code/Intelligent Portfolio Management.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intelligent Portfolio Management via NLP Analysis on Financial 10-K Statements\n",
    "\n",
    "### Install Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "!{sys.executable} -m pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import pprint\n",
    "import project_helper\n",
    "import project_tests\n",
    "\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download NLP Corpora\n",
    "Here we need two corpora to run this project: the stopwords corpus for removing stopwords and wordnet for lemmatizing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!pip install pandas==0.23.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get 10ks\n",
    "Here, we will be running NLP analysis on 10-k documents. To do that, we first need to download the documents. For this project, we'll download 10-ks for a few companies. To lookup documents for these companies, we'll use their CIK. If you would like to run this against other stocks, we've provided the dict `additional_cik` for more stocks. However, the more stocks you try, the long it will take to run.\n",
    "\n",
    "## Creating a diversified portfolio\n",
    "To test our hypothesis, I have taken a diversified portfolio. The portfolio contains 7 companies, all belonging from different sectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1962-01-01</th>\n",
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       "      <th>1964-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
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       "    <tr>\n",
       "      <th>1965-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
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       "      <th>1966-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
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      "text/plain": [
       "ticker      AMZN  LLY  CNP  XOM  NKE  FRT         BA\n",
       "date                                                \n",
       "1962-01-01   nan  nan  nan  nan  nan  nan 0.64071188\n",
       "1963-01-01   nan  nan  nan  nan  nan  nan 0.61913618\n",
       "1964-01-01   nan  nan  nan  nan  nan  nan 1.18873457\n",
       "1965-01-01   nan  nan  nan  nan  nan  nan 2.25681785\n",
       "1966-01-01   nan  nan  nan  nan  nan  nan 1.13263776"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pricing = pricing.filter(['AMZN', 'LLY', 'CNP', 'XOM', 'NKE', 'FRT', 'BA'])\n",
    "pricing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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       "      <th>LLY</th>\n",
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       "      <th>XOM</th>\n",
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       "      <th>2014-01-01</th>\n",
       "      <td>310.35000000</td>\n",
       "      <td>62.89207161</td>\n",
       "      <td>19.98816704</td>\n",
       "      <td>81.45436267</td>\n",
       "      <td>46.12386666</td>\n",
       "      <td>120.93327682</td>\n",
       "      <td>118.05963288</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>675.89000000</td>\n",
       "      <td>78.84732142</td>\n",
       "      <td>16.47694351</td>\n",
       "      <td>71.06372586</td>\n",
       "      <td>60.60044596</td>\n",
       "      <td>135.81997252</td>\n",
       "      <td>134.64696530</td>\n",
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       "      <th>2016-01-01</th>\n",
       "      <td>749.87000000</td>\n",
       "      <td>70.66763184</td>\n",
       "      <td>23.21314562</td>\n",
       "      <td>85.20695722</td>\n",
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       "      <th>2017-01-01</th>\n",
       "      <td>1169.47000000</td>\n",
       "      <td>83.27072791</td>\n",
       "      <td>27.76055779</td>\n",
       "      <td>81.97170489</td>\n",
       "      <td>62.18976773</td>\n",
       "      <td>130.63600821</td>\n",
       "      <td>291.93724356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>1699.73000000</td>\n",
       "      <td>86.56000000</td>\n",
       "      <td>27.75000000</td>\n",
       "      <td>82.31000000</td>\n",
       "      <td>76.55000000</td>\n",
       "      <td>126.28000000</td>\n",
       "      <td>333.18000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ticker              AMZN         LLY         CNP         XOM         NKE  \\\n",
       "date                                                                       \n",
       "2014-01-01  310.35000000 62.89207161 19.98816704 81.45436267 46.12386666   \n",
       "2015-01-01  675.89000000 78.84732142 16.47694351 71.06372586 60.60044596   \n",
       "2016-01-01  749.87000000 70.66763184 23.21314562 85.20695722 49.87045562   \n",
       "2017-01-01 1169.47000000 83.27072791 27.76055779 81.97170489 62.18976773   \n",
       "2018-01-01 1699.73000000 86.56000000 27.75000000 82.31000000 76.55000000   \n",
       "\n",
       "ticker              FRT           BA  \n",
       "date                                  \n",
       "2014-01-01 120.93327682 118.05963288  \n",
       "2015-01-01 135.81997252 134.64696530  \n",
       "2016-01-01 135.50155863 149.88750981  \n",
       "2017-01-01 130.63600821 291.93724356  \n",
       "2018-01-01 126.28000000 333.18000000  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pricing.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "cik_lookup = {\n",
    "    'AMZN': '0001018724',\n",
    "    'LLY': '0000059478',   \n",
    "    'CNP': '0001130310',\n",
    "    'XOM': '0000034088',\n",
    "    'NKE': '0000320187',\n",
    "    'FRT': '0000034903',\n",
    "    'BA': '0000012927'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>EOD/NKE - Adj_Close</th>\n",
       "      <th>EOD/AMZN - Adj_Close</th>\n",
       "      <th>EOD/LLY - Adj_Close</th>\n",
       "      <th>EOD/CNP - Adj_Close</th>\n",
       "      <th>EOD/XOM - Adj_Close</th>\n",
       "      <th>EOD/FRT - Adj_Close</th>\n",
       "      <th>EOD/BA - Adj_Close</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>48.71997276</td>\n",
       "      <td>749.87000000</td>\n",
       "      <td>67.76311638</td>\n",
       "      <td>21.47765641</td>\n",
       "      <td>75.51173213</td>\n",
       "      <td>125.63876199</td>\n",
       "      <td>144.26808190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>60.75508539</td>\n",
       "      <td>1169.47000000</td>\n",
       "      <td>79.84821168</td>\n",
       "      <td>25.68508946</td>\n",
       "      <td>72.64460115</td>\n",
       "      <td>121.12736200</td>\n",
       "      <td>280.99223356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>72.81849743</td>\n",
       "      <td>1501.97000000</td>\n",
       "      <td>112.14461953</td>\n",
       "      <td>26.64533341</td>\n",
       "      <td>61.67406248</td>\n",
       "      <td>110.32123549</td>\n",
       "      <td>313.39217857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>100.56316987</td>\n",
       "      <td>1847.84000000</td>\n",
       "      <td>129.50269881</td>\n",
       "      <td>26.54040495</td>\n",
       "      <td>66.14668434</td>\n",
       "      <td>124.10869121</td>\n",
       "      <td>323.81890018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>130.06000000</td>\n",
       "      <td>3195.69000000</td>\n",
       "      <td>148.96000000</td>\n",
       "      <td>20.65000000</td>\n",
       "      <td>33.50000000</td>\n",
       "      <td>77.65000000</td>\n",
       "      <td>164.61000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            EOD/NKE - Adj_Close  EOD/AMZN - Adj_Close  EOD/LLY - Adj_Close  \\\n",
       "Date                                                                         \n",
       "2016-12-31          48.71997276          749.87000000          67.76311638   \n",
       "2017-12-31          60.75508539         1169.47000000          79.84821168   \n",
       "2018-12-31          72.81849743         1501.97000000         112.14461953   \n",
       "2019-12-31         100.56316987         1847.84000000         129.50269881   \n",
       "2020-12-31         130.06000000         3195.69000000         148.96000000   \n",
       "\n",
       "            EOD/CNP - Adj_Close  EOD/XOM - Adj_Close  EOD/FRT - Adj_Close  \\\n",
       "Date                                                                        \n",
       "2016-12-31          21.47765641          75.51173213         125.63876199   \n",
       "2017-12-31          25.68508946          72.64460115         121.12736200   \n",
       "2018-12-31          26.64533341          61.67406248         110.32123549   \n",
       "2019-12-31          26.54040495          66.14668434         124.10869121   \n",
       "2020-12-31          20.65000000          33.50000000          77.65000000   \n",
       "\n",
       "            EOD/BA - Adj_Close  \n",
       "Date                            \n",
       "2016-12-31        144.26808190  \n",
       "2017-12-31        280.99223356  \n",
       "2018-12-31        313.39217857  \n",
       "2019-12-31        323.81890018  \n",
       "2020-12-31        164.61000000  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = pricing.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>AMZN</th>\n",
       "      <th>LLY</th>\n",
       "      <th>CNP</th>\n",
       "      <th>XOM</th>\n",
       "      <th>NKE</th>\n",
       "      <th>FRT</th>\n",
       "      <th>BA</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>675.89000000</td>\n",
       "      <td>78.84732142</td>\n",
       "      <td>16.47694351</td>\n",
       "      <td>71.06372586</td>\n",
       "      <td>60.60044596</td>\n",
       "      <td>135.81997252</td>\n",
       "      <td>134.64696530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>749.87000000</td>\n",
       "      <td>70.66763184</td>\n",
       "      <td>23.21314562</td>\n",
       "      <td>85.20695722</td>\n",
       "      <td>49.87045562</td>\n",
       "      <td>135.50155863</td>\n",
       "      <td>149.88750981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>1169.47000000</td>\n",
       "      <td>83.27072791</td>\n",
       "      <td>27.76055779</td>\n",
       "      <td>81.97170489</td>\n",
       "      <td>62.18976773</td>\n",
       "      <td>130.63600821</td>\n",
       "      <td>291.93724356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>1699.73000000</td>\n",
       "      <td>86.56000000</td>\n",
       "      <td>27.75000000</td>\n",
       "      <td>82.31000000</td>\n",
       "      <td>76.55000000</td>\n",
       "      <td>126.28000000</td>\n",
       "      <td>333.18000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>1847.84000000</td>\n",
       "      <td>129.50269881</td>\n",
       "      <td>26.54040495</td>\n",
       "      <td>66.14668434</td>\n",
       "      <td>100.56316987</td>\n",
       "      <td>124.10869121</td>\n",
       "      <td>323.81890018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ticker              AMZN          LLY         CNP         XOM          NKE  \\\n",
       "date                                                                         \n",
       "2015-01-01  675.89000000  78.84732142 16.47694351 71.06372586  60.60044596   \n",
       "2016-01-01  749.87000000  70.66763184 23.21314562 85.20695722  49.87045562   \n",
       "2017-01-01 1169.47000000  83.27072791 27.76055779 81.97170489  62.18976773   \n",
       "2018-01-01 1699.73000000  86.56000000 27.75000000 82.31000000  76.55000000   \n",
       "2019-01-01 1847.84000000 129.50269881 26.54040495 66.14668434 100.56316987   \n",
       "\n",
       "ticker              FRT           BA  \n",
       "date                                  \n",
       "2015-01-01 135.81997252 134.64696530  \n",
       "2016-01-01 135.50155863 149.88750981  \n",
       "2017-01-01 130.63600821 291.93724356  \n",
       "2018-01-01 126.28000000 333.18000000  \n",
       "2019-01-01 124.10869121 323.81890018  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pricing.loc[pd.to_datetime('2019-01-01')] = [1847.84000000, 129.50269881, 26.54040495, 66.14668434, 100.56316987, 124.10869121, 323.81890018]\n",
    "pricing.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get list of 10-ks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "sec_api = project_helper.SecAPI()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the class constructed, let's pull a list of filled 10-ks from the SEC for each company."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "\n",
    "def get_sec_data(cik, doc_type, start=0, count=60):\n",
    "    newest_pricing_data = pd.to_datetime('2018-01-01')\n",
    "    rss_url = 'https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany' \\\n",
    "        '&CIK={}&type={}&start={}&count={}&owner=exclude&output=atom' \\\n",
    "        .format(cik, doc_type, start, count)\n",
    "    sec_data = sec_api.get(rss_url)\n",
    "    feed = BeautifulSoup(sec_data.encode('ascii'), 'xml').feed\n",
    "    entries = [\n",
    "        (\n",
    "            entry.content.find('filing-href').getText(),\n",
    "            entry.content.find('filing-type').getText(),\n",
    "            entry.content.find('filing-date').getText())\n",
    "        for entry in feed.find_all('entry', recursive=False)\n",
    "        if pd.to_datetime(entry.content.find('filing-date').getText()) <= newest_pricing_data]\n",
    "\n",
    "    return entries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's pull the list using the `get_sec_data` function, then display some of the results. For displaying some of the data, we'll use Amazon as an example. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('https://www.sec.gov/Archives/edgar/data/1018724/000101872417000011/0001018724-17-000011-index.htm',\n",
      "  '10-K',\n",
      "  '2017-02-10'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000101872416000172/0001018724-16-000172-index.htm',\n",
      "  '10-K',\n",
      "  '2016-01-29'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000101872415000006/0001018724-15-000006-index.htm',\n",
      "  '10-K',\n",
      "  '2015-01-30'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000101872414000006/0001018724-14-000006-index.htm',\n",
      "  '10-K',\n",
      "  '2014-01-31'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000119312513028520/0001193125-13-028520-index.htm',\n",
      "  '10-K',\n",
      "  '2013-01-30')]\n"
     ]
    }
   ],
   "source": [
    "example_ticker = 'NKE'\n",
    "sec_data = {}\n",
    "\n",
    "\n",
    "for ticker, cik in cik_lookup.items():\n",
    "    sec_data[ticker] = get_sec_data(cik, '10-K')\n",
    "    \n",
    "\n",
    "pprint.pprint(sec_data['AMZN'][:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download 10-ks\n",
    "As you see, this is a list of urls. These urls point to a file that contains metadata related to each filling. Since we don't care about the metadata, we'll pull the filling by replacing the url with the filling url."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading AMZN Fillings: 100%|██████████| 22/22 [00:11<00:00,  1.93filling/s]\n",
      "Downloading LLY Fillings: 100%|██████████| 25/25 [00:10<00:00,  2.50filling/s]\n",
      "Downloading CNP Fillings: 100%|██████████| 19/19 [00:07<00:00,  2.38filling/s]\n",
      "Downloading XOM Fillings: 100%|██████████| 25/25 [00:08<00:00,  3.00filling/s]\n",
      "Downloading NKE Fillings: 100%|██████████| 26/26 [00:07<00:00,  3.54filling/s]\n",
      "Downloading FRT Fillings: 100%|██████████| 29/29 [00:06<00:00,  4.83filling/s]\n",
      "Downloading BA Fillings: 100%|██████████| 26/26 [00:08<00:00,  3.04filling/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example Document:\n",
      "\n",
      "<SEC-DOCUMENT>0000320187-17-000090.txt : 20170720\n",
      "<SEC-HEADER>0000320187-17-000090.hdr.sgml : 20170720\n",
      "<ACCEPTANCE-DATETIME>20170720161816\n",
      "ACCESSION NUMBER:\t\t0000320187-17-000090\n",
      "CONFORMED SUBMISSION TYPE:\t10-K\n",
      "PUBLIC DOCUMENT COUNT:\t\t98\n",
      "CONFORMED PERIOD OF REPORT:\t20170531\n",
      "FILED AS OF DATE:\t\t20170720\n",
      "DATE AS OF CHANGE:\t\t20170720\n",
      "\n",
      "FILER:\n",
      "\n",
      "\tCOMPANY DATA:\t\n",
      "\t\tCOMPANY CONFORMED NAME:\t\t\tNIKE INC\n",
      "\t\tCENTRAL INDEX KEY:\t\t\t0000320187\n",
      "\t\tSTANDARD INDUSTRIAL CLASSIFICATION:\tRUBBER & PLASTICS FOOTWEAR [3021]\n",
      "\t\tIRS NUMBER:\t\t\t\t930584541\n",
      "\t\tSTATE OF INCORPORATION:\t\t\tOR\n",
      "\t\tFISCAL YEAR END:\t\t\t0531\n",
      "\n",
      "\tFILING VALUES:\n",
      "\t\tFORM TYPE:\t\t10-K\n",
      "\t\tSEC ACT:\t\t1934 Act\n",
      "\t\tSEC FILE NUMBER:\t001-10635\n",
      "\t\tFILM NUMBER:\t\t17974568\n",
      "\n",
      "\tBUSINESS ADDRESS:\t\n",
      "\t\tSTREET 1:\t\tONE BOWERMAN DR\n",
      "\t\tCITY:\t\t\tBEAVERTON\n",
      "\t\tSTATE:\t\t\tOR\n",
      "\t\tZIP:\t\t\t97005-6453\n",
      "\t\tBUSINESS PHONE:\t\t5036713173\n",
      "\n",
      "\tMAIL ADDRESS:\t\n",
      "\t\tSTREET 1:\t\tONE BOWERMAN DR\n",
      "\t\tCITY:\t\t\tBEAVERTON\n",
      "\t\tSTATE:\t\t\tOR\n",
      "\t\tZIP:\t\t\t97005-6453\n",
      "</SEC-HEADER>\n",
      "<DOCUMENT>\n",
      "<TYPE>10-K\n",
      "<SEQUENCE>1\n",
      "<FILENAME>nke-5312017x1...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "raw_fillings_by_ticker = {}\n",
    "\n",
    "for ticker, data in sec_data.items():\n",
    "    raw_fillings_by_ticker[ticker] = {}\n",
    "    for index_url, file_type, file_date in tqdm(data, desc='Downloading {} Fillings'.format(ticker), unit='filling'):\n",
    "        if (file_type == '10-K'):\n",
    "            file_url = index_url.replace('-index.htm', '.txt').replace('.txtl', '.txt')            \n",
    "            \n",
    "            raw_fillings_by_ticker[ticker][file_date] = sec_api.get(file_url)\n",
    "\n",
    "\n",
    "print('Example Document:\\n\\n{}...'.format(next(iter(raw_fillings_by_ticker[example_ticker].values()))[:1000]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Documents\n",
    "With theses fillings downloaded, we want to break them into their associated documents. These documents are sectioned off in the fillings with the tags `<DOCUMENT>` for the start of each document and `</DOCUMENT>` for the end of each document. There's no overlap with these documents, so each `</DOCUMENT>` tag should come after the `<DOCUMENT>` with no `<DOCUMENT>` tag in between.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "\n",
    "def get_documents(text):\n",
    "    \"\"\"\n",
    "    Extract the documents from the text\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    text : str\n",
    "        The text with the document strings inside\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    extracted_docs : list of str\n",
    "        The document strings found in `text`\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Implement\n",
    "    final_docs = []\n",
    "    start_regex = re.compile(r'<DOCUMENT>')\n",
    "    end_regex = re.compile(r'</DOCUMENT>')\n",
    "    \n",
    "    start_idx = [x.end() for x in re.finditer(start_regex, text)]\n",
    "    end_idx = [x.start() for x in re.finditer(end_regex, text)]\n",
    "    \n",
    "    for start_i, end_i in zip(start_idx, end_idx):\n",
    "        final_docs.append(text[start_i:end_i])\n",
    "    \n",
    "    \n",
    "    return final_docs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `get_documents` function implemented, let's extract all the documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Getting Documents from AMZN Fillings: 100%|██████████| 17/17 [00:01<00:00,  8.53filling/s]\n",
      "Getting Documents from LLY Fillings: 100%|██████████| 21/21 [00:00<00:00, 49.41filling/s]\n",
      "Getting Documents from CNP Fillings: 100%|██████████| 15/15 [00:00<00:00, 41.03filling/s]\n",
      "Getting Documents from XOM Fillings: 100%|██████████| 22/22 [00:00<00:00, 48.63filling/s]\n",
      "Getting Documents from NKE Fillings: 100%|██████████| 21/21 [00:00<00:00, 73.07filling/s]\n",
      "Getting Documents from FRT Fillings: 100%|██████████| 19/19 [00:00<00:00, 27.05filling/s]\n",
      "Getting Documents from BA Fillings: 100%|██████████| 24/24 [00:04<00:00,  5.97filling/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Document 0 Filed on 2017-07-20:\n",
      "\n",
      "<TYPE>10-K\n",
      "<SEQUENCE>1\n",
      "<FILENAME>nke-5312017x10k.htm\n",
      "<DESCRIPTION>10-K\n",
      "<TEXT>\n",
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http://www.w3.org/TR/html4/loose.dtd\">\n",
      "<html>\n",
      "\t<head>\n",
      "\t\t<...\n",
      "\n",
      "Document 1 Filed on 2017-07-20:\n",
      "\n",
      "<TYPE>EX-10.20\n",
      "<SEQUENCE>2\n",
      "<FILENAME>nke-5312017xexhibit1020.htm\n",
      "<DESCRIPTION>COVENANT NOT TO COMPETE AND NON-DISCLOSURE AGREEMENT\n",
      "<TEXT>\n",
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN...\n",
      "\n",
      "Document 2 Filed on 2017-07-20:\n",
      "\n",
      "<TYPE>EX-12.1\n",
      "<SEQUENCE>3\n",
      "<FILENAME>nke-5312017xexhibit121.htm\n",
      "<DESCRIPTION>COMPUTATION OF RATIO OF EARNINGS TO FIXED CHARGES\n",
      "<TEXT>\n",
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"ht...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "filling_documents_by_ticker = {}\n",
    "\n",
    "for ticker, raw_fillings in raw_fillings_by_ticker.items():\n",
    "    filling_documents_by_ticker[ticker] = {}\n",
    "    for file_date, filling in tqdm(raw_fillings.items(), desc='Getting Documents from {} Fillings'.format(ticker), unit='filling'):\n",
    "        filling_documents_by_ticker[ticker][file_date] = get_documents(filling)\n",
    "\n",
    "\n",
    "print('\\n\\n'.join([\n",
    "    'Document {} Filed on {}:\\n{}...'.format(doc_i, file_date, doc[:200])\n",
    "    for file_date, docs in filling_documents_by_ticker[example_ticker].items()\n",
    "    for doc_i, doc in enumerate(docs)][:3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Document Types\n",
    "Now that we have all the documents, we want to find the 10-k form in this 10-k filing. The `get_document_type` function returns the type of document given. The document type is located on a line with the `<TYPE>` tag. For example, a form of type \"TEST\" would have the line `<TYPE>TEST`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_document_type(doc):\n",
    "    \"\"\"\n",
    "    Return the document type lowercased\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    doc : str\n",
    "        The document string\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    doc_type : str\n",
    "        The document type lowercased\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Implement\n",
    "    # Regex explaination : Here I am tryng to do a positive lookbehind\n",
    "    # (?<=a)b (positive lookbehind) matches the b (and only the b) in cab, but does not match bed or debt.\n",
    "    # More reference : https://www.regular-expressions.info/lookaround.html\n",
    "    \n",
    "    type_regex = re.compile(r'(?<=<TYPE>)\\w+[^\\n]+') # gives out \\w\n",
    "    type_idx = re.search(type_regex, doc).group(0).lower()\n",
    "    return type_idx\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `get_document_type` function, we'll filter out all non 10-k documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    CIK: '0000320187'\n",
      "    ten_k: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>nke-5312017x...\n",
      "    file_date: '2017-07-20'},\n",
      "  {\n",
      "    CIK: '0000320187'\n",
      "    ten_k: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>nke-5312016x...\n",
      "    file_date: '2016-07-21'},\n",
      "  {\n",
      "    CIK: '0000320187'\n",
      "    ten_k: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>nke-5312015x...\n",
      "    file_date: '2015-07-23'},\n",
      "  {\n",
      "    CIK: '0000320187'\n",
      "    ten_k: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>nke-5312014x...\n",
      "    file_date: '2014-07-25'},\n",
      "  {\n",
      "    CIK: '0000320187'\n",
      "    ten_k: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>nke-5312013x...\n",
      "    file_date: '2013-07-23'},\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "ten_ks_by_ticker = {}\n",
    "\n",
    "for ticker, filling_documents in filling_documents_by_ticker.items():\n",
    "    ten_ks_by_ticker[ticker] = []\n",
    "    for file_date, documents in filling_documents.items():\n",
    "        for document in documents:\n",
    "            if get_document_type(document) == '10-k':\n",
    "                ten_ks_by_ticker[ticker].append({\n",
    "                    'CIK': cik_lookup[ticker],\n",
    "                    'ten_k': document,\n",
    "                    'file_date': file_date})\n",
    "\n",
    "example_ticker='NKE'\n",
    "project_helper.print_ten_k_data(ten_ks_by_ticker[example_ticker][:5], ['CIK', 'ten_k', 'file_date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess the Data\n",
    "### Clean Up\n",
    "As you can see, the text for the documents are very messy. To clean this up, we'll remove the html and lowercase all the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_html_tags(text):\n",
    "    text = BeautifulSoup(text, 'html.parser').get_text()\n",
    "    \n",
    "    return text\n",
    "\n",
    "\n",
    "def clean_text(text):\n",
    "    text = text.lower()\n",
    "    text = remove_html_tags(text)\n",
    "    \n",
    "    return text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `clean_text` function, we'll clean up all the documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cleaning AMZN 10-Ks: 100%|██████████| 17/17 [00:40<00:00,  2.39s/10-K]\n",
      "Cleaning LLY 10-Ks: 100%|██████████| 21/21 [00:32<00:00,  1.56s/10-K]\n",
      "Cleaning CNP 10-Ks: 100%|██████████| 15/15 [01:07<00:00,  4.48s/10-K]\n",
      "Cleaning XOM 10-Ks: 100%|██████████| 22/22 [02:19<00:00,  6.35s/10-K]\n",
      "Cleaning NKE 10-Ks: 100%|██████████| 21/21 [00:46<00:00,  2.20s/10-K]\n",
      "Cleaning FRT 10-Ks: 100%|██████████| 19/19 [01:04<00:00,  3.38s/10-K]\n",
      "Cleaning BA 10-Ks: 100%|██████████| 24/24 [00:59<00:00,  2.47s/10-K]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\nnke-5312017x10k.htm\\n10-k\\n\\n\\n\\n\\n\\n\\...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\nnke-5312016x10k.htm\\n10-k\\n\\n\\n\\n\\n\\n\\...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\nnke-5312015x10k.htm\\n10-k\\n\\n\\n\\n\\n\\n\\...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\nnke-5312014x10k.htm\\n10-k\\n\\n\\n\\n\\n\\n\\...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\nnke-5312013x10k.htm\\n10-k\\n\\n\\n\\n\\n\\n\\...},\n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    for ten_k in tqdm(ten_ks, desc='Cleaning {} 10-Ks'.format(ticker), unit='10-K'):\n",
    "        ten_k['file_clean'] = clean_text(ten_k['ten_k'])\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data(ten_ks_by_ticker[example_ticker][:5], ['file_clean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lemmatize\n",
    "With the text cleaned up, it's time to distill the verbs down. The `lemmatize_words` function lemmatizes verbs in the list of words provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import wordnet\n",
    "\n",
    "\n",
    "def lemmatize_words(words):\n",
    "    \"\"\"\n",
    "    Lemmatize words \n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    words : list of str\n",
    "        List of words\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    lemmatized_words : list of str\n",
    "        List of lemmatized words\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Implement\n",
    "    wnl = WordNetLemmatizer()\n",
    "    lemmatized_words = [wnl.lemmatize(word, 'v') for word in words]\n",
    "    \n",
    "    return lemmatized_words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `lemmatize_words` function implemented, let's lemmatize all the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lemmatize AMZN 10-Ks: 100%|██████████| 17/17 [00:04<00:00,  3.8010-K/s]\n",
      "Lemmatize LLY 10-Ks: 100%|██████████| 21/21 [00:04<00:00,  4.4010-K/s]\n",
      "Lemmatize CNP 10-Ks: 100%|██████████| 15/15 [00:08<00:00,  1.8410-K/s]\n",
      "Lemmatize XOM 10-Ks: 100%|██████████| 22/22 [00:06<00:00,  3.4410-K/s]\n",
      "Lemmatize NKE 10-Ks: 100%|██████████| 21/21 [00:05<00:00,  3.5710-K/s]\n",
      "Lemmatize FRT 10-Ks: 100%|██████████| 19/19 [00:06<00:00,  3.1510-K/s]\n",
      "Lemmatize BA 10-Ks: 100%|██████████| 24/24 [00:09<00:00,  2.5810-K/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    preprocessed_ten_k: '['10', 'k', '1', 'nke', '5312017x10k', 'htm', '10...},\n",
      "  {\n",
      "    preprocessed_ten_k: '['10', 'k', '1', 'nke', '5312016x10k', 'htm', '10...},\n",
      "  {\n",
      "    preprocessed_ten_k: '['10', 'k', '1', 'nke', '5312015x10k', 'htm', '10...},\n",
      "  {\n",
      "    preprocessed_ten_k: '['10', 'k', '1', 'nke', '5312014x10k', 'htm', '10...},\n",
      "  {\n",
      "    preprocessed_ten_k: '['10', 'k', '1', 'nke', '5312013x10k', 'htm', '10...},\n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word_pattern = re.compile('\\w+')\n",
    "\n",
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    for ten_k in tqdm(ten_ks, desc='Lemmatize {} 10-Ks'.format(ticker), unit='10-K'):\n",
    "        ten_k['preprocessed_ten_k'] = lemmatize_words(word_pattern.findall(ten_k['file_clean']))\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data(ten_ks_by_ticker['NKE'][:5], ['preprocessed_ten_k'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove Stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Remove Stop Words for AMZN 10-Ks: 100%|██████████| 17/17 [00:01<00:00,  8.5710-K/s]\n",
      "Remove Stop Words for LLY 10-Ks: 100%|██████████| 21/21 [00:02<00:00,  9.9010-K/s]\n",
      "Remove Stop Words for CNP 10-Ks: 100%|██████████| 15/15 [00:03<00:00,  4.2310-K/s]\n",
      "Remove Stop Words for XOM 10-Ks: 100%|██████████| 22/22 [00:02<00:00,  7.4910-K/s]\n",
      "Remove Stop Words for NKE 10-Ks: 100%|██████████| 21/21 [00:02<00:00,  8.0010-K/s]\n",
      "Remove Stop Words for FRT 10-Ks: 100%|██████████| 19/19 [00:02<00:00,  6.9210-K/s]\n",
      "Remove Stop Words for BA 10-Ks: 100%|██████████| 24/24 [00:04<00:00,  5.9510-K/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stop Words Removed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import stopwords\n",
    "\n",
    "\n",
    "lemma_english_stopwords = lemmatize_words(stopwords.words('english'))\n",
    "\n",
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    for ten_k in tqdm(ten_ks, desc='Remove Stop Words for {} 10-Ks'.format(ticker), unit='10-K'):\n",
    "        ten_k['preprocessed_ten_k'] = [word for word in ten_k['preprocessed_ten_k'] if word not in lemma_english_stopwords]\n",
    "\n",
    "\n",
    "print('Stop Words Removed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis on 10ks\n",
    "### Loughran McDonald Sentiment Word Lists\n",
    "We'll be using the Loughran and McDonald sentiment word lists. These word lists cover the following sentiment:\n",
    "- Negative \n",
    "- Positive\n",
    "- Uncertainty\n",
    "- Litigious\n",
    "- Constraining\n",
    "- Superfluous\n",
    "- Modal\n",
    "\n",
    "This will allow us to do the sentiment analysis on the 10-ks. Let's first load these word lists. We'll be looking into a few of these sentiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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>negative</th>\n",
       "      <th>positive</th>\n",
       "      <th>uncertainty</th>\n",
       "      <th>litigious</th>\n",
       "      <th>constraining</th>\n",
       "      <th>interesting</th>\n",
       "      <th>word</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84770</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>wrongdoing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84771</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>wrongdoings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84775</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>wrongful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84776</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>wrongfully</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84784</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>wrongly</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       negative  positive  uncertainty  litigious  constraining  interesting  \\\n",
       "84770      True     False        False      False         False        False   \n",
       "84771      True     False        False      False         False        False   \n",
       "84775      True     False        False      False         False        False   \n",
       "84776      True     False        False      False         False        False   \n",
       "84784      True     False        False      False         False        False   \n",
       "\n",
       "              word  \n",
       "84770   wrongdoing  \n",
       "84771  wrongdoings  \n",
       "84775     wrongful  \n",
       "84776   wrongfully  \n",
       "84784      wrongly  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "sentiments = ['negative', 'positive', 'uncertainty', 'litigious', 'constraining', 'interesting']\n",
    "\n",
    "sentiment_df = pd.read_csv(os.path.join('..', '..', 'data', 'project_5_loughran_mcdonald', 'loughran_mcdonald_master_dic_2016.csv'))\n",
    "sentiment_df.columns = [column.lower() for column in sentiment_df.columns] # Lowercase the columns for ease of use\n",
    "\n",
    "# Remove unused information\n",
    "sentiment_df = sentiment_df[sentiments + ['word']]\n",
    "sentiment_df[sentiments] = sentiment_df[sentiments].astype(bool)\n",
    "sentiment_df = sentiment_df[(sentiment_df[sentiments]).any(1)]\n",
    "\n",
    "# Apply the same preprocessing to these words as the 10-k words\n",
    "sentiment_df['word'] = lemmatize_words(sentiment_df['word'].str.lower())\n",
    "sentiment_df = sentiment_df.drop_duplicates('word')\n",
    "\n",
    "\n",
    "sentiment_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bag of Words\n",
    "using the sentiment word lists, let's generate sentiment bag of words from the 10-k documents. The `get_bag_of_words` function generates a bag of words that counts the number of sentiment words in each doc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict, Counter\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "\n",
    "def get_bag_of_words(sentiment_words, docs):\n",
    "    \"\"\"\n",
    "    Generate a bag of words from documents for a certain sentiment\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    sentiment_words: Pandas Series\n",
    "        Words that signify a certain sentiment\n",
    "    docs : list of str\n",
    "        List of documents used to generate bag of words\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    bag_of_words : 2-d Numpy Ndarray of int\n",
    "        Bag of words sentiment for each document\n",
    "        The first dimension is the document.\n",
    "        The second dimension is the word.\n",
    "    \"\"\"\n",
    "    \n",
    "    # reference : https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html\n",
    "    \n",
    "    c_vectorizer = CountVectorizer(vocabulary=sentiment_words.values)\n",
    "    \n",
    "    # tokenization\n",
    "    X = c_vectorizer.fit_transform(docs)\n",
    "    \n",
    "    return X.toarray()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `get_bag_of_words` function, we'll generate a bag of words for all the documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17\n",
      "21\n",
      "15\n",
      "22\n",
      "21\n",
      "19\n",
      "24\n",
      "[\n",
      "  {\n",
      "    negative: '[[0 0 0 ..., 0 0 0]\\n [0 0 0 ..., 0 0 0]\\n [0 0 0...\n",
      "    positive: '[[9 0 0 ..., 0 0 0]\\n [9 0 0 ..., 0 0 0]\\n [9 0 0...\n",
      "    uncertainty: '[[ 0  0  1 ...,  2  3 17]\\n [ 0  0  1 ...,  2  3 ...\n",
      "    litigious: '[[0 0 0 ..., 0 0 0]\\n [0 0 0 ..., 0 0 0]\\n [0 0 0...\n",
      "    constraining: '[[0 2 0 ..., 0 0 0]\\n [0 2 0 ..., 0 0 0]\\n [0 2 0...\n",
      "    interesting: '[[1 0 0 ..., 0 0 0]\\n [1 0 0 ..., 0 0 0]\\n [1 0 0...},\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "sentiment_bow_ten_ks = {}\n",
    "\n",
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    lemma_docs = [' '.join(ten_k['preprocessed_ten_k']) for ten_k in ten_ks]\n",
    "    print(len(lemma_docs))\n",
    "    #print(lemma_docs[:2])\n",
    "    \n",
    "    sentiment_bow_ten_ks[ticker] = {\n",
    "        sentiment: get_bag_of_words(sentiment_df[sentiment_df[sentiment]]['word'], lemma_docs)\n",
    "        for sentiment in sentiments}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `get_jaccard_similarity` function, let's plot the similarities over time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TFIDF\n",
    "using the sentiment word lists, let's generate sentiment TFIDF from the 10-k documents. The `get_tfidf` function generates TFIDF from each document, using sentiment words as the terms. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "\n",
    "def get_tfidf(sentiment_words, docs):\n",
    "    \"\"\"\n",
    "    Generate TFIDF values from documents for a certain sentiment\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    sentiment_words: Pandas Series\n",
    "        Words that signify a certain sentiment\n",
    "    docs : list of str\n",
    "        List of documents used to generate bag of words\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    tfidf : 2-d Numpy Ndarray of float\n",
    "        TFIDF sentiment for each document\n",
    "        The first dimension is the document.\n",
    "        The second dimension is the word.\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Implement\n",
    "    tfidf_vector = TfidfVectorizer(vocabulary=sentiment_words.values)\n",
    "    X = tfidf_vector.fit_transform(docs)\n",
    "    \n",
    "    return X.toarray()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `get_tfidf` function, let's generate the TFIDF values for all the documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    negative: '[[ 0.  0.  0. ...,  0.  0.  0.]\\n [ 0.  0.  0. .....\n",
      "    positive: '[[ 0.10940495  0.          0.         ...,  0.   ...\n",
      "    uncertainty: '[[ 0.          0.          0.00263439 ...,  0.007...\n",
      "    litigious: '[[ 0.  0.  0. ...,  0.  0.  0.]\\n [ 0.  0.  0. .....\n",
      "    constraining: '[[ 0.          0.02561362  0.         ...,  0.   ...\n",
      "    interesting: '[[ 0.00467568  0.          0.         ...,  0.   ...},\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "sentiment_tfidf_ten_ks = {}\n",
    "\n",
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    lemma_docs = [' '.join(ten_k['preprocessed_ten_k']) for ten_k in ten_ks]\n",
    "    \n",
    "    sentiment_tfidf_ten_ks[ticker] = {\n",
    "        sentiment: get_tfidf(sentiment_df[sentiment_df[sentiment]]['word'], lemma_docs)\n",
    "        for sentiment in sentiments}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cosine Similarity\n",
    "Using the TFIDF values, we'll calculate the cosine similarity and plot it over time. Function `get_cosine_similarity`  returns the cosine similarities between each tick in time. Since the input, `tfidf_matrix`, is a TFIDF vector for each time period in order, we just need to computer the cosine similarities for each neighboring vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "\n",
    "def get_cosine_similarity(tfidf_matrix):\n",
    "    \"\"\"\n",
    "    Get cosine similarities for each neighboring TFIDF vector/document\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    tfidf : 2-d Numpy Ndarray of float\n",
    "        TFIDF sentiment for each document\n",
    "        The first dimension is the document.\n",
    "        The second dimension is the word.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    cosine_similarities : list of float\n",
    "        Cosine similarities for neighboring documents\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Implement\n",
    "    # Use k>0 for diagonals above the main diagonal, and k<0 for diagonals below the main diagonal.\n",
    "    cosine_sim = list(np.diag(cosine_similarity(tfidf_matrix, tfidf_matrix), k=1))\n",
    "    #print(cosine_sim)\n",
    "    return cosine_sim\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "cik_lookup = {\n",
    "    'AMZN': '0001018724',\n",
    "    'LLY': '0000059478',   \n",
    "    'CNP': '0001130310',\n",
    "    'XOM': '0000034088',\n",
    "    'NKE': '0000320187',\n",
    "    'FRT': '0000034903',\n",
    "    'BA': '0000012927'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the cosine similarities over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative = [0.98431114162338007, 0.99037047297085057, 0.99021273198863835, 0.96815726216763043, 0.97972290373447191, 0.98380272386352263, 0.9800784195072233, 0.97417601601869874, 0.96817200448645624, 0.97986414372584052, 0.95094070267614772, 0.94833579367193643, 0.90449923679845246, 0.9356766219564725, 0.73475381687218722, 0.76463549655228635, 0.90650841757975797, 0.71727732621060325, 0.57739092293041261, 0.63772300952814276, 0.61740082360731408, 0.9215537370703013, 0.86990102048943763]\n",
      "\n",
      "positive = [0.99694333190095996, 0.99820168711676127, 0.9977519415644186, 0.99792359626227056, 0.98915277965019432, 0.99161093488300089, 0.99326240222250839, 0.99545865133840461, 0.98957973148159373, 0.99115818536634248, 0.98727578217091105, 0.97559268792354015, 0.9772759417379987, 0.96293937848790712, 0.50012826685462231, 0.47858141440826651, 0.97847386435334727, 0.94801490585620851, 0.22126690649420189, 0.26849014171030661, 0.77671418061443209, 0.86285717711905363, 0.843177844634447]\n",
      "\n",
      "litigious = [0.99846310377535052, 0.99925468528606209, 0.99125305501055827, 0.99609165605110495, 0.99700862352731767, 0.99589913168631594, 0.99402865318486544, 0.99443830889787821, 0.98820464721588697, 0.99486039380910873, 0.98429753933077702, 0.99377322290600001, 0.97911962023745303, 0.97452832950709811, 0.26118852973901652, 0.35825913997570225, 0.94608033328556307, 0.7817294919248583, 0.47994030896818241, 0.97659062345763925, 0.88176812174811647, 0.95323310519745985, 0.78343024618589208]\n",
      "\n",
      "constraining = [0.99924338270082624, 0.99908815367270976, 0.99776637792804768, 0.99247971345280306, 0.99214496155877885, 0.99687751124919932, 0.99448721561655506, 0.99333407358444659, 0.9954636706325255, 0.99663644601216106, 0.97957088436144402, 0.96409232050310556, 0.99074907325754957, 0.97378847868062102, 0.55204105779597124, 0.61519175268299031, 0.97621644747484138, 0.98941294842710825, 0.76543414515816821, 0.77187949263140199, 0.86507647568012325, 0.96288754140044364, 0.9011245694677118]\n",
      "\n",
      "interesting = [0.99880431064118147, 0.98329592258972098, 0.9959835644188505, 0.99591176256021696, 0.99738582386575281, 0.99347564043510628, 0.98967382343947274, 0.98859802976561961, 0.9874230134901133, 0.99399175127742434, 0.98781942788543264, 0.98243048486531415, 0.99411928497639623, 0.99137840475212391, 0.98287269208880401, 0.98387780016356929, 0.9967188706022202, 0.98065587937136767, 0.95561959355919612, 0.96427711040398767, 0.93322701215288684, 0.97743920798167838, 0.89848392929966137]\n",
      "\n",
      "date = ['2016-02-10', '2015-02-12', '2014-02-14', '2013-02-11', '2012-02-09', '2011-02-09', '2010-02-08', '2009-02-09', '2008-02-15', '2007-02-16', '2006-02-28', '2005-02-28', '2004-03-05', '2003-02-27', '2002-03-08', '2001-03-09', '2000-03-08', '1999-03-05', '1998-03-04', '1997-03-10', '1996-03-13', '1995-03-09', '1994-03-17']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f29099cada0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cosine_similarities = {\n",
    "    ticker: {\n",
    "        sentiment_name: get_cosine_similarity(sentiment_values)\n",
    "        for sentiment_name, sentiment_values in ten_k_sentiments.items()}\n",
    "    for ticker, ten_k_sentiments in sentiment_tfidf_ten_ks.items()}\n",
    "\n",
    "example_ticker = 'BA'\n",
    "sentiments =['negative', 'positive', 'litigious', 'constraining', 'interesting']\n",
    "for sentiment in sentiments:\n",
    "    print('{} = {}\\n'.format(sentiment, cosine_similarities[example_ticker][sentiment]))\n",
    "\n",
    "print('{} = {}'.format('date', file_dates[example_ticker][1:]))\n",
    "\n",
    "project_helper.plot_similarities(\n",
    "    [cosine_similarities[example_ticker][sentiment] for sentiment in sentiments],\n",
    "    file_dates[example_ticker][1:],\n",
    "    'Cosine Similarities for {} Sentiment'.format(example_ticker),\n",
    "    sentiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate Alpha Factors\n",
    "Let's evaluate the alpha factors. \n",
    "### Price Data\n",
    "Let's get yearly pricing to run the factor against, since 10-Ks are produced annually."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dict to DataFrame\n",
    "The alphalens library uses dataframes, so we we'll need to turn our dictionary into a dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "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>A</th>\n",
       "      <th>AA</th>\n",
       "      <th>AAAP</th>\n",
       "      <th>AABA</th>\n",
       "      <th>AAC</th>\n",
       "      <th>AADR</th>\n",
       "      <th>AAIT</th>\n",
       "      <th>AAL</th>\n",
       "      <th>AAMC</th>\n",
       "      <th>AAME</th>\n",
       "      <th>...</th>\n",
       "      <th>ZUMZ</th>\n",
       "      <th>ZUO</th>\n",
       "      <th>ZVZZC</th>\n",
       "      <th>ZVZZCNX</th>\n",
       "      <th>ZX</th>\n",
       "      <th>ZXYZ_A</th>\n",
       "      <th>ZYME</th>\n",
       "      <th>ZYNE</th>\n",
       "      <th>ZZK</th>\n",
       "      <th>ZZZ</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>1962-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 11941 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ticker       A  AA  AAAP  AABA  AAC  AADR  AAIT  AAL  AAMC  AAME ...   ZUMZ  \\\n",
       "date                                                             ...          \n",
       "1962-01-01 nan nan   nan   nan  nan   nan   nan  nan   nan   nan ...    nan   \n",
       "1963-01-01 nan nan   nan   nan  nan   nan   nan  nan   nan   nan ...    nan   \n",
       "1964-01-01 nan nan   nan   nan  nan   nan   nan  nan   nan   nan ...    nan   \n",
       "1965-01-01 nan nan   nan   nan  nan   nan   nan  nan   nan   nan ...    nan   \n",
       "1966-01-01 nan nan   nan   nan  nan   nan   nan  nan   nan   nan ...    nan   \n",
       "\n",
       "ticker      ZUO  ZVZZC  ZVZZCNX  ZX  ZXYZ_A  ZYME  ZYNE  ZZK  ZZZ  \n",
       "date                                                               \n",
       "1962-01-01  nan    nan      nan nan     nan   nan   nan  nan  nan  \n",
       "1963-01-01  nan    nan      nan nan     nan   nan   nan  nan  nan  \n",
       "1964-01-01  nan    nan      nan nan     nan   nan   nan  nan  nan  \n",
       "1965-01-01  nan    nan      nan nan     nan   nan   nan  nan  nan  \n",
       "1966-01-01  nan    nan      nan nan     nan   nan   nan  nan  nan  \n",
       "\n",
       "[5 rows x 11941 columns]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5 year data was curated by using Quandl end of day US Stock Prices database, Accessed: 2020-10\n",
    "pricing = pd.read_csv('../../data/project_5_yr/yr-quotemedia.csv', parse_dates=['date'])\n",
    "pricing = pricing.pivot(index='date', columns='ticker', values='adj_close')\n",
    "\n",
    "\n",
    "pricing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "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>date</th>\n",
       "      <th>ticker</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.98065125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.95951741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.98838551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.97472377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.97585100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.97658031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.98103103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.93653965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.98808037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.98288202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.92710892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.95953508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.79804240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2003-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.95749124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.87599369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1999-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.92859157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.99505527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.99446855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.99288655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.98234804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date ticker sentiment      value\n",
       "0  2016-01-01   AMZN  negative 0.98065125\n",
       "1  2015-01-01   AMZN  negative 0.95951741\n",
       "2  2014-01-01   AMZN  negative 0.98838551\n",
       "3  2013-01-01   AMZN  negative 0.97472377\n",
       "4  2012-01-01   AMZN  negative 0.97585100\n",
       "5  2011-01-01   AMZN  negative 0.97658031\n",
       "6  2010-01-01   AMZN  negative 0.98103103\n",
       "7  2009-01-01   AMZN  negative 0.93653965\n",
       "8  2008-01-01   AMZN  negative 0.98808037\n",
       "9  2007-01-01   AMZN  negative 0.98288202\n",
       "10 2006-01-01   AMZN  negative 0.92710892\n",
       "11 2005-01-01   AMZN  negative 0.95953508\n",
       "12 2004-01-01   AMZN  negative 0.79804240\n",
       "13 2003-01-01   AMZN  negative 0.95749124\n",
       "14 2000-01-01   AMZN  negative 0.87599369\n",
       "15 1999-01-01   AMZN  negative 0.92859157\n",
       "16 2016-01-01   AMZN  positive 0.99505527\n",
       "17 2015-01-01   AMZN  positive 0.99446855\n",
       "18 2014-01-01   AMZN  positive 0.99288655\n",
       "19 2013-01-01   AMZN  positive 0.98234804"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_similarities_df_dict = {'date': [], 'ticker': [], 'sentiment': [], 'value': []}\n",
    "\n",
    "\n",
    "for ticker, ten_k_sentiments in cosine_similarities.items():\n",
    "    for sentiment_name, sentiment_values in ten_k_sentiments.items():\n",
    "        for sentiment_values, sentiment_value in enumerate(sentiment_values):\n",
    "            cosine_similarities_df_dict['ticker'].append(ticker)\n",
    "            cosine_similarities_df_dict['sentiment'].append(sentiment_name)\n",
    "            cosine_similarities_df_dict['value'].append(sentiment_value)\n",
    "            cosine_similarities_df_dict['date'].append(file_dates[ticker][1:][sentiment_values])\n",
    "\n",
    "cosine_similarities_df = pd.DataFrame(cosine_similarities_df_dict)\n",
    "cosine_similarities_df['date'] = pd.DatetimeIndex(cosine_similarities_df['date']).year\n",
    "cosine_similarities_df['date'] = pd.to_datetime(cosine_similarities_df['date'], format='%Y')\n",
    "\n",
    "\n",
    "cosine_similarities_df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alphalens Format\n",
    "In order to use a lot of the alphalens functions, we need to aligned the indices and convert the time to unix timestamp. In this next cell, we'll do just that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "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></th>\n",
       "      <th>1D</th>\n",
       "      <th>factor</th>\n",
       "      <th>factor_quantile</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>asset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1994-01-01</th>\n",
       "      <th>BA</th>\n",
       "      <td>0.69666598</td>\n",
       "      <td>0.86990102</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRT</th>\n",
       "      <td>0.17159556</td>\n",
       "      <td>0.47730392</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LLY</th>\n",
       "      <td>0.77387678</td>\n",
       "      <td>0.48325900</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XOM</th>\n",
       "      <td>0.39421669</td>\n",
       "      <td>0.71147771</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <th>BA</th>\n",
       "      <td>0.37622173</td>\n",
       "      <td>0.92155374</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         1D     factor  factor_quantile\n",
       "date       asset                                       \n",
       "1994-01-01 BA    0.69666598 0.86990102                5\n",
       "           FRT   0.17159556 0.47730392                1\n",
       "           LLY   0.77387678 0.48325900                2\n",
       "           XOM   0.39421669 0.71147771                4\n",
       "1995-01-01 BA    0.37622173 0.92155374                3"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import alphalens as al\n",
    "\n",
    "\n",
    "factor_data = {}\n",
    "skipped_sentiments = []\n",
    "\n",
    "for sentiment in sentiments:\n",
    "    cs_df = cosine_similarities_df[(cosine_similarities_df['sentiment'] == sentiment)]\n",
    "    cs_df = cs_df.pivot(index='date', columns='ticker', values='value')\n",
    "\n",
    "    try:\n",
    "        data = al.utils.get_clean_factor_and_forward_returns(cs_df.stack(), pricing, quantiles=5, bins=None, periods=[1])\n",
    "        factor_data[sentiment] = data\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        skipped_sentiments.append(sentiment)\n",
    "\n",
    "if skipped_sentiments:\n",
    "    print('\\nSkipped the following sentiments:\\n{}'.format('\\n'.join(skipped_sentiments)))\n",
    "factor_data[sentiments[0]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alphalens Format with Unix Time\n",
    "Alphalen's `factor_rank_autocorrelation` and `mean_return_by_quantile` functions require unix timestamps to work, so we'll also create factor dataframes with unix time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "unixt_factor_data = {\n",
    "    factor: data.set_index(pd.MultiIndex.from_tuples(\n",
    "        [(x.timestamp(), y) for x, y in data.index.values],\n",
    "        names=['date', 'asset']))\n",
    "    for factor, data in factor_data.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install pandas==0.23.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Factor Returns\n",
    "Let's view the factor returns over time. We should be seeing it generally move up and to the right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f29099adcf8>"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2909a26fd0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls_factor_returns = pd.DataFrame()\n",
    "\n",
    "for factor_name, data in factor_data.items():\n",
    "    ls_factor_returns[factor_name] = al.performance.factor_returns(data).iloc[:, 0]\n",
    "\n",
    "(1 + ls_factor_returns).cumprod().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "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>negative</th>\n",
       "      <th>positive</th>\n",
       "      <th>litigious</th>\n",
       "      <th>constraining</th>\n",
       "      <th>interesting</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1994-01-01</th>\n",
       "      <td>1.07783158</td>\n",
       "      <td>1.22948522</td>\n",
       "      <td>1.03736869</td>\n",
       "      <td>1.14338904</td>\n",
       "      <td>0.79465418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>1.25570995</td>\n",
       "      <td>0.99927924</td>\n",
       "      <td>1.23057429</td>\n",
       "      <td>0.99592302</td>\n",
       "      <td>0.62405698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-01-01</th>\n",
       "      <td>1.28334794</td>\n",
       "      <td>1.20477159</td>\n",
       "      <td>1.21111898</td>\n",
       "      <td>1.05407954</td>\n",
       "      <td>0.66007750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-01-01</th>\n",
       "      <td>1.07836475</td>\n",
       "      <td>1.40322961</td>\n",
       "      <td>1.01912584</td>\n",
       "      <td>1.10952651</td>\n",
       "      <td>0.54914314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01</th>\n",
       "      <td>0.79271995</td>\n",
       "      <td>1.03153234</td>\n",
       "      <td>0.74917266</td>\n",
       "      <td>0.81562737</td>\n",
       "      <td>0.69460401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01</th>\n",
       "      <td>0.40024761</td>\n",
       "      <td>1.41513221</td>\n",
       "      <td>0.72170192</td>\n",
       "      <td>0.84857263</td>\n",
       "      <td>0.57643152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>0.34733111</td>\n",
       "      <td>1.31934719</td>\n",
       "      <td>0.63270725</td>\n",
       "      <td>0.84809568</td>\n",
       "      <td>0.51386187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>0.36411780</td>\n",
       "      <td>1.32020254</td>\n",
       "      <td>0.63261812</td>\n",
       "      <td>0.85211241</td>\n",
       "      <td>0.52477385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01</th>\n",
       "      <td>0.41862029</td>\n",
       "      <td>1.35249926</td>\n",
       "      <td>0.65663356</td>\n",
       "      <td>0.88604649</td>\n",
       "      <td>0.60924239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-01</th>\n",
       "      <td>0.42827438</td>\n",
       "      <td>1.48622820</td>\n",
       "      <td>0.63122065</td>\n",
       "      <td>0.90434356</td>\n",
       "      <td>0.58963814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01</th>\n",
       "      <td>0.42951064</td>\n",
       "      <td>1.64311166</td>\n",
       "      <td>0.65930679</td>\n",
       "      <td>0.95655104</td>\n",
       "      <td>0.60382821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>0.46108737</td>\n",
       "      <td>1.84211214</td>\n",
       "      <td>0.72930577</td>\n",
       "      <td>1.10514112</td>\n",
       "      <td>0.69225814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>0.30262638</td>\n",
       "      <td>1.47032630</td>\n",
       "      <td>0.43423718</td>\n",
       "      <td>0.93304280</td>\n",
       "      <td>0.78864413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>0.27609385</td>\n",
       "      <td>1.41485678</td>\n",
       "      <td>0.39195041</td>\n",
       "      <td>0.84838697</td>\n",
       "      <td>0.75122812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>0.39105347</td>\n",
       "      <td>0.98479370</td>\n",
       "      <td>0.51139129</td>\n",
       "      <td>0.93349021</td>\n",
       "      <td>0.83769827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>0.40667011</td>\n",
       "      <td>0.99763086</td>\n",
       "      <td>0.51827574</td>\n",
       "      <td>0.99262189</td>\n",
       "      <td>0.86952853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>0.40243663</td>\n",
       "      <td>0.98696018</td>\n",
       "      <td>0.53133932</td>\n",
       "      <td>0.98877030</td>\n",
       "      <td>0.85274586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>0.42954664</td>\n",
       "      <td>0.98587414</td>\n",
       "      <td>0.55157283</td>\n",
       "      <td>1.01158836</td>\n",
       "      <td>0.96391214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>0.41895983</td>\n",
       "      <td>0.75460084</td>\n",
       "      <td>0.68589319</td>\n",
       "      <td>0.91652095</td>\n",
       "      <td>1.21518342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.43842523</td>\n",
       "      <td>0.77895909</td>\n",
       "      <td>0.72765809</td>\n",
       "      <td>0.78637045</td>\n",
       "      <td>1.30869296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>0.52452235</td>\n",
       "      <td>0.68358124</td>\n",
       "      <td>0.75405059</td>\n",
       "      <td>0.52107409</td>\n",
       "      <td>1.56867097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>0.48342352</td>\n",
       "      <td>0.82119110</td>\n",
       "      <td>0.74915459</td>\n",
       "      <td>0.47695748</td>\n",
       "      <td>1.67788421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.58093968</td>\n",
       "      <td>0.92035624</td>\n",
       "      <td>0.91030653</td>\n",
       "      <td>0.48358431</td>\n",
       "      <td>1.83393802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             negative   positive  litigious  constraining  interesting\n",
       "date                                                                  \n",
       "1994-01-01 1.07783158 1.22948522 1.03736869    1.14338904   0.79465418\n",
       "1995-01-01 1.25570995 0.99927924 1.23057429    0.99592302   0.62405698\n",
       "1996-01-01 1.28334794 1.20477159 1.21111898    1.05407954   0.66007750\n",
       "1997-01-01 1.07836475 1.40322961 1.01912584    1.10952651   0.54914314\n",
       "1998-01-01 0.79271995 1.03153234 0.74917266    0.81562737   0.69460401\n",
       "1999-01-01 0.40024761 1.41513221 0.72170192    0.84857263   0.57643152\n",
       "2000-01-01 0.34733111 1.31934719 0.63270725    0.84809568   0.51386187\n",
       "2001-01-01 0.36411780 1.32020254 0.63261812    0.85211241   0.52477385\n",
       "2002-01-01 0.41862029 1.35249926 0.65663356    0.88604649   0.60924239\n",
       "2003-01-01 0.42827438 1.48622820 0.63122065    0.90434356   0.58963814\n",
       "2004-01-01 0.42951064 1.64311166 0.65930679    0.95655104   0.60382821\n",
       "2005-01-01 0.46108737 1.84211214 0.72930577    1.10514112   0.69225814\n",
       "2006-01-01 0.30262638 1.47032630 0.43423718    0.93304280   0.78864413\n",
       "2007-01-01 0.27609385 1.41485678 0.39195041    0.84838697   0.75122812\n",
       "2008-01-01 0.39105347 0.98479370 0.51139129    0.93349021   0.83769827\n",
       "2009-01-01 0.40667011 0.99763086 0.51827574    0.99262189   0.86952853\n",
       "2010-01-01 0.40243663 0.98696018 0.53133932    0.98877030   0.85274586\n",
       "2011-01-01 0.42954664 0.98587414 0.55157283    1.01158836   0.96391214\n",
       "2012-01-01 0.41895983 0.75460084 0.68589319    0.91652095   1.21518342\n",
       "2013-01-01 0.43842523 0.77895909 0.72765809    0.78637045   1.30869296\n",
       "2014-01-01 0.52452235 0.68358124 0.75405059    0.52107409   1.56867097\n",
       "2015-01-01 0.48342352 0.82119110 0.74915459    0.47695748   1.67788421\n",
       "2016-01-01 0.58093968 0.92035624 0.91030653    0.48358431   1.83393802"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1 + ls_factor_returns).cumprod()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basis Points Per Day per Quantile\n",
    "It is not enough to look just at the factor weighted return. A good alpha is also monotonic in quantiles. Let's looks the basis points for the factor returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f29097340f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f29099be668>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f290972c390>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2909796080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f290978e438>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f290978e358>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2909781da0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f290978b2b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f290976aa20>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f29097bbcf8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2909827f28>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2909811978>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2909801128>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f29097f24a8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f290985e9e8>]], dtype=object)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2909987358>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "qr_factor_returns = pd.DataFrame()\n",
    "\n",
    "for factor_name, data in unixt_factor_data.items():\n",
    "    qr_factor_returns[factor_name] = al.performance.mean_return_by_quantile(data)[0].iloc[:, 0]\n",
    "\n",
    "(10000*qr_factor_returns).plot.bar(\n",
    "    subplots=True,\n",
    "    sharey=True,\n",
    "    layout=(5,3),\n",
    "    figsize=(14, 14),\n",
    "    legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Turnover Analysis\n",
    "Without doing a full and formal backtest, we can analyze how stable the alphas are over time. Stability in this sense means that from period to period, the alpha ranks do not change much. Since trading is costly, we always prefer, all other things being equal, that the ranks do not change significantly per period. We can measure this with the **Factor Rank Autocorrelation (FRA)**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f290969cd30>"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2909969978>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls_FRA = pd.DataFrame()\n",
    "\n",
    "for factor, data in unixt_factor_data.items():\n",
    "    ls_FRA[factor] = al.performance.factor_rank_autocorrelation(data)\n",
    "\n",
    "ls_FRA.plot(title=\"Factor Rank Autocorrelation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sharpe Ratio of the Alphas\n",
    "The last analysis we'll do on the factors will be sharpe ratio. Let's see what the sharpe ratio for the factors are. Generally, a Sharpe Ratio of near 1.0 or higher is an acceptable single alpha for this universe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "negative       -0.24000000\n",
       "positive        1.02000000\n",
       "litigious       0.89000000\n",
       "constraining   -2.98000000\n",
       "interesting     4.10000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_annualization_factor = np.sqrt(252)\n",
    "\n",
    "(daily_annualization_factor * ls_factor_returns.mean() / ls_factor_returns.std()).round(2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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