backtesting_CVX.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "import pandas as pd\n",
    "import alphalens as al\n",
    "import numpy as np\n",
    "import alphalens as al\n",
    "import cvxpy as cvx\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style\n",
    "from scipy.stats.stats import pearsonr\n",
    "from scipy.stats.stats import spearmanr\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from tqdm import tqdm\n",
    "from scipy.stats import gaussian_kde\n",
    "style.use('ggplot')\n",
    "from IPython.core.pylabtools import figsize\n",
    "figsize(15, 5)\n",
    "\n",
    "mod_path = '/Users/richard.wuebker/Projects/price_data_utils'\n",
    "if mod_path not in sys.path:\n",
    "    sys.path.append(mod_path)\n",
    "\n",
    "libs_path = '/Users/richard.wuebker/Projects/backtesting/libs'\n",
    "if libs_path not in sys.path:\n",
    "    sys.path.append(libs_path)\n",
    "\n",
    "    \n",
    "from risk_model import RiskModelPCA\n",
    "from optimal_holdings import OptimalHoldingsCVX\n",
    "    \n",
    "from data_loader import DataLoader\n",
    "from mp_historical_prices import HistoricalPrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction Date: 2019-08-09\n"
     ]
    }
   ],
   "source": [
    "dl = DataLoader(prices_dir='../price_data_utils/prices', info_dir='../price_data_utils/info', \n",
    "                analysis_date_str='2019-08-07', cached_dir='../price_data_utils/cached')\n",
    "dl.load_data()\n",
    "data = dl.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Date</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>ticker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [index, Date, High, Low, Open, Close, Volume, Adj Close, ticker]\n",
       "Index: []"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hp = HistoricalPrices(prices_dir='../price_data_utils/prices', \n",
    "                      cached_dir='../price_data_utils/cached',\n",
    "                      date_str='2019-08-09')\n",
    "hp.get_prices()\n",
    "prices = hp.prices\n",
    "\n",
    "# look for duplicates\n",
    "p = prices.reset_index()\n",
    "p.loc[p.duplicated(subset=['Date', 'ticker'], keep='first')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>ticker</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990-01-02</td>\n",
       "      <td>17.947027</td>\n",
       "      <td>17.527565</td>\n",
       "      <td>17.557526</td>\n",
       "      <td>17.947027</td>\n",
       "      <td>626200.0</td>\n",
       "      <td>7.267582</td>\n",
       "      <td>KMB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990-01-03</td>\n",
       "      <td>17.976990</td>\n",
       "      <td>17.827181</td>\n",
       "      <td>17.976990</td>\n",
       "      <td>17.887104</td>\n",
       "      <td>749200.0</td>\n",
       "      <td>7.243319</td>\n",
       "      <td>KMB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1990-01-04</td>\n",
       "      <td>17.976990</td>\n",
       "      <td>17.797220</td>\n",
       "      <td>17.857143</td>\n",
       "      <td>17.857143</td>\n",
       "      <td>439700.0</td>\n",
       "      <td>7.231189</td>\n",
       "      <td>KMB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990-01-05</td>\n",
       "      <td>17.857143</td>\n",
       "      <td>17.617449</td>\n",
       "      <td>17.797220</td>\n",
       "      <td>17.737297</td>\n",
       "      <td>712500.0</td>\n",
       "      <td>7.182656</td>\n",
       "      <td>KMB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990-01-08</td>\n",
       "      <td>17.917067</td>\n",
       "      <td>17.707335</td>\n",
       "      <td>17.737297</td>\n",
       "      <td>17.917067</td>\n",
       "      <td>495600.0</td>\n",
       "      <td>7.255455</td>\n",
       "      <td>KMB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date       High        Low       Open      Close    Volume  \\\n",
       "0  1990-01-02  17.947027  17.527565  17.557526  17.947027  626200.0   \n",
       "1  1990-01-03  17.976990  17.827181  17.976990  17.887104  749200.0   \n",
       "2  1990-01-04  17.976990  17.797220  17.857143  17.857143  439700.0   \n",
       "3  1990-01-05  17.857143  17.617449  17.797220  17.737297  712500.0   \n",
       "4  1990-01-08  17.917067  17.707335  17.737297  17.917067  495600.0   \n",
       "\n",
       "   Adj Close ticker  \n",
       "0   7.267582    KMB  \n",
       "1   7.243319    KMB  \n",
       "2   7.231189    KMB  \n",
       "3   7.182656    KMB  \n",
       "4   7.255455    KMB  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_prices(df, end_date_str):\n",
    "    prices_temp = df.pivot(index='Date', columns='ticker', values='Adj Close')\n",
    "    end_date = dt.datetime.strptime(end_date_str, '%Y-%m-%d')\n",
    "    start_date = end_date - pd.DateOffset(years=5)\n",
    "    prices_temp.index = pd.to_datetime(prices_temp.index)\n",
    "    prices = prices_temp.loc[start_date:end_date]\n",
    "    return prices.dropna(axis=1)\n",
    "new_p = transform_prices(prices, '2016-01-05')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to make the index of data we have with sector the same as in returns and prices\n",
    "\n",
    "sector_data = data['sector']\n",
    "index_ = sector_data.index.intersection(new_p.columns)\n",
    "sector_data = sector_data.loc[index_]\n",
    "p = new_p.loc[:, index_].copy() # to use only the stocks that are in sector_data\n",
    "sectors = sector_data.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1257, 741)\n",
      "(741,)\n",
      "(1258, 741)\n"
     ]
    }
   ],
   "source": [
    "returns = p.pct_change().drop(p.index[0])\n",
    "print(returns.shape)\n",
    "print(sector_data.shape)\n",
    "print(p.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ticker</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>UAL</th>\n",
       "      <th>TROW</th>\n",
       "      <th>ISRG</th>\n",
       "      <th>NVR</th>\n",
       "      <th>PRGO</th>\n",
       "      <th>LECO</th>\n",
       "      <th>TPR</th>\n",
       "      <th>DVN</th>\n",
       "      <th>CR</th>\n",
       "      <th>...</th>\n",
       "      <th>PGR</th>\n",
       "      <th>WAT</th>\n",
       "      <th>NNN</th>\n",
       "      <th>IEX</th>\n",
       "      <th>LRCX</th>\n",
       "      <th>NUS</th>\n",
       "      <th>NWL</th>\n",
       "      <th>UAA</th>\n",
       "      <th>BLK</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>16.319235</td>\n",
       "      <td>25.350000</td>\n",
       "      <td>50.676487</td>\n",
       "      <td>91.209999</td>\n",
       "      <td>692.690002</td>\n",
       "      <td>59.998005</td>\n",
       "      <td>28.536177</td>\n",
       "      <td>40.534866</td>\n",
       "      <td>70.415245</td>\n",
       "      <td>34.523262</td>\n",
       "      <td>...</td>\n",
       "      <td>14.976269</td>\n",
       "      <td>77.790001</td>\n",
       "      <td>17.154980</td>\n",
       "      <td>33.694031</td>\n",
       "      <td>43.528183</td>\n",
       "      <td>23.909542</td>\n",
       "      <td>14.824180</td>\n",
       "      <td>6.94250</td>\n",
       "      <td>151.752335</td>\n",
       "      <td>16.587673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-06</th>\n",
       "      <td>16.460661</td>\n",
       "      <td>25.750000</td>\n",
       "      <td>50.676487</td>\n",
       "      <td>89.559998</td>\n",
       "      <td>718.400024</td>\n",
       "      <td>62.723892</td>\n",
       "      <td>28.326279</td>\n",
       "      <td>40.797176</td>\n",
       "      <td>69.994743</td>\n",
       "      <td>34.397305</td>\n",
       "      <td>...</td>\n",
       "      <td>14.842355</td>\n",
       "      <td>76.050003</td>\n",
       "      <td>16.839489</td>\n",
       "      <td>33.711502</td>\n",
       "      <td>43.555824</td>\n",
       "      <td>23.958487</td>\n",
       "      <td>14.856651</td>\n",
       "      <td>6.83000</td>\n",
       "      <td>150.116272</td>\n",
       "      <td>16.470205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-07</th>\n",
       "      <td>16.476372</td>\n",
       "      <td>25.950001</td>\n",
       "      <td>50.264294</td>\n",
       "      <td>89.133331</td>\n",
       "      <td>735.150024</td>\n",
       "      <td>62.590939</td>\n",
       "      <td>28.334854</td>\n",
       "      <td>40.758602</td>\n",
       "      <td>70.200493</td>\n",
       "      <td>34.976746</td>\n",
       "      <td>...</td>\n",
       "      <td>14.686119</td>\n",
       "      <td>75.209999</td>\n",
       "      <td>16.681742</td>\n",
       "      <td>33.589188</td>\n",
       "      <td>43.472923</td>\n",
       "      <td>24.031902</td>\n",
       "      <td>14.669928</td>\n",
       "      <td>6.69375</td>\n",
       "      <td>148.875351</td>\n",
       "      <td>16.315643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-10</th>\n",
       "      <td>16.334949</td>\n",
       "      <td>26.200001</td>\n",
       "      <td>50.342079</td>\n",
       "      <td>90.526665</td>\n",
       "      <td>749.960022</td>\n",
       "      <td>62.932850</td>\n",
       "      <td>28.647541</td>\n",
       "      <td>41.267784</td>\n",
       "      <td>70.057358</td>\n",
       "      <td>35.111115</td>\n",
       "      <td>...</td>\n",
       "      <td>14.358768</td>\n",
       "      <td>74.680000</td>\n",
       "      <td>16.497704</td>\n",
       "      <td>33.807583</td>\n",
       "      <td>44.891319</td>\n",
       "      <td>24.553984</td>\n",
       "      <td>14.678050</td>\n",
       "      <td>6.90250</td>\n",
       "      <td>151.096298</td>\n",
       "      <td>16.290915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-11</th>\n",
       "      <td>16.405655</td>\n",
       "      <td>25.690001</td>\n",
       "      <td>50.715374</td>\n",
       "      <td>93.663330</td>\n",
       "      <td>751.900024</td>\n",
       "      <td>63.474232</td>\n",
       "      <td>29.354307</td>\n",
       "      <td>41.653553</td>\n",
       "      <td>72.347855</td>\n",
       "      <td>35.279057</td>\n",
       "      <td>...</td>\n",
       "      <td>14.336452</td>\n",
       "      <td>76.300003</td>\n",
       "      <td>16.333384</td>\n",
       "      <td>33.982292</td>\n",
       "      <td>45.572895</td>\n",
       "      <td>24.456089</td>\n",
       "      <td>14.507563</td>\n",
       "      <td>6.79875</td>\n",
       "      <td>152.874680</td>\n",
       "      <td>16.303284</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 741 columns</p>\n",
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      ],
      "text/plain": [
       "ticker           CSCO        UAL       TROW       ISRG         NVR       PRGO  \\\n",
       "Date                                                                            \n",
       "2011-01-05  16.319235  25.350000  50.676487  91.209999  692.690002  59.998005   \n",
       "2011-01-06  16.460661  25.750000  50.676487  89.559998  718.400024  62.723892   \n",
       "2011-01-07  16.476372  25.950001  50.264294  89.133331  735.150024  62.590939   \n",
       "2011-01-10  16.334949  26.200001  50.342079  90.526665  749.960022  62.932850   \n",
       "2011-01-11  16.405655  25.690001  50.715374  93.663330  751.900024  63.474232   \n",
       "\n",
       "ticker           LECO        TPR        DVN         CR    ...            PGR  \\\n",
       "Date                                                      ...                  \n",
       "2011-01-05  28.536177  40.534866  70.415245  34.523262    ...      14.976269   \n",
       "2011-01-06  28.326279  40.797176  69.994743  34.397305    ...      14.842355   \n",
       "2011-01-07  28.334854  40.758602  70.200493  34.976746    ...      14.686119   \n",
       "2011-01-10  28.647541  41.267784  70.057358  35.111115    ...      14.358768   \n",
       "2011-01-11  29.354307  41.653553  72.347855  35.279057    ...      14.336452   \n",
       "\n",
       "ticker            WAT        NNN        IEX       LRCX        NUS        NWL  \\\n",
       "Date                                                                           \n",
       "2011-01-05  77.790001  17.154980  33.694031  43.528183  23.909542  14.824180   \n",
       "2011-01-06  76.050003  16.839489  33.711502  43.555824  23.958487  14.856651   \n",
       "2011-01-07  75.209999  16.681742  33.589188  43.472923  24.031902  14.669928   \n",
       "2011-01-10  74.680000  16.497704  33.807583  44.891319  24.553984  14.678050   \n",
       "2011-01-11  76.300003  16.333384  33.982292  45.572895  24.456089  14.507563   \n",
       "\n",
       "ticker          UAA         BLK        PPL  \n",
       "Date                                        \n",
       "2011-01-05  6.94250  151.752335  16.587673  \n",
       "2011-01-06  6.83000  150.116272  16.470205  \n",
       "2011-01-07  6.69375  148.875351  16.315643  \n",
       "2011-01-10  6.90250  151.096298  16.290915  \n",
       "2011-01-11  6.79875  152.874680  16.303284  \n",
       "\n",
       "[5 rows x 741 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ticker</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>UAL</th>\n",
       "      <th>TROW</th>\n",
       "      <th>ISRG</th>\n",
       "      <th>NVR</th>\n",
       "      <th>PRGO</th>\n",
       "      <th>LECO</th>\n",
       "      <th>TPR</th>\n",
       "      <th>DVN</th>\n",
       "      <th>CR</th>\n",
       "      <th>...</th>\n",
       "      <th>PGR</th>\n",
       "      <th>WAT</th>\n",
       "      <th>NNN</th>\n",
       "      <th>IEX</th>\n",
       "      <th>LRCX</th>\n",
       "      <th>NUS</th>\n",
       "      <th>NWL</th>\n",
       "      <th>UAA</th>\n",
       "      <th>BLK</th>\n",
       "      <th>PPL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-29</th>\n",
       "      <td>24.640928</td>\n",
       "      <td>59.299999</td>\n",
       "      <td>66.267441</td>\n",
       "      <td>184.456665</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>141.577072</td>\n",
       "      <td>48.883698</td>\n",
       "      <td>29.317432</td>\n",
       "      <td>31.023300</td>\n",
       "      <td>45.610756</td>\n",
       "      <td>...</td>\n",
       "      <td>29.112070</td>\n",
       "      <td>136.169998</td>\n",
       "      <td>34.340061</td>\n",
       "      <td>73.815681</td>\n",
       "      <td>75.976669</td>\n",
       "      <td>35.107853</td>\n",
       "      <td>39.816341</td>\n",
       "      <td>41.125000</td>\n",
       "      <td>314.309814</td>\n",
       "      <td>29.093164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>24.427971</td>\n",
       "      <td>57.509998</td>\n",
       "      <td>65.351830</td>\n",
       "      <td>184.169998</td>\n",
       "      <td>1649.540039</td>\n",
       "      <td>140.273254</td>\n",
       "      <td>48.753433</td>\n",
       "      <td>29.024263</td>\n",
       "      <td>29.830099</td>\n",
       "      <td>45.245197</td>\n",
       "      <td>...</td>\n",
       "      <td>28.986509</td>\n",
       "      <td>135.639999</td>\n",
       "      <td>34.271770</td>\n",
       "      <td>73.255249</td>\n",
       "      <td>75.430962</td>\n",
       "      <td>35.071419</td>\n",
       "      <td>39.375340</td>\n",
       "      <td>40.480000</td>\n",
       "      <td>312.891907</td>\n",
       "      <td>29.008694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>24.099659</td>\n",
       "      <td>57.299999</td>\n",
       "      <td>64.807915</td>\n",
       "      <td>182.053329</td>\n",
       "      <td>1643.000000</td>\n",
       "      <td>139.751724</td>\n",
       "      <td>48.278931</td>\n",
       "      <td>29.077566</td>\n",
       "      <td>30.792358</td>\n",
       "      <td>44.842140</td>\n",
       "      <td>...</td>\n",
       "      <td>28.520142</td>\n",
       "      <td>134.580002</td>\n",
       "      <td>34.186417</td>\n",
       "      <td>72.770805</td>\n",
       "      <td>74.725288</td>\n",
       "      <td>34.515739</td>\n",
       "      <td>39.672337</td>\n",
       "      <td>40.305000</td>\n",
       "      <td>311.492279</td>\n",
       "      <td>28.831297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>23.616772</td>\n",
       "      <td>55.610001</td>\n",
       "      <td>63.629417</td>\n",
       "      <td>182.463333</td>\n",
       "      <td>1555.660034</td>\n",
       "      <td>137.626907</td>\n",
       "      <td>48.325451</td>\n",
       "      <td>29.886017</td>\n",
       "      <td>31.100277</td>\n",
       "      <td>44.710915</td>\n",
       "      <td>...</td>\n",
       "      <td>27.910280</td>\n",
       "      <td>130.350006</td>\n",
       "      <td>33.768162</td>\n",
       "      <td>71.944405</td>\n",
       "      <td>73.135201</td>\n",
       "      <td>34.415535</td>\n",
       "      <td>39.096333</td>\n",
       "      <td>39.830002</td>\n",
       "      <td>304.704834</td>\n",
       "      <td>28.451166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>23.509462</td>\n",
       "      <td>55.060001</td>\n",
       "      <td>63.892326</td>\n",
       "      <td>183.986664</td>\n",
       "      <td>1594.089966</td>\n",
       "      <td>137.800766</td>\n",
       "      <td>48.167278</td>\n",
       "      <td>30.179193</td>\n",
       "      <td>31.591042</td>\n",
       "      <td>44.542198</td>\n",
       "      <td>...</td>\n",
       "      <td>28.170368</td>\n",
       "      <td>129.809998</td>\n",
       "      <td>34.382748</td>\n",
       "      <td>71.241478</td>\n",
       "      <td>73.530373</td>\n",
       "      <td>33.559250</td>\n",
       "      <td>39.078335</td>\n",
       "      <td>40.075001</td>\n",
       "      <td>305.491516</td>\n",
       "      <td>28.755270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 741 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ticker           CSCO        UAL       TROW        ISRG          NVR  \\\n",
       "Date                                                                   \n",
       "2015-12-29  24.640928  59.299999  66.267441  184.456665  1650.000000   \n",
       "2015-12-30  24.427971  57.509998  65.351830  184.169998  1649.540039   \n",
       "2015-12-31  24.099659  57.299999  64.807915  182.053329  1643.000000   \n",
       "2016-01-04  23.616772  55.610001  63.629417  182.463333  1555.660034   \n",
       "2016-01-05  23.509462  55.060001  63.892326  183.986664  1594.089966   \n",
       "\n",
       "ticker            PRGO       LECO        TPR        DVN         CR    ...      \\\n",
       "Date                                                                  ...       \n",
       "2015-12-29  141.577072  48.883698  29.317432  31.023300  45.610756    ...       \n",
       "2015-12-30  140.273254  48.753433  29.024263  29.830099  45.245197    ...       \n",
       "2015-12-31  139.751724  48.278931  29.077566  30.792358  44.842140    ...       \n",
       "2016-01-04  137.626907  48.325451  29.886017  31.100277  44.710915    ...       \n",
       "2016-01-05  137.800766  48.167278  30.179193  31.591042  44.542198    ...       \n",
       "\n",
       "ticker            PGR         WAT        NNN        IEX       LRCX        NUS  \\\n",
       "Date                                                                            \n",
       "2015-12-29  29.112070  136.169998  34.340061  73.815681  75.976669  35.107853   \n",
       "2015-12-30  28.986509  135.639999  34.271770  73.255249  75.430962  35.071419   \n",
       "2015-12-31  28.520142  134.580002  34.186417  72.770805  74.725288  34.515739   \n",
       "2016-01-04  27.910280  130.350006  33.768162  71.944405  73.135201  34.415535   \n",
       "2016-01-05  28.170368  129.809998  34.382748  71.241478  73.530373  33.559250   \n",
       "\n",
       "ticker            NWL        UAA         BLK        PPL  \n",
       "Date                                                     \n",
       "2015-12-29  39.816341  41.125000  314.309814  29.093164  \n",
       "2015-12-30  39.375340  40.480000  312.891907  29.008694  \n",
       "2015-12-31  39.672337  40.305000  311.492279  28.831297  \n",
       "2016-01-04  39.096333  39.830002  304.704834  28.451166  \n",
       "2016-01-05  39.078335  40.075001  305.491516  28.755270  \n",
       "\n",
       "[5 rows x 741 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(504, 741)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ticker</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>UAL</th>\n",
       "      <th>TROW</th>\n",
       "      <th>ISRG</th>\n",
       "      <th>NVR</th>\n",
       "      <th>PRGO</th>\n",
       "      <th>LECO</th>\n",
       "      <th>TPR</th>\n",
       "      <th>DVN</th>\n",
       "      <th>CR</th>\n",
       "      <th>...</th>\n",
       "      <th>PGR</th>\n",
       "      <th>WAT</th>\n",
       "      <th>NNN</th>\n",
       "      <th>IEX</th>\n",
       "      <th>LRCX</th>\n",
       "      <th>NUS</th>\n",
       "      <th>NWL</th>\n",
       "      <th>UAA</th>\n",
       "      <th>BLK</th>\n",
       "      <th>PPL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>-0.011680</td>\n",
       "      <td>-0.002643</td>\n",
       "      <td>-0.014444</td>\n",
       "      <td>-0.010597</td>\n",
       "      <td>-0.008684</td>\n",
       "      <td>-0.002346</td>\n",
       "      <td>-0.013737</td>\n",
       "      <td>-0.003920</td>\n",
       "      <td>-0.011476</td>\n",
       "      <td>-0.007286</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.020169</td>\n",
       "      <td>-0.007800</td>\n",
       "      <td>0.002308</td>\n",
       "      <td>-0.016520</td>\n",
       "      <td>-0.009183</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>-0.007096</td>\n",
       "      <td>-0.003666</td>\n",
       "      <td>-0.011597</td>\n",
       "      <td>-0.014623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>-0.000909</td>\n",
       "      <td>0.058839</td>\n",
       "      <td>0.008963</td>\n",
       "      <td>-0.017236</td>\n",
       "      <td>0.002212</td>\n",
       "      <td>0.007381</td>\n",
       "      <td>0.001563</td>\n",
       "      <td>0.005902</td>\n",
       "      <td>-0.002126</td>\n",
       "      <td>0.001198</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014221</td>\n",
       "      <td>-0.011893</td>\n",
       "      <td>0.013487</td>\n",
       "      <td>0.006746</td>\n",
       "      <td>-0.000185</td>\n",
       "      <td>-0.040994</td>\n",
       "      <td>0.006837</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.004380</td>\n",
       "      <td>-0.001012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>0.001365</td>\n",
       "      <td>-0.014768</td>\n",
       "      <td>-0.001561</td>\n",
       "      <td>-0.006748</td>\n",
       "      <td>0.006161</td>\n",
       "      <td>-0.008429</td>\n",
       "      <td>-0.014900</td>\n",
       "      <td>-0.006757</td>\n",
       "      <td>-0.012781</td>\n",
       "      <td>-0.010473</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006454</td>\n",
       "      <td>0.003570</td>\n",
       "      <td>0.004544</td>\n",
       "      <td>-0.006154</td>\n",
       "      <td>-0.012792</td>\n",
       "      <td>0.006419</td>\n",
       "      <td>-0.009259</td>\n",
       "      <td>-0.004368</td>\n",
       "      <td>-0.000318</td>\n",
       "      <td>0.004389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>0.013630</td>\n",
       "      <td>-0.017022</td>\n",
       "      <td>0.008416</td>\n",
       "      <td>0.007602</td>\n",
       "      <td>-0.011456</td>\n",
       "      <td>0.010789</td>\n",
       "      <td>0.014117</td>\n",
       "      <td>0.009309</td>\n",
       "      <td>0.020415</td>\n",
       "      <td>0.009828</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005350</td>\n",
       "      <td>0.020226</td>\n",
       "      <td>0.006462</td>\n",
       "      <td>0.004954</td>\n",
       "      <td>0.012394</td>\n",
       "      <td>0.033168</td>\n",
       "      <td>0.000623</td>\n",
       "      <td>0.001963</td>\n",
       "      <td>0.009202</td>\n",
       "      <td>0.005714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-08</th>\n",
       "      <td>-0.000896</td>\n",
       "      <td>0.060222</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>0.017926</td>\n",
       "      <td>-0.003925</td>\n",
       "      <td>0.022319</td>\n",
       "      <td>-0.003409</td>\n",
       "      <td>-0.011884</td>\n",
       "      <td>-0.002765</td>\n",
       "      <td>0.001497</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011909</td>\n",
       "      <td>0.000996</td>\n",
       "      <td>-0.006421</td>\n",
       "      <td>0.001369</td>\n",
       "      <td>0.005008</td>\n",
       "      <td>0.001235</td>\n",
       "      <td>-0.003425</td>\n",
       "      <td>0.001383</td>\n",
       "      <td>-0.000315</td>\n",
       "      <td>-0.009024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 741 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ticker          CSCO       UAL      TROW      ISRG       NVR      PRGO  \\\n",
       "Date                                                                     \n",
       "2014-01-02 -0.011680 -0.002643 -0.014444 -0.010597 -0.008684 -0.002346   \n",
       "2014-01-03 -0.000909  0.058839  0.008963 -0.017236  0.002212  0.007381   \n",
       "2014-01-06  0.001365 -0.014768 -0.001561 -0.006748  0.006161 -0.008429   \n",
       "2014-01-07  0.013630 -0.017022  0.008416  0.007602 -0.011456  0.010789   \n",
       "2014-01-08 -0.000896  0.060222  0.000596  0.017926 -0.003925  0.022319   \n",
       "\n",
       "ticker          LECO       TPR       DVN        CR    ...          PGR  \\\n",
       "Date                                                  ...                \n",
       "2014-01-02 -0.013737 -0.003920 -0.011476 -0.007286    ...    -0.020169   \n",
       "2014-01-03  0.001563  0.005902 -0.002126  0.001198    ...    -0.014221   \n",
       "2014-01-06 -0.014900 -0.006757 -0.012781 -0.010473    ...    -0.006454   \n",
       "2014-01-07  0.014117  0.009309  0.020415  0.009828    ...    -0.005350   \n",
       "2014-01-08 -0.003409 -0.011884 -0.002765  0.001497    ...    -0.011909   \n",
       "\n",
       "ticker           WAT       NNN       IEX      LRCX       NUS       NWL  \\\n",
       "Date                                                                     \n",
       "2014-01-02 -0.007800  0.002308 -0.016520 -0.009183 -0.001085 -0.007096   \n",
       "2014-01-03 -0.011893  0.013487  0.006746 -0.000185 -0.040994  0.006837   \n",
       "2014-01-06  0.003570  0.004544 -0.006154 -0.012792  0.006419 -0.009259   \n",
       "2014-01-07  0.020226  0.006462  0.004954  0.012394  0.033168  0.000623   \n",
       "2014-01-08  0.000996 -0.006421  0.001369  0.005008  0.001235 -0.003425   \n",
       "\n",
       "ticker           UAA       BLK       PPL  \n",
       "Date                                      \n",
       "2014-01-02 -0.003666 -0.011597 -0.014623  \n",
       "2014-01-03  0.000115  0.004380 -0.001012  \n",
       "2014-01-06 -0.004368 -0.000318  0.004389  \n",
       "2014-01-07  0.001963  0.009202  0.005714  \n",
       "2014-01-08  0.001383 -0.000315 -0.009024  \n",
       "\n",
       "[5 rows x 741 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets = returns.loc['2014-01-01':'2015-12-31']\n",
    "print(rets.shape)\n",
    "rets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "risk_model = RiskModelPCA(rets, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 20 artists>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AJSXQAQAAlJRABwAAUFICHQAAQEkJdAAAACUl0AEAAJSUQAcAAFBSAh0AAEBJCXQAAAAlJdABAACUlEAHAABQUgIdAABASQl0AAAAJSXQAQAAlJRABwAAUFKN4xnU09OTzZs3p1qtZvHixVmyZMmo/c8880zuuuuu/OIXv8jKlSszf/78kX3bt2/P/fffnyRZunRprrjiiomrHgAA4DQ25hW6arWaTZs25eabb86GDRvyyCOP5MUXXxw1pqWlJStWrMjll18+6vmhoaHcd999ufXWW3Prrbfmvvvuy9DQ0MQeAQAAwGlqzEDX29ubtra2tLa2prGxMQsWLMjOnTtHjZkxY0Z+4zd+I3V1daOe7+npydy5c9PU1JSmpqbMnTs3PT09E3sEAAAAp6kxl1wODg6mubl5ZLu5uTl79uwZ1w//9bmVSiWDg4NHjOvu7k53d3eSZM2aNWlpaRnXzz+Z9hX42pOxH2XT2NiojwXS/2Lpf7H0v1j6Xyz9L5b+F+tk9X9c99CdaF1dXenq6hrZ3r9/f4HVTD76cfxaWlr0sUD6Xyz9L5b+F0v/i6X/xdL/Yh1P/2fOnDnusWMuuaxUKhkYGBjZHhgYSKVSGdcP//W5g4OD454LAADAWxsz0LW3t6evry/9/f0ZHh7Ojh070tnZOa4fPm/evOzevTtDQ0MZGhrK7t27M2/evOMuGgAAgHEsuWxoaMjy5cuzevXqVKvVLFy4MLNmzcqWLVvS3t6ezs7O9Pb25rbbbstrr72WXbt25d5778369evT1NSUT3/601m1alWS5KqrrkpTU9MJPygAAIDTwbjuoevo6EhHR8eo55YtWzby+MILL8wdd9xx1LmLFi3KokWLjqNEAAAAjmbMJZcAAABMTgIdAABASQl0AAAAJSXQAQAAlJRABwAAUFICHQAAQEkJdAAAACUl0AEAAJSUQAcAAFBSAh0AAEBJCXQAAAAlJdABAACUlEAHAABQUgIdAABASQl0AAAAJSXQAQAAlJRABwAAUFICHQAAQEkJdAAAACUl0AEAAJSUQAcAAFBSAh0AAEBJCXQAAAAlJdABAACUlEAHAABQUgIdAABASTUWXQDH7/AXPlnYazfc+U+FvTYAAJzuXKEDAAAoKYEOAACgpAQ6AACAkhLoAAAASkqgAwAAKKlxfctlT09PNm/enGq1msWLF2fJkiWj9h86dCgbN27M3r17M23atKxcuTIzZsxIf39/rrvuusycOTNJMmfOnFxzzTUTfxQAAACnoTEDXbVazaZNm3LLLbekubk5q1atSmdnZ84///yRMdu2bcvZZ5+db3/723nkkUfy/e9/P9ddd12SpK2tLWvXrj1xRwAAAHCaGnPJZW9vb9ra2tLa2prGxsYsWLAgO3fuHDXmscceyxVXXJEkmT9/fp566qnUarUTUjAAAABvGPMK3eDgYJqbm0e2m5ubs2fPnmOOaWhoyNSpU3PgwIEkSX9/f2644Ya8613vyh/8wR/kN3/zNyeyfgAAgNPWuO6he6fOPffc3H777Zk2bVr27t2btWvXZt26dZk6deqocd3d3enu7k6SrFmzJi0tLSeyrHdkX4GvPVY/JnNtk0VjY2Npaj0V6X+x9L9Y+l8s/S+W/hdL/4t1svo/ZqCrVCoZGBgY2R4YGEilUjnqmObm5hw+fDivv/56pk2blrq6upxxxhlJkgsuuCCtra3p6+tLe3v7qPldXV3p6uoa2d6/f/9xHdSpZjL3YzLX9mYtLS2lqfVUpP/F0v9i6X+x9L9Y+l8s/S/W8fT//75UcjzGvIeuvb09fX196e/vz/DwcHbs2JHOzs5RYy699NJs3749SfLoo4/mkksuSV1dXV599dVUq9Ukyb59+9LX15fW1ta3cSgAAAAcy5hX6BoaGrJ8+fKsXr061Wo1CxcuzKxZs7Jly5a0t7ens7MzixYtysaNG/PlL385TU1NWblyZZLkmWeeyb333puGhobU19fnC1/4Qpqamk74QQEAAJwOxnUPXUdHRzo6OkY9t2zZspHHU6ZMyfXXX3/EvPnz52f+/PnHWSIAAABHM+aSSwAAACYngQ4AAKCkBDoAAICSEugAAABKSqADAAAoKYEOAACgpAQ6AACAkhLoAAAASkqgAwAAKCmBDgAAoKQEOgAAgJJqLLoATl2Hv/DJwl674c5/Kuy1AQDgZHGFDgAAoKQEOgAAgJKy5JLTkuWgAACcClyhAwAAKCmBDgAAoKQEOgAAgJIS6AAAAEpKoAMAACgpgQ4AAKCkBDoAAICSEugAAABKSqADAAAoqcaiCwD+v8Nf+GRhr91w5z8V9toAALwzrtABAACUlCt0wLgcz9XDfcfxuq4cAgAcmyt0AAAAJeUKHVBqk/m+w6Jqc1UTAE4fAh3AaWayhuDJWldS3JLjREAH4K0JdABQUqdqCD4ek7WuZPLW5o8GUG4CHQAAxzRZr1AL50enZ2/PqfAHjXEFup6enmzevDnVajWLFy/OkiVLRu0/dOhQNm7cmL1792batGlZuXJlZsyYkSTZunVrtm3blvr6+lx99dWZN2/exB8FAADAaWjMb7msVqvZtGlTbr755mzYsCGPPPJIXnzxxVFjtm3blrPPPjvf/va384lPfCLf//73kyQvvvhiduzYkfXr1+erX/1qNm3alGq1emKOBAAA4DQzZqDr7e1NW1tbWltb09jYmAULFmTnzp2jxjz22GO54oorkiTz58/PU089lVqtlp07d2bBggU544wzMmPGjLS1taW3t/eEHAgAAMDpZsxANzg4mObm5pHt5ubmDA4OHnNMQ0NDpk6dmgMHDhwxt1KpHDEXAACAd2ZSfClKd3d3uru7kyRr1qzJzJkzC67oKP7lsaIrOLbJWtskq2vUeTXJahsxWetKJm9tk7WuZFLV5vw/TpO1tslaVzKpaivF+Z9M3trU9fZNotqc/8U6GblmzCt0lUolAwMDI9sDAwOpVCrHHHP48OG8/vrrmTZt2hFzBwcHj5ibJF1dXVmzZk3WrFnzjg9ksrvpppuKLuG0pv/F0v9i6X+x9L9Y+l8s/S+W/hfrZPV/zEDX3t6evr6+9Pf3Z3h4ODt27EhnZ+eoMZdeemm2b9+eJHn00UdzySWXpK6uLp2dndmxY0cOHTqU/v7+9PX15cILLzwhBwIAAHC6GXPJZUNDQ5YvX57Vq1enWq1m4cKFmTVrVrZs2ZL29vZ0dnZm0aJF2bhxY7785S+nqakpK1euTJLMmjUrH/nIR3L99denvr4+n//851NfP2aGBAAAYBzGdQ9dR0dHOjo6Rj23bNmykcdTpkzJ9ddff9S5S5cuzdKlS4+jxFNDV1dX0SWc1vS/WPpfLP0vlv4XS/+Lpf/F0v9inaz+19VqtdpJeSUAAAAmlPWPAAAAJSXQAQAAlNSk+HfoTiU9PT3ZvHlzqtVqFi9enCVLlozaf+jQoWzcuDF79+7NtGnTsnLlysyYMaOgak8t+/fvz3e+85386le/Sl1dXbq6uvJ7v/d7o8Y8/fTT+eu//uuRnn/4wx/OVVddVUS5p6Rrr702Z511Vurr69PQ0HDEP0VSq9WyefPm/OxnP8uZZ56ZFStW5IILLiio2lPLSy+9lA0bNoxs9/f35zOf+Uw+8YlPjDzn/J9Yt99+ex5//PFMnz4969atS5IMDQ1lw4YN+e///u+cd955ue6669LU1HTE3O3bt+f+++9P8sa95ldcccXJLP2UcLT+33333dm1a1caGxvT2tqaFStW5Oyzzz5i7li/qxjb0fp/77335qGHHsq73/3uJMlnP/vZI76DIRn7sxJjO1r/N2zYkJdeeilJ8vrrr2fq1KlZu3btEXOd/8fvWJ85C3sPqDFhDh8+XPvSl75U+6//+q/aoUOHan/+539e+8///M9RYx544IHad7/73VqtVqv95Cc/qa1fv76IUk9Jg4ODteeee65Wq9Vqr7/+eu0rX/nKEf1/6qmnan/1V39VRHmnhRUrVtReeeWVY+7ftWtXbfXq1bVqtVp79tlna6tWrTqJ1Z0+Dh8+XPuTP/mTWn9//6jnnf8T6+mnn64999xzteuvv37kubvvvru2devWWq1Wq23durV29913HzHvwIEDtWuvvbZ24MCBUY95e47W/56entrw8HCtVnvjv8XR+l+rjf27irEdrf9btmyp/eAHP3jLeeP5rMTYjtb/N7vrrrtq//AP/3DUfc7/43esz5xFvQdYcjmBent709bWltbW1jQ2NmbBggXZuXPnqDGPPfbYSAqfP39+nnrqqdR8L82EOPfcc0eu9rzrXe/Ke9/73gwODhZcFW/22GOP5Xd+53dSV1eXD3zgA3nttdfy8ssvF13WKefJJ59MW1tbzjvvvKJLOaVdfPHFR/zldefOnfnYxz6WJPnYxz52xHtA8sbViblz56apqSlNTU2ZO3duenp6TkrNp5Kj9f+3fuu30tDQkCT5wAc+4D3gBDpa/8djPJ+VGNtb9b9Wq+Xf/u3f8tu//dsnuarTx7E+cxb1HmDJ5QQaHBxMc3PzyHZzc3P27NlzzDENDQ2ZOnVqDhw4MLI8gYnR39+f559//qj/kP3Pf/7z/MVf/EXOPffc/NEf/VFmzZpVQIWnrtWrVydJfvd3f/eIr+sdHBxMS0vLyHZzc3MGBwdz7rnnntQaT3WPPPLIMd/Inf8n1iuvvDJyPp9zzjl55ZVXjhjz6+8VlUpF8DgBtm3blgULFhxz/1v9ruKd+9GPfpSHH344F1xwQT73uc8dETrG81mJ4/Pv//7vmT59et7znvccc4zzf+K8+TNnUe8BAh2nnIMHD2bdunX54z/+40ydOnXUvve///25/fbbc9ZZZ+Xxxx/P2rVr83d/93cFVXrq+da3vpVKpZJXXnklf/mXf5mZM2fm4osvLrqs08rw8HB27dqVP/zDPzxin/P/5Kqrq0tdXV3RZZyW7r///jQ0NOSjH/3oUff7XXViXHnllSP35W7ZsiV///d/nxUrVhRc1ennrf6olzj/J9JbfeY8me8BllxOoEqlkoGBgZHtgYGBVCqVY445fPhwXn/99UybNu2k1nkqGx4ezrp16/LRj340H/7wh4/YP3Xq1Jx11llJko6Ojhw+fDivvvrqyS7zlPV/5/v06dNz2WWXpbe394j9+/fvH9k+2v8jHJ+f/exnef/7359zzjnniH3O/xNv+vTpI8uIX3755aOuvvj194rBwUH/H0yg7du3Z9euXfnKV75yzA9TY/2u4p0555xzUl9fn/r6+ixevDjPPffcEWPG81mJd+7w4cP56U9/+pZXp53/E+NonzmLeg8Q6CZQe3t7+vr60t/fn+Hh4ezYsSOdnZ2jxlx66aXZvn17kuTRRx/NJZdc4i+4E6RWq+WOO+7Ie9/73vz+7//+Ucf86le/Grlnsbe3N9VqVaCeIAcPHsz//M//jDx+4oknMnv27FFjOjs78/DDD6dWq+XnP/95pk6darnlBHurv8w6/0+8zs7O/PjHP06S/PjHP85ll112xJh58+Zl9+7dGRoaytDQUHbv3p158+ad7FJPST09PfnBD36QG2+8MWeeeeZRx4zndxXvzJvvif7pT3961CXd4/msxDv35JNPZubMmaOW9L2Z839iHOszZ1HvAXU138gxoR5//PHcddddqVarWbhwYZYuXZotW7akvb09nZ2d+d///d9s3Lgxzz//fJqamrJy5cq0trYWXfYp4T/+4z/y9a9/PbNnzx4JyZ/97GdHrghdeeWVeeCBB/Lggw+moaEhU6ZMyec+97lcdNFFRZZ9yti3b19uu+22JG/8hfDyyy/P0qVL8+CDDyZ5o/+1Wi2bNm3K7t27M2XKlKxYsSLt7e1Fln1KOXjwYFasWJGNGzeOLP14c/+d/xPrb/7mb/LMM8/kwIEDmT59ej7zmc/ksssuy4YNG7J///5RX1n93HPP5V//9V/zZ3/2Z0neuL9r69atSd74yuqFCxcWeSildLT+b926NcPDwyP3bc2ZMyfXXHNNBgcH893vfjerVq065u8q3p6j9f/pp5/OCy+8kLq6upx33nm55pprcu65547qf3L0z0q8PUfr/6JFi/Kd73wnc+bMyZVXXjnxbxwLAAAASElEQVQy1vk/8Y71mXPOnDmFvAcIdAAAACVlySUAAEBJCXQAAAAlJdABAACUlEAHAABQUgIdAABASQl0AAAAJSXQAQAAlNT/A7ar0XO/U4vlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(np.arange(risk_model.n_components), risk_model.pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# looking at factor returns\n",
    "risk_model.factor_returns.cumsum().plot(legend=False)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def density_plot(data): \n",
    "    density = gaussian_kde(data)\n",
    "    xs = np.linspace(np.min(data),np.max(data),200)\n",
    "    density.covariance_factor = lambda : .25\n",
    "    density._compute_covariance()\n",
    "    plt.plot(xs,density(xs))\n",
    "    plt.xlabel('Daily Returns')\n",
    "    plt.ylabel('Density')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ACYCyB+CEmZls8QK57v3k0tJ8x0E113eoFE6XvTfDdxQAAJAAKHsATtzaT6Qd26Qe/X0nwSFcVrZc70GyebNkVVW+4wAAAM8oewBOmBXPlyS5Hv08J8HnuSEjpV07gnMqAQBAnUbZA3DCbPECqcNpco2a+I6Cz+vRX8puKJsz3XcSAADgWTgeL1JRUaHbb79dVVVVikQiGjJkiL761a9q8+bNeuCBB7Rr1y516tRJEydOVDgcl0gATpLt2il9skxu/Nd8R8FRuHC6XP/hsjlvycr2ydXP9B0JAAB4Epcje+np6br99tt1zz336Le//a2Kioq0fPlyPfXUUzrvvPP0hz/8QdnZ2Zo2bVo84gA4Bbb0fcmMLRcSmBs8UqoolxXN8R0FAAB4FJey55xT/fr1JUmRSESRSETOOS1ZskRDhgyRJI0aNUrz5s2LRxwAp6J4vtSwsdS+s+8kOJYuZ0i5zVmVEwCAOi5ucyaj0ah+/OMfa+PGjTrrrLPUokULZWVlKa162facnByVlpYe9blTp07V1KlTJUl333238vLy4hU7KYXDYb5HiMmJjhWLRLRlaZHq9x+mxs2b12IynKpdo87W3klPq2k4pLQmOaf8+fi5glgxVhArxgpixVg5eXEre6FQSPfcc4/27Nmj3/3ud9qwYUPMzy0sLFRhYeGB6yUlJbURMWXk5eXxPUJMTnSs2MqPZLt2qPz0HoyxBGe9BknP/01bX3tRobHnn/Ln4+cKYsVYQawYK4gVY+VI+fn5MT0u7qtxZmdnq3v37lq+fLn27t2rSCQiSSotLVVOzqm/+wyg9ljxfMmF5Ar6+o6C43D57aS2HZnKCQBAHRaXsrdz507t2bNHUrAy56JFi9S6dWt1795dc+YECwhMnz5dAwaw4AOQyKx4gdS5m1x2A99REAM3eJS0arlsU+wzKQAAQOqIyzTObdu26eGHH1Y0GpWZaejQoerfv7/atGmjBx54QP/4xz/UsWNHjRkzJh5xAJwE214qrV0p95Vv+I6CGLlBI2TPPyF7b7rcBV/3HQcAAMRZXMpe+/bt9dvf/vaI21u0aKG77rorHhEAnCJbslCS5Hr295wEsXJNc6WuPWVzpsvOv1zOOd+RAABAHMX9nD0AycmK50tNcqXWHXxHwQlwQ0ZJWzZKq5b7jgIAAOKMsgfguKyqSlpaJNezP0eHkozrO1QKp7NQCwAAdRBlD8DxrfxQ2rdXrieLKCUbl5Ut9R4omzcrKO0AAKDOoOwBOC4rni+lhaUzevmOgpMQGjJK2rVD+vAD31EAAEAcUfYAHJcVL5BO7y5XP8t3FJyMHv2lrAayOdN9JwEAAHFE2QPwhWzrZmnDWrkerMKZrFw4XW7AcFnRHFnZPt9xAABAnFD2AHwhK14gSXK9OF8vmbnBI6WKclnRe76jAACAOKHsAfhCVjxfatZSatHadxScii4FUk4zVuUEAKAOoewBOCarrJA++kCuB1suJDsXCskNHiEtfV+2c7vvOAAAIA4oewCObdliqaKCLRdShBs8SopGZfPe9h0FAADEAWUPwDHZ4gVSej2paw/fUVADXOv2UpsOsvem+44CAADigLIH4JiseL7UrZdcvQzfUVBD3OCR0qrlss0bfEcBAAC1jLIH4Khs0wZp82dyPdlyIZW4QSMk52TvzfQdBQAA1DLKHoCjsuL5ksT+einG5TSTTusue2+GzMx3HAAAUIsoewCOyornS63ayjVr6TsKapgbPFLa9Km0ZoXvKAAAoBZR9gAcwcr2ScsXM4UzRbn+w6VwmD33AABIcZQ9AEf6aJFUVcUUzhTlshtIPQfI5s2SRSO+4wAAgFpC2QNwBCteIGVkSqcV+I6CWhIaPErasS0o9gAAICVR9gAcxsxki+dLBb3lwum+46C29BogZWbL5kz3nQQAANSSmMvevHnzFIkw3QdIeRvWSqUlcj0H+E6CWuTS68n1GypbOEdWXu47DgAAqAUxl71nn31W1157rR577DF9/PHHtZkJgEe2iC0X6go3eKRUvk+2aK7vKAAAoBaEY33gPffco9WrV2vWrFm69957lZGRoREjRujMM89U8+bNazMjgDiyxfOlth3lmub6joLa1rWH1CQnWJVz4Jm+0wAAgBoWc9mTpA4dOqhDhw668sorVVxcrCeffFLPPvusunXrpsLCQg0fPlyhEKcBAsnK9u6WVnwod/bFvqMgDlwoTW7QCNmb/5Ht3inXoJHvSAAAoAadcDPbuHGjnnvuOf3lL39RRUWFLrvsMo0dO1ZTpkzRfffdVxsZAcTL0iIpGmV/vTrEDR4pRSKy+bN9RwEAADUs5iN7U6ZM0axZs/TZZ59p2LBhuuGGG3T66acfuH/w4MH69re/XSshAcSHFS+QshpIHbv6joJ4adtJatU2mMo56hzfaQAAQA2KuewVFRVp/PjxGjBggNLTj1yOPSMjQz/84Q9rNByA+LFoVLZ4gVz3vnJpab7jIE6cc3KDR8omPyXbulkul3OwAQBIFTFP4ywoKNDQoUOPKHovvfTSgY979+5dc8kAxNfaldLO7RJbLtQ5bvBISWLPPQAAUkzMZe/5558/odsBJBcrXiA5J9ejn+8oiDOX10I6rUA2Z7rMzHccAABQQ447jXPx4sWSpEgkcuDj/TZt2qTMzMzaSQYgrqx4vtThNLmGjX1HgQduyGjZkw9Lq1dIHU/zHQcAANSA45a9Rx55RJJUWVl54GMpOM+jSZMm+ta3vlV76QDEhe3aIa3+WO78y31HgSduwHDZM3+WzXlLjrIHAEBKOG7Ze/jhhyVJDz30kG644YZaDwQg/mzJQsmMLRfqMJfVQK7PYNncmbJLr5ELH7kQFwAASC4xn7NH0QNSWPECqWFjqV1n30ngkRs6Wtq9U1q80HcUAABQA77wyN7NN9+s+++/X5J03XXXHfNxh07vBJBcLBqRLV4o13uQXCjm93+Qigr6Sg0bK/ruW0rrM9h3GgAAcIq+sOx95zvfOfDxxIkTaz0MAA8+WSbt3c2WC5ALh+UGjZDNeFW2Z7dcdgPfkQAAwCn4wrLXrVu3Ax8XFBTUehgA8WfFC6RQSK6gj+8oSABu6BjZm/+RzX9bbuTZvuMAAIBTEPOcrZdeekmrV6+WJC1fvlzXXXedrr/+ei1fvry2sgGIAyueL3XuxlEcBNp1kvLbyd6d5jsJAAA4RTGXvZdfflnNmzeXJD3zzDMaP368Lr74Yj3xxBO1lQ1ALYts3SKtWyXHFE5Uc87JDRktrfxItnmD7zgAAOAUxFz29u7dq6ysLO3bt0+rV6/WOeecozFjxmjDBn4ZAJJV+YLZkiTXa5DnJEgkbsgoyYVk73B0DwCAZBZz2cvNzdWyZcs0e/ZsnXHGGQqFQtq7d69CrN4HJK3y+e9Iuc2l/La+oyCBuKa5UkFv2bvTZNGI7zgAAOAkxdzUrrzySt13332aNGmSLrnkEknSwoUL1aVLl1oLB6D2WEW5Kj6YJ9droJxzvuMgwbjhhVJpifTRIt9RAADASfrC1TgP1a9fP/3pT3867LYhQ4ZoyJAhNR4KQBx8tEiqKJfrzRROHMn1GSzLypbNflOuoK/vOAAA4CTEXPak4Ly9DRs2qKys7LDbe/ToUaOhANQ+WzRPrn6mdDr/fnEkl15PbtBI2eypsr27JeX5jgQAAE5QzGVv+vTpeuyxx1S/fn3Vq1fvwO3OOT300EO1Eg5A7TAz2aL5yug9SFXp6b7jIEG54WNl01+RzZ0ltevgOw4AADhBMZe9Z555Rj/4wQ/Uty/TeYCkt361tK1EGQOGq8p3FiSu9l2k1u1l77wpXXKV7zQAAOAExbxASzQaVe/evWszC4A4sUXzJEn1BgzznASJzDknN2ystGq5qtat8h0HAACcoJjL3oUXXqjnn39e0Wi0NvMAiAP7YK7U8XSlNcnxHQUJzg0ZJaWlad+bL/uOAgAATlDM0zhffvllbd++Xf/+97/VoEGDw+575JFHajwYgNphO7dJqz+Wu+By31GQBFyjJlLPASqbMUU6+xK58Amt6wUAADyK+X/tiRMnnvSLlJSU6OGHH9b27dvlnFNhYaHOPfdc7d69W/fff7+2bNmiZs2a6eabbz6iSAKoWVa8UDKT6zXQdxQkidDwQkWL3lNo8XypD9vtAACQLGIuewUFBSf9ImlpabrqqqvUqVMn7du3Tz/5yU/Uq1cvTZ8+XT179tSECRM0efJkTZ48WVdeeeVJvw6A47NF86QmuVLbTr6jIFn0HKBQ0zxFZ76uNMoeAABJI+Zz9iorK/XMM8/ohhtu0De/+U1J0gcffKApU6Yc97lNmzZVp07BL5aZmZlq3bq1SktLNW/ePI0cOVKSNHLkSM2bN+9kvgYAMbLKCmnJQrleA+Wc8x0HScKlpSlz7HnS4oWy0i2+4wAAgBjFfGTvr3/9q0pLS3XjjTfq17/+tSSpbdu2+utf/6qzzz475hfcvHmzVq1apS5dumjHjh1q2rSpJKlJkybasWPHUZ8zdepUTZ06VZJ09913Ky+PzX2/SDgc5nuEoyqfP1vby8vUeNQ4ZeTlMVYQu7MmaM9zf1Xm+++owWX/5TsNEhg/VxArxgpixVg5eTGXvblz5+rBBx9U/fr1DxwRyMnJUWlpacwvVlZWpnvvvVdXX321srKyDrvPOXfMIw2FhYUqLCw8cL2kpCTm16yL8vLy+B7hqKIz35Ays7SzVXu5khLGCmKWl9dCKuijPa+/qH2jx8uF0nxHQoLi5wpixVhBrBgrR8rPz4/pcTFP4wyHw0dsu7Bz5041bNgwpudXVVXp3nvv1ZlnnqnBgwdLkho3bqxt27ZJkrZt26ZGjRrFGgfACbJoRFb0nlyP/nLhdN9xkIRCZ46TSkukJUW+owAAgBjEXPaGDBmihx56SJs3b5YUlLPHHntMw4Ydf1NmM9Ojjz6q1q1ba/z48QduHzBggGbMmCFJmjFjhgYOZHVAoNasXCbt2iH1Heo7CZJVn8FSw8aKznrNdxIAABCDmMve17/+dbVo0UK33HKL9u7dqxtvvFFNmzbVpZdeetznLlu2TDNnztTixYt166236tZbb9XChQs1YcIELVq0SDfeeKOKi4s1YcKEU/piABybFc2RwmG5Hv18R0GScuF0uWFjpA/myrbHPoUfAAD4EfM5exs3blR+fr4uuugiRaNRDRo0SO3atYvpud26ddOzzz571Ptuu+22WCMAOElmJnt/jtStt1xm1vGfAByD+9I42WuTZO+8KXfu8d/sAwAA/hy37JmZHnnkEc2YMUO5ublq2rSpSktL9dxzz2nEiBG67rrrWMIdSHSfrpG2bJQ7+yu+kyDJuZatpdN7yN5+Q3b2xXKhmCeIAACAODtu2Zs6daqWLl2qO++8U126dDlw+4oVK/T73/9eb7zxhsaNG1erIQGcGiuaIzkn13uw7yhIAW7EWbK/3CstfV/q0d93HAAAcAzHfUt25syZuuaaaw4repLUpUsXXX311Zo1a1athQNQM+z9OVKnrnKNm/qOghTg+g8LFmp56xXfUQAAwBc4btlbv369CgoKjnpfQUGB1q9fX+OhANQc27pZWvuJXN8hvqMgRbhwutyZZ0nF82Ulm3zHAQAAx3DcsheNRpWZmXnU+zIzM4/Yew9AYrGi9yRJrg9lDzXHjTxLkpPNmOI7CgAAOIbjnrMXiUS0ePHiY95P2QMSmy18R8pvJ9ci33cUpBCX00zqM0j29uuyCy6XS6/nOxIAAPic45a9xo0b65FHHjnm/Y0aNarRQABqjm3fKn28VO78y31HQQoKjT5P0ffnyOa9Hey/BwAAEspxy97DDz8cjxwAaoEteFcykxsw3HcUpKJuvaSWbWTTX5EoewAAJBw2SAJSmM1/W2rdXq5VW99RkIKcc3KjzpVWLZetWeE7DgAA+BzKHpCibNtWacVSuQFf8h0FKcwNHS1l1Je99bLvKAAA4HMoe0CKsgWzJYkpnKhVLitbbuho2XszZTu3+Y4DAAAOQdkDUpTNf1tq01GuZRvfUZDi3NgLpKpK2fRXfUcBAACHoOwBKchKt0grP+KoHuLCtWwt9R4km/6qrKLcdxwAAFCNsgekIFvwjiSCgFNyAAAgAElEQVRxvh7iJvTlC6VdO2TvzfAdBQAAVKPsASnI5r8ttevERuqIn9N7SO06yd54UWbmOw0AABBlD0g5tnWz9Mkyjuohrpxzcl++UPpsnbRkoe84AABAlD0g5dj86lU4+3O+HuLLDfiS1CRH0Tde9B0FAACIsgekHJvzltSpq1zzVr6joI5x4XS5MeOlpUWy9at8xwEAoM6j7AEpxNatktavlhsy2ncU1FFuxFlSvQzZ65N9RwEAoM6j7AEpxN6dJqWF5QZyvh78cNkN5UacJXtvhmzLRt9xAACo0yh7QIqwSCRY9r7XALkGjXzHQR3mxl0khUKyKS/4jgIAQJ1G2QNSxYdF0s7tCjGFE565prlywwtl70yVlZb4jgMAQJ1F2QNShL37lpTdUOo5wHcUQO7si6VoVPb6JN9RAACosyh7QAqwfXtl78+RG3imXHq67ziAXF4LuSGjZbNek+3c5jsOAAB1EmUPSAG28B2pskJuKFM4kTjcOZdIlZWyN/7tOwoAAHUSZQ9IAfbuW1LzfKnj6b6jAAe4lq3lBnxJ9tYrsj27fMcBAKDOoewBSc62bpaWFcsNHS3nnO84wGHcuZdK5fvYdw8AAA8oe0CSs7enSs4xhRMJybXpIDfwTNnUf8u2l/qOAwBAnULZA5KYRSKyt1+XuveTy23uOw5wVG7CFVKkSvbyP31HAQCgTqHsAcls0Txpe6lCI8/2nQQ4Jtc8X+7Ms2SzXpdt2uA7DgAAdQZlD0hi0RmvSk1y2VsPCc+Nv0xKC8smP+U7CgAAdQZlD0hStmWjtOR9uTPHyaWl+Y4DfCHXuKncuAmy+W/L1qzwHQcAgDqBsgckKZv1muRCcl/6su8oQEzcuIukBo0Uff6vvqMAAFAnUPaAJGRVlcEqnL0HyuXk+Y4DxMRlZsmdd6n04Qey4gW+4wAAkPIoe0ASsvffk3btUGgEC7MgubiR50otWyv6zJ9klRW+4wAAkNIoe0ASshmvSrnNpe59fEcBTohLT1fo69+VtmyUvfq87zgAAKQ0yh6QZGzDWmlZcbAwS4iFWZB83Bm9g43WX31OtpmtGAAAqC2UPSDJ2BsvSun15JjCiSTmvvotKRxW9O9/kpn5jgMAQEqi7AFJxHZsk815S274WLmGjXzHAU6aa5Ird+EV0pL3pYXv+o4DAEBKouwBScSmvSxFInKFF/qOApwyN/o8qU1HRf/5F9nePb7jAACQcih7QJKw8jLZ9FekPoPlWuT7jgOcMpeWptBV35N2lMqe+bPvOAAApBzKHpAkbPZUae9uhcZd5DsKUGNcp65y531VNuctRefO9B0HAICUQtkDkoBFI8HCLJ27yXU5w3ccoEa58y6TOnWVPfWIbOsW33EAAEgZlD0gGbw/RyrZpNC4Cb6TADXOpaUp9O1bpGhU0f+7TxaN+I4EAEBKoOwBCc7MFH1tktSspdRnsO84QK1wzVrKXX6ttHyJbMoLvuMAAJASKHtAolu8UFq1XO6sr7CJOlKaGzZGrv9w2YtPyxYv9B0HAICkF47Hi/zxj3/UwoUL1bhxY917772SpN27d+v+++/Xli1b1KxZM918881q0KBBPOIAScPMFH3xaSm3udzwsb7jALXKOSddPVG26VNF//xbhX78G7nW7X3HAgAgacXlyN6oUaP0s5/97LDbJk+erJ49e+rBBx9Uz549NXny5HhEAZLLB+9Ja1bInf81uXC67zRArXP1sxSa+EupXoaif/gf2c5tviMBAJC04lL2CgoKjjhqN2/ePI0cOVKSNHLkSM2bNy8eUYCkYdGoopOflprnyw0Z7TsOEDcup5lCN/xC2rVd0YfulFWU+44EAEBS8nbO3o4dO9S0aVNJUpMmTbRjxw5fUYCEZAvekT5dI3fB5XJpnKuHusV1OE2h/7pFWv2xov97r6yq0nckAACSTlzO2Tse51xwrsYxTJ06VVOnTpUk3X333crLy4tXtKQUDof5HiU5i0S09eV/Sm07KvfsCbVW9hgriJWXsTLufO2tLNOuv9yv8F/uVZMf3SlXLyO+GXDC+LmCWDFWECvGysnzVvYaN26sbdu2qWnTptq2bZsaNWp0zMcWFhaqsLDwwPWSkpJ4RExaeXl5fI+SXPTdt2SfrlHouz/W1m21d84SYwWx8jZWBo+WKytXxdOPaPMdNyl0/c/lMurHPwdixs8VxIqxglgxVo6Un58f0+O8TeMcMGCAZsyYIUmaMWOGBg4c6CsKkFCsolz24tNS245S36G+4wDehUaeLXfNTdJHxYo+cLts7x7fkQAASApxKXsPPPCAfvGLX2jDhg367ne/q2nTpmnChAlatGiRbrzxRhUXF2vChAnxiAIkPJvyvLR1s0KXfVsuxFaYgCSFho5W6Du3SquWK3rXrbJP1/qOBABAwovLNM6bbrrpqLffdttt8Xh5IGlYySbZlBfkBp4p17Wn7zhAQnH9hyvUoJGif75H0V/fInfV9QoNGeU7FgAACYvDBkACiT77mOSc3CXX+I4CJCTXtadCv3xAat9Z9th9ij79iKyywncsAAASEmUPSBC29H3p/Tly514ql8OKU8CxuCY5Ct1yp9xZF8mmv6roHTfKlhb5jgUAQMKh7AEJwKoqFX3mf6VmLeXGXeQ7DpDwXFqaQpdco9DNv5Jkit5/m6J/uVe2s/ZWrwUAINlQ9oAEYG/+R9q4XqHL/lsuPd13HCBpuII+Ct3+oNz4y2TzZyv6y+8p+urzsvJy39EAAPCOsgd4ZhvWyiY/LfUZLNebLUiAE+XqZSh04RUK3f6g1Kmb7IW/KvrzaxV96xVZVaXveAAAeEPZAzyyqipFH7tfqp+p0FXf8x0HSGquVRulff92hW69S2rWSvb3RxX9xXWKzpgiq6T0AQDqHsoe4JG9/Ky0dqVCV31PrlFT33GAlOBO767Qj+5S6Pu3S42ayJ76o6I//46ib74kq2B6JwCg7ojLPnsAjmSrPpa98qzckNFy/Yb5jgOkFOec1KO/Qt37SR8WKfrSP2X/+HPwb27cRXIjz5arn+k7JgAAtYqyB3hgFeWK/t99UuMcucv/23ccIGU556SCvkor6CtbtljRl/8pe+5x2ZTn5AovlBt9nlxWtu+YAADUCsoeEGdmJvv7n6SNnyp08/8nl9XAdySgTnBdeyitaw/Zyo8UfflZ2eSnZK9Pkhtzvlzh+XLZDX1HBACgRlH2gDizt16WzZ4qd95X5Qr6+o4D1Dmuczel3XibbM3K4EjfS/+QvfGi3OhzgymeDRv5jggAQI2g7AFxZMuKZf/8i9RroNwFX/cdB6jTXPvOSvvez2TrV8te+ZfstRdkM6bInXep3Jjxcun1fEcEAOCUsBonECe2dbOij/5Gat5Kof/6gVyIf35AInBtOih07a0K3f4HqcsZsueeCDZnnztTZuY7HgAAJ43fNoE4sPIyRf/4aylSpdD1P2dBCCABudbtlHbjbQrd/CspM1v2v79T9J6fyj5d4zsaAAAnhbIH1DKrKFf04TuldasV+vYtci3b+I4E4Au4gj4K/fI+uW/cIG1Yp+ivvq/ov/5PVrbXdzQAAE4I5+wBtciqKoOpmx8tkrvmJrleA31HAhADF0qTO3OcrM8Q2aS/yV6fLJs7S6Gvf0eu7xDf8QAAiAlH9oBaYpGIov/7O6l4vtyV1yk0dLTvSABOkGvYSKFv3KDQT34rZTdQ9I+/VuTRu2U7tvmOBgDAcVH2gFpgVVWy/7tfWviu3Nf+W6ERZ/uOBOAUuM7dFPrF/XITrpQ+mKvobdcrOnsqC7gAABIaZQ+oYbZ3j6J/+JVs7ky5r3xTobHn+44EoAa4cFih876q0G0PSvntZE88qOj9t8m2bPQdDQCAo6LsATXItm5W9Dc/lpYVy119o0LnXOw7EoAa5lq1UejWX8td8V1p1XJF77hB0dcnySIR39EAADgMC7QANcRWfazoQ/8jVVYq9P075M7o7TsSgFriQiG5UefKeg1S9O+Pyv71eLCAy9UT5dp09B0PAABJHNkDTplFo4q+PlnR3/5YSq+n0E9/S9ED6giXkxfsnXntrVLpFkX/3w8UnfSkrLLCdzQAADiyB5wKKy1R9PEHpI8WSX0GK/SNiXING/mOBSCOnHNyA8+UndFb9uxjslf+JVv4jkJX3SB3enff8QAAdRhlDzgJFo3K3psh+8efpaoquW/cIPelL8s55zsaAE9cg0Zy37pZNniUok8+rOg9P5UbebbcxVfLZWb5jgcAqIMoe8AJspUfKfrPv0irlksdT1fov34g1yLfdywACcJ176vQHX+Qvfi07M3/yD6Yp9CV18n1HuQ7GgCgjqHsATGyLRuDX97emyE1zpG75vtyQ0bLhTj1FcDhXP1Mucu+LRt4pqJ/e0jRh/6f1HuQQpdcLdeyje94AIA6grIHHIetXSmb8oJs/mwpLU3u3K/KnXOxXP1M39EAJDjXqatCv7hP9saLslf+pegdE+VGnC13/uWc3wsAqHWUPeAorKpSWjRP0RlTpKVFUv1MuS9fKDf2fLmcPN/xACQRF06XO+cS2fCxsn8/I5v+qmzOdLnCC4KfKdkNfEcEAKQoyh5QzcyktStl70yTzZ0h7d4lNcmR+8o35UaeJZfFL2QATp5r1FTuyu/JxowPtmf4zzOyqS/KjR4v9+UL5BpwpA8AULMoe6jTrKpK+niJrOg92Qdzpa2bpXBYrs8QuWFjpYI+cmlpvmMCSCEuv53Srv+5bN0qRV/+p+yVZ4PSN2S03Ohz5dp08B0RAJAiKHuoUywSkdZ+IlteLFu2WPp4iVS2T0qvFxS7874q128Y06oA1DrXtqPSvvsT2adrZW9Mkr07TTZzinRagdzIc+T6DJbLqO87JgAgiVH2kNKCcrdStqy63K1YGpQ7SWrZWm7QCLke/YOixy9VADxwrdvJXf192SXXyGa/KZvxquwv98oy6sv1Hiw3aITUvY9cON13VABAkqHsIaXYvr3SJ8tkK5bKVnwY7IVXXhbc2bKN3OCRUteecqf3kGvc1G9YADiEa9BI7qyLZF++MJhePnembME7wTnE9TOlgr5yPfvL9egn1yTXd1wAQBKg7CGp2dYtshVLpZUfBuVu/RrJopILSW3aB+fdnVZAuQOQNFwoFLwp1bWn7PJrpSVFskVzZcULZAvfkUlSq7ZypxUEP99O6y7lNJNzznd0AECCoewhaZiZtOlT2YeLgne9V34olZYEd2bUlzp1Dc65O+0MqWNXucwsv4EB4BS5cLrUe6Bc74HBz8BP1wSlb/li2bxZ0szXgvLXsLHUtqNc205Su07BZYtWciEWmAKAuoyyh4Rmu3bIPvxAWlok+7DoYLlrkiPXpUAad4ZclzOkNh1ZNRNASnPOSW06BKt1nnOxLBqR1q8JZjesXSlbt0o29d9SpCoogPUygse37Sjlt5Nr1VZq3U5q2ISjgABQR1D2kFCsolxasVS29IOg3K39JLgjK1vq1kvunEvlCvpIzVryywqAOs2F0oKjeO06HbjNqiqlz9bL1n4irftEtu6T4Ajg3j1BAZSk7IZSflu5Vu2CEpjfVspvJzWiBAJAqqHswSuLRqX1q2RLi2RLi6QVH0qVFVJaWOrcTe7CK4Jy16EL05EA4DhcOL16OmdHSWMlVU+B31EqbVgn27BW+iy4tPmfK4FZDQ4rf65VdQls3JQSCABJirKHuLOSTbKPFgVTMz9aJO3aEdyR305u5NlBuTutu1z9TL9BASAFOOekJrlSk9zg52u1oARuO1D+9pdBmz9b2vvawRLYqInUrrNc+85y7btI7TpLOXkUQABIApQ91Cozk0o2yZYVS8sXB3vdlW4J7mzcVK57X+mMPnIFvVlKHADiKCiBOcE50Gf0PnC7mUk7t0sb1gYlcM1K2dqVsiXvyywaPKhBI6l9Z7n2p8l16SZ16iaX3cDTVwIAOBbKHmqUVVUGvyCsXhGsmLlssbStelGVBo2k03vInXWR3Ok9pNbteWcYABKMc05q3DR4Q+7QElheHky7X7tSWrNCtuYT2ZTngun4UrAdRJcztK/PIFmLNlLzVvyMBwDPKHsJzqoqpYoKKVIlVVYGl6E0KRyW0vZfpktpacHeTHHLVSWVbpY++1S2cb20cb1s3Srp09VSVVXwoIaNg1LX9ZLgMr8t//EDQJJyGRnBudSdux24zcrLpFXLZSs+lK38SLZgtnbOej24s2FjqfMZcvv3AmzbUS7Mrx0AEE/81PXE9u2VNm+QbdoglWySdmyT7SgNzp/YvVPat1fauydYrCRWoZAUTteWzCxF62UEe89l1JcyMqX69eX2f5xRX6pf/fH+0hiq/mNRKRKp/lMp7d0bZNm3R7Znl7Rtq7R9azDFx+zgazdsHBypG3u+1L6LXPvOUjPe1QWAVOYy6gcrJXfrJSlYdKtp2W6Vzn9HWvFhUAKL5gTn/+3fD7VLQbAhfKeuwfMBALWGsleLLBoNitz61UGp2/SpbPMGadOGoCwdKjOretpMjlzrDsFWA5nZwe31MqT09GCFyrQ0KRoNjvBVVQWlrKqyupxVSVWVynBOZTu2ycr2SeVl0r490vatwTuw5fuksrLgObHKyAxyZDeQmuYGq7w1zZVym8u1bCO1bC2X3bBGv3cAgOTjQiGF23VSKKuRNOJsSZJt3yr7+MNgav+KpbKX/hGcF5iWFiz8clpBsG9qlwK5ho08fwUAkFooezXEysulDWtk6z6R1q0OLtevCcrVfo2aSM3z5XoOkFrkyzXPl1rkS3ktanTlyUZ5eaooKfnivFVVUkXZwcIYrT6aFwpVl8pQMD20fiablQMATpprkis38EvSwC9JkmzvHmnlR7L95W/ay7LXJwcPzmsRrPjZoUtw2b6zXBYLvwDAyaLsnSIzU/TOW4LNv/evUpaZJbXpIDdsTHCOQpsOUss2cplZXrMeyoXDUpj/QAEA8eWysqWe/eV69pckWWWFtHqFbOWHweWaFdKC2Qe3fmieL9ehS/D/acvWUss2Ul5Lzv8DgBjwk/IUOeeC1cp6DZBr20lq0yF4Z5Jz1QAAOC6XXk86rfo8vmq2e2ew5cPqj2VrVsg+XirNnXmwAKalSc1aBacR7D+dIKdZcIpBk1z2aT0OMzvkdJDqy6rK6suq4Jz9qs/dX32bHfU5lUf5fId8HlMwc8i54DIUklxIO7OyFK2slNLrSRkZUr36wakrGfWDBYHqZVSfSpIp1c8K3kzPyKToAyfA+7+WoqIiPf7444pGoxo7dqwmTJjgO9IJC138Td8RAABIGa5BI6l732Av1mq2d09w7vtn1StAb/pU+my9rHiBFKmSHfoJMrODPQSb5gZ7uDbJkbIbStkN5Ro0DM5Bz66+zGogF06P+9d4PBaJBOfd7z/fvry8+rJMVnbkbSovk8r2SRXlB8/ZP+y5ZYcXstqQlhacChIOV18Gq4XLuWC9AYtKUau+jKpM1aeVVJYfXMl7/9f/Ra9Tr15Q/vYXwPqZwWknmftvO6Qc1s+S+9z14PFZUr16vDmPlOe17EWjUT322GP6xS9+odzcXP30pz/VgAED1KZNG5+xAABAgnFZ2VLH0+U6nn7Y7RaJSFs3SaUlsu1bpW2l0rbqj7eXBhvD79h+4FSLo5aIw1awrh+UgeqPXUb94MjT/hITTj9YZtLTg5WspUNWqLZDXsSC8+ErK4PVtauqLysrpMrKYAprRXlQ0j5fzk50Ne76mdUrbmccXHm7URO5+q2Cj+tlHJ49HD64fVP4YEFzn7/9Cx572O0nsQVUXl6eSqrXGLBIJPhelJcFawpUlB8ss2X7ZPv2SWXVK4SX7ZWqr9v+61u3yMr2HnxMJHLgdY5ZHJ07+PeYdsjf7bEuQ2mHHZ10h153ocOOWh78+PCjmXLukD/77z/kYxeqvn6M2+Sqt94KMrnDMh4ld1o4GL/p6VJ6vTq/DoMdusjhoUem9x+JrqwK1rEw04F/y01y5Jq19B39pHkteytWrFDLli3VokULSdKwYcM0b948yh4AAIiJS0uTmucH5/Yd4zEWjQYlYM9uac8uafeuYDuhvbul3buC+8qCknHgqFnZvmBbpLJ9B1a7PjA98URWtN5v/y/c4fRDPq6evpiZHRyF/PwWSfUObpXkDi2iB24/uI1Ssh+hcmlpwZG3Y6xvcCJfnZkFf0f7i2DZviPL4b59wd/zgamnlQf3M66slEU+Vwb27Q0KwIEjlFFZ9eWBP/vvj0aOOIp58Hr1baaDaz2cgi88Ano0LnSg+B0xHtPrVV8Pxqb7/G2ff044/fBC6w792FUX4kMKq3TI986OerT3sO9xZWX130GldqWnK7pz54HrqqwI9qKuqjrscYf9PX7+32yk6rA3AWL+lp19sVwSz+LzWvZKS0uVm5t74Hpubq4+/vhjj4kAAECqcaGQlBVM2VT1O/SnUo2Cc96qtzyKVB38bPsLlzvkFaqPgCV7GUsmzrnqYlIvWAn90Ps8ZToW21/+DiuCx7ktGjn6kamqz5XVys8VoANHlg890nxIcdp/1HlPcGTZKg+5rarq4HNO5Ouroe/T3v1HkNMPPYqZfkgRDVaQV7iRFE4/5IjnUY50Hu2obVo4KLdp6cGK9DrkCGxe8xr6Kvzwfs5eLKZOnaqpU6dKku6++27l5eV5TpTYwuEw3yPEhLGCWDFWECvGCmLFWEk+Fo1KVcEUZKusPOSoZlBILRI5pJgeehQvcuAIn3MumBIbcgemxrrPTXV1oVAw7bT6SGJ6vXqqqqqlc01TnNeyl5OTo61btx64vnXrVuXk5BzxuMLCQhUWFh64XnKcPeTqukPnwANfhLGCWDFWECvGCmLFWEkV+0uapNApLnZUfaqcolGpqkzaVyaJsXI0+fn5MT3uxM6krWGdO3fWZ599ps2bN6uqqkrvvPOOBgwY4DMSAAAAAKQEr0f20tLS9K1vfUt33nmnotGoRo8erbZt2/qMBAAAAAApwfs5e/369VO/fv18xwAAAACAlOJ1GicAAAAAoHZQ9gAAAAAgBVH2AAAAACAFUfYAAAAAIAVR9gAAAAAgBVH2AAAAACAFUfYAAAAAIAU5MzPfIQAAAAAANYsjeynoJz/5ie8ISBKMFcSKsYJYMVYQK8YKYsVYOXmUPQAAAABIQZQ9AAAAAEhBaXfccccdvkOg5nXq1Ml3BCQJxgpixVhBrBgriBVjBbFirJwcFmgBAAAAgBTENE4AAAAASEFh3wFwcnbv3q37779fW7ZsUbNmzXTzzTerQYMGRzxu+vTpeuGFFyRJX/nKVzRq1KjD7v/Nb36jzZs36957741HbHhwKmOlvLxc9913nzZt2qRQKKT+/fvriiuuiPeXgFpUVFSkxx9/XNFoVGPHjtWECRMOu7+yslIPPfSQPvnkEzVs2FA33XSTmjdvLkmaNGmSpk2bplAopGuuuUZ9+vTx8SUgTk52rCxatEhPP/20qqqqFA6HddVVV6lHjx6evgrEw6n8XJGkkpIS3Xzzzbr00kt1wQUXxDs+4uhUxsqaNWv05z//Wfv27ZNzTnfddZfq1avn48tIbIak9OSTT9qkSZPMzGzSpEn25JNPHvGYXbt22fXXX2+7du067OP95syZYw888ID94Ac/iFtuxN+pjJWysjIrLi42M7PKykr75S9/aQsXLoxrftSeSCRiN9xwg23cuNEqKyvthz/8oa1bt+6wx0yZMsX+9Kc/mZnZ22+/bffdd5+Zma1bt85++MMfWkVFhW3atMluuOEGi0Qicf8aEB+nMlY++eQT27p1q5mZrVmzxq699tr4hkdcncpY2e93v/ud3Xvvvfbiiy/GLTfi71TGSlVVld1yyy22atUqMzPbuXMn/wcdA9M4k9S8efM0cuRISdLIkSM1b968Ix5TVFSkXr16qUGDBmrQoIF69eqloqIiSf9/e/cfU1X9x3H8eS8IjPh5QWSKyC9NpQzy1gh3IayRSv5hrUZljTVZFrj5V65ZG0FN3WpJE4VZaZpBzkqX/WUIQjLFYLEpktyypA25kwsoECAXvn84WUSWeb/cC9fX4y/uvZ/zOe/Pve+x+77nfc6BgYEBjh49ytNPP+3SuMX1nMkVX1/fsV/gvb29iY2NpbOz06Xxy+SxWq1ERkYya9YsvL29SU1NnZAfP/zww1hHQEpKCmfPnmV0dJQzZ86QmprKjBkziIiIIDIyEqvV6oZViCs4kyuxsbGYTCYA5s6dy9DQENevX3f1EsRFnMkVgPr6eiIiIoiKinJ16OJizuRKU1MT0dHRxMTEABAYGIjRqLLm7+hdmaZ6enoIDQ0FICQkhJ6englj7HY7YWFhY49NJhN2ux2AiooKVq9ercPddwFnc+Wmvr4+GhoauP/++yc3YHGZv37uYWFhEz73P4/x8vLC39+fa9eu3VbOiOdwJlf+7PTp08TFxTFjxozJD1rcwplcGRgY4MiRIzzzzDMujVncw5lcaW9vx2Aw8O6777Jp0yaOHDni0tinE52zN4UVFRXR3d094fns7Oxxjw0GAwaD4bbn/fXXX+no6CAnJwebzeZ0nOJ+k5UrNzkcDoqLi1m5ciWzZs264zhF5O7V1tbGgQMH2Lx5s7tDkSnq4MGDZGVl4efn5+5QZIpzOBy0tLSwZcsWfH19KSwsJC4uTj9I/w0Ve1PYW2+9dcvXgoOD6erqIjQ0lK6uLoKCgiaMMZlMNDc3jz222+0sXryYCxcu8Msvv5CXl4fD4aCnp4eCggJ0y8Xpa7Jy5aaysjIiIyPJysr6/wYubmUymca15XZ2do612/11TFhYGA6Hg/7+fgIDAydsa7fbJ2wrnsOZXLk5/r333iMvL4/IyEiXxi6u5UyuWK1WTp8+zYEDB+jr68NgMODj48OKFStcvQxxAWdyJSwsjEWLFo19p0lOTubixYsq9v6G2jinKbPZzORwRRoAAAfESURBVIkTJwA4ceIEDz300IQxSUlJNDU10dvbS29vL01NTSQlJZGZmUlZWRklJSUUFhYye/ZsFXoezJlcgRstv/39/eTk5LgybHGB+Ph42tvbsdlsDA8PU1dXh9lsHjdm6dKlVFdXA3Dq1CkSExMxGAyYzWbq6uq4fv06NpuN9vZ2EhIS3LAKcQVncqWvr4+tW7fy/PPPs3DhQjdEL67kTK4UFhZSUlJCSUkJq1atYs2aNSr0PJgzufLAAw/Q1tbG4OAgDoeD8+fP6zzPW9BN1aepa9eu8cEHH3DlypVxl9P/+eefOXbsGOvXrwfg+PHjfP3118CNy+lnZGSMm8dms7Ft2zbdesGDOZMrnZ2dvPrqq8yZMwdv7xuNACtWrOCxxx5z23rk/6uxsZFPP/2UkZERMjIyeOqpp/jiiy+Ij4/HbDYzNDTEjh07uHjxIgEBAWzcuHGslferr76iqqoKo9FITk4OycnJbl6NTKY7zZUvv/ySw4cPjzui9+abbxIcHOzG1chkcub/yk0HDx7Ez89Pt17wcM7kSk1NDYcPH8ZgMJCcnMzatWvdvJqpScWeiIiIiIiIB1Ibp4iIiIiIiAdSsSciIiIiIuKBVOyJiIiIiIh4IBV7IiIiIiIiHkjFnoiIiIiIiAdSsSciIh7nypUrvPjii4yMjABQUFBAZWWlm6MSERFxLW93ByAiIvJXeXl5dHd34+XlhdFoJCoqirS0NB5//HGMxn//nTI8PJz9+/c7HUd1dTW7du3Cx8cHo9FIREQE2dnZLF269La2LygowGKx6N6UIiLiFir2RERkStq0aRNLliyhv7+f5uZm9uzZg9Vq5bXXXnNpHAsWLKCoqIiRkREqKyvZvn07paWl3HPPPZO+b4fDgZeX16TvR0REPJOKPRERmdL8/f0xm82EhISwefNmnnzySaKjo2lsbKSiooKOjg78/f3JyMjg2WefBcBms5Gfn095efm4Yml4eJjc3FzefvttoqOjAejp6SEvL4+dO3cSFBR0yziMRiNpaWns3r2b9vZ2EhISALhw4QL79u3j999/Z+bMmeTk5JCYmEh5eTnnz5+ntbWVvXv38uijj7J69eoJcf356F91dTWVlZXEx8dTU1NDZmYmkZGRVFZWMn/+fKqqqvD392fdunUkJycDN44+Hjp0iKtXrxIYGEh2djYWi2VSPgsREZleVOyJiMi0kJCQgMlkoqWlhejoaHx9fcnPzycqKoq2tjbeeecdYmJiePjhh285h7e3N8uWLaOmpoa1a9cCcPLkSe67775/LPQARkZGqKqqwsvLi5kzZwJgt9vZunUr+fn5JCUlcfbsWd5//322b9/Oc889x08//TSujdNms/3rOltbW0lNTWX37t04HA7q6uqwWq2kp6fz8ccf891331FaWkppaSmDg4Ps2bOHLVu2MHv2bLq6uujt7b3dt1RERDycLtAiIiLThslkGitmEhMTiY6Oxmg0Mm/ePJYtW0Zzc/O/zpGens7JkycZHR0FoKamhrS0tFuOb21tJScnhxdeeIH9+/ezYcMGgoODx7ZNTk7mwQcfxGg0smTJEuLj42lsbLzjNYaGhrJy5Uq8vLzw8fEBbpyDePN8xfT0dLq6uujp6QHAYDBw6dIlhoaGCA0NZe7cuXe8bxER8Sw6siciItOG3W4nICAAuFGEff7551y6dInh4WGGh4dJSUn51znmz5+Pr68v586dIzQ0lMuXL2M2m/9xfFFREQMDA+zatYuWlhZSU1OBG1f9PHXqFA0NDWPjHQ4HiYmJd7zG8PDwCc+FhISM/e3r6wvAwMAAISEhbNy4kW+++YbS0lLuvfdeXnrpJebMmXPH+xcREc+hYk9ERKYFq9WK3W5n4cKFAHz44Yc88cQTvPHGG/j4+LB3716uXr16W3Olp6dTW1tLSEgIKSkpY0fQ/omfnx+5ubnk5+eTkZFBbGwsYWFhWCwW1q9f/7fbGAyGCXMADA4O4u/vD0B3d/dtxXwrSUlJJCUlMTQ0REVFBWVlZRQWFjo1p4iIeAa1cYqIyJTW399PQ0MDxcXFWCyWsQur/PHHHwQEBODj44PVauX777+/7TktFgv19fXU1tb+YwvnXwUEBLB8+XIOHTo0Nk9DQwM//vgjIyMjDA0Nce7cOTo7OwEIDg6mo6NjbPugoCBMJhO1tbWMjIxw/Pjxca//V93d3Zw5c4aBgQG8vb3x8/ObUGCKiMjdS0f2RERkStq2bRteXl4YDAaioqLIysoiMzNz7PV169axb98+PvnkExYvXswjjzxCX1/fbc0dHh5OXFwcly9fZtGiRf8prqysLDZs2MBvv/3GvHnzeP311/nss88oLi7GaDSSkJBAbm4uAKtWraKkpIRjx45hsVh4+eWXeeWVV/joo48oLy9n+fLlLFiw4D/t/89GR0c5evQoO3bswGAwEBMTM7ZvERERw+jNM9RFRETuIjt37sRkMpGdne3uUERERCaF2jhFROSuY7PZqK+vZ/ny5e4ORUREZNKojVNERO4qFRUVfPvtt6xZs4aIiAh3hyMiIjJp1MYpIiIiIiLigdTGKSIiIiIi4oFU7ImIiIiIiHggFXsiIiIiIiIeSMWeiIiIiIiIB1KxJyIiIiIi4oFU7ImIiIiIiHig/wEukaY6kqxLJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "density_plot(risk_model.unwinz_returns.iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "density_plot(risk_model.returns.iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.001349527665317139, 0.001349527665317139, 0.001349527665317139, 0.001349527665317139, 0.001349527665317139]\n",
      "this is h2.shape:  (741,)\n",
      "this is ivv.shape:  (741, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.13830879851271666"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holdings = [1 / len(rets.columns)] * len(rets.columns)\n",
    "print(holdings[:5])\n",
    "np.sqrt(risk_model.predict_portfolio_risk_opt(holdings, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is h2.shape:  (741,)\n",
      "this is ivv.shape:  (741, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.13830879851271666"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aum = 50000000\n",
    "holdings = np.asarray(holdings) * aum\n",
    "np.sqrt(risk_model.predict_portfolio_risk_opt(holdings, aum))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_momentum_factor(prices, sector_data):\n",
    "\n",
    "    # demean by sector\n",
    "    df = prices.pct_change(252).dropna()\n",
    "    sectors = sector_data.unique()\n",
    "    \n",
    "    # demean by sector\n",
    "    for sector in sectors:\n",
    "        col_filter = sector_data[sector_data==sector].index\n",
    "        means = df.loc[:, col_filter].mean(axis=1)\n",
    "        df.loc[:, col_filter] = df.loc[:, col_filter].subtract(means, axis=0)\n",
    "        \n",
    "    ranked = df.rank(axis=1, method='first')\n",
    "    means = ranked.mean(axis=1)\n",
    "    stds = ranked.std(axis=1)\n",
    "    demeaned = ranked.subtract(means, axis=0)\n",
    "    zscored = demeaned.div(stds, axis=0)\n",
    "    return zscored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>ticker</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>UAL</th>\n",
       "      <th>TROW</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>-0.471847</td>\n",
       "      <td>-1.326777</td>\n",
       "      <td>-0.228916</td>\n",
       "      <td>1.691173</td>\n",
       "      <td>-0.369068</td>\n",
       "      <td>1.588395</td>\n",
       "      <td>1.130563</td>\n",
       "      <td>0.728793</td>\n",
       "      <td>-1.200640</td>\n",
       "      <td>1.032457</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261618</td>\n",
       "      <td>-0.686747</td>\n",
       "      <td>0.149496</td>\n",
       "      <td>-0.158839</td>\n",
       "      <td>-1.051144</td>\n",
       "      <td>1.621097</td>\n",
       "      <td>-1.130563</td>\n",
       "      <td>1.214655</td>\n",
       "      <td>0.126137</td>\n",
       "      <td>-0.172855</td>\n",
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       "      <th>2012-01-06</th>\n",
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       "      <td>-1.382838</td>\n",
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       "      <td>-0.583969</td>\n",
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       "      <td>0.752152</td>\n",
       "      <td>-1.256701</td>\n",
       "      <td>1.027785</td>\n",
       "      <td>...</td>\n",
       "      <td>0.355053</td>\n",
       "      <td>-0.551266</td>\n",
       "      <td>0.219572</td>\n",
       "      <td>-0.182198</td>\n",
       "      <td>-1.004426</td>\n",
       "      <td>1.588395</td>\n",
       "      <td>-1.065159</td>\n",
       "      <td>1.275388</td>\n",
       "      <td>0.233587</td>\n",
       "      <td>-0.149496</td>\n",
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       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>-0.513892</td>\n",
       "      <td>-1.448242</td>\n",
       "      <td>-0.294320</td>\n",
       "      <td>1.663143</td>\n",
       "      <td>-0.738136</td>\n",
       "      <td>1.513647</td>\n",
       "      <td>1.172609</td>\n",
       "      <td>0.752152</td>\n",
       "      <td>-1.130563</td>\n",
       "      <td>1.023113</td>\n",
       "      <td>...</td>\n",
       "      <td>0.327022</td>\n",
       "      <td>-0.537251</td>\n",
       "      <td>0.303664</td>\n",
       "      <td>-0.144824</td>\n",
       "      <td>-0.976396</td>\n",
       "      <td>1.588395</td>\n",
       "      <td>-1.027785</td>\n",
       "      <td>1.340792</td>\n",
       "      <td>0.154168</td>\n",
       "      <td>0.070076</td>\n",
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       "    <tr>\n",
       "      <th>2012-01-10</th>\n",
       "      <td>-0.551266</td>\n",
       "      <td>-1.508975</td>\n",
       "      <td>-0.163511</td>\n",
       "      <td>1.649128</td>\n",
       "      <td>-0.896976</td>\n",
       "      <td>1.555693</td>\n",
       "      <td>1.125892</td>\n",
       "      <td>0.569953</td>\n",
       "      <td>-1.191296</td>\n",
       "      <td>1.027785</td>\n",
       "      <td>...</td>\n",
       "      <td>0.415786</td>\n",
       "      <td>-0.560610</td>\n",
       "      <td>0.336366</td>\n",
       "      <td>-0.228916</td>\n",
       "      <td>-1.097861</td>\n",
       "      <td>1.522990</td>\n",
       "      <td>-1.069831</td>\n",
       "      <td>1.261372</td>\n",
       "      <td>0.116794</td>\n",
       "      <td>-0.004672</td>\n",
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       "    <tr>\n",
       "      <th>2012-01-11</th>\n",
       "      <td>-0.527908</td>\n",
       "      <td>-1.457586</td>\n",
       "      <td>-0.303664</td>\n",
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       "      <td>-0.873617</td>\n",
       "      <td>1.504303</td>\n",
       "      <td>1.083846</td>\n",
       "      <td>0.284977</td>\n",
       "      <td>-1.219327</td>\n",
       "      <td>1.018441</td>\n",
       "      <td>...</td>\n",
       "      <td>0.485862</td>\n",
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       "      <td>0.355053</td>\n",
       "      <td>-0.228916</td>\n",
       "      <td>-1.079174</td>\n",
       "      <td>1.518319</td>\n",
       "      <td>-0.995083</td>\n",
       "      <td>1.331449</td>\n",
       "      <td>0.130809</td>\n",
       "      <td>-0.121465</td>\n",
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      ],
      "text/plain": [
       "ticker          CSCO       UAL      TROW      ISRG       NVR      PRGO  \\\n",
       "Date                                                                     \n",
       "2012-01-05 -0.471847 -1.326777 -0.228916  1.691173 -0.369068  1.588395   \n",
       "2012-01-06 -0.476518 -1.382838 -0.196213  1.686502 -0.583969  1.532334   \n",
       "2012-01-09 -0.513892 -1.448242 -0.294320  1.663143 -0.738136  1.513647   \n",
       "2012-01-10 -0.551266 -1.508975 -0.163511  1.649128 -0.896976  1.555693   \n",
       "2012-01-11 -0.527908 -1.457586 -0.303664  1.649128 -0.873617  1.504303   \n",
       "\n",
       "ticker          LECO       TPR       DVN        CR    ...          PGR  \\\n",
       "Date                                                  ...                \n",
       "2012-01-05  1.130563  0.728793 -1.200640  1.032457    ...     0.261618   \n",
       "2012-01-06  1.130563  0.752152 -1.256701  1.027785    ...     0.355053   \n",
       "2012-01-09  1.172609  0.752152 -1.130563  1.023113    ...     0.327022   \n",
       "2012-01-10  1.125892  0.569953 -1.191296  1.027785    ...     0.415786   \n",
       "2012-01-11  1.083846  0.284977 -1.219327  1.018441    ...     0.485862   \n",
       "\n",
       "ticker           WAT       NNN       IEX      LRCX       NUS       NWL  \\\n",
       "Date                                                                     \n",
       "2012-01-05 -0.686747  0.149496 -0.158839 -1.051144  1.621097 -1.130563   \n",
       "2012-01-06 -0.551266  0.219572 -0.182198 -1.004426  1.588395 -1.065159   \n",
       "2012-01-09 -0.537251  0.303664 -0.144824 -0.976396  1.588395 -1.027785   \n",
       "2012-01-10 -0.560610  0.336366 -0.228916 -1.097861  1.522990 -1.069831   \n",
       "2012-01-11 -0.551266  0.355053 -0.228916 -1.079174  1.518319 -0.995083   \n",
       "\n",
       "ticker           UAA       BLK       PPL  \n",
       "Date                                      \n",
       "2012-01-05  1.214655  0.126137 -0.172855  \n",
       "2012-01-06  1.275388  0.233587 -0.149496  \n",
       "2012-01-09  1.340792  0.154168  0.070076  \n",
       "2012-01-10  1.261372  0.116794 -0.004672  \n",
       "2012-01-11  1.331449  0.130809 -0.121465  \n",
       "\n",
       "[5 rows x 741 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "momentum = create_momentum_factor(p, sector_data)\n",
    "momentum.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2015-12-29</th>\n",
       "      <td>-0.378412</td>\n",
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       "      <td>-0.967052</td>\n",
       "      <td>-0.887632</td>\n",
       "      <td>1.401525</td>\n",
       "      <td>-1.471601</td>\n",
       "      <td>-1.139907</td>\n",
       "      <td>-0.682075</td>\n",
       "      <td>-0.854930</td>\n",
       "      <td>-0.803541</td>\n",
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       "      <td>1.093189</td>\n",
       "      <td>0.042046</td>\n",
       "      <td>0.102778</td>\n",
       "      <td>0.397099</td>\n",
       "      <td>-0.317679</td>\n",
       "      <td>-1.294075</td>\n",
       "      <td>0.607327</td>\n",
       "      <td>1.051144</td>\n",
       "      <td>-0.467175</td>\n",
       "      <td>0.780182</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>-0.406442</td>\n",
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       "      <td>-0.990411</td>\n",
       "      <td>-0.840915</td>\n",
       "      <td>1.350136</td>\n",
       "      <td>-1.476273</td>\n",
       "      <td>-1.107205</td>\n",
       "      <td>-0.742808</td>\n",
       "      <td>-0.925006</td>\n",
       "      <td>-0.798869</td>\n",
       "      <td>...</td>\n",
       "      <td>1.107205</td>\n",
       "      <td>0.093435</td>\n",
       "      <td>0.140152</td>\n",
       "      <td>0.406442</td>\n",
       "      <td>-0.308335</td>\n",
       "      <td>-1.177281</td>\n",
       "      <td>0.509221</td>\n",
       "      <td>1.009098</td>\n",
       "      <td>-0.411114</td>\n",
       "      <td>0.780182</td>\n",
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       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>-0.345709</td>\n",
       "      <td>-0.672732</td>\n",
       "      <td>-0.976396</td>\n",
       "      <td>-0.920335</td>\n",
       "      <td>1.373494</td>\n",
       "      <td>-1.476273</td>\n",
       "      <td>-1.130563</td>\n",
       "      <td>-0.682075</td>\n",
       "      <td>-0.910991</td>\n",
       "      <td>-0.794197</td>\n",
       "      <td>...</td>\n",
       "      <td>1.055815</td>\n",
       "      <td>0.158839</td>\n",
       "      <td>0.196213</td>\n",
       "      <td>0.387755</td>\n",
       "      <td>-0.266290</td>\n",
       "      <td>-1.195968</td>\n",
       "      <td>0.644701</td>\n",
       "      <td>1.032457</td>\n",
       "      <td>-0.397099</td>\n",
       "      <td>0.808213</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>-0.401770</td>\n",
       "      <td>-0.747480</td>\n",
       "      <td>-0.981067</td>\n",
       "      <td>-0.719449</td>\n",
       "      <td>1.289403</td>\n",
       "      <td>-1.452914</td>\n",
       "      <td>-1.093189</td>\n",
       "      <td>-0.434473</td>\n",
       "      <td>-0.915663</td>\n",
       "      <td>-0.784854</td>\n",
       "      <td>...</td>\n",
       "      <td>1.060487</td>\n",
       "      <td>0.032702</td>\n",
       "      <td>0.200885</td>\n",
       "      <td>0.397099</td>\n",
       "      <td>-0.341038</td>\n",
       "      <td>-1.065159</td>\n",
       "      <td>0.649373</td>\n",
       "      <td>1.102533</td>\n",
       "      <td>-0.425129</td>\n",
       "      <td>0.794197</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-0.369068</td>\n",
       "      <td>-0.854930</td>\n",
       "      <td>-0.981067</td>\n",
       "      <td>-0.574625</td>\n",
       "      <td>1.317433</td>\n",
       "      <td>-1.448242</td>\n",
       "      <td>-1.102533</td>\n",
       "      <td>-0.317679</td>\n",
       "      <td>-0.910991</td>\n",
       "      <td>-0.798869</td>\n",
       "      <td>...</td>\n",
       "      <td>1.041800</td>\n",
       "      <td>-0.014015</td>\n",
       "      <td>0.102778</td>\n",
       "      <td>0.298992</td>\n",
       "      <td>-0.308335</td>\n",
       "      <td>-1.139907</td>\n",
       "      <td>0.565282</td>\n",
       "      <td>1.181953</td>\n",
       "      <td>-0.378412</td>\n",
       "      <td>0.850258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 741 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ticker          CSCO       UAL      TROW      ISRG       NVR      PRGO  \\\n",
       "Date                                                                     \n",
       "2015-12-29 -0.378412 -0.327022 -0.967052 -0.887632  1.401525 -1.471601   \n",
       "2015-12-30 -0.406442 -0.551266 -0.990411 -0.840915  1.350136 -1.476273   \n",
       "2015-12-31 -0.345709 -0.672732 -0.976396 -0.920335  1.373494 -1.476273   \n",
       "2016-01-04 -0.401770 -0.747480 -0.981067 -0.719449  1.289403 -1.452914   \n",
       "2016-01-05 -0.369068 -0.854930 -0.981067 -0.574625  1.317433 -1.448242   \n",
       "\n",
       "ticker          LECO       TPR       DVN        CR    ...          PGR  \\\n",
       "Date                                                  ...                \n",
       "2015-12-29 -1.139907 -0.682075 -0.854930 -0.803541    ...     1.093189   \n",
       "2015-12-30 -1.107205 -0.742808 -0.925006 -0.798869    ...     1.107205   \n",
       "2015-12-31 -1.130563 -0.682075 -0.910991 -0.794197    ...     1.055815   \n",
       "2016-01-04 -1.093189 -0.434473 -0.915663 -0.784854    ...     1.060487   \n",
       "2016-01-05 -1.102533 -0.317679 -0.910991 -0.798869    ...     1.041800   \n",
       "\n",
       "ticker           WAT       NNN       IEX      LRCX       NUS       NWL  \\\n",
       "Date                                                                     \n",
       "2015-12-29  0.042046  0.102778  0.397099 -0.317679 -1.294075  0.607327   \n",
       "2015-12-30  0.093435  0.140152  0.406442 -0.308335 -1.177281  0.509221   \n",
       "2015-12-31  0.158839  0.196213  0.387755 -0.266290 -1.195968  0.644701   \n",
       "2016-01-04  0.032702  0.200885  0.397099 -0.341038 -1.065159  0.649373   \n",
       "2016-01-05 -0.014015  0.102778  0.298992 -0.308335 -1.139907  0.565282   \n",
       "\n",
       "ticker           UAA       BLK       PPL  \n",
       "Date                                      \n",
       "2015-12-29  1.051144 -0.467175  0.780182  \n",
       "2015-12-30  1.009098 -0.411114  0.780182  \n",
       "2015-12-31  1.032457 -0.397099  0.808213  \n",
       "2016-01-04  1.102533 -0.425129  0.794197  \n",
       "2016-01-05  1.181953 -0.378412  0.850258  \n",
       "\n",
       "[5 rows x 741 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "momentum.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ticker</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>UAL</th>\n",
       "      <th>TROW</th>\n",
       "      <th>ISRG</th>\n",
       "      <th>NVR</th>\n",
       "      <th>PRGO</th>\n",
       "      <th>LECO</th>\n",
       "      <th>TPR</th>\n",
       "      <th>DVN</th>\n",
       "      <th>CR</th>\n",
       "      <th>...</th>\n",
       "      <th>PGR</th>\n",
       "      <th>WAT</th>\n",
       "      <th>NNN</th>\n",
       "      <th>IEX</th>\n",
       "      <th>LRCX</th>\n",
       "      <th>NUS</th>\n",
       "      <th>NWL</th>\n",
       "      <th>UAA</th>\n",
       "      <th>BLK</th>\n",
       "      <th>PPL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-24</th>\n",
       "      <td>-0.000730</td>\n",
       "      <td>0.024507</td>\n",
       "      <td>-0.004124</td>\n",
       "      <td>-0.000511</td>\n",
       "      <td>-0.001761</td>\n",
       "      <td>-0.001083</td>\n",
       "      <td>-0.003024</td>\n",
       "      <td>-0.018930</td>\n",
       "      <td>-0.015060</td>\n",
       "      <td>0.007036</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.003299</td>\n",
       "      <td>0.001808</td>\n",
       "      <td>-0.000373</td>\n",
       "      <td>-0.012554</td>\n",
       "      <td>-0.014908</td>\n",
       "      <td>-0.009635</td>\n",
       "      <td>-0.006595</td>\n",
       "      <td>0.000584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-28</th>\n",
       "      <td>-0.002557</td>\n",
       "      <td>-0.012881</td>\n",
       "      <td>0.003727</td>\n",
       "      <td>-0.000621</td>\n",
       "      <td>-0.003015</td>\n",
       "      <td>-0.011522</td>\n",
       "      <td>-0.014407</td>\n",
       "      <td>-0.000919</td>\n",
       "      <td>-0.030887</td>\n",
       "      <td>-0.027743</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001250</td>\n",
       "      <td>0.000448</td>\n",
       "      <td>0.009360</td>\n",
       "      <td>-0.004769</td>\n",
       "      <td>-0.004478</td>\n",
       "      <td>-0.029839</td>\n",
       "      <td>0.017427</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>0.014967</td>\n",
       "      <td>-0.003502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-29</th>\n",
       "      <td>0.016844</td>\n",
       "      <td>0.004914</td>\n",
       "      <td>0.005226</td>\n",
       "      <td>0.011442</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.005143</td>\n",
       "      <td>0.016838</td>\n",
       "      <td>0.011649</td>\n",
       "      <td>0.017356</td>\n",
       "      <td>0.028535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015645</td>\n",
       "      <td>0.016194</td>\n",
       "      <td>0.008271</td>\n",
       "      <td>0.006345</td>\n",
       "      <td>0.008997</td>\n",
       "      <td>0.030757</td>\n",
       "      <td>-0.002930</td>\n",
       "      <td>0.012682</td>\n",
       "      <td>0.003358</td>\n",
       "      <td>0.008787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>-0.008642</td>\n",
       "      <td>-0.030186</td>\n",
       "      <td>-0.013817</td>\n",
       "      <td>-0.001554</td>\n",
       "      <td>-0.000279</td>\n",
       "      <td>-0.009209</td>\n",
       "      <td>-0.002665</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>-0.038461</td>\n",
       "      <td>-0.008015</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004313</td>\n",
       "      <td>-0.003892</td>\n",
       "      <td>-0.001989</td>\n",
       "      <td>-0.007592</td>\n",
       "      <td>-0.007183</td>\n",
       "      <td>-0.001038</td>\n",
       "      <td>-0.011076</td>\n",
       "      <td>-0.015684</td>\n",
       "      <td>-0.004511</td>\n",
       "      <td>-0.002903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>-0.013440</td>\n",
       "      <td>-0.003652</td>\n",
       "      <td>-0.008323</td>\n",
       "      <td>-0.011493</td>\n",
       "      <td>-0.003965</td>\n",
       "      <td>-0.003718</td>\n",
       "      <td>-0.009733</td>\n",
       "      <td>0.001836</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>-0.008908</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016089</td>\n",
       "      <td>-0.007815</td>\n",
       "      <td>-0.002491</td>\n",
       "      <td>-0.006613</td>\n",
       "      <td>-0.009355</td>\n",
       "      <td>-0.015844</td>\n",
       "      <td>0.007543</td>\n",
       "      <td>-0.004323</td>\n",
       "      <td>-0.004473</td>\n",
       "      <td>-0.006115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 741 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ticker          CSCO       UAL      TROW      ISRG       NVR      PRGO  \\\n",
       "Date                                                                     \n",
       "2015-12-24 -0.000730  0.024507 -0.004124 -0.000511 -0.001761 -0.001083   \n",
       "2015-12-28 -0.002557 -0.012881  0.003727 -0.000621 -0.003015 -0.011522   \n",
       "2015-12-29  0.016844  0.004914  0.005226  0.011442  0.000048  0.005143   \n",
       "2015-12-30 -0.008642 -0.030186 -0.013817 -0.001554 -0.000279 -0.009209   \n",
       "2015-12-31 -0.013440 -0.003652 -0.008323 -0.011493 -0.003965 -0.003718   \n",
       "\n",
       "ticker          LECO       TPR       DVN        CR    ...          PGR  \\\n",
       "Date                                                  ...                \n",
       "2015-12-24 -0.003024 -0.018930 -0.015060  0.007036    ...     0.000000   \n",
       "2015-12-28 -0.014407 -0.000919 -0.030887 -0.027743    ...    -0.001250   \n",
       "2015-12-29  0.016838  0.011649  0.017356  0.028535    ...     0.015645   \n",
       "2015-12-30 -0.002665 -0.010000 -0.038461 -0.008015    ...    -0.004313   \n",
       "2015-12-31 -0.009733  0.001836  0.032258 -0.008908    ...    -0.016089   \n",
       "\n",
       "ticker           WAT       NNN       IEX      LRCX       NUS       NWL  \\\n",
       "Date                                                                     \n",
       "2015-12-24  0.000149  0.003299  0.001808 -0.000373 -0.012554 -0.014908   \n",
       "2015-12-28  0.000448  0.009360 -0.004769 -0.004478 -0.029839  0.017427   \n",
       "2015-12-29  0.016194  0.008271  0.006345  0.008997  0.030757 -0.002930   \n",
       "2015-12-30 -0.003892 -0.001989 -0.007592 -0.007183 -0.001038 -0.011076   \n",
       "2015-12-31 -0.007815 -0.002491 -0.006613 -0.009355 -0.015844  0.007543   \n",
       "\n",
       "ticker           UAA       BLK       PPL  \n",
       "Date                                      \n",
       "2015-12-24 -0.009635 -0.006595  0.000584  \n",
       "2015-12-28  0.000246  0.014967 -0.003502  \n",
       "2015-12-29  0.012682  0.003358  0.008787  \n",
       "2015-12-30 -0.015684 -0.004511 -0.002903  \n",
       "2015-12-31 -0.004323 -0.004473 -0.006115  \n",
       "\n",
       "[5 rows x 741 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "risk_model.returns.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(504, 741)\n",
      "(373464,)\n",
      "Dropped 0.2% entries from factor data: 0.2% 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"
     ]
    }
   ],
   "source": [
    "factor_data = {}\n",
    "start = pd.to_datetime('2014-01-01')\n",
    "end = pd.to_datetime('2015-12-31')\n",
    "\n",
    "factor_data['momentum'] = momentum.loc[start:end,:].stack()\n",
    "factor_data['momentum'].head()\n",
    "factor_prices = p.loc[start:end, :]\n",
    "print(factor_prices.shape)\n",
    "print(factor_data['momentum'].shape)\n",
    "factor_returns = al.utils.get_clean_factor_and_forward_returns(factor=factor_data['momentum'], prices=factor_prices, periods=[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x152761ba8>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(20,8)\n",
    "factor_returns_data = al.performance.factor_returns(factor_returns)\n",
    "factor_returns_data.cumsum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1D    0.835379\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sharpe = factor_returns_data.mean() * np.sqrt(252) / factor_returns_data.std()\n",
    "sharpe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x129a6f9b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1527485c0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x151b0dcc0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x128865400>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1299dbcf8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x129a11208>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x129a82a20>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x128853c88>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "qr_factor_returns = al.performance.mean_return_by_quantile(factor_returns)[0].iloc[:, 0]\n",
    "\n",
    "(10000*qr_factor_returns).plot.bar(\n",
    "    subplots=True,\n",
    "    sharey=True,\n",
    "    layout=(4,2),\n",
    "    figsize=(14, 14),\n",
    "    legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12b0d5b70>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls_FRA = al.performance.factor_rank_autocorrelation(factor_returns)\n",
    "\n",
    "ls_FRA.plot(title=\"Factor Rank Autocorrelation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "741"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets_index = rets.index[-1]\n",
    "alpha_vector = momentum.loc[rets_index, :]\n",
    "print(type(alpha_vector))\n",
    "len(alpha_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00134953, 0.00134953, 0.00134953, 0.00134953, 0.00134953])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "previous = np.asarray(holdings) / 50000000\n",
    "len(previous)\n",
    "previous[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OptimalHoldingsCVX:\n",
    "\n",
    "    def __init__(self, risk_model, alpha_vector, previous, risk_cap=0.05, factor_max=10.0, aum=50e6, lambda_reg=0.00,\n",
    "                 factor_min=-10.0, weights_max=0.55, weights_min=-0.55, risk_aversion=1.0e-6):\n",
    "\n",
    "        self.risk_model = risk_model\n",
    "        self.alpha_vector = alpha_vector\n",
    "        self.previous = previous\n",
    "        self.risk_cap = risk_cap\n",
    "        self.factor_min = factor_min\n",
    "        self.factor_max = factor_max\n",
    "        self.weights_min = weights_min\n",
    "        self.weights_max = weights_max\n",
    "        self.risk_aversion = risk_aversion\n",
    "        self.aum = aum\n",
    "        self.lambda_reg = lambda_reg\n",
    "\n",
    "    def _get_obj(self, h1):\n",
    "        ra = self.risk_aversion\n",
    "        Q = self.risk_model.Q # Q is k x N\n",
    "        ivv = self.risk_model.i_var_vector\n",
    "        av = -self.alpha_vector\n",
    "        #h0 = self.previous.flatten()\n",
    "        #lv = np.ones((len(av), 1)) * 0.1/2000000\n",
    "        #obj_func = cvx.Minimize(np.array(av.T) * h1 + self.lambda_reg * cvx.norm(h1, 2) + (h1-h0)**2 @ lv)\n",
    "        obj_func = cvx.Minimize(np.array(av.T) * h1 + self.lambda_reg * cvx.norm(h1, 2))\n",
    "\n",
    "        return obj_func\n",
    "\n",
    "    def _get_constraints(self, h1, risk):\n",
    "        c = []\n",
    "        factor_exposures = self.risk_model.factor_exposures\n",
    "        c.append(risk <= self.risk_cap**2)\n",
    "        c.append(factor_exposures.T @ h1 <= self.factor_max)\n",
    "        c.append(factor_exposures.T @ h1 >= self.factor_min)\n",
    "        c.append(sum(h1) == 0)\n",
    "        c.append(sum(cvx.abs(h1)) <= 1.0)\n",
    "        c.append(h1 >= self.weights_min)\n",
    "        c.append(h1 <= self.weights_max)\n",
    "        return c\n",
    "\n",
    "    def get_risk(self, h1):\n",
    "        av_index = self.alpha_vector.index\n",
    "        ivm = self.risk_model.i_var_matrix.loc[av_index].values\n",
    "        f = self.risk_model.factor_exposures.loc[av_index].values.T @ h1\n",
    "        X = self.risk_model.factor_cov_matrix\n",
    "        risk = cvx.quad_form(f, X) + cvx.quad_form(h1, ivm)\n",
    "        return risk\n",
    "\n",
    "    def find(self):\n",
    "        h1 = cvx.Variable(len(self.alpha_vector))\n",
    "        risk = self.get_risk(h1)\n",
    "        obj_func = self._get_obj(h1)\n",
    "        constraints = self._get_constraints(h1, risk)\n",
    "        prob = cvx.Problem(obj_func, constraints)\n",
    "        prob.solve(max_iters=100, verbose=True)\n",
    "        optimal_weights = np.asarray(h1.value).flatten()\n",
    "        df = pd.DataFrame(optimal_weights, index=self.alpha_vector.index)\n",
    "        return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ECOS 2.0.7 - (C) embotech GmbH, Zurich Switzerland, 2012-15. Web: www.embotech.com/ECOS\n",
      "\n",
      "It     pcost       dcost      gap   pres   dres    k/t    mu     step   sigma     IR    |   BT\n",
      " 0  +2.001e-17  -2.487e+03  +1e+06  8e-01  7e-01  1e+00  1e+00    ---    ---    1  1  - |  -  - \n",
      " 1  -7.894e+01  -3.745e+02  +1e+05  6e-02  9e-02  2e-01  1e-01  0.9890  1e-01   2  2  2 |  0  0\n",
      " 2  -1.468e+01  -4.998e+01  +2e+04  7e-03  1e-02  3e-02  2e-02  0.8922  2e-02   3  2  3 |  0  0\n",
      " 3  -3.075e+00  -8.503e+00  +3e+03  1e-03  2e-03  4e-03  3e-03  0.9519  1e-01   5  4  4 |  0  0\n",
      " 4  -1.459e+00  -2.214e+00  +4e+02  2e-04  2e-04  4e-04  4e-04  0.8536  1e-02   8  7  8 |  0  0\n",
      " 5  -1.227e+00  -1.306e+00  +4e+01  2e-05  2e-05  7e-05  4e-05  0.9368  3e-02   1  1  1 |  0  0\n",
      " 6  -1.176e+00  -1.182e+00  +3e+00  1e-06  1e-06  5e-06  3e-06  0.9352  1e-02   2  2  2 |  0  0\n",
      " 7  -1.172e+00  -1.173e+00  +5e-01  2e-07  2e-07  7e-07  5e-07  0.8954  8e-02   2  2  2 |  0  0\n",
      " 8  -1.171e+00  -1.172e+00  +2e-01  7e-08  8e-08  2e-07  2e-07  0.8427  2e-01   2  2  2 |  0  0\n",
      " 9  -1.171e+00  -1.171e+00  +5e-02  2e-08  2e-08  4e-08  4e-08  0.9052  2e-01   1  1  1 |  0  0\n",
      "10  -1.171e+00  -1.171e+00  +1e-02  5e-09  5e-09  9e-09  1e-08  0.9381  2e-01   2  2  2 |  0  0\n",
      "11  -1.171e+00  -1.171e+00  +1e-03  5e-10  6e-10  9e-10  1e-09  0.9628  8e-02   2  2  2 |  0  0\n",
      "12  -1.171e+00  -1.171e+00  +4e-04  2e-10  2e-10  3e-10  4e-10  0.7603  1e-01   2  2  2 |  0  0\n",
      "13  -1.171e+00  -1.171e+00  +8e-05  5e-11  4e-11  6e-11  7e-11  0.9890  2e-01   2  2  2 |  0  0\n",
      "14  -1.171e+00  -1.171e+00  +6e-06  1e-11  3e-12  4e-12  5e-12  0.9890  6e-02   2  2  1 |  0  0\n",
      "15  -1.171e+00  -1.171e+00  +8e-07  5e-12  4e-13  6e-13  7e-13  0.9890  1e-01   2  1  2 |  0  0\n",
      "16  -1.171e+00  -1.171e+00  +5e-07  4e-12  2e-13  4e-13  5e-13  0.9890  6e-01   1  1  1 |  0  0\n",
      "17  -1.171e+00  -1.171e+00  +4e-07  3e-12  2e-13  3e-13  4e-13  0.9890  8e-01   2  2  1 |  0  0\n",
      "18  -1.171e+00  -1.171e+00  +3e-07  3e-12  1e-13  2e-13  3e-13  0.9890  7e-01   2  1  1 |  0  0\n",
      "19  -1.171e+00  -1.171e+00  +2e-07  2e-12  7e-14  1e-13  1e-13  0.9890  5e-01   2  1  1 |  0  0\n",
      "20  -1.171e+00  -1.171e+00  +1e-07  2e-12  5e-14  8e-14  1e-13  0.9890  7e-01   2  1  1 |  0  0\n",
      "21  -1.171e+00  -1.171e+00  +1e-07  1e-12  5e-14  7e-14  9e-14  0.9890  9e-01   2  1  1 |  0  0\n",
      "22  -1.171e+00  -1.171e+00  +9e-08  1e-12  4e-14  5e-14  8e-14  0.9890  7e-01   2  1  1 |  0  0\n",
      "23  -1.171e+00  -1.171e+00  +7e-08  7e-13  3e-14  3e-14  7e-14  0.9890  5e-01   2  1  1 |  0  0\n",
      "24  -1.171e+00  -1.171e+00  +1e-07  6e-13  6e-14  4e-14  1e-13  0.9890  8e-01   2  1  1 |  0  0\n",
      "25  -1.171e+00  -1.171e+00  +1e-07  6e-13  5e-14  9e-14  1e-13  0.9890  9e-01   2  1  1 |  0  0\n",
      "26  -1.171e+00  -1.171e+00  +9e-08  5e-13  4e-14  6e-14  8e-14  0.9890  7e-01   2  1  1 |  0  0\n",
      "27  -1.171e+00  -1.171e+00  +8e-08  3e-13  4e-14  3e-14  7e-14  0.9890  4e-01   2  1  1 |  0  0\n",
      "28  -1.171e+00  -1.171e+00  +1e-07  2e-13  6e-14  5e-14  1e-13  0.9890  8e-01   2  1  1 |  0  0\n",
      "29  -1.171e+00  -1.171e+00  +1e-07  2e-13  5e-14  9e-14  1e-13  0.9890  9e-01   2  1  1 |  0  0\n",
      "30  -1.171e+00  -1.171e+00  +8e-08  2e-13  4e-14  6e-14  7e-14  0.9890  7e-01   2  1  1 |  0  0\n",
      "31  -1.171e+00  -1.171e+00  +8e-08  1e-13  4e-14  2e-14  8e-14  0.9890  4e-01   2  1  1 |  0  0\n",
      "32  -1.171e+00  -1.171e+00  +1e-07  9e-14  6e-14  5e-14  1e-13  0.9890  8e-01   2  1  1 |  0  0\n",
      "33  -1.171e+00  -1.171e+00  +1e-07  8e-14  5e-14  8e-14  1e-13  0.9890  9e-01   2  1  1 |  0  0\n",
      "34  -1.171e+00  -1.171e+00  +7e-08  6e-14  3e-14  5e-14  7e-14  0.9890  6e-01   2  1  1 |  0  0\n",
      "35  -1.171e+00  -1.171e+00  +9e-08  3e-14  4e-14  2e-14  8e-14  0.9890  3e-01   2  1  1 |  0  0\n",
      "36  -1.171e+00  -1.171e+00  +1e-07  3e-14  5e-14  6e-14  1e-13  0.9890  9e-01   2  1  1 |  0  0\n",
      "37  -1.171e+00  -1.171e+00  +1e-07  3e-14  5e-14  8e-14  1e-13  0.9890  9e-01   2  1  1 |  0  0\n",
      "38  -1.171e+00  -1.171e+00  +7e-08  2e-14  3e-14  5e-14  6e-14  0.9890  6e-01   2  1  1 |  0  0\n",
      "39  -1.171e+00  -1.171e+00  +1e-07  1e-14  5e-14  1e-14  9e-14  0.9890  3e-01   2  1  1 |  0  0\n",
      "40  -1.171e+00  -1.171e+00  +1e-07  2e-14  5e-14  7e-14  1e-13  0.9890  9e-01   2  1  1 |  0  0\n",
      "41  -1.171e+00  -1.171e+00  +1e-07  2e-14  5e-14  7e-14  1e-13  0.9890  9e-01   2  1  1 |  0  0\n",
      "42  -1.171e+00  -1.171e+00  +6e-08  8e-15  3e-14  4e-14  6e-14  0.9890  5e-01   2  1  1 |  0  0\n",
      "43  -1.171e+00  -1.171e+00  +1e-07  5e-15  5e-14  1e-14  1e-13  0.9890  2e-01   2  1  1 |  0  0\n",
      "44  -1.171e+00  -1.171e+00  +1e-07  4e-15  5e-14  5e-14  1e-13  0.7038  9e-01   2  1  1 |  0  0\n",
      "45  -1.171e+00  -1.171e+00  +3e-08  6e-15  1e-14  2e-14  2e-14  0.9890  2e-01   2  1  1 |  0  0\n",
      "SIGINT intercepted, stopping.\n",
      "\n",
      "Close to OPTIMAL (within feastol=1.2e-14, reltol=2.3e-08, abstol=2.7e-08).\n",
      "Runtime: 35.009839 seconds.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "oh = OptimalHoldingsCVX(risk_model, alpha_vector, previous, lambda_reg=5.0)\n",
    "optimal_weights = oh.find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/richard.wuebker/.pyenv/versions/zipline/lib/python3.5/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
      "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  \"Adding an axes using the same arguments as a previous axes \"\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(20, 8)\n",
    "optimal_weights.plot.bar(legend=None, title='Portfolio % Holdings by Stock')\n",
    "x_axis = plt.axes().get_xaxis()\n",
    "x_axis.set_visible(False)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x12f464630>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(alpha_vector, optimal_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "741\n",
      "741\n",
      "ticker\n",
      "CSCO   -0.345709\n",
      "UAL    -0.672732\n",
      "TROW   -0.976396\n",
      "ISRG   -0.920335\n",
      "NVR     1.373494\n",
      "Name: 2015-12-31 00:00:00, dtype: float64\n",
      "                   0\n",
      "ticker              \n",
      "CSCO   -9.181216e-15\n",
      "UAL    -2.435021e-14\n",
      "TROW   -7.766664e-14\n",
      "ISRG   -5.837649e-14\n",
      "NVR     3.016487e-03\n"
     ]
    }
   ],
   "source": [
    "print(len(alpha_vector))\n",
    "print(len(optimal_weights))\n",
    "print(alpha_vector.head())\n",
    "print(optimal_weights.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_transfer_coefficient(a, b):\n",
    "    tc, p = spearmanr(a, b)\n",
    "    return tc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(741,)\n",
      "(741,)\n"
     ]
    }
   ],
   "source": [
    "av = alpha_vector.values\n",
    "r = optimal_weights.values.flatten()\n",
    "print(av.shape)\n",
    "print(r.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_transfer_coefficient(av, r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OptimalHoldingsCVX:\n",
    "\n",
    "    def __init__(self, risk_model, alpha_vector, previous, risk_cap=0.05, factor_max=10.0, gmv=5e3, lambda_reg=0.00,\n",
    "                 factor_min=-10.0, weights_max=0.02, weights_min=-0.02, risk_aversion=1.0e-6):\n",
    "\n",
    "        self.risk_model = risk_model\n",
    "        self.alpha_vector = alpha_vector\n",
    "        self.previous = previous\n",
    "        self.risk_cap = risk_cap\n",
    "        self.factor_min = factor_min\n",
    "        self.factor_max = factor_max\n",
    "        self.weights_min = weights_min * gmv\n",
    "        self.weights_max = weights_max * gmv\n",
    "        self.risk_aversion = risk_aversion\n",
    "        self.gmv = gmv\n",
    "        self.lambda_reg = lambda_reg\n",
    "\n",
    "    def _get_obj(self, h1):\n",
    "        ra = self.risk_aversion\n",
    "        Q = self.risk_model.Q # Q is k x N\n",
    "        ivv = self.risk_model.i_var_vector\n",
    "        av = -self.alpha_vector\n",
    "        h0 = self.previous.flatten()\n",
    "        lv = np.ones((len(av), 1)) * 0.1/10000000\n",
    "        obj_func = cvx.Minimize(np.array(av.T) * h1 + self.lambda_reg * cvx.norm(h1, 2) + (h1-h0)**2 @ lv)\n",
    "        #obj_func = cvx.Minimize(np.array(av.T) * h1 + self.lambda_reg * cvx.norm(h1, 2))\n",
    "\n",
    "        return obj_func\n",
    "\n",
    "    def _get_constraints(self, h1, risk):\n",
    "        c = []\n",
    "        factor_exposures = self.risk_model.factor_exposures\n",
    "        c.append(risk <= self.risk_cap**2)\n",
    "        c.append(factor_exposures.T @ h1 <= self.factor_max)\n",
    "        c.append(factor_exposures.T @ h1 >= self.factor_min)\n",
    "        c.append(sum(h1) == 0)\n",
    "        c.append(sum(cvx.abs(h1)) <= self.gmv)\n",
    "        c.append(h1 >= self.weights_min)\n",
    "        c.append(h1 <= self.weights_max)\n",
    "        return c\n",
    "\n",
    "    def get_risk(self, h1):\n",
    "        av_index = self.alpha_vector.index\n",
    "        ivm = self.risk_model.i_var_matrix.loc[av_index].values\n",
    "        f = self.risk_model.factor_exposures.loc[av_index].values.T @ h1\n",
    "        X = self.risk_model.factor_cov_matrix\n",
    "        risk = cvx.quad_form(f, X) + cvx.quad_form(h1/self.gmv, ivm)\n",
    "        return risk\n",
    "\n",
    "    def find(self):\n",
    "        h1 = cvx.Variable(len(self.alpha_vector))\n",
    "        risk = self.get_risk(h1)\n",
    "        obj_func = self._get_obj(h1)\n",
    "        constraints = self._get_constraints(h1, risk)\n",
    "        prob = cvx.Problem(obj_func, constraints)\n",
    "        prob.solve(max_iters=100, verbose=True)\n",
    "        optimal_weights = np.asarray(h1.value).flatten()\n",
    "        df = pd.DataFrame(optimal_weights, index=self.alpha_vector.index, columns=['holdings'])\n",
    "        return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(741, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_holdings = np.zeros((len(alpha_vector), 1))  / len(alpha_vector)\n",
    "print(initial_holdings.shape)\n",
    "initial_holdings[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ECOS 2.0.7 - (C) embotech GmbH, Zurich Switzerland, 2012-15. Web: www.embotech.com/ECOS\n",
      "\n",
      "It     pcost       dcost      gap   pres   dres    k/t    mu     step   sigma     IR    |   BT\n",
      " 0  +1.577e-16  -1.866e+05  +1e+06  9e-02  8e-01  1e+00  1e+00    ---    ---    2  1  - |  -  - \n",
      " 1  -1.880e+00  -3.007e+04  +2e+05  1e-02  2e-01  2e-01  2e-01  0.8425  6e-03   2  2  2 |  0  0\n",
      " 2  -7.276e+00  -4.359e+03  +3e+04  2e-03  3e-02  4e-02  3e-02  0.9541  9e-02   2  2  2 |  0  0\n",
      " 3  -2.605e+01  -2.664e+03  +2e+04  1e-03  2e-02  3e-02  1e-02  0.4534  8e-02   3  3  4 |  0  0\n",
      " 4  -2.854e+01  -2.965e+03  +2e+04  1e-03  2e-02  3e-02  1e-02  0.1426  9e-01   3  4  4 |  0  0\n",
      " 5  -3.205e+01  -3.180e+03  +2e+04  2e-03  2e-02  4e-02  1e-02  0.1000  8e-01   3  4  4 |  0  0\n",
      " 6  -6.360e+01  -1.336e+03  +5e+03  7e-04  8e-03  2e-02  5e-03  0.8161  2e-01   3  4  4 |  0  0\n",
      " 7  -1.321e+02  -1.089e+03  +3e+03  5e-04  6e-03  2e-02  3e-03  0.5709  3e-01   3  3  3 |  0  0\n",
      " 8  -4.779e+02  -1.318e+03  +1e+03  5e-04  9e-03  3e-02  1e-03  0.9374  3e-01   2  2  2 |  0  0\n",
      " 9  -7.782e+02  -1.689e+03  +8e+02  6e-04  1e-02  5e-02  7e-04  0.4330  3e-01   2  2  2 |  0  0\n",
      "10  -1.935e+03  -3.191e+03  +4e+02  8e-04  2e-02  1e-01  4e-04  0.7430  4e-01   2  2  2 |  0  0\n",
      "11  -1.926e+03  -3.211e+03  +4e+02  8e-04  2e-02  1e-01  4e-04  0.0529  9e-01   2  2  2 |  0  0\n",
      "12  -3.400e+03  -4.953e+03  +2e+02  1e-03  4e-02  3e-01  2e-04  0.8110  4e-01   2  2  1 |  0  0\n",
      "13  -3.304e+03  -4.274e+03  +1e+02  6e-04  2e-02  2e-01  1e-04  0.6148  3e-01   2  2  3 |  0  0\n",
      "14  -3.519e+03  -4.272e+03  +8e+01  5e-04  2e-02  2e-01  7e-05  0.6904  4e-01   2  2  2 |  0  0\n",
      "15  -3.838e+03  -4.399e+03  +4e+01  4e-04  2e-02  2e-01  4e-05  0.6132  3e-01   2  1  1 |  0  0\n",
      "16  -4.043e+03  -4.465e+03  +3e+01  3e-04  1e-02  2e-01  2e-05  0.6087  4e-01   2  2  2 |  0  0\n",
      "17  -4.255e+03  -4.567e+03  +2e+01  2e-04  1e-02  2e-01  2e-05  0.4539  2e-01   2  1  2 |  0  0\n",
      "18  -4.239e+03  -4.544e+03  +2e+01  2e-04  1e-02  2e-01  1e-05  0.2816  7e-01   2  2  2 |  0  0\n",
      "19  -4.436e+03  -4.644e+03  +9e+00  1e-04  8e-03  2e-01  9e-06  0.5630  3e-01   2  1  1 |  0  0\n",
      "20  -4.467e+03  -4.647e+03  +8e+00  1e-04  7e-03  2e-01  7e-06  0.4588  6e-01   2  2  2 |  0  0\n",
      "21  -4.588e+03  -4.712e+03  +5e+00  8e-05  5e-03  1e-01  4e-06  0.5495  3e-01   2  1  2 |  0  0\n",
      "22  -4.659e+03  -4.741e+03  +3e+00  5e-05  4e-03  9e-02  3e-06  0.6480  4e-01   2  2  2 |  0  0\n",
      "23  -4.740e+03  -4.786e+03  +1e+00  3e-05  2e-03  6e-02  1e-06  0.7119  3e-01   1  1  1 |  0  0\n",
      "24  -4.803e+03  -4.816e+03  +4e-01  8e-06  6e-04  2e-02  4e-07  0.9603  2e-01   2  2  2 |  0  0\n",
      "25  -4.823e+03  -4.827e+03  +1e-01  2e-06  2e-04  6e-03  1e-07  0.7577  7e-02   2  1  2 |  0  0\n",
      "26  -4.830e+03  -4.831e+03  +2e-02  4e-07  3e-05  8e-04  2e-08  0.8651  3e-02   1  2  2 |  0  0\n",
      "27  -4.830e+03  -4.831e+03  +1e-02  2e-07  2e-05  5e-04  1e-08  0.5915  5e-01   1  1  2 |  0  0\n",
      "28  -4.831e+03  -4.831e+03  +1e-02  2e-07  2e-05  5e-04  1e-08  0.0576  9e-01   1  1  1 |  0  0\n",
      "29  -4.831e+03  -4.831e+03  +7e-03  1e-07  9e-06  3e-04  6e-09  0.9890  5e-01   1  2  2 |  0  0\n",
      "30  -4.831e+03  -4.831e+03  +3e-03  6e-08  4e-06  1e-04  3e-09  0.7796  3e-01   2  2  1 |  0  0\n",
      "31  -4.831e+03  -4.831e+03  +1e-03  2e-08  2e-06  5e-05  1e-09  0.7611  2e-01   1  1  2 |  0  0\n",
      "32  -4.831e+03  -4.831e+03  +1e-03  2e-08  2e-06  5e-05  1e-09  0.1222  8e-01   2  2  2 |  0  0\n",
      "33  -4.831e+03  -4.831e+03  +5e-04  1e-08  7e-07  2e-05  5e-10  0.9890  4e-01   2  2  1 |  0  0\n",
      "34  -4.831e+03  -4.831e+03  +2e-04  6e-09  3e-07  5e-06  2e-10  0.9890  3e-01   1  2  2 |  0  0\n",
      "35  -4.831e+03  -4.831e+03  +4e-05  3e-09  1e-07  1e-06  3e-11  0.9575  2e-01   2  2  2 |  0  0\n",
      "36  -4.831e+03  -4.831e+03  +1e-05  4e-09  9e-08  3e-07  1e-11  0.8698  2e-01   4  3  4 |  0  0\n",
      "37  -4.832e+03  -4.832e+03  +9e-06  6e-09  6e-08  3e-07  9e-12  0.5345  7e-01   3  4  4 |  0  0\n",
      "38  -4.832e+03  -4.832e+03  +7e-06  7e-09  6e-08  2e-07  7e-12  0.9890  8e-01   4  3  3 |  0  0\n",
      "39  -4.832e+03  -4.832e+03  +2e-06  1e-08  5e-08  6e-08  2e-12  0.9890  3e-01   3  3  4 |  0  0\n",
      "40  -4.832e+03  -4.832e+03  +4e-07  3e-08  3e-08  1e-08  4e-13  0.9101  8e-02   4  4  4 |  0  0\n",
      "41  -4.832e+03  -4.832e+03  +4e-07  4e-08  3e-08  9e-09  3e-13  0.9890  8e-01   4  5  5 |  0  0\n",
      "42  -4.832e+03  -4.832e+03  +3e-08  4e-07  2e-08  7e-10  2e-14  0.9890  6e-02   5  5  6 |  0  0\n",
      "43  -4.832e+03  -4.832e+03  -1e+13  -2e-02  -4e-08  -7e-10  -1e+07  0.0000  1e+00   0  1  0 |  0  0\n",
      "Unreliable search direction detected, recovering best iterate (35) and stopping.\n",
      "\n",
      "Close to OPTIMAL (within feastol=1.1e-07, reltol=7.4e-09, abstol=3.6e-05).\n",
      "Runtime: 38.357430 seconds.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "oh = OptimalHoldingsCVX(risk_model, alpha_vector, initial_holdings, lambda_reg=5.0)\n",
    "optimal_weights = oh.find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(20, 8)\n",
    "optimal_weights.plot.bar(legend=None, title='Portfolio % Holdings by Stock')\n",
    "x_axis = plt.axes().get_xaxis()\n",
    "x_axis.set_visible(False)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>holdings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ticker</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CSCO</th>\n",
       "      <td>-8.080527e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UAL</th>\n",
       "      <td>-9.134056e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TROW</th>\n",
       "      <td>-5.071902e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISRG</th>\n",
       "      <td>-1.136673e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NVR</th>\n",
       "      <td>2.047374e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            holdings\n",
       "ticker              \n",
       "CSCO   -8.080527e-07\n",
       "UAL    -9.134056e-07\n",
       "TROW   -5.071902e-06\n",
       "ISRG   -1.136673e+01\n",
       "NVR     2.047374e+01"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimal_weights.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average long holdings:  6.7568\n",
      "max long holding:  74.8945\n",
      "average short holdings:  -6.7385\n",
      "max short holding:  -70.7802\n",
      "gmv =  holdings    4999.999022\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('average long holdings: ', optimal_weights.loc[optimal_weights['holdings'] > 0].mean().values[0].round(4))\n",
    "print('max long holding: ', optimal_weights.loc[optimal_weights['holdings'] > 0].max().values[0].round(4))\n",
    "print('average short holdings: ', optimal_weights.loc[optimal_weights['holdings'] <= 0].mean().values[0].round(4))\n",
    "print('max short holding: ', optimal_weights.loc[optimal_weights['holdings'] < 0].min().values[0].round(4))\n",
    "print('gmv = ', optimal_weights.loc[optimal_weights['holdings'] > 0].sum() + optimal_weights.loc[optimal_weights['holdings'] < 0].abs().sum().values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is h2.shape:  (741,)\n",
      "this is ivv.shape:  (741, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.016695784202779708"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(risk_model.predict_portfolio_risk_opt(optimal_weights.values.flatten(), 5000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def backtest(test_period, holdings, returns, alpha, gmv):\n",
    "    port = {}\n",
    "    for d in tqdm(test_period):\n",
    "        str_d = d.strftime('%Y%m%d')\n",
    "        end_date = d\n",
    "        start_date = d - dt.timedelta(days=365)\n",
    "        av = alpha.loc[end_date, :]\n",
    "        rets = returns.loc[start_date:end_date, :]\n",
    "        lambda_vector = np.ones((1, len(av))) * 0.1/10000000\n",
    "        rm = RiskModelPCA(rets, 20)\n",
    "        start = time.time()\n",
    "        oh = OptimalHoldingsCVX(rm, av, holdings, lambda_reg=7.0, gmv=gmv)\n",
    "        end = time.time()\n",
    "        start = time.time()\n",
    "        new_holdings = oh.find()\n",
    "        end = time.time()\n",
    "        data = np.matmul(rm.factor_exposures.values.T, new_holdings.values).flatten()\n",
    "        risk_exposures = pd.Series(data=data.T, index=rm.factor_exposures.columns.values) \n",
    "        alpha_exposures = pd.Series(av * new_holdings['holdings'], index=av.index) \n",
    "        transaction_costs = np.dot(lambda_vector, (new_holdings - holdings)**2).flatten()\n",
    "        print('TRANSACTION COSTS: ', transaction_costs)\n",
    "        result = {\n",
    "            'h.opt.previous': holdings,\n",
    "            'h.opt': new_holdings,\n",
    "            'risk.exposures': risk_exposures,\n",
    "            'alpha.exposures': alpha_exposures,\n",
    "            't.costs': transaction_costs,\n",
    "            'factor.exposures': rm.factor_exposures,\n",
    "            'alpha.vector': av,\n",
    "            'rm': rm\n",
    "        }\n",
    "        print('RISK: ', np.sqrt(rm.predict_portfolio_risk_opt(new_holdings.values.flatten(), gmv)))\n",
    "        port[str_d] = result\n",
    "        holdings = np.asarray(new_holdings)\n",
    "    return port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/64 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2015-05-01', '2015-05-04', '2015-05-05', '2015-05-06',\n",
      "               '2015-05-07'],\n",
      "              dtype='datetime64[ns]', name='Date', freq=None)\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-86-633f026b32c1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_period\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0minitial_holdings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmomentum\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mport\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbacktest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_period\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial_holdings\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmomentum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-84-b8d23b3ea48a>\u001b[0m in \u001b[0;36mbacktest\u001b[0;34m(test_period, holdings, returns, alpha, gmv)\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0mnew_holdings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m         \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfactor_exposures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_holdings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-85-6f5e90bd989e>\u001b[0m in \u001b[0;36mfind\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     54\u001b[0m         \u001b[0mconstraints\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_constraints\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrisk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m         \u001b[0mprob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcvx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mProblem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconstraints\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m         \u001b[0mprob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_iters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m         \u001b[0moptimal_weights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m         \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimal_weights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malpha_vector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'holdings'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/zipline/lib/python3.5/site-packages/cvxpy/problems/problem.py\u001b[0m in \u001b[0;36msolve\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    287\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m             \u001b[0msolve_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mProblem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_solve\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 289\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msolve_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    290\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    291\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/zipline/lib/python3.5/site-packages/cvxpy/problems/problem.py\u001b[0m in \u001b[0;36m_solve\u001b[0;34m(self, solver, warm_start, verbose, parallel, gp, qcp, **kwargs)\u001b[0m\n\u001b[1;32m    567\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_construct_chains\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolver\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msolver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m         data, solving_inverse_data = self._solving_chain.apply(\n\u001b[0;32m--> 569\u001b[0;31m             self._intermediate_problem)\n\u001b[0m\u001b[1;32m    570\u001b[0m         solution = self._solving_chain.solve_via_data(\n\u001b[1;32m    571\u001b[0m             self, data, warm_start, verbose, kwargs)\n",
      "\u001b[0;32m~/.pyenv/versions/zipline/lib/python3.5/site-packages/cvxpy/reductions/chain.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, problem)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0minverse_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreductions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0mproblem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m             \u001b[0minverse_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mproblem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minverse_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/zipline/lib/python3.5/site-packages/cvxpy/reductions/solvers/conic_solvers/ecos_conif.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, problem)\u001b[0m\n\u001b[1;32m    107\u001b[0m         \u001b[0minv_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNEQ_CONSTR\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mneq_constr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m         data[s.G], data[s.H] = self.group_coeff_offset(\n\u001b[0;32m--> 109\u001b[0;31m             problem, neq_constr, ECOS.EXP_CONE_ORDER)\n\u001b[0m\u001b[1;32m    110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minv_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/zipline/lib/python3.5/site-packages/cvxpy/reductions/solvers/conic_solvers/conic_solver.py\u001b[0m in \u001b[0;36mgroup_coeff_offset\u001b[0;34m(self, problem, constraints, exp_cone_order)\u001b[0m\n\u001b[1;32m    283\u001b[0m         \u001b[0mmatrices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffsets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mcons\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mconstraints\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m             \u001b[0mcoeff\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_constr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcons\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexp_cone_order\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    286\u001b[0m             \u001b[0mmatrices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoeff\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m             \u001b[0moffsets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/zipline/lib/python3.5/site-packages/cvxpy/reductions/solvers/conic_solvers/conic_solver.py\u001b[0m in \u001b[0;36mformat_constr\u001b[0;34m(self, problem, constr, exp_cone_order)\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0mcoeffs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffsets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mconstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 209\u001b[0;31m             \u001b[0mcoeff\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mConicSolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_coeff_offset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    210\u001b[0m             \u001b[0mcoeffs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoeff\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m             \u001b[0moffsets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/zipline/lib/python3.5/site-packages/cvxpy/reductions/solvers/conic_solvers/conic_solver.py\u001b[0m in \u001b[0;36mget_coeff_offset\u001b[0;34m(expr)\u001b[0m\n\u001b[1;32m    143\u001b[0m             \u001b[0;31m# expr is c.T*x + d\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m             \u001b[0moffset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m             \u001b[0mcoeff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    146\u001b[0m         \u001b[0;31m# Convert scalars to sparse matrices.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoeff\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/zipline/lib/python3.5/site-packages/cvxpy/atoms/atom.py\u001b[0m in \u001b[0;36mvalue\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    329\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 331\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_value_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/zipline/lib/python3.5/site-packages/cvxpy/atoms/atom.py\u001b[0m in \u001b[0;36m_value_impl\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    352\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m                     \u001b[0marg_values\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    355\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/zipline/lib/python3.5/site-packages/cvxpy/atoms/affine/index.py\u001b[0m in \u001b[0;36mnumeric\u001b[0;34m(self, values)\u001b[0m\n\u001b[1;32m     68\u001b[0m         \"\"\" Returns the index/slice into the given value.\n\u001b[1;32m     69\u001b[0m         \"\"\"\n\u001b[0;32m---> 70\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_orig_key\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mshape_from_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/zipline/lib/python3.5/site-packages/scipy/sparse/_index.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_sliceXslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_sliceXarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/zipline/lib/python3.5/site-packages/scipy/sparse/compressed.py\u001b[0m in \u001b[0;36m_get_sliceXslice\u001b[0;34m(self, row, col)\u001b[0m\n\u001b[1;32m    650\u001b[0m         \u001b[0mmajor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_swap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    651\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mminor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 652\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_submatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    653\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_major_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_minor_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mminor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    654\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/zipline/lib/python3.5/site-packages/scipy/sparse/compressed.py\u001b[0m in \u001b[0;36m_get_submatrix\u001b[0;34m(self, major, minor, copy)\u001b[0m\n\u001b[1;32m    787\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m         indptr, indices, data = get_csr_submatrix(\n\u001b[0;32m--> 789\u001b[0;31m             M, N, self.indptr, self.indices, self.data, i0, i1, j0, j1)\n\u001b[0m\u001b[1;32m    790\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    791\u001b[0m         \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_swap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mi0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mj0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "test_period = momentum.loc['2015-05-01':'2015-07-31'].index\n",
    "print(test_period[:5])\n",
    "initial_holdings = np.zeros((len(momentum.columns), 1))\n",
    "port = backtest(test_period, initial_holdings, returns, momentum, 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "factor_0    0.002008\n",
      "factor_1    0.006009\n",
      "factor_2    0.003795\n",
      "factor_3    0.015021\n",
      "factor_4    0.002467\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/richard.wuebker/.pyenv/versions/zipline/lib/python3.5/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
      "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  \"Adding an axes using the same arguments as a previous axes \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "index_ = list(port.keys())[-1]\n",
    "p = port[index_]\n",
    "print(p['risk.exposures'].head())\n",
    "\n",
    "optimal_weights = p['h.opt']\n",
    "figsize(20, 8)\n",
    "optimal_weights.plot.bar(legend=None, title='Portfolio % Holdings by Stock')\n",
    "x_axis = plt.axes().get_xaxis()\n",
    "x_axis.set_visible(False)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average long holdings:  13.1234\n",
      "max long holding:  95.9873\n",
      "average short holdings:  -13.8889\n",
      "max short holding:  -148.7307\n",
      "gmv =  holdings    10000.000002\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('average long holdings: ', optimal_weights.loc[optimal_weights['holdings'] > 0].mean().values[0].round(4))\n",
    "print('max long holding: ', optimal_weights.loc[optimal_weights['holdings'] > 0].max().values[0].round(4))\n",
    "print('average short holdings: ', optimal_weights.loc[optimal_weights['holdings'] <= 0].mean().values[0].round(4))\n",
    "print('max short holding: ', optimal_weights.loc[optimal_weights['holdings'] < 0].min().values[0].round(4))\n",
    "print('gmv = ', optimal_weights.loc[optimal_weights['holdings'] > 0].sum() + optimal_weights.loc[optimal_weights['holdings'] < 0].abs().sum().values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ticker          AAPL\n",
      "Date                \n",
      "2014-01-02 -0.014064\n",
      "2014-01-03 -0.021966\n",
      "2014-01-06  0.005453\n",
      "2014-01-07 -0.007152\n",
      "2014-01-08  0.006333\n",
      "ticker          AAPL\n",
      "Date                \n",
      "2014-01-02  0.005453\n",
      "2014-01-03 -0.007152\n",
      "2014-01-06  0.006333\n",
      "2014-01-07 -0.012770\n",
      "2014-01-08 -0.006673\n"
     ]
    }
   ],
   "source": [
    "shifted_rets = rets.shift(-2)\n",
    "print(rets.loc[:,['AAPL']].head())\n",
    "print(shifted_rets.loc[:,['AAPL']].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wins(x, a=-0.50, b=0.50):\n",
    "    return np.where(x <= a,a, np.where(x >= b, b, x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def estimate_factor_returns(exposures, returns):\n",
    "    lr = LinearRegression()\n",
    "    lr.fit(exposures, returns)\n",
    "    return lr.coef_\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_pnl_attribution(port, returns, alpha, date_index):\n",
    "    df = pd.DataFrame(index=date_index)\n",
    "    \n",
    "    for date_ in date_index:\n",
    "        date_str = date_.strftime('%Y%m%d')\n",
    "        rets = returns.loc[date_, :].apply(wins)\n",
    "        p = port[date_str]\n",
    "        \n",
    "        df.at[date_, 'daily.pnl'] = np.sum(p['h.opt']['holdings'] * rets)\n",
    "        \n",
    "        alpha_returns = estimate_factor_returns(p['alpha.vector'].values.reshape((len(p['alpha.vector']), 1)), wins(rets, -0.25, 0.25))\n",
    "        df.at[date_, 'attribution.alpha.pnl'] = np.sum(alpha_returns * p['alpha.exposures'])\n",
    "        \n",
    "        factor_returns = estimate_factor_returns(p['factor.exposures'], wins(rets, -0.25, 0.25))\n",
    "        df.at[date_, 'attribution.risk.pnl'] = np.sum(factor_returns * p['risk.exposures'])\n",
    "        df.at[date_, 't.costs'] = p['t.costs']\n",
    "        \n",
    "        \n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = build_pnl_attribution(port, shifted_rets, momentum, test_period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "attr = build_pnl_attribution(port, shifted_rets, momentum, test_period)\n",
    "\n",
    "for column in attr.columns:\n",
    "    plt.plot(attr[column].cumsum(), label=column)\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('PnL Attribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_portfolio_characteristics(my_dates, port): \n",
    "    df = pd.DataFrame(index = my_dates)\n",
    "    \n",
    "    for dt in my_dates:\n",
    "        date = dt.strftime('%Y%m%d')\n",
    "  \n",
    "        p = port[date]\n",
    "        h = p['h.opt']\n",
    "        h0 = pd.DataFrame(p['h.opt.previous'], index=h.index)\n",
    "        \n",
    "        # TODO: Implement\n",
    "        longs = np.sum(h[h>0]).values\n",
    "        shorts = np.sum(h[h<0]).values\n",
    "\n",
    "        df.at[dt,\"long\"] = longs\n",
    "        df.at[dt,\"short\"] = shorts\n",
    "        df.at[dt,\"net\"] = longs + shorts\n",
    "        df.at[dt,\"gmv\"] = longs + np.abs(shorts)\n",
    "        \n",
    "        #df.at[dt,\"traded\"] = np.sum(np.abs(h - h0)).values\n",
    "        \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pchar = build_portfolio_characteristics(test_period, port)\n",
    "\n",
    "for column in pchar.columns:\n",
    "    plt.plot(pchar[column], label=column)\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Portfolio')\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}