmjXpXjif0Zei1GM7IrMvhql9BQUF6aOPPsqwza1f5hxdrS0rdaa2dcVqYXXr1tUnn3yiXbt2KSAgQAkJCXbXQ9WpU0ejR49WfHy8du3aJbPZnOn1UtLN1f0+++wz7d+/X99//712796tiRMnavLkyfroo4/UsmVL23No0KCBBgwYcMfPo1q1arZrNZo3b66iRYvqo48+UuXKldWzZ09JWRuHzkpJSclwn4+PT4b7HP1Sn1UBAQGKjIzUpk2bdPLkSVvYrlGjhiwWi5YuXaq+fftqx44dOnPmjPr37++S48bHx+uxxx5TYmKi+vTpI4vFIn9/f5nNZk2fPj3dFT9zevW7rKykWLZsWXl5eenQoUOZtktdhOGfC1Rk9LxuXXhHurn4Tv/+/VW6dGmNGDFCJUuWlI+Pj0wmk4YNG5bu50RmYycrnyuOMAxDFovF7jrCW916nSKA/yFMAbDx8PBQx44dNX36dPn4+Khdu3YZtk09O+Pr65tpWJGk33//XYcPH9aQIUP07LPP2u376quv7rjuO1GmTBn9+eefql69uvz9/V3ad3BwsPz9/XXs2LE0+2JiYhQVFWULAMHBwfLz87Odafmn9B6fmfvvv1/e3t7auXOnAgIC5OPjY3cBee3atZWcnKxdu3Zpz549qly5sgoVKuRQ39WqVVO1atUkSX///bc6duyoCRMmqGXLlgoKClLBggV19erV244JZzzxxBNasmSJJkyYoPbt2ysgICBL4/B2MjprdKerEGaH1C/tt04969q1q9577z3t379fS5Yskbe3t8OLXdzOjh07dOHCBb3//vtppv/eeqPo1DN2d7IaXOrZmaNHj6Y5a5L6nkjvDI6jvL291aRJE3377bfaunVrhotQpH5G/XNlwNT3S3R0tO2MkZT+WFmzZo1SUlI0c+ZMu3oTEhLSnN3LKleE1TJlyujKlSuqV69els4cA7iJdw0AOz169NDQoUP11ltvZXotwQMPPKDg4GDNnDkz3S+g165ds02PSf0/6Fv/onrkyJF0l0bPSR07dpTVatX48ePT3X8nU8TMZrOaNm2qgwcPauvWrXb7ZsyYIavVqhYtWki6GWSbNm2qAwcO2P2F3zAMzZo1K0vH9fLyUmRkpH777Tdt2bJFkZGRdveNslgsKly4sD799FPbKn+3c+vqbpIUGhqqoKAg2zQls9ms9u3ba//+/Rne9Dcry7zfqkCBAho0aJCio6M1b948SVkbh9LNe3HFxMSk+9f9smXL6o8//tD58+dt25KSkvT55587XXN2uHz5svbu3Stvb+801+516NBB3t7emjVrlr799lu1atXKZTfbTT1bcutrt23btjS3LKhUqZIqVqyopUuX6ujRo2n6+mcf/v7+6f7uGjZsKD8/Py1YsMDud3j16lUtWLBAfn5+thUrnfXss8/K29tbb775ZrpLiC9ZskTr1q1T/fr17a5lTA3xt16z9dlnn6XpI6OzTNOnT0/3jHxWpF6LdSdT/zp27KioqKh0a5eyd5oscDfgzBQAO/fee6+eeeaZ27bz8/PTmDFjNGTIELVp00ZdunRRmTJlFBsbqxMnTujbb7/V5MmTVbduXYWFhalixYqaNWuWrl27pnLlyumPP/7Ql19+KYvFot9++y0Hnln62rRpo86dO2vBggX67bff1LRpUxUpUkTnzp3Tvn37dPLkSW3atMnp/ocPH67t27dryJAhevTRR1W6dGnt3r1b69atU+3atdWpUydb2+eff15bt27V4MGD1atXL4WGhmrLli3pBpnbqVu3rnbu3Km9e/em+X2aTCbVqlXLtvjF7RafkKSpU6fqhx9+0IMPPqiSJUvKMAxt2bJFJ06csFsgY9iwYdqzZ4+ef/55/etf/1L16tVVoEABnT17Vlu3btV9992nDz74IMvPJ1WHDh30ySefaM6cOerdu7cCAwMdHofSzQUdtmzZorffflv333+/PDw8VK9ePQUHB+uxxx7T2rVr1bdvX/Xo0UM3btzQypUrXXoT3qw6f/68Vq5cKenm4gVnz57V0qVLFRsbq2HDhqW5zrBQoUJq3bq1Vq1aJUm2hSNcoWbNmgoJCdGYMWN05swZhYaG6tChQ1q5cqUsFouOHDlia2symfT++++rb9++6tq1q21p9NjYWP30009q1KiR7Z5e1atXt51xDAsLs/0RomDBgnrhhRf09ttvq1u3brb3yvLly3Xy5Em9/fbbt1084nYsFovGjx+vESNGqH379rYFMxITE7V161Z9//33qlKliv7zn//YnQV66KGH9PHHH+uNN97QiRMnVKRIEW3dulVXrlxJc4wWLVpozpw5GjBggLp3764CBQrohx9+0O+//55mSfSsql69uhYsWKC33npLTZo0UYECBVStWrUsnbF7/PHHtX37dn344Yf68ccfVa9ePQUEBOjs2bP68ccf5eXlpfnz599RncDdjDAFwGmNGjXSkiVLNGPGDK1atUpXrlxRwYIFVbp0afXt29d2U0sPDw9Nnz5dY8aM0fLly5WYmKiKFStqzJgxOnz4sFvDlCSNHj1adevW1eLFizV9+nTduHFDISEhioiI0IgRI+6o7xIlSmjx4sWaOHGiVq1apbi4OBUvXlyDBg3SU089ZbeYQunSpfX5559rzJgxWrBggby8vNSoUSN9+OGHWZ7C9s+AlN6Zp9q1a2vjxo3y8PCwuzdQRlq0aKGoqCht2LBBFy9elI+Pj8qUKaN3333XbqW4wMBALVy4ULNnz9aGDRu0adMmeXh4KDQ0VDVr1rzjL/eenp4aOHCg3nzzTc2dO1dDhw51eBxKUp8+fXTq1Cl9/fXXWrRokaxWq+bNm6fg4GDVrFlTH3zwgaZNm6axY8eqWLFi6tmzp6pUqZJmZcmccujQIb300ku2n/39/VW5cmWNGDEiw2m43bt316pVq1SmTBmHzjo6qmDBgpo1a5bGjh2rBQsWKDk5WVWqVNHMmTO1ZMkSuzAl3ZwSumTJEk2ZMkXr16/XokWLVLhwYVWrVs3uXkjDhg1TTEyMvvjiC8XGxsowDG3atEl+fn567LHHVKxYMX366af65JNPJN0865V6o2RXaNGihdasWaNZs2bpu+++0+LFi233CuvYsaPef//9NGeXAgICNGPGDI0ePVrTp0+Xn5+fWrVqpbFjx6a5/rBmzZqaNGmSpkyZov/85z/y9vZWgwYNtGDBAvXq1euOan/ooYd06NAhrV27Vhs2bJDVatXo0aOzFKYKFCig6dOn64svvtDKlSs1adIkSTevNaxatardH3wApGUyXH0lIwAAcJv9+/era9euGj58uN0qjnDchQsX1KtXL124cEGzZs1y6A8OAPInrpkCAOAusmDBAhUoUMDh+y0hrWLFimnu3LkKCgrSwIED070XFABITPMDACDPS0hI0JYtW3T06FGtWrVK3bp1U0hIiLvLytPuuecebd682d1lAMjlmOYHAEAed/r0aTVv3lx+fn5q3Lix3nvvvTSLUwAAXI8wBQAAAABO4JopAAAAAHACYQoAAAAAnJDvF6C4ciVeViszHbMiODhAly5dvX1DwAUYb8hpjDnkJMYbchLjLevMZpOKFPHPcH++D1NWq0GYcgKvGXIS4w05jTGHnMR4Q05ivLkW0/wAAAAAwAmEKQAAAABwAmEKAAAAAJxAmAIAAAAAJxCmAAAAAMAJ+X41v9tJTr6h+PhYXb+eKKs1xd3l5AoXLphltVpd2qeHRwEFBBSSr2/GS08CAAAAuQlhKhPJyTd0+fJ5+fkFKigoVB4eHjKZTO4uy+08Pc1KTnZdmDIMQzduXFd09EV5ehZQgQJeLusbAAAAyC5M88tEfHys/PwCFRBQSJ6engSpbGIymeTl5SN//0K6ejXa3eUAAAAADiFMZeL69UT5+DDtLKf4+Pjqxo0kd5cBAAAAOIQwlQmrNUUeHh7uLiPfMJs9uC4NAAAAeQZh6jaY2pdzeK0BAACQl7AABQAAuKv5F/SVn7drvvIkXE9WfGyiS/oCkPcRppzkyg9mZ93JB/qFC+f1xRfz9fvvh3Ts2BElJiZq4sRpqlGjll27Rx5pr3Pn/pYkmc1m+fn5qXjxUFWufJ/atm2vatUi7/RpAACQrfy8PWV+YbVL+rKOa694l/QE4G5AmHKSKz+YnXUnH+inT5/Sxo1fy2KppJo1a2vbtq0Zto2MrKHBg5+RJCUkxOuvv/7QunVrtGbNSnXq1FUjRrzsZBUAAABA3kWYyqciI2tozZpvJUlbt/430zAVGBioKlWq2n5u0KCBOnfurrFjR2v58q9Utmw5denSLdtrBgAAAHITFqDIp8zmO/vVm81mPf/8CypcuLAWLpzvoqoAAACAvIMwBad5e3urVq26Onfub50/f87d5QAAAAA5ijCFO1K8eKgk6dKli26uBAAAAMhZhCncEcMw3F0CAAAA4BaEKdyR1Ol9RYuGuLkSAAAAIGcRpuC069ev6+efd+nee0uoWLHi7i4HAAAAyFGEKTjFarVqwoRxio6O1qOP9nZ3OQAAAECO4z5T+diWLRslSYcOHZQk7du3RzEx0fLx8VX9+g1t7eLi4nTgwK+SpMTEeP31159au3aNjhw5rEce6a6OHR/J+eIBAAAANyNM5WOvvz7S7ufZs2dIkkJD79GSJatt2/ft26PBg5+QyWSSr6+fQkNDFRFRRcOGvaiqVavnaM0AAABAbkGYclLC9WRZx7V3ew13Ytu23bdt889QlcrT06zkZOsdHRsAAADI6/JcmNqxY4dWrlypvXv36ty5cypUqJCqVaumZ555RuHh4TlWR3xsouJz7GgAAAAAcps8F6YWLlyo6Oho9e3bV2FhYbp48aJmzZqlRx55RPPnz1dkZKS7SwQAAACQD+S5MPXmm28qODjYbtsDDzyg5s2b69NPP9WkSZPcVBkAAACA/CTPLY1+a5CSpIIFC6pMmTI6d+6cGyoCAAAAkB/luTCVnsuXL+vo0aOqWLGiu0sBAAAAkE/k+TBlGIZef/11Wa1W9e/f393lAAAAAMgn8tw1U7f68MMPtXHjRo0ePVphYWFZfnxwcECG+y5cMMvTM8/nzWyRXa+L2WxWSEhgtvSNvIsxgZzGmENmXD0+GG/ISYw318rTYerjjz/W7Nmz9eqrr6pz585O9XHp0lVZrUa6+6xWK/dTSkd23mfKarUqKiouW/pG3hQSEsiYQI5izN19XP3l0ZXjg/GGnMR4yzqz2ZTpyZc8G6b+85//aNq0aXrxxRf1+OOPu7scAAAAQP4FfeXn7bqv2AnXkxUfm+iy/uBaeTJMTZ48WVOmTNFzzz2nJ5980t3lAAAAAJIkP29PmV9Y7bL+rOPaK95lvcHV8lyYmj17tiZNmqSmTZuqQYMG2rdvn22fl5eXIiIi3FdcHrFnz249++zgdPd9/vkSlSlTVpL0yCPtde7c35JuXsvk5+enYsWK6777qupf/3pI1apF5lDFAAAAQO6T58LUli1bbP+b+t+pSpQooc2bN+dIHUUKecjTyy9HjpWR5KQEXYlJcfrxTz31jKpXr2G3LTT0HrufIyNraPDgZyRJCQnx+vPPE9qwYZ1Wr16hTp26asSIl50+PgAAAJCX5bkwNX/+fHeXIEny9PLTHxO83FpDueeTJDl/EWGpUmVUpUrVTNsEBgbatalTp5569HhUo0e/p+XLv1LZsuXUpUs3p2sAAAAA8irW/UaWmc1mPf/8CypcuLAWLswd4RYAAADIaYSpfGzs2PfVpEldtW7dRC+99LwOHz7k8GO9vb1Vq1ZdnTv3t86fP5eNVQIAAAC5U56b5oc7FxAQoG7deur++2uqYMFC+vPPP7RgwVw9/XR/TZo0Q/fdV8WhfooXD5UkXbp00fbfAAAAQH5BmMqHLJZKslgq2X6uXv1+PfBAYz3+eHfNmDFF//nPFIf6MYz0b3YMAAAA5AdM84MkKTi4qGrXrqeDB391+DGp0/uKFg3JrrIAAACAXIswBRvDsEoyOdT2+vXr+vnnXbr33hIqVqx49hYGAAAA5EKEKUi6ed3TTz/tcuh6KavVqgkTxik6OlqPPto7B6oDAAAAch+umcqH3nrrNd17bwlZLJUUGBiov/76U59/Pk/Xr1/T4MFD7drGxcXpwIGbU/8SE+P1xx9/6Ouv1+r33w/rkUe6q2PHR9zxFAAAAAC3I0zlQ+XLV9CmTd9o6dIvlZiYqEKFCuv++2uoT5/+Kl++gl3bffv2aPDgJ2QymeTr66fixYurSpVqev75F1W1anU3PQMAAADA/QhTTkpOSlC555PcXoMzevfuq969+9623ZIlq9Pd7ulpVnKy1aljAwAAAHcLwpSTrsSkSIpzdxkAAAAA3IQFKAAAAADACYQpAAAAAHACYQoAAAAAnECYAgAAAAAnEKYAAAAAwAmEKQAAAABwAmEKAAAAAJxAmAIAAAAAJxCmAAAAAMAJhCkAAAAAcIKnuwvIq/wLecvPy8utNSQkJSk+5rpTj71w4by++GK+fv/9kI4dO6LExERNnDhNNWrUStP2p59+1MyZ03Ts2FH5+fnpwQebatCgZxQYGGhrs27dar3//lu2n728vBUYGKhy5cqrfv2GateugwICApyqFQAAAMiNCFNO8vPyksdnL7i1hpQnxilezoWp06dPaePGr2WxVFLNmrW1bdvWdNvt2bNbL7zwnBo1elADBjylixejNG3aZB0/fkyffDJLZrP9yc3XXntLJUuWVkpKsi5evKi9e3drxowpWrToc40ZM14WSyWn6gUAAAByG8JUPhUZWUNr1nwrSdq69b8ZhqmpUyeqfPkwvf32aFtwKlasmJ577mlt2bJRzZu3smsfFlZBFSuG235u3rylOnXqqiFDBmjkyBFauHCZvL29s+lZAQAAADmHa6byqVvPKKUnKuqCDh06qNat29q1r1u3nkJCium//93s0LHCwiqoT5/+unDhvDZv/tbpmgEAAIDchDCFDJ04cVySVL58hTT7ypevoD/+OO5wXw0bNpIk7du3xzXFAQAAAG6W5Wl+MTExunTpkkwmk4KCglSoUKHsqAu5QExMjCSpYMGCafYVLFhQR44cdriv4sVDJUmXLl10TXEAAACAm902TFmtVm3atEkbNmzQrl27dPGi/ZfhokWLqk6dOvrXv/6lZs2aOTR9DHmNKf2tpvS3p89wTSkAAABALpFhmEpJSdHChQs1ffp0RUVFydfXV1WrVlWTJk1UuHBhGYahmJgYnTx5Ups3b9batWsVEhKiwYMHq0ePHvLw8MjJ54FskHrWMTY2Js2+2NjYdM9YZeT8+XOSpKJFQ1xTHAAAAOBmGYapdu3a6dy5c2rbtq06dOig2rVrZ3jWyWq1aufOnVq1apXGjh2rBQsWaP369dlWNHJGuXLlJUknThxTnTr17PadOHFMVapUc7ivbdu+l3RzFUEAAADgbpBhmGrcuLEGDhyookWL3rYTs9ms+vXrq379+ho+fLhmzpzp0iLhHsWKFVelShH65psN6tbtUVuY/umnnYqKuqAmTZo61M+JE8c0b95sFS8eqmbNWmZnyQAAAECOyTBMvfLKK051GBIS4vRjkbO2bNkoSTp06KCkmyvtxcREy8fHV/XrN5QkPfXUMxo+fKj+/e9X9fDDnf7/pr2TFBFRRU2btkjT5/Hjx3T9epJSUlJ06dJF7dnzkzZsWKuCBQvpgw/Gy8vLK+eeIAAAAJCNuGlvPvb66yPtfp49e4YkKTT0Hi1ZslqSVLNmbY0Z87E+/XS6Xnrpefn5+alx46YaPPiZdK+Le/fdNyVJBQoUUMGCBVWuXJgGDHhKDz3UQf7+Adn8jAAAAICc43CYOnnypE6ePKnGjRvbtv3yyy+aOnWqoqOj1alTJ3Xv3j1bisyNEpKSlPLEOLfXcCe2bdvtULt69RqoXr0Gtp89Pc1KTrbatWnbtr3atm1/R/UAAAAAeYnDYWrcuHGKjo62hanLly9rwIABSkhIkLe3t/79738rODhYLVqknfp1N4qPua54XXd3GQAAAADcxOGbQh04cEANGvzv7MTatWt19epVLVu2TDt27FD16tU1d+7cbCkSAAAAAHIbh8PU5cuXVaxYMdvP33//vWrUqCGLxSIvLy+1bdtWx48fz5YiAQAAACC3cThM+fr6Ki4uTtLNG/r+/PPPqlWrlm2/j4+Prl696voKAQAAACAXcjhMVaxYUStXrtSVK1e0ePFiJSQkqGHDhrb9Z86cUVBQULYUCQAAAAC5jcMLUPTv319PP/207bqpypUr252Z+uGHHxQREeH6CgEAAAAgF3I4TD344IOaO3euNm3apICAAPXq1Usmk0mSdOXKFYWGhqpjx47ZVScAAAAA5CpZumlv7dq1Vbt27TTbixQposmTJ7usKAAAAADI7bIUplKdOHFCp06dkiSVKlVK5cuXd2lRAAAAAJDbZSlM7dixQ++++65OnDhht718+fJ67bXXVL9+fZcWl5Fz585p1qxZ+u2333T48GElJCRo3rx5qlu3bo4cHwAAAAAcDlM7duzQgAEDVKBAAXXt2lUVKlSQYRg6fvy41qxZowEDBmjmzJk5EqhOnjyptWvXKiIiQvXq1dPmzZuz/Zi3KhLoIU8fvxw/7j8lX0vQlbiULD9u9+5d+vrrdTpwYL8uXDivggULqXLl+9S//yCFhVWwtVu/fo22b9+m338/pLNnzygysoYmT56Rpr9161br/fffsv3s5eWtwMBAlStXXvXrN1S7dh0UEBDg3JMEAAAAcimHw9THH3+s4OBgLV68WMWLF7fb9/TTT6tbt26aMGFCjoSp2rVra8eOHZKkjRs3uiVMefr46WAfU44f958i5hrS/9/7KytWrFiq2NgYde/+qMqUKafLly/riy/macCAxzVx4nRVqVJVkvT11+t05coVRUbW0LVr127b72uvvaWSJUsrJSVZFy9e1N69uzVjxhQtWvS5xowZL4ulUpZrBQAAAHIrh8PU77//rkGDBqUJUpIUGhqq7t27a8aMtGctsoPZ7PDtsZCOESNeVpEi9vcEq1Onnrp2fVgLF87Te++NlSSNHz/Z9lr37fvobfsNC6ugihXDbT83b95SnTp11ZAhAzRy5AgtXLhM3t7eLnwmAAAAgPs4nEoCAwPl7++f4f6AgAAFBga6pChkr1uDlHTz91uqVClduHDBts0VoTUsrIL69OmvCxfOa/Pmb++4PwAAACC3cPjbcps2bbR27VolJyen2Xfjxg2tXbtWbdq0cWlxyDlXrlzRiRPHVb58mMv7btiwkSRp3749Lu8bAIC87FryDYWEBLrkn38hZn8AOc3haX49evTQnj171KtXL/Xp00fly5eXyWTSsWPHNHfuXKWkpKhnz546e/as3ePuvfdelxftSsHBGS+McOGCWZ6euXtKoSvqMwxD48a9L8Mw1KvX4+n2aTJJJpPJtu+fbczmm9eOeXik/3qVKHFzDFy+fOm29ZrNZoWEcIYT9hgTyGmMOWTG1ePD47MXXNLPjcfelZ8La7MmXZPZy8dl/cF5rhxzfL65lsNh6qGHHpLJZJJhGPrll1/s9hmGYWtzq0OHDt1hidnr0qWrslqNdPdZrVYlJ1tzuKKscUV9kydP0HffbdErr7ypUqXKptunYdz8PScnW+XpabZrk/r6paSk/3olJ6f8fx/Gbeu1Wq2Kisr6ohq4e4WEBDImkKMYc3cfV395dOX4cGVtZi8fly6OFTHX4L3ghOwIK676PfD5lnVmsynTky8Oh6khQ4bIZHLv6nVwvenTP9GiRQv03HMvqG3b9tlyjPPnz0mSihYNyZb+AQAAAHdwOEw988wz2VkH3GDWrGmaP/8zPf30s+ratUe2HWfbtu8lSZGRNbLtGAAAAEBOczhM5TYbNmyQJP3666+SpJ9++klXrlyRr6+vmjRp4s7S8oTZs2dozpxZevLJwXr00cez7TgnThzTvHmzVbx4qJo1a5ltxwEAAAByWpbD1Lp167Rx40adOnVKklSqVCm1aNFCbdu2dXlxmXnuuefsfp40aZIkqUSJEm65iW9esnDhAs2ePUMNGjRSrVp1deDAr7Z9Xl4FbDfX/eOPE/rzzxOSpPj4q0pOTtaWLRvl4WGWxVJZoaH32PV7/PgxXb+epJSUFF26dFF79vykDRvWqmDBQvrgg/Hy8vLKuScJAAAAZDOHw1RiYqKefvpp/fjjjzIMQwULFpRhGPr111+1fv16ffnll5o6dar8/Pyys16b33//PUeOczf64YetkqTt27/X9u3f2+0LDb1HS5asliRt3vytPvtspt3+118fKUl65ZU301xj9e67b0qSChQooIIFC6pcuTANGPCUHnqog/z9M75wDwAAAMiLHA5T48eP144dO9S7d28NHDhQISE3FxOIiorSjBkzNH/+fH388cd69dVXs63Y3CT5WoIi5qa/CmBO1uCMyZNnONSuf/9B6t9/UJrtt67m17Zt+2xbvAIAAADIrRwOU+vXr1ebNm3ShKWQkBC9+uqrOn/+vNavX59vwtSVuBQpjqUlAQAAgPzK4Tu+Xr16VXXr1s1wf7169XT16lWXFAUAAAAAuZ3DYSo8PFwnT57McP/JkydlsVhcUhQAAAAA5HYOh6nnn39eixcvTnelvI0bN+qrr77SsGHDXFocAAAAAORWGV4zNWrUqDTbSpYsqSFDhqhcuXIKCwuTyWTSsWPH9Mcff8hisWj16tWqX79+thYMAAAA5BfW5GsKCQl0WV9wrQzD1PLlyzN80IkTJ3TixAm7bb///ruOHDmi999/33XV5QKGYchkMrm7jHzBMNy7OiIAAEBuY/b00R8TXHOvznLPJ0m64ZK+cFOGYerw4cM5WUeu5OFRQDduXJeXl4+7S8kXbtxIkodHlu8jDQAAALgF31wzERBQSNHRF+XvX0g+Pr4ymz04S5UNDMPQjRtJio6OUmBgEXeXAwBAhlw55QpA3keYyoSvr788PQvo6tVoxcfHyGpNcXdJuYLZbJbVar19wyzw8PBUYGAR+fr6u7RfAABcyZVTrqTUaVcA8qoMw9SLL76oZ599VqVKlcpSh3/++acmT56scePG3XFxuUGBAl4qUqSYu8vIVUJCAhUVxQ2LAQAAkL9lGKb++usv/etf/1KzZs3UoUMHNWzYUD4+6V87FB8fr++//14rV67U999/rypVqmRbwQAAAACQG2QYpr788kutXr1aU6ZM0ZAhQ+Tp6akKFSqodOnSKlSokAzDUExMjP7880+dOHFCKSkpqlChgj788EO1bds2J58DAAAAAOS4TK+Zat++vdq3b68dO3Zo/fr12r17tzZu3Gi7XsZsNissLEw9evRQmzZtVLt27RwpGgAAAADczaEFKOrXr2+7Ga/ValV0dLQkqUiRIqxuBwAAACBfyvJqfmazWUFBQdlRCwAAAADkGWZ3FwAAAAAAeRFhCgAAAACcQJgCAAAAACcQpgAAAADACYQpAAAAAHACYQoAAAAAnJDlpdElKTExUdHR0TIMI82+e++9946LAgAAAIDczuEwZbVaNWvWLM2fP18XL17MsN2hQ4dcUhgAAAAA5GYOh6lx48Zp9uzZqlixolq3bq3ChQtnY1kAAAAAkLs5HKZWrVqlRo0aaebMmdlZDwAAAADkCQ4vQBEbG6vmzZtnZy0AAAAAkGc4HKYsFouioqKysxYAAAAAyDMcDlNDhw7VokWL9Pfff2dnPQAAAACQJzh8zdSBAwd07733qm3btmrZsqVKliwps9k+i5lMJg0ZMsTlRQIAAABAbuNwmJo8ebLtv1etWpVuG8IUAAAAgPzC4TC1adOm7KwDAAAAAPIUh8JUQkKCli9frurVq6tRo0bZXRMAAAAA5HoOLUDh5+en6dOn69y5c9ldDwAAAADkCQ6v5le6dGmWRgcAAACA/+fwNVOPPvqoZs2apZ49e6pIkSLZWRMAAACAXK5IoIc8ffxc0lfytQRdiUtxSV85yeEw5e/vr0KFCqlNmzbq1KmTypQpI19f3zTtOnbs6Mr6AAAAAORCnj5+OtjH5JK+IuYaUlycS/rKSQ6HqZEjR9r+e86cOem2MZlMhCkAAAAA+YLDYWrevHnZWQcAAAAA5CkOh6k6depkZx0AAAAAkKc4vJofAAAAAOB/HD4zNXny5Nu2MZlMGjJkyB0VBAAAAAB5gUvClMlkkmEYORam4uPj9fHHH2vDhg2KjY1VhQoVNGTIEDVv3jzbjw0AAAAAUhbC1KZNm9JsS0lJ0V9//aU5c+bo6tWr+uCDD1xaXEaGDh2qgwcP6oUXXlDJkiW1fPlyDR06VNOmTVOTJk1ypAYAAAAA+ZvDYapEiRLpbi9durQaNmyoxx57TMuWLdPw4cNdVlx6vvvuO23fvl2TJ09Wy5YtJUn16tXTqVOn9MEHHxCmAAAAAOQIlyxAYTKZ1Lp1a61YscIV3WXq22+/VWBgoN2UPpPJpE6dOunEiRM6duxYttcAAAAAAA6fmbqdGzduKDo62lXdZejo0aOqUKGCzGb7HBgeHi5JOnLkiCpUqJDtdQAAAAB5ybXkGwoJCXR3GXcVl4SpX3/9VfPmzVNYWJgrustUdHS0ypYtm2Z7oUKFbPsBAAAA2PPxLCCPz15wWX8pT4xzWV95lcNhKqOV8mJiYhQfHy8PDw+9++67LissMyaTyal96QkODrjTcrLFtRsp8ing4bL+rMmJMnv6uqSvxOQkl/5VIyUpUR5erqnNmnRNZi8fl/SVnzDenMN4c54rx5wrx5vk2jGXbE2Rp9l17y0459qNFFnHtXdJX9bkRJV7PsklfUk3x5urvpCmJCUqYq7hkr6km59xnMXIOleON8m1Y86V401y7ZjLq+PN4TB17733ptlmMpl03333qWzZsurWrZtKlizp0uLSU7hw4XTPPsXExEj63xkqR126dFVWq+s+eFwlJCRQ5hdWu6w/67j2+mOCl0v6Kvd8ksv/qnGwT9ZCcEYi5hqKiopzSV/5CePNOYw357lyzLlyvEmuHXMpT4xjjOQSrv0tuK63kJBAF48RV4+3Gy7uL39w/bveNT26frxJrn22uW+8mc2mTE++OBym5s+f75KC7lSFChX0zTffyGq12l03deTIEUmSxWJxV2kAAAAA8hGHV/NbsWKFTp8+neH+06dP58hqfi1btlRsbKw2b95st33FihUqV64ci08AAAAAyBEOn5kaNWqUPvzwwwyn8u3fv1+jRo1Sx44dXVVbupo0aaK6devq1VdfVXR0tEqWLKkVK1bo559/1pQpU7L12ABy3rXkG1zgCgAAciWHw5RhZH5d0Y0bN9IsV54dTCaTpkyZovHjx+vjjz9WbGysKlSooMmTJ6tZs2bZfnwAOcvHs4BL53fnxYtbAQBA7pSlpdEzWikvNjZW3333nUJCQlxS1O0EBATojTfe0BtvvJEjxwMAAACAW2UapiZPnqxPPvlE0s0g9eKLL+rFF1/MsP0TTzzh2uoAAAAAIJfKNExVqlRJHTt2lGEYWrFihWrVqqVSpUqlaefv76/q1avroYceyrZCAQAAACA3yTRMtWjRQi1atJAknTlzRk8//bTq16+fI4UBAAAAQG6W5+4zBSD3syZfc9nd2q3J11zSDwAAgKtlaQGKlJQUrV69Wtu2bdOlS5f04osvKiIiQjExMdqyZYvq16+v4sWLZ1etAPIIs6ePy1bgu7n6Xu67IzoAAIDDYSoxMVH9+vXT3r175evrq2vXrikmJkbSzdX1xo0bpy5dumjYsGHZViwAAAAA5BYO3xhq0qRJOnDggCZPnqxNmzbZ3XfKw8NDrVq10rZt27KlSAAAAADIbRwOUxs2bFD37t3VokWLdO83Vbp0aZ05c8alxQEAAABAbuXwNL8LFy4oPDw8w/2+vr6Kj493SVEAALjLteQbSnlinEv6SkhyzUIsAIDcyeEwVbhwYZ0/fz7D/UePHlWxYsVcUhQAAO7i41nAZQuoAADubg5P86tfv76WLVumxMTENPtOnTqlpUuXqlGjRi4tDgAAAAByK4fD1NChQxUbG6tHHnlECxculMlk0vfff6+PPvpInTt3lpeXlwYNGpSdtQIAAABAruFwmCpTpozmzJkjDw8PTZw4UYZhaPbs2Zo5c6ZCQ0M1d+5c3XPPPdlZKwAAAADkGlm6aW+VKlW0atUqHTlyRMePH5dhGCpbtqwiIiKyqz4AAAAAyJWyFKZSWSwWWSwWu22nT5/WlClT9P7777ukMADIDsnXEhQx17h9Qwf7AgAA+ZdDYcowDF2+fFmBgYHy8vKy23f27FlNnTpVy5cvV0pKCmEKQK52JS5FimOlNgAAcOduG6ZmzJihWbNmKS4uTmazWa1bt9Z7772nAgUKaOLEiZozZ46SkpJUo0YNPf300zlRMwAAAAC4XaZhavny5Ro/frx8fX1133336e+//9b69esVEBCgqKgobdmyRbVr19bQoUNVt27dnKoZAAAAANwu0zC1ePFilSxZUl988YWKFSum5ORkDR8+XF999ZW8vb01fvx4tW3bNqdqBQAAAIBcI9Ol0Y8ePaquXbuqWLFikiRPT08NHDhQhmHoySefJEgBAAAAyLcyDVPx8fEKDQ2123bvvfdKkqpWrZp9VQEAAABALpdpmDIMQ2azfROTySRJaVb1AwAAAID85Lar+R04cEDe3t62n+Pj4yVJP//8s+LSWV64VatWLiwPAAAAAHKn24apefPmad68eWm2T5482XaWSrp5FstkMunQoUOurRAAgExYk6+p3PNJLu0PAABHZBqmRo8enVN1AADgFLOnj6KiXHcj5pCQQEk3XNYfAODulWmY6tSpU07VAQAAAAB5SqYLUAAAAAAA0keYAgAAAAAnEKYAAAAAwAmEKQAAAABwAmEKAAAAAJxAmAIAAAAAJxCmAAAAAMAJmd5n6lYJCQlas2aN/vzzT0VHR8swDLv9JpNJ77//vksLBAAAAIDcyOEwtX//fg0cOFDR0dEZtiFMAQAckXA9WdZx7V3WFwAA7uBwmBo9erSSk5M1YcIE1atXT4ULF87GsgAAd7P42ETFu7sIAADukMNh6rffftOgQYPUpk2b7KwH2cSafE3lnk9ySV+JyUlKeWKcS/oCAAAA8iqHw1RAQABno/Iws6ePoqLiXNJXSEigy/pK7Q8AAADIaxxeza9ly5batm1bdtYCAAAAAHmGw2HqxRdf1OXLl/XOO+/or7/+SrOSHwAAAADkJw5P86tVq5ZMJpP279+vL774It02JpNJBw8edFlxAAAAAJBbORymOnbsKJPJlJ21AAAAAECe4XCY+uCDD7KzDgAAAADIUxwOU7nF0aNHtWDBAv3222/6/ffflZSUpE2bNqlkyZLuLg1OSr6WoIi5rrkGL/lagkv6AQAAAG7HqTAVHx+vuLg4Wa3WNPvuvffeOy4qMwcOHNCWLVsUEREhf39//fjjj9l6PGS/K3EpUpzrlloHAAAAckKWwtTatWs1depUHT9+PMM2hw4duuOiMtOhQwd16tRJkjRnzhzCFAAAAAC3cHhp9I0bN2rEiBFKTk5W9+7dZRiG2rVrpzZt2sjT01MREREaMmRIdtYqSTKbHS4ZAAAAALKNw2emPv30U4WFhWnZsmWKj4/XokWL1KVLF9WvX19HjhxRz549ValSpeysFQAAAAByDYdP8/z+++/q2LGjvL29bWeHUq+Zslgs6tatm2bMmJE9VQIAAABALuPwmSmr1arChQtLknx8fCRJcf9YNKB8+fJatGhRlg6+c+dOPf744w613bFjh4KCgrLUvyOCgwNc3mduFRISmCv7wt2J8Ya8jDGHnMR4Q05ivLmWw2GqePHiOnv2rKSbYSo4OFgHDhxQmzZtJEknTpyQr69vlg5evnx5jR492qG2AQHZE3ouXboqq9U1y3K7UnYM9Kgo16yYFxIS6LK+kDsw3oD/YcwhJzHekJMYb1lnNpsyPfnicJiqUaOGduzYoeeee06S1KxZM82bN08+Pj4yDENffPGFmjZtmqXiQkJC1Llz5yw9BgAAAAByA4fDVM+ePbVx40Zdu3ZNPj4+GjZsmPbv36/JkydLkipWrKiXX3452woFAAAAgNzE4TBVrVo1VatWzfZzUFCQVq5cqcOHD8vDw0NhYWE5smx5YmKivvvuO0k3F8WQpK1btyooKEhBQUGqU6dOttcAAAAAAFm6aW96cno59EuXLtmmGqZ66623JEl16tTR/Pnzc7QeAAAAAPlTlsPUTz/9pG3btunSpUt64oknFBYWpvj4eB08eFDh4eEqWLBgdtRpU7JkSdsZKQAAAABwF4fDVEpKikaMGKGvv/5ahmHIZDKpXbt2CgsLk6enp4YMGaJ+/fpp8ODB2VkvAAAAAOQKDoepmTNn6ptvvtHIkSPVqFEjtW3b1rbP29tbLVq00HfffUeYAvKghOvJso5r79L+AAAA7nYOh6kVK1aoQ4cO6tOnj65cuZJmf1hYmLZu3erS4gDkjPjYRMW7uwgAAIA8xuHl986cOaP7778/w/0FCxZUTEyMS4oCAAAAgNzO4TDl7++v6OjoDPefPHlSQUFBrqgJAAAAAHI9h8NUzZo1tXr1ahmGkWZfTEyMli5dqrp167q0OAAAAADIrRwOU4MHD9aff/6pxx9/XP/9738l3bxp7qJFi9SpUyclJiZq4MCB2VUnAAAAAOQqDi9AUbVqVU2ePFmvvvqqRo0aJUkaM2aMDMNQcHCwJk+erAoVKmRboQAAAACQm2Tppr1NmjTR5s2b9cMPP+j48eMyDENly5bVAw88IF9f3+yqEQAAAABynSyFKUny8vJS06ZN1bRp0+yoBwAAAADyBIevmQIAAAAA/E+mZ6Yef/zxLHVmMpk0d+7cOyoIAAAAAPKCTMPUrl275OnpqQIFCjjUmclkcklRAAAAAJDbZRqmPD1v7m7QoIE6d+6spk2bymxmZiAAAAAAZJqMtm7dquHDh+uvv/7S0KFD1bhxY40dO1YnTpzIqfoAAAAAIFfKNEwFBQWpX79+Wr16tb788ks1a9ZMixcvVrt27dS9e3d99dVXunr1ak7VCgAAAAC5hsNz9qpVq6a3335b27Zt05gxY+Tr66s33nhDjRo10sqVK7OzRgAAAADIdbJ8nylvb289/PDDKlGihMxms7Zv365Tp05lR20AAAAAkGtlKUydP39eK1eu1LJly3Ty5EkVK1ZMgwYNUpcuXbKrPgAAAADIlW4bpm7cuKFNmzZp2bJl+uGHH2Q2m9WsWTONGjVKjRo1YnU/AAAAAPlSpmHq3Xff1erVqxUbG6vw8HC9/PLLevjhh1W4cOEcKg8AAAAAcqdMw9SCBQvk4+Ojdu3a6b777lNKSoqWL1+eYXuTyaS+ffu6ukYAAAAAyHVuO83v2rVrWrNmjdasWXPbzghTAAAAAPKLTMPUvHnzcqoOAAAAAMhTMg1TderUyak6AAAAACBPYSk+AAAAAHACYQoAAAAAnECYAgAAAAAnEKYAAAAAwAmEKQAAAABwAmEKAAAAAJxAmAIAAAAAJxCmAAAAAMAJhCkAAAAAcAJhCgAAAACcQJgCAAAAACcQpgAAAADACYQpAAAAAHACYQoAAAAAnECYAgAAAAAnEKYAAAAAwAmEKQAAAABwgqe7C8iqr7/+WuvXr9evv/6qqKgoFS1aVLVr19YzzzyjkiVLurs8AAAAAPlEngtTs2bNUtGiRTVkyBCVLFlSZ86c0dSpU9W5c2ctXbpUpUqVcneJAAAAAPKBPBempk2bpuDgYLtttWrVUsuWLfX5559r5MiRbqoMAAAAQH6S566ZujVISVKpUqVUpEgRnTt3zg0VAQAAAMiP8lyYSs+RI0d0+fJlVaxY0d2lAAAAAMgn8nyYSkpK0quvvqrChQurZ8+e7i4HAAAAQD7h1mumdu7cqccff9yhtjt27FBQUJDdtpSUFL300ks6dOiQpk+fnma/I4KDA7L8mLwqJCQwV/YF3A7jDTmNMYecxHhDTmK8uZZbw1T58uU1evRoh9oGBNiHHqvVqlGjRunbb7/Vxx9/rIYNGzpVw6VLV2W1Gk49Njtlx0CPiopzST8hIYEu6wu4HcYbchpjDjmJ8YacxHjLOrPZlOnJF7eGqZCQEHXu3DnLj7NarXrllVe0Zs0ajR07Vq1atcqG6gAAAAAgY3luaXTDMPTaa69p5cqVGj16tNq1a+fukgAAAADkQ3kuTL377rtaunSpunXrprJly2rfvn22fQEBAapQoYL7igMAAACQb+S5MLVlyxZJ0uLFi7V48WK7fXXq1NH8+fPdURYAAACAfCbPhanNmze7uwQAAAAAyPv3mQIAAAAAdyBMAQAAAIATCFMAAAAA4ATCFAAAAAA4gTAFAAAAAE4gTAEAAACAEwhTAAAAAOAEwhQAAAAAOIEwBQAAAABOIEwBAAAAgBMIUwAAAADgBMIUAAAAADiBMAUAAAAATiBMAQAAAIATCFMAAAAA4ATCFAAAAAA4gTAFAAAAAE4gTAEAAACAEwhTAAAAAOAEwhQAAAAAOIEwBQAAAABO8HR3AUhfwvVkWce1d2l/AAAAAFyHMJVLxccmKt7dRQAAAADIENP8AAAAAMAJhCkAAAAAcAJhCgAAAACcQJgCAAAAACcQpgAAAADACYQpAAAAAHACYQoAAAAAnECYAgAAAAAnEKYAAAAAwAmEKQAAAABwAmEKAAAAAJzg6e4C3M1sNrm7hDyJ1w05ifGGnMaYQ05ivCEnMd6y5navl8kwDCOHagEAAACAuwbT/AAAAADACYQpAAAAAHACYQoAAAAAnECYAgAAAAAnEKYAAAAAwAmEKQAAAABwAmEKAAAAAJxAmAIAAAAAJxCmAAAAAMAJhCkAAAAAcAJhCgAAwA2SkpI0fvx4nT592t2lAHASYQoAgH+4fPmyrFaru8tAPnD9+nXNnDlT586dc3cpAJxkMgzDcHcRyLu2bt2qt956S5s2bXJ3Kcjj9u/fr1mzZun8+fOyWCx64oknVL58ebs2hw4d0tChQxlvuGPr1q3T4sWL5enpqSeeeEINGzbUunXr9NZbbyk2NlYFCxbUc889p0cffdTdpSKP69SpU4b7UlJSdOTIEZUpU0Z+fn4ymUxatmxZDlaHu9Eff/yhEiVKyMvLy7Zt//79mjBhgvbt2yeTyaTIyEiNGDFCERERbqz07sCZKdyRxMREnT171t1lII87cuSIevXqpR9//FFWq1WrV69Wp06dtGLFCrt2SUlJjDfcsa1bt2r48OH6888/df78eQ0ePFibNm3SSy+9pPvvv1/PPPOMKlWqpHfeeUffffedu8tFHnfo0CFFRUUpICAg3X+S5Ovrq4CAAPn7+7u5WtwN2rZtq8OHD9t+/u2339SrVy8dPHhQTZs2VZMmTXTgwAE99thjOnbsmBsrvTt4ursA5E4//fSTQ+2OHj2azZUgP5g0aZIqVqyoOXPmKDAwUJcvX9brr7+uUaNGKT4+Xo899pi7S8Rd5NNPP1Xjxo01depUeXh4aOLEiRo5cqRat26tjz76SJL09NNP68knn9T8+fPVpEkTN1eMvGzIkCGaNWuWwsPDNWzYMLvAFBsbqzp16ujVV19V7dq13Vgl7ia3TjqbNGmSihcvroULF6po0aKSpKioKPXo0UNTp061fe7BOYQppKt3794ymUy3bWcYhkPtgMzs379fo0aNUmBgoCQpKChIn3zyicaMGaN3331XSUlJeuKJJ9xcJe4WR44c0TvvvCMPDw9JUo8ePTRlyhS1b9/erl3nzp319ttvu6NE3EWeeeYZtWnTRq+99pratWunN954Q82aNZMk/v8TOWL37t16+eWXbUFKkkJCQvTkk0/qk08+cWNldwfCFNLl6+urBg0aZDrXW5L27t2r2bNn51BVuFvFxMTYfcinevnll+Xl5aUPP/xQSUlJqlevnhuqw90mPj5ehQoVsv0cHBwsSWnGYEhIiK5evZqjteHuVLFiRS1atEifffaZRowYoUaNGun111+Xj4+Pu0tDPpCYmJjmGmRJCgsLU3R0dM4XdJchTCFdlStXVlJSklq0aJFpu5SUlByqCHez0NBQHT9+XLVq1Uqzb9iwYTKbzZowYYIefPDBnC8Od53ChQsrKirK9rPZbFarVq1UuHBhu3ZXrlyxXdMC3CmTyaR+/fqpZcuWeu2119S2bVsNHDiQs1PIFrt377Z9zhUpUkQxMTFp2sTGxsrPzy+nS7vrEKaQroiICK1fv96htiwIiTt1//33a/369erevXu6+5977jl5eHho8uTJfPHAHatYsaL27t2rtm3bSrr5JXfixIlp2v32228qW7ZsDleHu12pUqU0d+5cffnllxo3bhz/H4psMXbsWLuxtWvXLtv00lS//fabSpQokdOl3XUIU0jXgAED1KpVq9u2a926td2KMYAzHn74YS1atEiXL19WUFBQum2GDh0qPz8/bdmyJYerw91m6NChio2NvW27M2fOpLmOCnCV7t27q1WrVjp//rxKly7t7nJwF5k3b16abanXJP/TqVOnbH9UgvO4zxQAAAAAOIH7TAEAAACAEwhTAAAAAOAEwhQAAAAAOIEwBQAAAABOIEwBAAAAgBP+DyUMbSog5FXgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x7056 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_summary_tear_sheet(lasso_factor_data);"
   ]
  }
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