5-nlp-financial-statements/project_5_starter.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",
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     ]
    }
   ],
   "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",
    "    newest_pricing_data = pd.to_datetime('2018-01-01')\n",
    "    rss_url = 'https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany' \\\n",
    "        '&CIK={}&type={}&start={}&count={}&owner=exclude&output=atom' \\\n",
    "        .format(cik, doc_type, start, count)\n",
    "    sec_data = sec_api.get(rss_url)\n",
    "    feed = BeautifulSoup(sec_data.encode('ascii'), 'xml').feed\n",
    "    entries = [\n",
    "        (\n",
    "            entry.content.find('filing-href').getText(),\n",
    "            entry.content.find('filing-type').getText(),\n",
    "            entry.content.find('filing-date').getText())\n",
    "        for entry in feed.find_all('entry', recursive=False)\n",
    "        if pd.to_datetime(entry.content.find('filing-date').getText()) <= newest_pricing_data]\n",
    "\n",
    "    return entries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's pull the list using the `get_sec_data` function, then display some of the results. For displaying some of the data, we'll use Amazon as an example. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('https://www.sec.gov/Archives/edgar/data/1018724/000101872417000011/0001018724-17-000011-index.htm',\n",
      "  '10-K',\n",
      "  '2017-02-10'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000101872416000172/0001018724-16-000172-index.htm',\n",
      "  '10-K',\n",
      "  '2016-01-29'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000101872415000006/0001018724-15-000006-index.htm',\n",
      "  '10-K',\n",
      "  '2015-01-30'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000101872414000006/0001018724-14-000006-index.htm',\n",
      "  '10-K',\n",
      "  '2014-01-31'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000119312513028520/0001193125-13-028520-index.htm',\n",
      "  '10-K',\n",
      "  '2013-01-30')]\n"
     ]
    }
   ],
   "source": [
    "example_ticker = '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%|██████████| 22/22 [00:03<00:00,  6.36filling/s]\n",
      "Downloading BMY Fillings: 100%|██████████| 27/27 [00:05<00:00,  4.83filling/s]\n",
      "Downloading CNP Fillings: 100%|██████████| 19/19 [00:04<00:00,  4.66filling/s]\n",
      "Downloading CVX Fillings: 100%|██████████| 25/25 [00:05<00:00,  4.22filling/s]\n",
      "Downloading FL Fillings: 100%|██████████| 22/22 [00:04<00:00,  4.49filling/s]\n",
      "Downloading FRT Fillings: 100%|██████████| 29/29 [00:06<00:00,  4.58filling/s]\n",
      "Downloading HON Fillings: 100%|██████████| 25/25 [00:05<00:00,  4.92filling/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example Document:\n",
      "\n",
      "<SEC-DOCUMENT>0001018724-17-000011.txt : 20170210\n",
      "<SEC-HEADER>0001018724-17-000011.hdr.sgml : 20170210\n",
      "<ACCEPTANCE-DATETIME>20170209175636\n",
      "ACCESSION NUMBER:\t\t0001018724-17-000011\n",
      "CONFORMED SUBMISSION TYPE:\t10-K\n",
      "PUBLIC DOCUMENT COUNT:\t\t92\n",
      "CONFORMED PERIOD OF REPORT:\t20161231\n",
      "FILED AS OF DATE:\t\t20170210\n",
      "DATE AS OF CHANGE:\t\t20170209\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\t17588807\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",
      "<FILENAME...\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",
    "    start_pattern = re.compile(r'<DOCUMENT>')\n",
    "    end_pattern = re.compile(r'</DOCUMENT>')\n",
    "    \n",
    "    start = [x.end() for x in start_pattern.finditer(text)]\n",
    "    end = [x.start() for x in end_pattern.finditer(text)]\n",
    "    \n",
    "    extracted_docs = []\n",
    "    for start_index, end_index in zip(start, end):\n",
    "        extracted_docs.append(text[start_index:end_index])\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%|██████████| 17/17 [00:00<00:00, 88.89filling/s]\n",
      "Getting Documents from BMY Fillings: 100%|██████████| 23/23 [00:00<00:00, 42.08filling/s]\n",
      "Getting Documents from CNP Fillings: 100%|██████████| 15/15 [00:00<00:00, 31.63filling/s]\n",
      "Getting Documents from CVX Fillings: 100%|██████████| 21/21 [00:00<00:00, 31.49filling/s]\n",
      "Getting Documents from FL Fillings: 100%|██████████| 16/16 [00:00<00:00, 60.03filling/s]\n",
      "Getting Documents from FRT Fillings: 100%|██████████| 19/19 [00:00<00:00, 53.06filling/s]\n",
      "Getting Documents from HON Fillings: 100%|██████████| 20/20 [00:00<00:00, 43.55filling/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Document 0 Filed on 2017-02-10:\n",
      "\n",
      "<TYPE>10-K\n",
      "<SEQUENCE>1\n",
      "<FILENAME>amzn-20161231x10k.htm\n",
      "<DESCRIPTION>FORM 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<he...\n",
      "\n",
      "Document 1 Filed on 2017-02-10:\n",
      "\n",
      "<TYPE>EX-12.1\n",
      "<SEQUENCE>2\n",
      "<FILENAME>amzn-20161231xex121.htm\n",
      "<DESCRIPTION>COMPUTATION OF RATIO OF EARNINGS TO FIXED CHARGES\n",
      "<TEXT>\n",
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http:...\n",
      "\n",
      "Document 2 Filed on 2017-02-10:\n",
      "\n",
      "<TYPE>EX-21.1\n",
      "<SEQUENCE>3\n",
      "<FILENAME>amzn-20161231xex211.htm\n",
      "<DESCRIPTION>LIST OF SIGNIFICANT SUBSIDIARIES\n",
      "<TEXT>\n",
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http://www.w3.org/TR/h...\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-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",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2013123...\n",
      "    file_date: '2014-01-31'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>d445434d10k....\n",
      "    file_date: '2013-01-30'},\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%|██████████| 17/17 [00:33<00:00,  1.96s/10-K]\n",
      "Cleaning BMY 10-Ks: 100%|██████████| 23/23 [01:09<00:00,  3.03s/10-K]\n",
      "Cleaning CNP 10-Ks: 100%|██████████| 15/15 [00:51<00:00,  3.41s/10-K]\n",
      "Cleaning CVX 10-Ks: 100%|██████████| 21/21 [01:42<00:00,  4.89s/10-K]\n",
      "Cleaning FL 10-Ks: 100%|██████████| 16/16 [00:25<00:00,  1.57s/10-K]\n",
      "Cleaning FRT 10-Ks: 100%|██████████| 19/19 [00:51<00:00,  2.69s/10-K]\n",
      "Cleaning HON 10-Ks: 100%|██████████| 20/20 [00:57<00:00,  2.86s/10-K]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\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",
      "    file_clean: '\\n10-k\\n1\\namzn-20131231x10k.htm\\nform 10-k\\n\\n\\n...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\nd445434d10k.htm\\nform 10-k\\n\\n\\nform 1...},\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%|██████████| 17/17 [00:04<00:00,  3.5410-K/s]\n",
      "Lemmatize BMY 10-Ks: 100%|██████████| 23/23 [00:10<00:00,  2.2210-K/s]\n",
      "Lemmatize CNP 10-Ks: 100%|██████████| 15/15 [00:08<00:00,  1.7410-K/s]\n",
      "Lemmatize CVX 10-Ks: 100%|██████████| 21/21 [00:09<00:00,  2.1210-K/s]\n",
      "Lemmatize FL 10-Ks: 100%|██████████| 16/16 [00:04<00:00,  3.9610-K/s]\n",
      "Lemmatize FRT 10-Ks: 100%|██████████| 19/19 [00:06<00:00,  3.0210-K/s]\n",
      "Lemmatize HON 10-Ks: 100%|██████████| 20/20 [00:05<00:00,  3.3510-K/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\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",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20131231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'd445434d10k', 'htm', 'form', '1...},\n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word_pattern = re.compile('\\w+')\n",
    "\n",
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    for ten_k in tqdm(ten_ks, desc='Lemmatize {} 10-Ks'.format(ticker), unit='10-K'):\n",
    "        ten_k['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%|██████████| 17/17 [00:02<00:00,  7.9410-K/s]\n",
      "Remove Stop Words for BMY 10-Ks: 100%|██████████| 23/23 [00:04<00:00,  5.0310-K/s]\n",
      "Remove Stop Words for CNP 10-Ks: 100%|██████████| 15/15 [00:03<00:00,  3.9610-K/s]\n",
      "Remove Stop Words for CVX 10-Ks: 100%|██████████| 21/21 [00:04<00:00,  4.7010-K/s]\n",
      "Remove Stop Words for FL 10-Ks: 100%|██████████| 16/16 [00:01<00:00,  8.9610-K/s]\n",
      "Remove Stop Words for FRT 10-Ks: 100%|██████████| 19/19 [00:02<00:00,  6.5710-K/s]\n",
      "Remove Stop Words for HON 10-Ks: 100%|██████████| 20/20 [00:02<00:00,  7.2310-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 scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>negative</th>\n",
       "      <th>positive</th>\n",
       "      <th>uncertainty</th>\n",
       "      <th>litigious</th>\n",
       "      <th>constraining</th>\n",
       "      <th>interesting</th>\n",
       "      <th>word</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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": 19,
   "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",
    "    vectorizer = CountVectorizer(vocabulary=sentiment_words)\n",
    "    vectors = vectorizer.fit_transform(docs)\n",
    "    words_list = vectorizer.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": 20,
   "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: '[[16  0  0 ...,  0  0  0]\\n [16  0  0 ...,  0  0 ...\n",
      "    uncertainty: '[[0 0 0 ..., 1 1 3]\\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": 21,
   "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",
    "    bag_of_words = np.array(bag_of_words_matrix, dtype=bool)\n",
    "    \n",
    "    jaccard_similarities = []\n",
    "    \n",
    "    for i in range(len(bag_of_words) - 1):\n",
    "        jaccard_similarities.append(jaccard_similarity_score(bag_of_words[i], bag_of_words[i + 1]))\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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0ca4a2eb8>"
      ]
     },
     "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": 23,
   "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",
    "    vectorizer = TfidfVectorizer(vocabulary=sentiment_words)\n",
    "    tfidf = vectorizer.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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    negative: '[[ 0.          0.          0.         ...,  0.   ...\n",
      "    positive: '[[ 0.22288432  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: '[[ 0.01673784  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": 25,
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0ca4a1c18>"
      ]
     },
     "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ticker</th>\n",
       "      <th>A</th>\n",
       "      <th>AA</th>\n",
       "      <th>AAAP</th>\n",
       "      <th>AABA</th>\n",
       "      <th>AAC</th>\n",
       "      <th>AADR</th>\n",
       "      <th>AAIT</th>\n",
       "      <th>AAL</th>\n",
       "      <th>AAMC</th>\n",
       "      <th>AAME</th>\n",
       "      <th>...</th>\n",
       "      <th>ZUMZ</th>\n",
       "      <th>ZUO</th>\n",
       "      <th>ZVZZC</th>\n",
       "      <th>ZVZZCNX</th>\n",
       "      <th>ZX</th>\n",
       "      <th>ZXYZ_A</th>\n",
       "      <th>ZYME</th>\n",
       "      <th>ZYNE</th>\n",
       "      <th>ZZK</th>\n",
       "      <th>ZZZ</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966-01-01</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <td>nan</td>\n",
       "      <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",
       "      <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>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",
       "      <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>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",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>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>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",
       "      <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>1976-01-01</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>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",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01</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>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": 27,
     "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",
    "\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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>ticker</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.98065125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.95951741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.98838551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.97472377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.97585100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date ticker sentiment      value\n",
       "0 2016-01-01   AMZN  negative 0.98065125\n",
       "1 2015-01-01   AMZN  negative 0.95951741\n",
       "2 2014-01-01   AMZN  negative 0.98838551\n",
       "3 2013-01-01   AMZN  negative 0.97472377\n",
       "4 2012-01-01   AMZN  negative 0.97585100"
      ]
     },
     "execution_count": 28,
     "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": 29,
   "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 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>1D</th>\n",
       "      <th>factor</th>\n",
       "      <th>factor_quantile</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>asset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1994-01-01</th>\n",
       "      <th>BMY</th>\n",
       "      <td>0.53264104</td>\n",
       "      <td>0.44784189</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CVX</th>\n",
       "      <td>0.22211880</td>\n",
       "      <td>0.91363233</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRT</th>\n",
       "      <td>0.17159556</td>\n",
       "      <td>0.47730392</td>\n",
       "      <td>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>0.89403523</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CVX</th>\n",
       "      <td>0.28478156</td>\n",
       "      <td>0.91066582</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 0.44784189                1\n",
       "           CVX   0.22211880 0.91363233                5\n",
       "           FRT   0.17159556 0.47730392                3\n",
       "1995-01-01 BMY   0.32152919 0.89403523                1\n",
       "           CVX   0.28478156 0.91066582                3"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import alphalens as al\n",
    "\n",
    "\n",
    "factor_data = {}\n",
    "skipped_sentiments = []\n",
    "\n",
    "for sentiment in sentiments:\n",
    "    cs_df = cosine_similarities_df[(cosine_similarities_df['sentiment'] == sentiment)]\n",
    "    cs_df = cs_df.pivot(index='date', columns='ticker', values='value')\n",
    "\n",
    "    try:\n",
    "        data = al.utils.get_clean_factor_and_forward_returns(cs_df.stack(), pricing, quantiles=5, bins=None, periods=[1])\n",
    "        factor_data[sentiment] = data\n",
    "    except:\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": 30,
   "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc0ca9c5518>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0ca9d39b0>"
      ]
     },
     "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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fc0caa2b278>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc0ca96e358>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc0cac0f7f0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc0cab25470>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc0cab42cf8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc0cab42f98>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc0cab4a5c0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc0cac3bd30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc0cab09588>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc0cab94cc0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc0cab50b00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc0caaaa208>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fc0cac06860>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc0caac1a90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fc0caf7e9e8>]], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0ca95d550>"
      ]
     },
     "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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc0caa50160>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0caea0ef0>"
      ]
     },
     "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "negative       0.39000000\n",
       "positive       4.17000000\n",
       "uncertainty    3.05000000\n",
       "litigious      1.97000000\n",
       "constraining   1.92000000\n",
       "interesting    3.49000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "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."
   ]
  }
 ],
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