NLP_on_Financial_Statements.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project 5: NLP on Financial Statements\n",
    "## Instructions\n",
    "Each problem consists of a function to implement and instructions on how to implement the function.  The parts of the function that need to be implemented are marked with a `# TODO` comment. After implementing the function, run the cell to test it against the unit tests we've provided. For each problem, we provide one or more unit tests from our `project_tests` package. These unit tests won't tell you if your answer is correct, but will warn you of any major errors. Your code will be checked for the correct solution when you submit it to Udacity.\n",
    "\n",
    "## Packages\n",
    "When you implement the functions, you'll only need to you use the packages you've used in the classroom, like [Pandas](https://pandas.pydata.org/) and [Numpy](http://www.numpy.org/). These packages will be imported for you. We recommend you don't add any import statements, otherwise the grader might not be able to run your code.\n",
    "\n",
    "The other packages that we're importing are `project_helper` and `project_tests`. These are custom packages built to help you solve the problems.  The `project_helper` module contains utility functions and graph functions. The `project_tests` contains the unit tests for all the problems.\n",
    "\n",
    "### Install Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[33mYou are using pip version 9.0.1, however version 19.0.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import sys\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",
    "You'll 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]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get 10ks\n",
    "We'll 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cik_lookup = {\n",
    "    'AMZN': '0001018724',\n",
    "    'BMY': '0000014272',   \n",
    "    'CNP': '0001130310',\n",
    "    'CVX': '0000093410',\n",
    "    'FL': '0000850209',\n",
    "    'FRT': '0000034903',\n",
    "    'HON': '0000773840'}\n",
    "\n",
    "additional_cik = {\n",
    "    'AEP': '0000004904',\n",
    "    'AXP': '0000004962',\n",
    "    'BA': '0000012927', \n",
    "    'BK': '0001390777',\n",
    "    'CAT': '0000018230',\n",
    "    'DE': '0000315189',\n",
    "    'DIS': '0001001039',\n",
    "    'DTE': '0000936340',\n",
    "    'ED': '0001047862',\n",
    "    'EMR': '0000032604',\n",
    "    'ETN': '0001551182',\n",
    "    'GE': '0000040545',\n",
    "    'IBM': '0000051143',\n",
    "    'IP': '0000051434',\n",
    "    'JNJ': '0000200406',\n",
    "    'KO': '0000021344',\n",
    "    'LLY': '0000059478',\n",
    "    'MCD': '0000063908',\n",
    "    'MO': '0000764180',\n",
    "    'MRK': '0000310158',\n",
    "    'MRO': '0000101778',\n",
    "    'PCG': '0001004980',\n",
    "    'PEP': '0000077476',\n",
    "    'PFE': '0000078003',\n",
    "    'PG': '0000080424',\n",
    "    'PNR': '0000077360',\n",
    "    'SYY': '0000096021',\n",
    "    'TXN': '0000097476',\n",
    "    'UTX': '0000101829',\n",
    "    'WFC': '0000072971',\n",
    "    'WMT': '0000104169',\n",
    "    'WY': '0000106535',\n",
    "    'XOM': '0000034088'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get list of 10-ks\n",
    "The SEC has a limit on the number of calls you can make to the website per second. In order to avoid hiding that limit, we've created the `SecAPI` class. This will cache data from the SEC and prevent you from going over the limit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "\n",
    "def get_sec_data(cik, doc_type, start=0, count=60):\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",
    "\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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('https://www.sec.gov/Archives/edgar/data/1018724/000101872419000004/0001018724-19-000004-index.htm',\n",
      "  '10-K',\n",
      "  '2019-02-01'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000101872418000005/0001018724-18-000005-index.htm',\n",
      "  '10-K',\n",
      "  '2018-02-02'),\n",
      " ('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"
     ]
    }
   ],
   "source": [
    "example_ticker = 'AMZN'\n",
    "sec_data = {}\n",
    "\n",
    "for ticker, cik in cik_lookup.items():\n",
    "    sec_data[ticker] = get_sec_data(cik, '10-K')\n",
    "\n",
    "pprint.pprint(sec_data[example_ticker][: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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading AMZN Fillings: 100%|██████████| 24/24 [00:03<00:00,  6.21filling/s]\n",
      "Downloading BMY Fillings: 100%|██████████| 28/28 [00:06<00:00,  4.28filling/s]\n",
      "Downloading CNP Fillings: 100%|██████████| 20/20 [00:04<00:00,  4.57filling/s]\n",
      "Downloading CVX Fillings: 100%|██████████| 26/26 [00:06<00:00,  3.79filling/s]\n",
      "Downloading FL Fillings: 100%|██████████| 23/23 [00:03<00:00,  5.77filling/s]\n",
      "Downloading FRT Fillings: 100%|██████████| 30/30 [00:04<00:00,  6.53filling/s]\n",
      "Downloading HON Fillings: 100%|██████████| 26/26 [00:05<00:00,  5.04filling/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example Document:\n",
      "\n",
      "<SEC-DOCUMENT>0001018724-19-000004.txt : 20190201\n",
      "<SEC-HEADER>0001018724-19-000004.hdr.sgml : 20190201\n",
      "<ACCEPTANCE-DATETIME>20190131202240\n",
      "ACCESSION NUMBER:\t\t0001018724-19-000004\n",
      "CONFORMED SUBMISSION TYPE:\t10-K\n",
      "PUBLIC DOCUMENT COUNT:\t\t106\n",
      "CONFORMED PERIOD OF REPORT:\t20181231\n",
      "FILED AS OF DATE:\t\t20190201\n",
      "DATE AS OF CHANGE:\t\t20190131\n",
      "\n",
      "FILER:\n",
      "\n",
      "\tCOMPANY DATA:\t\n",
      "\t\tCOMPANY CONFORMED NAME:\t\t\tAMAZON COM INC\n",
      "\t\tCENTRAL INDEX KEY:\t\t\t0001018724\n",
      "\t\tSTANDARD INDUSTRIAL CLASSIFICATION:\tRETAIL-CATALOG & MAIL-ORDER HOUSES [5961]\n",
      "\t\tIRS NUMBER:\t\t\t\t911646860\n",
      "\t\tSTATE OF INCORPORATION:\t\t\tDE\n",
      "\t\tFISCAL YEAR END:\t\t\t1231\n",
      "\n",
      "\tFILING VALUES:\n",
      "\t\tFORM TYPE:\t\t10-K\n",
      "\t\tSEC ACT:\t\t1934 Act\n",
      "\t\tSEC FILE NUMBER:\t000-22513\n",
      "\t\tFILM NUMBER:\t\t19557427\n",
      "\n",
      "\tBUSINESS ADDRESS:\t\n",
      "\t\tSTREET 1:\t\t410 TERRY AVENUE NORTH\n",
      "\t\tCITY:\t\t\tSEATTLE\n",
      "\t\tSTATE:\t\t\tWA\n",
      "\t\tZIP:\t\t\t98109\n",
      "\t\tBUSINESS PHONE:\t\t2062661000\n",
      "\n",
      "\tMAIL ADDRESS:\t\n",
      "\t\tSTREET 1:\t\t410 TERRY AVENUE NORTH\n",
      "\t\tCITY:\t\t\tSEATTLE\n",
      "\t\tSTATE:\t\t\tWA\n",
      "\t\tZIP:\t\t\t98109\n",
      "</SEC-HEADER>\n",
      "<DOCUMENT>\n",
      "<TYPE>10-K\n",
      "<SEQUENCE>1\n",
      "<FILENAM...\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",
    "\n",
    "Implement `get_documents` to return a list of these documents from a filling. Make sure not to include the tag in the returned document text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "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",
    "    extracted_docs = []\n",
    "    \n",
    "    doc_start_pattern = re.compile(r'<DOCUMENT>')\n",
    "    doc_end_pattern = re.compile(r'</DOCUMENT>')   \n",
    "    \n",
    "    doc_start_is = [x.end() for x in doc_start_pattern.finditer(text)]\n",
    "    doc_end_is = [x.start() for x in doc_end_pattern.finditer(text)]\n",
    "    \n",
    "    for doc_start_i, doc_end_i in zip(doc_start_is, doc_end_is):\n",
    "            extracted_docs.append(text[doc_start_i:doc_end_i])\n",
    "    \n",
    "    return extracted_docs\n",
    "\n",
    "\n",
    "project_tests.test_get_documents(get_documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `get_documents` function implemented, let's extract all the documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Getting Documents from AMZN Fillings: 100%|██████████| 19/19 [00:00<00:00, 45.12filling/s]\n",
      "Getting Documents from BMY Fillings: 100%|██████████| 24/24 [00:00<00:00, 24.71filling/s]\n",
      "Getting Documents from CNP Fillings: 100%|██████████| 16/16 [00:00<00:00, 21.88filling/s]\n",
      "Getting Documents from CVX Fillings: 100%|██████████| 22/22 [00:00<00:00, 24.16filling/s]\n",
      "Getting Documents from FL Fillings: 100%|██████████| 17/17 [00:00<00:00, 24.68filling/s]\n",
      "Getting Documents from FRT Fillings: 100%|██████████| 20/20 [00:00<00:00, 21.77filling/s]\n",
      "Getting Documents from HON Fillings: 100%|██████████| 21/21 [00:00<00:00, 21.05filling/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Document 0 Filed on 2019-02-01:\n",
      "\n",
      "<TYPE>10-K\n",
      "<SEQUENCE>1\n",
      "<FILENAME>amzn-20181231x10k.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...\n",
      "\n",
      "Document 1 Filed on 2019-02-01:\n",
      "\n",
      "<TYPE>EX-10.7\n",
      "<SEQUENCE>2\n",
      "<FILENAME>amzn-20181231xex107.htm\n",
      "<DESCRIPTION>EXHIBIT 10.7\n",
      "<TEXT>\n",
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http://www.w3.org/TR/html4/loose.dtd\">\n",
      "<ht...\n",
      "\n",
      "Document 2 Filed on 2019-02-01:\n",
      "\n",
      "<TYPE>EX-21.1\n",
      "<SEQUENCE>3\n",
      "<FILENAME>amzn-20181231xex211.htm\n",
      "<DESCRIPTION>EXHIBIT 21.1\n",
      "<TEXT>\n",
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http://www.w3.org/TR/html4/loose.dtd\">\n",
      "<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. Implement the `get_document_type` function to return 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`. Make sure to return the type as lowercase, so this example would be returned as \"test\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "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",
    "    type_pattern = re.compile(r'<TYPE>[^\\n]+')\n",
    "    \n",
    "    doc_type = type_pattern.findall(doc)[0][len('<TYPE>'):] \n",
    "    \n",
    "    return doc_type.lower()\n",
    "\n",
    "\n",
    "project_tests.test_get_document_type(get_document_type)"
   ]
  },
  {
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2018123...\n",
      "    file_date: '2019-02-01'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2017123...\n",
      "    file_date: '2018-02-02'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2016123...\n",
      "    file_date: '2017-02-10'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2015123...\n",
      "    file_date: '2016-01-29'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2014123...\n",
      "    file_date: '2015-01-30'},\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",
    "                    'file': document,\n",
    "                    'file_date': file_date})\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data(ten_ks_by_ticker[example_ticker][:5], ['cik', 'file', '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": 13,
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cleaning AMZN 10-Ks: 100%|██████████| 19/19 [00:40<00:00,  2.14s/10-K]\n",
      "Cleaning BMY 10-Ks: 100%|██████████| 24/24 [01:21<00:00,  3.39s/10-K]\n",
      "Cleaning CNP 10-Ks: 100%|██████████| 16/16 [01:00<00:00,  3.81s/10-K]\n",
      "Cleaning CVX 10-Ks: 100%|██████████| 22/22 [02:04<00:00,  5.67s/10-K]\n",
      "Cleaning FL 10-Ks: 100%|██████████| 17/17 [00:28<00:00,  1.70s/10-K]\n",
      "Cleaning FRT 10-Ks: 100%|██████████| 20/20 [01:00<00:00,  3.03s/10-K]\n",
      "Cleaning HON 10-Ks: 100%|██████████| 21/21 [01:04<00:00,  3.07s/10-K]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20181231x10k.htm\\n10-k\\n\\n\\n\\n\\n\\...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20171231x10k.htm\\n10-k\\n\\n\\n\\n\\n\\...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20161231x10k.htm\\nform 10-k\\n\\n\\n...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20151231x10k.htm\\nform 10-k\\n\\n\\n...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20141231x10k.htm\\nform 10-k\\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['file'])\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. Implement the `lemmatize_words` function to lemmatize verbs in the list of words provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "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",
    "    lemmatized_words = [WordNetLemmatizer().lemmatize(word, 'v') for word in words]\n",
    "    \n",
    "    return lemmatized_words\n",
    "\n",
    "\n",
    "project_tests.test_lemmatize_words(lemmatize_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `lemmatize_words` function implemented, let's lemmatize all the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lemmatize AMZN 10-Ks: 100%|██████████| 19/19 [00:05<00:00,  3.3910-K/s]\n",
      "Lemmatize BMY 10-Ks: 100%|██████████| 24/24 [00:11<00:00,  2.1510-K/s]\n",
      "Lemmatize CNP 10-Ks: 100%|██████████| 16/16 [00:09<00:00,  1.7110-K/s]\n",
      "Lemmatize CVX 10-Ks: 100%|██████████| 22/22 [00:10<00:00,  2.0610-K/s]\n",
      "Lemmatize FL 10-Ks: 100%|██████████| 17/17 [00:04<00:00,  3.7910-K/s]\n",
      "Lemmatize FRT 10-Ks: 100%|██████████| 20/20 [00:06<00:00,  2.9010-K/s]\n",
      "Lemmatize HON 10-Ks: 100%|██████████| 21/21 [00:06<00:00,  3.2010-K/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20181231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20171231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20161231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20151231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20141231x10k', 'htm', '...},\n",
      "]\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['file_lemma'] = lemmatize_words(word_pattern.findall(ten_k['file_clean']))\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data(ten_ks_by_ticker[example_ticker][:5], ['file_lemma'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove Stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Remove Stop Words for AMZN 10-Ks: 100%|██████████| 19/19 [00:02<00:00,  7.9210-K/s]\n",
      "Remove Stop Words for BMY 10-Ks: 100%|██████████| 24/24 [00:04<00:00,  4.9610-K/s]\n",
      "Remove Stop Words for CNP 10-Ks: 100%|██████████| 16/16 [00:04<00:00,  3.8910-K/s]\n",
      "Remove Stop Words for CVX 10-Ks: 100%|██████████| 22/22 [00:04<00:00,  4.5710-K/s]\n",
      "Remove Stop Words for FL 10-Ks: 100%|██████████| 17/17 [00:01<00:00,  8.7810-K/s]\n",
      "Remove Stop Words for FRT 10-Ks: 100%|██████████| 20/20 [00:03<00:00,  6.4410-K/s]\n",
      "Remove Stop Words for HON 10-Ks: 100%|██████████| 21/21 [00:02<00:00,  7.5110-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['file_lemma'] = [word for word in ten_k['file_lemma'] 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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>9</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>abandon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</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>abandonment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</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>abandonments</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</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>abdicate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</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>abdication</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    negative  positive  uncertainty  litigious  constraining  interesting  \\\n",
       "9       True     False        False      False         False        False   \n",
       "12      True     False        False      False         False        False   \n",
       "13      True     False        False      False         False        False   \n",
       "51      True     False        False      False         False        False   \n",
       "54      True     False        False      False         False        False   \n",
       "\n",
       "            word  \n",
       "9        abandon  \n",
       "12   abandonment  \n",
       "13  abandonments  \n",
       "51      abdicate  \n",
       "54    abdication  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\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.head()"
   ]
  },
  {
   "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. Implement `get_bag_of_words` to generate a bag of words that counts the number of sentiment words in each doc. You can ignore words that are not in `sentiment_words`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "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",
    "    # TODO: Implement\n",
    "    vec = CountVectorizer(vocabulary=sentiment_words)\n",
    "    vectors = vec.fit_transform(docs)\n",
    "    words_list = vec.get_feature_names()\n",
    "    bag_of_words = np.zeros([len(docs), len(words_list)])\n",
    "    \n",
    "    for i in range(len(docs)):\n",
    "        bag_of_words[i] = vectors[i].toarray()[0]\n",
    "\n",
    "    return bag_of_words.astype(int)\n",
    "\n",
    "\n",
    "project_tests.test_get_bag_of_words(get_bag_of_words)"
   ]
  },
  {
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    negative: '[[0 0 0 ..., 0 0 0]\\n [0 0 0 ..., 0 0 0]\\n [0 0 0...\n",
      "    positive: '[[15  0  0 ...,  0  0  0]\\n [15  0  0 ...,  0  0 ...\n",
      "    uncertainty: '[[0 0 0 ..., 1 1 2]\\n [0 0 0 ..., 1 1 3]\\n [0 0 0...\n",
      "    litigious: '[[0 0 0 ..., 0 0 0]\\n [0 0 0 ..., 0 0 0]\\n [0 0 0...\n",
      "    constraining: '[[0 0 0 ..., 0 0 2]\\n [0 0 0 ..., 0 0 2]\\n [0 0 0...\n",
      "    interesting: '[[2 0 0 ..., 0 0 0]\\n [2 0 0 ..., 0 0 0]\\n [2 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['file_lemma']) for ten_k in ten_ks]\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}\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data([sentiment_bow_ten_ks[example_ticker]], sentiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jaccard Similarity\n",
    "Using the bag of words, let's calculate the jaccard similarity on the bag of words and plot it over time. Implement `get_jaccard_similarity` to return the jaccard similarities between each tick in time. Since the input, `bag_of_words_matrix`, is a bag of words for each time period in order, you just need to compute the jaccard similarities for each neighboring bag of words. Make sure to turn the bag of words into a boolean array when calculating the jaccard similarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import jaccard_similarity_score\n",
    "\n",
    "\n",
    "def get_jaccard_similarity(bag_of_words_matrix):\n",
    "    \"\"\"\n",
    "    Get jaccard similarities for neighboring documents\n",
    "\n",
    "    Parameters\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",
    "    Returns\n",
    "    -------\n",
    "    jaccard_similarities : list of float\n",
    "        Jaccard similarities for neighboring documents\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Implement\n",
    "    jaccard_similarities = []\n",
    "    bag_of_words_matrix = np.array(bag_of_words_matrix, dtype=bool)\n",
    "    \n",
    "    for i in range(len(bag_of_words_matrix)-1):\n",
    "            u = bag_of_words_matrix[i]\n",
    "            v = bag_of_words_matrix[i+1]\n",
    "            jaccard_similarities.append(jaccard_similarity_score(u,v))    \n",
    "    \n",
    "    return jaccard_similarities\n",
    "\n",
    "\n",
    "project_tests.test_get_jaccard_similarity(get_jaccard_similarity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `get_jaccard_similarity` function, let's plot the similarities over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1d0a2ccb70>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get dates for the universe\n",
    "file_dates = {\n",
    "    ticker: [ten_k['file_date'] for ten_k in ten_ks]\n",
    "    for ticker, ten_ks in ten_ks_by_ticker.items()}  \n",
    "\n",
    "jaccard_similarities = {\n",
    "    ticker: {\n",
    "        sentiment_name: get_jaccard_similarity(sentiment_values)\n",
    "        for sentiment_name, sentiment_values in ten_k_sentiments.items()}\n",
    "    for ticker, ten_k_sentiments in sentiment_bow_ten_ks.items()}\n",
    "\n",
    "\n",
    "project_helper.plot_similarities(\n",
    "    [jaccard_similarities[example_ticker][sentiment] for sentiment in sentiments],\n",
    "    file_dates[example_ticker][1:],\n",
    "    'Jaccard Similarities for {} Sentiment'.format(example_ticker),\n",
    "    sentiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TFIDF\n",
    "using the sentiment word lists, let's generate sentiment TFIDF from the 10-k documents. Implement `get_tfidf` to generate TFIDF from each document, using sentiment words as the terms. You can ignore words that are not in `sentiment_words`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "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",
    "    vec = TfidfVectorizer(vocabulary=sentiment_words)\n",
    "    tfidf = vec.fit_transform(docs)\n",
    "    \n",
    "    return tfidf.toarray()\n",
    "\n",
    "\n",
    "project_tests.test_get_tfidf(get_tfidf)"
   ]
  },
  {
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    negative: '[[ 0.          0.          0.         ...,  0.   ...\n",
      "    positive: '[[ 0.24113923  0.          0.         ...,  0.   ...\n",
      "    uncertainty: '[[ 0.          0.          0.         ...,  0.005...\n",
      "    litigious: '[[ 0.  0.  0. ...,  0.  0.  0.]\\n [ 0.  0.  0. .....\n",
      "    constraining: '[[ 0.          0.          0.         ...,  0.   ...\n",
      "    interesting: '[[ 0.01878316  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['file_lemma']) 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",
    "\n",
    "    \n",
    "project_helper.print_ten_k_data([sentiment_tfidf_ten_ks[example_ticker]], sentiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cosine Similarity\n",
    "Using the TFIDF values, we'll calculate the cosine similarity and plot it over time. Implement `get_cosine_similarity` to return the cosine similarities between each tick in time. Since the input, `tfidf_matrix`, is a TFIDF vector for each time period in order, you just need to computer the cosine similarities for each neighboring vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "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",
    "    cosine_similarities = []    \n",
    "    \n",
    "    for i in range(len(tfidf_matrix)-1):\n",
    "        cosine_similarities.append(cosine_similarity(tfidf_matrix[i].reshape(1, -1),tfidf_matrix[i+1].reshape(1, -1))[0,0])\n",
    "    \n",
    "    return cosine_similarities\n",
    "\n",
    "\n",
    "project_tests.test_get_cosine_similarity(get_cosine_similarity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the cosine similarities over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1d09fd6780>"
      ]
     },
     "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",
    "\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",
    "Just like we did in project 4, let's evaluate the alpha factors. For this section, we'll just be looking at the cosine similarities, but it can be applied to the jaccard similarities as well.\n",
    "### Price Data\n",
    "Let's get yearly pricing to run the factor against, since 10-Ks are produced annually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ticker</th>\n",
       "      <th>A</th>\n",
       "      <th>AA</th>\n",
       "      <th>AAAP</th>\n",
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       "      <th>ZYME</th>\n",
       "      <th>ZYNE</th>\n",
       "      <th>ZZK</th>\n",
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       "    </tr>\n",
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       "      <th>date</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1962-01-01</th>\n",
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       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
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       "      <td>nan</td>\n",
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       "      <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",
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       "      <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",
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       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
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       "      <th>1966-01-01</th>\n",
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       "      <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",
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       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
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       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
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       "      <th>1967-01-01</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",
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       "      <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>1968-01-01</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>1969-01-01</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>1970-01-01</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>1971-01-01</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",
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       "      <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>1972-01-01</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",
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       "      <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>1973-01-01</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>1974-01-01</th>\n",
       "      <td>nan</td>\n",
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       "      <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",
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       "      <th>1975-01-01</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",
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       "      <td>nan</td>\n",
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       "      <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",
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       "      <th>1976-01-01</th>\n",
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       "      <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",
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       "      <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",
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       "      <th>1977-01-01</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>1978-01-01</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>1979-01-01</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>1980-01-01</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>1981-01-01</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",
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       "      <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>1983-01-01</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>1984-01-01</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>6.40835399</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>1985-01-01</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>10.61223421</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>1986-01-01</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>9.04493392</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>1987-01-01</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>3.80659044</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>1988-01-01</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>3.14982608</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>1989-01-01</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>2.32975302</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>1990-01-01</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>1.39785181</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>1991-01-01</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>0.69892591</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>1992-01-01</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>1.51433946</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>1993-01-01</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>1.63082711</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>1994-01-01</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>2.09677772</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>1995-01-01</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>2.15548749</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>1996-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.70833333</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.85161770</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>1997-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>4.32812500</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>4.71821581</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>1998-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>29.61250000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>4.54301839</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>1999-01-01</th>\n",
       "      <td>52.37855258</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>108.17500000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.15548749</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>2000-01-01</th>\n",
       "      <td>37.09385272</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>15.03000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.86380242</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>2001-01-01</th>\n",
       "      <td>19.31590395</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>8.87000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.05391026</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>2002-01-01</th>\n",
       "      <td>12.16813872</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>8.17500000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.51899897</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>2003-01-01</th>\n",
       "      <td>19.81048865</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>22.51500000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.79570362</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>2004-01-01</th>\n",
       "      <td>16.32807033</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>37.68000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.88889374</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>2005-01-01</th>\n",
       "      <td>22.55441748</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>39.18000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>35.76072005</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.51613326</td>\n",
       "      <td>...</td>\n",
       "      <td>21.61000000</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>2006-01-01</th>\n",
       "      <td>23.61133822</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>25.54000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>51.85015549</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.75842757</td>\n",
       "      <td>...</td>\n",
       "      <td>29.54000000</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>2007-01-01</th>\n",
       "      <td>24.89183834</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>23.26000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>14.16371007</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.30466169</td>\n",
       "      <td>...</td>\n",
       "      <td>24.36000000</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>2008-01-01</th>\n",
       "      <td>10.58953275</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>12.20000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>7.44292854</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.69147070</td>\n",
       "      <td>...</td>\n",
       "      <td>7.45000000</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>2009-01-01</th>\n",
       "      <td>21.05033797</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>16.78000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>4.66025539</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.19283355</td>\n",
       "      <td>...</td>\n",
       "      <td>12.72000000</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>2010-01-01</th>\n",
       "      <td>28.06937567</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>16.63000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>28.82785959</td>\n",
       "      <td>nan</td>\n",
       "      <td>9.63825546</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.89175945</td>\n",
       "      <td>...</td>\n",
       "      <td>26.87000000</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>2011-01-01</th>\n",
       "      <td>23.66553928</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>16.13000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>27.36247977</td>\n",
       "      <td>nan</td>\n",
       "      <td>4.88171380</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.85276561</td>\n",
       "      <td>...</td>\n",
       "      <td>27.76000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.99225131</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>2012-01-01</th>\n",
       "      <td>28.01179940</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>19.90000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>30.02536396</td>\n",
       "      <td>27.10695167</td>\n",
       "      <td>12.99864622</td>\n",
       "      <td>82.00000000</td>\n",
       "      <td>2.97706591</td>\n",
       "      <td>...</td>\n",
       "      <td>19.41000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.99000000</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>2013-01-01</th>\n",
       "      <td>39.53485221</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>40.44000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>36.74348283</td>\n",
       "      <td>31.27057728</td>\n",
       "      <td>24.31228275</td>\n",
       "      <td>930.00000000</td>\n",
       "      <td>3.96461868</td>\n",
       "      <td>...</td>\n",
       "      <td>26.00000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2.60000000</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>2014-01-01</th>\n",
       "      <td>39.43238724</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>50.51000000</td>\n",
       "      <td>30.92000000</td>\n",
       "      <td>36.88899069</td>\n",
       "      <td>32.90504993</td>\n",
       "      <td>51.89970750</td>\n",
       "      <td>310.12000000</td>\n",
       "      <td>3.94778326</td>\n",
       "      <td>...</td>\n",
       "      <td>38.63000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.43000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>100.00000000</td>\n",
       "      <td>0.50000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>40.79862571</td>\n",
       "      <td>nan</td>\n",
       "      <td>31.27000000</td>\n",
       "      <td>33.26000000</td>\n",
       "      <td>19.06000000</td>\n",
       "      <td>38.06921608</td>\n",
       "      <td>30.53000000</td>\n",
       "      <td>41.33893271</td>\n",
       "      <td>17.16000000</td>\n",
       "      <td>4.91270038</td>\n",
       "      <td>...</td>\n",
       "      <td>15.12000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.79000000</td>\n",
       "      <td>14.10000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>10.07000000</td>\n",
       "      <td>100.00000000</td>\n",
       "      <td>1.00000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>44.93909238</td>\n",
       "      <td>28.08000000</td>\n",
       "      <td>26.76000000</td>\n",
       "      <td>38.67000000</td>\n",
       "      <td>7.24000000</td>\n",
       "      <td>39.81959334</td>\n",
       "      <td>nan</td>\n",
       "      <td>46.08991196</td>\n",
       "      <td>53.50000000</td>\n",
       "      <td>4.05320152</td>\n",
       "      <td>...</td>\n",
       "      <td>21.85000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>100.01000000</td>\n",
       "      <td>14.26000000</td>\n",
       "      <td>1.19890000</td>\n",
       "      <td>8.00000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>15.59000000</td>\n",
       "      <td>100.00000000</td>\n",
       "      <td>25.00000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>66.65391782</td>\n",
       "      <td>53.87000000</td>\n",
       "      <td>81.62000000</td>\n",
       "      <td>69.85000000</td>\n",
       "      <td>9.00000000</td>\n",
       "      <td>58.83570736</td>\n",
       "      <td>nan</td>\n",
       "      <td>51.80358470</td>\n",
       "      <td>81.60000000</td>\n",
       "      <td>3.37888199</td>\n",
       "      <td>...</td>\n",
       "      <td>20.82500000</td>\n",
       "      <td>nan</td>\n",
       "      <td>100.10000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.21650000</td>\n",
       "      <td>nan</td>\n",
       "      <td>7.59310000</td>\n",
       "      <td>12.52000000</td>\n",
       "      <td>25.00000000</td>\n",
       "      <td>0.01000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>61.80000000</td>\n",
       "      <td>46.88000000</td>\n",
       "      <td>81.63000000</td>\n",
       "      <td>73.35000000</td>\n",
       "      <td>9.81000000</td>\n",
       "      <td>52.88000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>37.99000000</td>\n",
       "      <td>67.90000000</td>\n",
       "      <td>2.57500000</td>\n",
       "      <td>...</td>\n",
       "      <td>24.30000000</td>\n",
       "      <td>26.74000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.38000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>15.34000000</td>\n",
       "      <td>9.17000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>57 rows × 11941 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "ticker               A          AA        AAAP         AABA         AAC  \\\n",
       "date                                                                      \n",
       "1962-01-01         nan         nan         nan          nan         nan   \n",
       "1963-01-01         nan         nan         nan          nan         nan   \n",
       "1964-01-01         nan         nan         nan          nan         nan   \n",
       "1965-01-01         nan         nan         nan          nan         nan   \n",
       "1966-01-01         nan         nan         nan          nan         nan   \n",
       "1967-01-01         nan         nan         nan          nan         nan   \n",
       "1968-01-01         nan         nan         nan          nan         nan   \n",
       "1969-01-01         nan         nan         nan          nan         nan   \n",
       "1970-01-01         nan         nan         nan          nan         nan   \n",
       "1971-01-01         nan         nan         nan          nan         nan   \n",
       "1972-01-01         nan         nan         nan          nan         nan   \n",
       "1973-01-01         nan         nan         nan          nan         nan   \n",
       "1974-01-01         nan         nan         nan          nan         nan   \n",
       "1975-01-01         nan         nan         nan          nan         nan   \n",
       "1976-01-01         nan         nan         nan          nan         nan   \n",
       "1977-01-01         nan         nan         nan          nan         nan   \n",
       "1978-01-01         nan         nan         nan          nan         nan   \n",
       "1979-01-01         nan         nan         nan          nan         nan   \n",
       "1980-01-01         nan         nan         nan          nan         nan   \n",
       "1981-01-01         nan         nan         nan          nan         nan   \n",
       "1982-01-01         nan         nan         nan          nan         nan   \n",
       "1983-01-01         nan         nan         nan          nan         nan   \n",
       "1984-01-01         nan         nan         nan          nan         nan   \n",
       "1985-01-01         nan         nan         nan          nan         nan   \n",
       "1986-01-01         nan         nan         nan          nan         nan   \n",
       "1987-01-01         nan         nan         nan          nan         nan   \n",
       "1988-01-01         nan         nan         nan          nan         nan   \n",
       "1989-01-01         nan         nan         nan          nan         nan   \n",
       "1990-01-01         nan         nan         nan          nan         nan   \n",
       "1991-01-01         nan         nan         nan          nan         nan   \n",
       "1992-01-01         nan         nan         nan          nan         nan   \n",
       "1993-01-01         nan         nan         nan          nan         nan   \n",
       "1994-01-01         nan         nan         nan          nan         nan   \n",
       "1995-01-01         nan         nan         nan          nan         nan   \n",
       "1996-01-01         nan         nan         nan   0.70833333         nan   \n",
       "1997-01-01         nan         nan         nan   4.32812500         nan   \n",
       "1998-01-01         nan         nan         nan  29.61250000         nan   \n",
       "1999-01-01 52.37855258         nan         nan 108.17500000         nan   \n",
       "2000-01-01 37.09385272         nan         nan  15.03000000         nan   \n",
       "2001-01-01 19.31590395         nan         nan   8.87000000         nan   \n",
       "2002-01-01 12.16813872         nan         nan   8.17500000         nan   \n",
       "2003-01-01 19.81048865         nan         nan  22.51500000         nan   \n",
       "2004-01-01 16.32807033         nan         nan  37.68000000         nan   \n",
       "2005-01-01 22.55441748         nan         nan  39.18000000         nan   \n",
       "2006-01-01 23.61133822         nan         nan  25.54000000         nan   \n",
       "2007-01-01 24.89183834         nan         nan  23.26000000         nan   \n",
       "2008-01-01 10.58953275         nan         nan  12.20000000         nan   \n",
       "2009-01-01 21.05033797         nan         nan  16.78000000         nan   \n",
       "2010-01-01 28.06937567         nan         nan  16.63000000         nan   \n",
       "2011-01-01 23.66553928         nan         nan  16.13000000         nan   \n",
       "2012-01-01 28.01179940         nan         nan  19.90000000         nan   \n",
       "2013-01-01 39.53485221         nan         nan  40.44000000         nan   \n",
       "2014-01-01 39.43238724         nan         nan  50.51000000 30.92000000   \n",
       "2015-01-01 40.79862571         nan 31.27000000  33.26000000 19.06000000   \n",
       "2016-01-01 44.93909238 28.08000000 26.76000000  38.67000000  7.24000000   \n",
       "2017-01-01 66.65391782 53.87000000 81.62000000  69.85000000  9.00000000   \n",
       "2018-01-01 61.80000000 46.88000000 81.63000000  73.35000000  9.81000000   \n",
       "\n",
       "ticker            AADR        AAIT         AAL         AAMC        AAME  \\\n",
       "date                                                                      \n",
       "1962-01-01         nan         nan         nan          nan         nan   \n",
       "1963-01-01         nan         nan         nan          nan         nan   \n",
       "1964-01-01         nan         nan         nan          nan         nan   \n",
       "1965-01-01         nan         nan         nan          nan         nan   \n",
       "1966-01-01         nan         nan         nan          nan         nan   \n",
       "1967-01-01         nan         nan         nan          nan         nan   \n",
       "1968-01-01         nan         nan         nan          nan         nan   \n",
       "1969-01-01         nan         nan         nan          nan         nan   \n",
       "1970-01-01         nan         nan         nan          nan         nan   \n",
       "1971-01-01         nan         nan         nan          nan         nan   \n",
       "1972-01-01         nan         nan         nan          nan         nan   \n",
       "1973-01-01         nan         nan         nan          nan         nan   \n",
       "1974-01-01         nan         nan         nan          nan         nan   \n",
       "1975-01-01         nan         nan         nan          nan         nan   \n",
       "1976-01-01         nan         nan         nan          nan         nan   \n",
       "1977-01-01         nan         nan         nan          nan         nan   \n",
       "1978-01-01         nan         nan         nan          nan         nan   \n",
       "1979-01-01         nan         nan         nan          nan         nan   \n",
       "1980-01-01         nan         nan         nan          nan         nan   \n",
       "1981-01-01         nan         nan         nan          nan         nan   \n",
       "1982-01-01         nan         nan         nan          nan         nan   \n",
       "1983-01-01         nan         nan         nan          nan         nan   \n",
       "1984-01-01         nan         nan         nan          nan  6.40835399   \n",
       "1985-01-01         nan         nan         nan          nan 10.61223421   \n",
       "1986-01-01         nan         nan         nan          nan  9.04493392   \n",
       "1987-01-01         nan         nan         nan          nan  3.80659044   \n",
       "1988-01-01         nan         nan         nan          nan  3.14982608   \n",
       "1989-01-01         nan         nan         nan          nan  2.32975302   \n",
       "1990-01-01         nan         nan         nan          nan  1.39785181   \n",
       "1991-01-01         nan         nan         nan          nan  0.69892591   \n",
       "1992-01-01         nan         nan         nan          nan  1.51433946   \n",
       "1993-01-01         nan         nan         nan          nan  1.63082711   \n",
       "1994-01-01         nan         nan         nan          nan  2.09677772   \n",
       "1995-01-01         nan         nan         nan          nan  2.15548749   \n",
       "1996-01-01         nan         nan         nan          nan  2.85161770   \n",
       "1997-01-01         nan         nan         nan          nan  4.71821581   \n",
       "1998-01-01         nan         nan         nan          nan  4.54301839   \n",
       "1999-01-01         nan         nan         nan          nan  2.15548749   \n",
       "2000-01-01         nan         nan         nan          nan  1.86380242   \n",
       "2001-01-01         nan         nan         nan          nan  2.05391026   \n",
       "2002-01-01         nan         nan         nan          nan  1.51899897   \n",
       "2003-01-01         nan         nan         nan          nan  2.79570362   \n",
       "2004-01-01         nan         nan         nan          nan  2.88889374   \n",
       "2005-01-01         nan         nan 35.76072005          nan  2.51613326   \n",
       "2006-01-01         nan         nan 51.85015549          nan  2.75842757   \n",
       "2007-01-01         nan         nan 14.16371007          nan  1.30466169   \n",
       "2008-01-01         nan         nan  7.44292854          nan  0.69147070   \n",
       "2009-01-01         nan         nan  4.66025539          nan  1.19283355   \n",
       "2010-01-01 28.82785959         nan  9.63825546          nan  1.89175945   \n",
       "2011-01-01 27.36247977         nan  4.88171380          nan  1.85276561   \n",
       "2012-01-01 30.02536396 27.10695167 12.99864622  82.00000000  2.97706591   \n",
       "2013-01-01 36.74348283 31.27057728 24.31228275 930.00000000  3.96461868   \n",
       "2014-01-01 36.88899069 32.90504993 51.89970750 310.12000000  3.94778326   \n",
       "2015-01-01 38.06921608 30.53000000 41.33893271  17.16000000  4.91270038   \n",
       "2016-01-01 39.81959334         nan 46.08991196  53.50000000  4.05320152   \n",
       "2017-01-01 58.83570736         nan 51.80358470  81.60000000  3.37888199   \n",
       "2018-01-01 52.88000000         nan 37.99000000  67.90000000  2.57500000   \n",
       "\n",
       "ticker         ...            ZUMZ         ZUO        ZVZZC     ZVZZCNX  \\\n",
       "date           ...                                                        \n",
       "1962-01-01     ...             nan         nan          nan         nan   \n",
       "1963-01-01     ...             nan         nan          nan         nan   \n",
       "1964-01-01     ...             nan         nan          nan         nan   \n",
       "1965-01-01     ...             nan         nan          nan         nan   \n",
       "1966-01-01     ...             nan         nan          nan         nan   \n",
       "1967-01-01     ...             nan         nan          nan         nan   \n",
       "1968-01-01     ...             nan         nan          nan         nan   \n",
       "1969-01-01     ...             nan         nan          nan         nan   \n",
       "1970-01-01     ...             nan         nan          nan         nan   \n",
       "1971-01-01     ...             nan         nan          nan         nan   \n",
       "1972-01-01     ...             nan         nan          nan         nan   \n",
       "1973-01-01     ...             nan         nan          nan         nan   \n",
       "1974-01-01     ...             nan         nan          nan         nan   \n",
       "1975-01-01     ...             nan         nan          nan         nan   \n",
       "1976-01-01     ...             nan         nan          nan         nan   \n",
       "1977-01-01     ...             nan         nan          nan         nan   \n",
       "1978-01-01     ...             nan         nan          nan         nan   \n",
       "1979-01-01     ...             nan         nan          nan         nan   \n",
       "1980-01-01     ...             nan         nan          nan         nan   \n",
       "1981-01-01     ...             nan         nan          nan         nan   \n",
       "1982-01-01     ...             nan         nan          nan         nan   \n",
       "1983-01-01     ...             nan         nan          nan         nan   \n",
       "1984-01-01     ...             nan         nan          nan         nan   \n",
       "1985-01-01     ...             nan         nan          nan         nan   \n",
       "1986-01-01     ...             nan         nan          nan         nan   \n",
       "1987-01-01     ...             nan         nan          nan         nan   \n",
       "1988-01-01     ...             nan         nan          nan         nan   \n",
       "1989-01-01     ...             nan         nan          nan         nan   \n",
       "1990-01-01     ...             nan         nan          nan         nan   \n",
       "1991-01-01     ...             nan         nan          nan         nan   \n",
       "1992-01-01     ...             nan         nan          nan         nan   \n",
       "1993-01-01     ...             nan         nan          nan         nan   \n",
       "1994-01-01     ...             nan         nan          nan         nan   \n",
       "1995-01-01     ...             nan         nan          nan         nan   \n",
       "1996-01-01     ...             nan         nan          nan         nan   \n",
       "1997-01-01     ...             nan         nan          nan         nan   \n",
       "1998-01-01     ...             nan         nan          nan         nan   \n",
       "1999-01-01     ...             nan         nan          nan         nan   \n",
       "2000-01-01     ...             nan         nan          nan         nan   \n",
       "2001-01-01     ...             nan         nan          nan         nan   \n",
       "2002-01-01     ...             nan         nan          nan         nan   \n",
       "2003-01-01     ...             nan         nan          nan         nan   \n",
       "2004-01-01     ...             nan         nan          nan         nan   \n",
       "2005-01-01     ...     21.61000000         nan          nan         nan   \n",
       "2006-01-01     ...     29.54000000         nan          nan         nan   \n",
       "2007-01-01     ...     24.36000000         nan          nan         nan   \n",
       "2008-01-01     ...      7.45000000         nan          nan         nan   \n",
       "2009-01-01     ...     12.72000000         nan          nan         nan   \n",
       "2010-01-01     ...     26.87000000         nan          nan         nan   \n",
       "2011-01-01     ...     27.76000000         nan          nan         nan   \n",
       "2012-01-01     ...     19.41000000         nan          nan         nan   \n",
       "2013-01-01     ...     26.00000000         nan          nan         nan   \n",
       "2014-01-01     ...     38.63000000         nan          nan         nan   \n",
       "2015-01-01     ...     15.12000000         nan          nan         nan   \n",
       "2016-01-01     ...     21.85000000         nan 100.01000000 14.26000000   \n",
       "2017-01-01     ...     20.82500000         nan 100.10000000         nan   \n",
       "2018-01-01     ...     24.30000000 26.74000000          nan         nan   \n",
       "\n",
       "ticker             ZX      ZXYZ_A        ZYME        ZYNE          ZZK  \\\n",
       "date                                                                     \n",
       "1962-01-01        nan         nan         nan         nan          nan   \n",
       "1963-01-01        nan         nan         nan         nan          nan   \n",
       "1964-01-01        nan         nan         nan         nan          nan   \n",
       "1965-01-01        nan         nan         nan         nan          nan   \n",
       "1966-01-01        nan         nan         nan         nan          nan   \n",
       "1967-01-01        nan         nan         nan         nan          nan   \n",
       "1968-01-01        nan         nan         nan         nan          nan   \n",
       "1969-01-01        nan         nan         nan         nan          nan   \n",
       "1970-01-01        nan         nan         nan         nan          nan   \n",
       "1971-01-01        nan         nan         nan         nan          nan   \n",
       "1972-01-01        nan         nan         nan         nan          nan   \n",
       "1973-01-01        nan         nan         nan         nan          nan   \n",
       "1974-01-01        nan         nan         nan         nan          nan   \n",
       "1975-01-01        nan         nan         nan         nan          nan   \n",
       "1976-01-01        nan         nan         nan         nan          nan   \n",
       "1977-01-01        nan         nan         nan         nan          nan   \n",
       "1978-01-01        nan         nan         nan         nan          nan   \n",
       "1979-01-01        nan         nan         nan         nan          nan   \n",
       "1980-01-01        nan         nan         nan         nan          nan   \n",
       "1981-01-01        nan         nan         nan         nan          nan   \n",
       "1982-01-01        nan         nan         nan         nan          nan   \n",
       "1983-01-01        nan         nan         nan         nan          nan   \n",
       "1984-01-01        nan         nan         nan         nan          nan   \n",
       "1985-01-01        nan         nan         nan         nan          nan   \n",
       "1986-01-01        nan         nan         nan         nan          nan   \n",
       "1987-01-01        nan         nan         nan         nan          nan   \n",
       "1988-01-01        nan         nan         nan         nan          nan   \n",
       "1989-01-01        nan         nan         nan         nan          nan   \n",
       "1990-01-01        nan         nan         nan         nan          nan   \n",
       "1991-01-01        nan         nan         nan         nan          nan   \n",
       "1992-01-01        nan         nan         nan         nan          nan   \n",
       "1993-01-01        nan         nan         nan         nan          nan   \n",
       "1994-01-01        nan         nan         nan         nan          nan   \n",
       "1995-01-01        nan         nan         nan         nan          nan   \n",
       "1996-01-01        nan         nan         nan         nan          nan   \n",
       "1997-01-01        nan         nan         nan         nan          nan   \n",
       "1998-01-01        nan         nan         nan         nan          nan   \n",
       "1999-01-01        nan         nan         nan         nan          nan   \n",
       "2000-01-01        nan         nan         nan         nan          nan   \n",
       "2001-01-01        nan         nan         nan         nan          nan   \n",
       "2002-01-01        nan         nan         nan         nan          nan   \n",
       "2003-01-01        nan         nan         nan         nan          nan   \n",
       "2004-01-01        nan         nan         nan         nan          nan   \n",
       "2005-01-01        nan         nan         nan         nan          nan   \n",
       "2006-01-01        nan         nan         nan         nan          nan   \n",
       "2007-01-01        nan         nan         nan         nan          nan   \n",
       "2008-01-01        nan         nan         nan         nan          nan   \n",
       "2009-01-01        nan         nan         nan         nan          nan   \n",
       "2010-01-01        nan         nan         nan         nan          nan   \n",
       "2011-01-01 2.99225131         nan         nan         nan          nan   \n",
       "2012-01-01 2.99000000         nan         nan         nan          nan   \n",
       "2013-01-01 2.60000000         nan         nan         nan          nan   \n",
       "2014-01-01 1.43000000         nan         nan         nan 100.00000000   \n",
       "2015-01-01 0.79000000 14.10000000         nan 10.07000000 100.00000000   \n",
       "2016-01-01 1.19890000  8.00000000         nan 15.59000000 100.00000000   \n",
       "2017-01-01 1.21650000         nan  7.59310000 12.52000000  25.00000000   \n",
       "2018-01-01 1.38000000         nan 15.34000000  9.17000000          nan   \n",
       "\n",
       "ticker             ZZZ  \n",
       "date                    \n",
       "1962-01-01         nan  \n",
       "1963-01-01         nan  \n",
       "1964-01-01         nan  \n",
       "1965-01-01         nan  \n",
       "1966-01-01         nan  \n",
       "1967-01-01         nan  \n",
       "1968-01-01         nan  \n",
       "1969-01-01         nan  \n",
       "1970-01-01         nan  \n",
       "1971-01-01         nan  \n",
       "1972-01-01         nan  \n",
       "1973-01-01         nan  \n",
       "1974-01-01         nan  \n",
       "1975-01-01         nan  \n",
       "1976-01-01         nan  \n",
       "1977-01-01         nan  \n",
       "1978-01-01         nan  \n",
       "1979-01-01         nan  \n",
       "1980-01-01         nan  \n",
       "1981-01-01         nan  \n",
       "1982-01-01         nan  \n",
       "1983-01-01         nan  \n",
       "1984-01-01         nan  \n",
       "1985-01-01         nan  \n",
       "1986-01-01         nan  \n",
       "1987-01-01         nan  \n",
       "1988-01-01         nan  \n",
       "1989-01-01         nan  \n",
       "1990-01-01         nan  \n",
       "1991-01-01         nan  \n",
       "1992-01-01         nan  \n",
       "1993-01-01         nan  \n",
       "1994-01-01         nan  \n",
       "1995-01-01         nan  \n",
       "1996-01-01         nan  \n",
       "1997-01-01         nan  \n",
       "1998-01-01         nan  \n",
       "1999-01-01         nan  \n",
       "2000-01-01         nan  \n",
       "2001-01-01         nan  \n",
       "2002-01-01         nan  \n",
       "2003-01-01         nan  \n",
       "2004-01-01         nan  \n",
       "2005-01-01         nan  \n",
       "2006-01-01         nan  \n",
       "2007-01-01         nan  \n",
       "2008-01-01         nan  \n",
       "2009-01-01         nan  \n",
       "2010-01-01         nan  \n",
       "2011-01-01         nan  \n",
       "2012-01-01         nan  \n",
       "2013-01-01         nan  \n",
       "2014-01-01  0.50000000  \n",
       "2015-01-01  1.00000000  \n",
       "2016-01-01 25.00000000  \n",
       "2017-01-01  0.01000000  \n",
       "2018-01-01         nan  \n",
       "\n",
       "[57 rows x 11941 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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",
    "pricing"
   ]
  },
  {
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>sentiment</th>\n",
       "      <th>ticker</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>negative</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>0.97186425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>negative</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>0.95941874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>negative</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>0.98131624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>negative</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>0.95916216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>negative</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>0.98854716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date sentiment ticker      value\n",
       "0 2018-01-01  negative   AMZN 0.97186425\n",
       "1 2017-01-01  negative   AMZN 0.95941874\n",
       "2 2016-01-01  negative   AMZN 0.98131624\n",
       "3 2015-01-01  negative   AMZN 0.95916216\n",
       "4 2014-01-01  negative   AMZN 0.98854716"
      ]
     },
     "execution_count": 29,
     "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()"
   ]
  },
  {
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 0.8% entries from factor data: 0.8% 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 0.8% entries from factor data: 0.8% 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 0.8% entries from factor data: 0.8% 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 0.8% entries from factor data: 0.8% 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 0.8% entries from factor data: 0.8% 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 0.8% entries from factor data: 0.8% 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>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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=\"3\" valign=\"top\">1994-01-01</th>\n",
       "      <th>BMY</th>\n",
       "      <td>0.53264104</td>\n",
       "      <td>0.44766877</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CVX</th>\n",
       "      <td>0.22211880</td>\n",
       "      <td>0.91255527</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRT</th>\n",
       "      <td>0.17159556</td>\n",
       "      <td>0.47850281</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1995-01-01</th>\n",
       "      <th>BMY</th>\n",
       "      <td>0.32152919</td>\n",
       "      <td>0.89198386</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CVX</th>\n",
       "      <td>0.28478156</td>\n",
       "      <td>0.91011711</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 BMY   0.53264104 0.44766877                1\n",
       "           CVX   0.22211880 0.91255527                5\n",
       "           FRT   0.17159556 0.47850281                3\n",
       "1995-01-01 BMY   0.32152919 0.89198386                1\n",
       "           CVX   0.28478156 0.91011711                3"
      ]
     },
     "execution_count": 30,
     "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.loc[cs_df.index], quantiles=5, bins=None, periods=[1])\n",
    "        factor_data[sentiment] = data\n",
    "    except:\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": 31,
   "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": "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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d09fd4a58>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1d09fbe908>"
      ]
     },
     "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": "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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f1d0a2c1978>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1d0a2c65f8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1d09f79780>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f1d0a228198>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1d0a24c828>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1d0a24c940>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f1d0a2204e0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1d0a287240>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1d0a1b67b8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f1d0a1d2e80>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1d0a168c88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1d0a180f60>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f1d0a18db38>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1d0a12fb00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1d0a13ef60>]], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1d0a044940>"
      ]
     },
     "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d0a0450b8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1d0a0ff208>"
      ]
     },
     "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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "negative       0.16000000\n",
       "positive       4.14000000\n",
       "uncertainty    2.98000000\n",
       "litigious      1.95000000\n",
       "constraining   1.26000000\n",
       "interesting    2.71000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it! You've successfully done sentiment analysis on 10-ks!\n",
    "## Submission\n",
    "Now that you're done with the project, it's time to submit it. Click the submit button in the bottom right. One of our reviewers will give you feedback on your project with a pass or not passed grade. You can continue to the next section while you wait for feedback."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}