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": "stderr",
     "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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\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",
    "    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": 16,
   "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",
    "\n",
    "sec_data = {}\n",
    "for ticker, cik in cik_lookup.items():\n",
    "    sec_data[ticker] = get_sec_data(cik, '10-K')\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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading AMZN Fillings: 100%|██████████| 22/22 [00:03<00:00,  6.66filling/s]\n",
      "Downloading BMY Fillings: 100%|██████████| 27/27 [00:05<00:00,  4.54filling/s]\n",
      "Downloading CNP Fillings: 100%|██████████| 19/19 [00:03<00:00,  5.14filling/s]\n",
      "Downloading CVX Fillings: 100%|██████████| 25/25 [00:06<00:00,  4.02filling/s]\n",
      "Downloading FL Fillings: 100%|██████████| 22/22 [00:03<00:00,  7.05filling/s]\n",
      "Downloading FRT Fillings: 100%|██████████| 29/29 [00:03<00:00,  7.35filling/s]\n",
      "Downloading HON Fillings: 100%|██████████| 25/25 [00:04<00:00,  5.49filling/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": 18,
   "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",
    "    doc_start_pattern = re.compile(r'<DOCUMENT>')\n",
    "    doc_end_pattern = re.compile(r'</DOCUMENT>')\n",
    "    doc_start_is = [x.end() for x in doc_start_pattern.finditer(text)]\n",
    "    doc_end_is = [x.start() for x in doc_end_pattern.finditer(text)]\n",
    "    extracted_docs = []\n",
    "    for doc_start_i, doc_end_i in zip(doc_start_is, doc_end_is):\n",
    "        doc = text[doc_start_i:doc_end_i]\n",
    "        extracted_docs.append(doc)\n",
    "    return extracted_docs\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": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Getting Documents from AMZN Fillings: 100%|██████████| 17/17 [00:00<00:00, 88.74filling/s]\n",
      "Getting Documents from BMY Fillings: 100%|██████████| 23/23 [00:00<00:00, 23.07filling/s]\n",
      "Getting Documents from CNP Fillings: 100%|██████████| 15/15 [00:01<00:00, 14.95filling/s]\n",
      "Getting Documents from CVX Fillings: 100%|██████████| 21/21 [00:01<00:00, 12.77filling/s]\n",
      "Getting Documents from FL Fillings: 100%|██████████| 16/16 [00:00<00:00, 56.50filling/s]\n",
      "Getting Documents from FRT Fillings: 100%|██████████| 19/19 [00:00<00:00, 31.93filling/s]\n",
      "Getting Documents from HON Fillings: 100%|██████████| 20/20 [00:01<00:00, 15.83filling/s]\n"
     ]
    },
    {
     "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"
     ]
    }
   ],
   "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": 20,
   "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",
    "\n",
    "    type_pattern = re.compile(r'<TYPE>[^\\n]+')\n",
    "    doc_type = [x[len('<TYPE>'):] for x in type_pattern.findall(doc)]\n",
    "    #print(doc_type)\n",
    "    return doc_type[0].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": 21,
   "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>d445434d10k....\n",
      "    file_date: '2013-01-30'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>d269317d10k....\n",
      "    file_date: '2012-02-01'},\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": 22,
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cleaning AMZN 10-Ks: 100%|██████████| 13/13 [00:29<00:00,  2.31s/10-K]\n",
      "Cleaning BMY 10-Ks: 100%|██████████| 19/19 [00:55<00:00,  2.90s/10-K]\n",
      "Cleaning CNP 10-Ks: 100%|██████████| 11/11 [00:46<00:00,  4.25s/10-K]\n",
      "Cleaning CVX 10-Ks: 100%|██████████| 17/17 [01:29<00:00,  5.24s/10-K]\n",
      "Cleaning FL 10-Ks: 100%|██████████| 14/14 [00:22<00:00,  1.62s/10-K]\n",
      "Cleaning FRT 10-Ks: 100%|██████████| 13/13 [00:33<00:00,  2.55s/10-K]\n",
      "Cleaning HON 10-Ks: 100%|██████████| 15/15 [00:51<00:00,  3.41s/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\\nd445434d10k.htm\\nform 10-k\\n\\n\\nform 1...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\nd269317d10k.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": 24,
   "metadata": {
    "scrolled": true
   },
   "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(w, pos='v') for w 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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lemmatize AMZN 10-Ks: 100%|██████████| 13/13 [00:03<00:00,  3.7310-K/s]\n",
      "Lemmatize BMY 10-Ks: 100%|██████████| 19/19 [00:07<00:00,  2.5410-K/s]\n",
      "Lemmatize CNP 10-Ks: 100%|██████████| 11/11 [00:06<00:00,  1.8110-K/s]\n",
      "Lemmatize CVX 10-Ks: 100%|██████████| 17/17 [00:07<00:00,  2.2210-K/s]\n",
      "Lemmatize FL 10-Ks: 100%|██████████| 14/14 [00:03<00:00,  4.3610-K/s]\n",
      "Lemmatize FRT 10-Ks: 100%|██████████| 13/13 [00:03<00:00,  3.3610-K/s]\n",
      "Lemmatize HON 10-Ks: 100%|██████████| 15/15 [00:04<00:00,  3.3010-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', 'd445434d10k', 'htm', 'form', '1...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'd269317d10k', '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": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Remove Stop Words for AMZN 10-Ks: 100%|██████████| 13/13 [00:01<00:00,  8.9910-K/s]\n",
      "Remove Stop Words for BMY 10-Ks: 100%|██████████| 19/19 [00:02<00:00,  6.5810-K/s]\n",
      "Remove Stop Words for CNP 10-Ks: 100%|██████████| 11/11 [00:02<00:00,  4.7310-K/s]\n",
      "Remove Stop Words for CVX 10-Ks: 100%|██████████| 17/17 [00:02<00:00,  5.7210-K/s]\n",
      "Remove Stop Words for FL 10-Ks: 100%|██████████| 14/14 [00:01<00:00, 10.6310-K/s]\n",
      "Remove Stop Words for FRT 10-Ks: 100%|██████████| 13/13 [00:01<00:00,  8.3810-K/s]\n",
      "Remove Stop Words for HON 10-Ks: 100%|██████████| 15/15 [00:01<00:00,  8.8310-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": 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></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": 27,
     "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": 28,
   "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",
    "    cnt_vec = CountVectorizer(vocabulary=sentiment_words)\n",
    "    bag_of_words = cnt_vec.fit_transform(docs).toarray()\n",
    "    \n",
    "    return bag_of_words\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": 29,
   "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  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   6   0   0   0   0   0   0   2...},\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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/metrics/_classification.py:660: FutureWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
      "  FutureWarning)\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 = bag_of_words_matrix.astype(bool)\n",
    "    jaccard_similarities = [jaccard_similarity_score(u,v) for u, v in zip(bag_of_words,bag_of_words[1:])]\n",
    "    return jaccard_similarities\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": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/metrics/_classification.py:660: FutureWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5aac113a58>"
      ]
     },
     "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": 32,
   "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).toarray()\n",
    "    \n",
    "    return tfidf\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": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    negative: '[[0.         0.         0.         ... 0.        ...\n",
      "    positive: '[[0.22323804 0.         0.         ... 0.        ...\n",
      "    uncertainty: '[[0.         0.         0.         ... 0.0051074 ...\n",
      "    litigious: '[[0. 0. 0. ... 0. 0. 0.]\\n [0. 0. 0. ... 0. 0. 0....\n",
      "    constraining: '[[0.         0.         0.         ... 0.        ...\n",
      "    interesting: '[[0.01673974 0.         0.         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": 34,
   "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",
    "\n",
    "    cos_similarity= cosine_similarity(tfidf_matrix[0:], tfidf_matrix[1:])\n",
    "    \n",
    "    return cos_similarity[0].tolist()\n",
    "    \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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5aa365ddd8>"
      ]
     },
     "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": 36,
   "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": 36,
     "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": 37,
   "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>0.98145323</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>0.95140972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.89912654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.86144322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.89725202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date ticker sentiment      value\n",
       "0 2016-01-01   AMZN  negative 0.98145323\n",
       "1 2015-01-01   AMZN  negative 0.95140972\n",
       "2 2013-01-01   AMZN  negative 0.89912654\n",
       "3 2012-01-01   AMZN  negative 0.86144322\n",
       "4 2011-01-01   AMZN  negative 0.89725202"
      ]
     },
     "execution_count": 37,
     "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": 38,
   "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 3.2% entries from factor data: 3.2% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 3.2% entries from factor data: 3.2% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 3.2% entries from factor data: 3.2% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 3.2% entries from factor data: 3.2% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 3.2% entries from factor data: 3.2% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 3.2% entries from factor data: 3.2% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "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>0.45855569</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CVX</th>\n",
       "      <td>0.22211880</td>\n",
       "      <td>0.64792283</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRT</th>\n",
       "      <td>0.17159556</td>\n",
       "      <td>0.51868302</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>0.43745347</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CVX</th>\n",
       "      <td>0.28478156</td>\n",
       "      <td>0.62932941</td>\n",
       "      <td>5</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 0.45855569                1\n",
       "           CVX   0.22211880 0.64792283                5\n",
       "           FRT   0.17159556 0.51868302                3\n",
       "1995-01-01 BMY   0.32152919 0.43745347                3\n",
       "           CVX   0.28478156 0.62932941                5"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import alphalens as al\n",
    "factor_data = {}\n",
    "skipped_sentiments = []\n",
    "for sentiment in sentiments:\n",
    "    cs_df = cosine_similarities_df[(cosine_similarities_df['sentiment'] == sentiment)]\n",
    "    cs_df = cs_df.pivot(index='date', columns='ticker', values='value')\n",
    "    \n",
    "    try:\n",
    "        data = al.utils.get_clean_factor_and_forward_returns(cs_df.stack(), pricing.loc[cs_df.index], quantiles=5, bins=None, periods=[1])\n",
    "        factor_data[sentiment] = data\n",
    "    except:\n",
    "        skipped_sentiments.append(sentiment)\n",
    "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": 39,
   "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": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5aa36fdb00>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5aa36f5ba8>"
      ]
     },
     "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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f5aa368bb38>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5aa3649b00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5aa3a46390>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f5aa36ac9b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5aa3600940>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5aa36000f0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f5aa3555f28>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5aa39ef198>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5aa39c6358>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f5aa3954898>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5aa3964438>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5aa3574e80>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f5aa39fdef0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5aa39e0d30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f5aa3918550>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eL/nkCSc+oW9xXbz04r6NPap8vwdnLr3X080nc21a3Wpgh57H2wNrxneqquOqaklVLVy0aNHAgpO0cTGXSJot5hNp4zDXptWdByxJshPwC+Bg4CXDDUmSJEnSTI3imbQ5VRxV1Z1JjgDOAjYBjq+qS2drfKdiSZIkSZrMnCqOAKpqObB82HFIkiRJml/mXHGkjYdn6iRJkjRKLI6kjYTFqCRJ0oaZa6vVSZIkSdJQWBxJkiRJEk6rkyRJGqpRXO5Y2lhZHEmSJGnesBjVVJxWJ0mSJEl45kiSJI131C3DjkCShsIzR5IkSZKExZEkSZIkARZHkiRJkgRYHEmSJEkSYHEkSZIkSYDFkSRJkiQBFkeSJEmSBFgcSZIkSRLQx+IoyVFJfpHkx+32nJ5tb0uyIsnlSZ7d075va1uR5Mh+xSZJkiRJ4y3o8/gfqqp/6W1IsjNwMPB4YFvga0ke3TYfC/wlsBo4L8myqvpJn2OUJEmSpL4XRxPZHzilqu4Afp5kBbB727aiqq4ESHJK62txJEmSJKnv+n3N0RFJLkpyfJItW9t2wKqePqtb22TtkiRJktR3G1QcJflakksmuO0PfAx4JLALcA3wwbHdJhiqpmif6HkPS3JFkrUrV67ckJcgaR4zl0iaLeYTaeOwQdPqqmrv6fRL8m/AGe3hamCHns3bA2va/cnaxz/vccBxALvtttuEBZQkrYu5RNJsMZ9IG4d+rlb3iJ6HBwKXtPvLgIOT3C/JTsAS4IfAecCSJDsl2ZRu0YZl/YpPkiRJknr1c0GGDyTZhW5q3FXAawGq6tIkp9EttHAncHhV3QWQ5AjgLGAT4PiqurSP8UmSJEnSH/StOKqql0+x7Wjg6AnalwPL+xWTJEmSJE2m36vVSZIkSdJIsDiSJEmSJCyOJEmSJAmwOJIkSZIkwOJIkiRJkgCLI0mSJEkCLI4kSZIkCbA4kiRJkiTA4kiSJEmSAIsjSZIkSQIsjiRJkiQJsDiSJEmSJMDiSJIkSZIAiyNJkiRJAiyOJEmSJAmwOJIkSZIkYAOLoyQHJbk0yd1Jdhu37W1JViS5PMmze9r3bW0rkhzZ075TknOTXJHk1CSbbkhskiRJkjQTG3rm6BLgBcB3ehuT7AwcDDwe2Bf4aJJNkmwCHAvsB+wMHNL6Arwf+FBVLQFuAg7dwNgkSZIkado2qDiqqsuq6vIJNu0PnFJVd1TVz4EVwO7ttqKqrqyq3wGnAPsnCfBM4PNt/xOBAzYkNkmSJEmaiX5dc7QdsKrn8erWNln7Q4Gbq+rOce2SJEmSNBDrLI6SfC3JJRPc9p9qtwnaaj3aJ4vpsHZt0tqVK1dO/QIkaRLmEkmzxXwibRwWrKtDVe29HuOuBnboebw9sKbdn6j9emCLJAva2aPe/hPFdBxwHMBuu+02aRElSVMxl0iaLeYTaePQr2l1y4CDk9wvyU7AEuCHwHnAkrYy3aZ0izYsq6oCvgm8sO2/FPhSn2KTJEmSpHvZ0KW8D0yyGtgTODPJWQBVdSlwGvAT4CvA4VV1VzsrdARwFnAZcFrrC/BW4O+SrKC7BunfNyQ2SZIkSZqJdU6rm0pVnQ6cPsm2o4GjJ2hfDiyfoP1KutXsJEmSJGngNqg4kiRJUzjqlmFHIEmagX5dcyRJkiRJI8XiSJIkSZKwOJIkSZIkwOJIkiRJkgCLI0mSJEkCIN3vr46uJGuBq/s0/CJgZZ/G7ifjHqxRjLufMe9YVQv7NHbfmEsmZNyDZdz3Zj65N/+dDJZxD9bQ88nIF0f9lGTtqCZl4x6cUYx7FGMeZaP6fhv3YBm3pmNU32/jHizjXn9Oq5vazcMOYD0Z92CNYtyjGPMoG9X327gHy7g1HaP6fhv3YBn3erI4mtqo/nqfcQ/WKMY9ijGPslF9v417sIxb0zGq77dxD5ZxryeLo6kdN+wA1pNxD9Yoxj2KMY+yUX2/jXuwjFvTMarvt3EPlnGvJ685kiRJkiQ8cyRJkiRJgMWRJEmSJAEWR5IkSZIEWBxJkiRJEmBxJEmSJEmAxZFmKMlVSfZO8vYkn5yi30uTfHWaY067ryTNhiSXJtlrtvtKmn2j+BlM8hdJLh92HJo5l/LWjCS5Cnh1VX2tp20x8HPgvlV153AikzQfmG8kTWaiY5QBPncBS6pqxaCfW7PLM0eSpI1KkgXDjkHSaEnH42JZHGn9JDkqyWfbw++0vzcnuS3JnklemeS7Pf33SXJ5kluSfDTJt5O8um0b3/epSc5rfc9L8tSebVcl2XuiOJJsluSzSW5IcnPbd5t+vg+SOkl2SPLFJGvbZ/AjSe6T5B+SXJ3kuiSfTrJ56784SSVZmmRlkuuT/H3PeLsnOT/JrUmuTfKvbdNk+eZ7ST6U5EbgqCSPTPKNFsv1Sf4jyRY94/8hl7Q8clqL71dtCs9u69l31yT/3bZ9LsmpSd7Ttzdemgd6pvRP+vlL8hlgEfDllhve0tr3SPL9dlxwYe/0vCTfSnJ0ku8BtwN/kmTzJP+e5Jokv0jyniSbtP6Pascvt7S8cmprH8tLF7bnfnGSvZKsHvca3pTkorb/qUk269n+lvaca5K8uuXHR/X3ndVELI40G57W/m5RVQ+qqh/0bkyyNfB54G3AQ4HLgacygSRbAWcCx7S+/wqcmeSh04hjKbA5sEPb93XAb2b8aiTNSDtwOAO4GlgMbAecAryy3Z4B/AnwIOAj43b/c+AxwLOAdyR5XGv/MPDhqnoI8EjgtNY+Wb55CnAl8DDgaCDAe4FtgcfR5YWjpngZf9Vi3gJYNkGc6+ybZFPgdOAEYCvgZODAKcaRNHMTfv6q6uXASuD5LTd8IMl2dMcU76H7TL4J+EKShT3jvRw4DHgwXQ47EbgTeBTwP4F9gFe3vv8EfBXYEtge+N/tucfy0pPac586SewvAvYFdgKeSJcfSbIv8HfA3u15n74e74tmicWRBuE5wKVV9cV2jcAxwC8n6ftc4Iqq+kxV3VlVJwM/BZ4/jef5PV1R9KiququqLqiqW2fjBUia0u50Rcibq+rXVfXbqvou8FLgX6vqyqq6je4LkoNzz2lv76qq31TVhcCFwJNa+++BRyXZuqpuq6pz1hHDmqr63y1v/KaqVlTV2VV1R1WtpfuiZaoDju9W1fKqugv4TE8cM+m7B7AAOKaqfl9VXwR+uI64Jc3MTD6rLwOWt/53V9XZwPl0xyVjTqiqS9vxyVbAfsDftlx2HfAh4ODW9/fAjsC2PXluJo6pqjVVdSPwZWCX1v4i4FMtjtuBd81wXM0iiyMNwrbAqrEH1a0CsnqKvlePa7ua7pvodfkMcBZwSjst/YEk912PeCXNzA7A1RMskDD+83w1XfHQO92194uS2+nOLgEcCjwa+GmbIvu8dcSwqvdBkoclOaVNi7kV+Cyw9RT7j49js0x+7dJkfbcFflH3XOnoHnFJ2mAz+azuCBzUptTdnORmurPVj+jps2pc//sC1/T0/wTdGWmAt9Cdlf5hm9L3vzYw9rF8d4/jJMwbQ2VxpNmwriUPr6E7/Qx0Fz32Ph5nDV1y6rUI+EW7/2vgAT3bHv6HILpvat9VVTvTTdt7HvCKdUYvaUOtAhZNcIAy/vO8iG66yrXrGrCqrqiqQ+gOSt4PfD7JA5k834xvf29re2KbmvcyuoOafroG2K7luDE79Pk5Jf3R+DywCvhMVW3Rc3tgVb1vkn1WAXcAW/f0f0hVPR6gqn5ZVa+pqm2B1wIfnaXrgu5xnIR5Y6gsjjQb1gJ3011TMJEzgSckOaAdPB1OT1EzznLg0UlekmRBkhcDO9NdzwDwY7ppOfdtF2G+cGzHJM9I8oR2/cOtdKe/79rQFydpnX5I9z/39yV5YLrFUf6M7pqb/zvJTkkeBPwzcOp0luBO8rIkC6vqbuDm1nwX6843Yx4M3Ea3cMN2wJvX65XNzA/oYjyi5a/96aYcShqMa7lnbvgs8Pwkz06ySctNeyWZ8AvaqrqG7pqiDyZ5SLpFZR6Z5OkASQ7q2fcmusJq7Dhj/HPPxGnAq5I8LskDgHes5ziaBRZH2mBtfuzRwPfaaeg9xm2/HjgI+ABwA12xcz7dtzPjx7qB7ozPG1vftwDPa2MA/CPdxdk30c3JPaln94fTLfxwK3AZ8G26xCipj9rc/+fTXUi8km7a7IuB4+mmu36H7reJfgv8zTSH3Re4NMltdIszHNzm+E+Zb3q8C9gVuIXuC5ovrs9rm4mq+h3wAropgTfTna06gwlynaS+eC/wDy03vKmqVgH7A2+n+2JlFd0XJVMd/74C2BT4Cd2xxuf54zS8JwPntry0DHhDVf28bTsKOLE994tmEnRV/Rfd9djfBFbQfdEC5o6h8EdgNXDpfkdgNfDSqvrmsOORpH5Jci7w8ar61LBjkTQa2qqdlwD388euB88zRxqIdkp7iyT3o/sGJ8C6Vp+SpJGS5OlJHt6m1S2lW673K8OOS9LcluTAJJsm2ZLuOssvWxgNh8WRBmVP4GfA9XTTbw6oKn+DSNLG5jF0S5LfQjc9+IXtOgZJmspr6ab+/YzuOqa/Hm4485fT6iRJkiQJzxxJkiRJEtD9GN9I23rrrWvx4sXDDkNSc8EFF1xfVQuHHcdMmUukucd8Imm2TDefjHxxtHjxYs4///xhhyGpSXL1sGNYH+YSae4xn0iaLdPNJ06rkyRJkiQsjiRJkiQJsDiSJEmSJMDiSJIkSZKAjWBBBkmS5qpjX/eNvo19+Mef2bexJWm+8syRJEmSJGFxJEmSJEmAxZEkSZIkARZHkiRJkgRYHEmSJEkSYHEkSZIkSYDFkSRJkiQBFkeSJEmSBFgcSZIkSRIwS8VRkuOTXJfkkp62rZKcneSK9nfL1p4kxyRZkeSiJLv27LO09b8iydLZiE2SJEmSpmO2zhydAOw7ru1I4OtVtQT4ensMsB+wpN0OAz4GXTEFvBN4CrA78M6xgkqSJEmS+m1WiqOq+g5w47jm/YET2/0TgQN62j9dnXOALZI8Ang2cHZV3VhVNwFnc++CS5IkSZL6op/XHG1TVdcAtL8Pa+3bAat6+q1ubZO1S5IkSVLfDWNBhkzQVlO033uA5LB2XdLalStXzmpwkuYPc4mk2WI+kTYO/SyOrm3T5Wh/r2vtq4EdevptD6yZov1equq4qlpSVQsXLVo064FLmh/MJZJmi/lE2jj0szhaBoytOLcU+FJP+yvaqnV7ALe0aXdnAfsk2bItxLBPa5MkSZKkvlswG4MkORnYC9g6yWq6VefeB5yW5FBgJXBQ674ceA6wArgdeBVAVd2Y5J+A81q/d1fV+EUeJEmSpHnlgy9+Xt/GfuOpZ/Rt7FE0K8VRVR0yyaZnTdC3gMMnGed44PjZiEmSJEmSZmIYCzJIkiRJ0pxjcSRJkiRJWBxJkiRJEmBxJEmSJEmAxZEkSZIkARZHkiRJkgRYHEmSJEkSYHEkSZIkSYDFkSRJkiQBFkeSJEmSBFgcSZIkSRJgcSRJkiRJgMWRJEmSJAEWR5IkSZIEWBxJkiRJEmBxJEmSJEnAAIqjJFcluTjJj5Oc39q2SnJ2kiva3y1be5Ick2RFkouS7Nrv+CRJkiQJBnfm6BlVtUtV7dYeHwl8vaqWAF9vjwH2A5a022HAxwYUnyRJkqR5bljT6vYHTmz3TwQO6Gn/dHXOAbZI8ohhBChJkiRpfhlEcVTAV5NckOSw1rZNVV0D0P4+rLVvB6zq2Xd1a5MkSZKkvlowgOf4s6pak+RhwNlJfjpF30zQVvfq1BVZbwa2WLhw4SyFKWm+MZdImi3mE2nj0PczR1W1pv29Djgd2B24dmy6XPt7Xeu+GtihZ/ftgTUTjHlcVS2pqoWLFi3qZ/iSNmLmEkmzxXwibRz6WhwleWCSB4/dB/YBLgGWAUtbt6XAl9r9ZcAr2qp1ewC3jE2/kyRJkqR+6ve0um2A05OMPddJVfWVJOcBpyU5FFgJHNT6LweeA6wAbgde1ef4JEmSJAnoc3FUVVcCT5qg/QbgWRO0F3B4P2OSJEmSpIkMYkEGbaDLHvu4vo39uJ9e1rexJUmSpFEyrN85kiRJkqQ5xeJIkiRJkrA4kiRJkiTA4kiSJEmSAIsjSZIkSQJEADAWAAANdElEQVRcrU6SJEnr4YMvfl7fxn7jqWf0bWxpKp45kiRJkiQsjiRJkiQJsDiSJEmSJMDiSJIkSZIAF2SQJEkaKhc2kOYOzxxJkiRJEhZHkiRJkgQ4rU6S1suxr/tG38Y+/OPP7NvYkiRpcvOqOLrssY/r29iP++llfRtbkiRJUv/NuWl1SfZNcnmSFUmOHHY8kiRJkuaHOVUcJdkEOBbYD9gZOCTJzsONSpIkSdJ8MNem1e0OrKiqKwGSnALsD/xkqFFpvTiNUZIkSaNkTp05ArYDVvU8Xt3aJEmSJKmvUlXDjuEPkhwEPLuqXt0evxzYvar+Zly/w4A3A1ssXLhw6+uuu27wwWqjNapnvOZK3EkuqKrd+hbMLJqPuWRUV9kb1bhH1Vx5v80n6gd/dHdw5tJ7Pd18MtfOHK0Gduh5vD2wZnynqjquqpZU1cJFixYNLDhJGxdziaTZYj6RNg5zrTg6D1iSZKckmwIHA8uGHJMkSZKkeWBOLchQVXcmOQI4C9gEOL6qLh1yWJIkSZLmgTlVHAFU1XJg+bDjkCRJkjS/zLVpdZIkSZI0FBZHkiRJkoTFkSRJkiQBFkeSJEmSBMzBBRkkSdJw+cO4kuYrzxxJkiRJEp45kqR5xTMCkua7N556xrBD0BxmcSRtJB7308uGHYIkSdJIc1qdJEmSJGFxJEmSJEmA0+qke3F6miRJ0vzkmSNJkiRJwuJIkiRJkgCLI0mSJEkCLI4kSZIkCbA4kiRJkiTA4kiSJEmSgD4u5Z3kKOA1wNrW9PaqWt62vQ04FLgLeH1VndXa9wU+DGwCfLKq3tev+CRJo+Pwjz9z2CFIkuaBfv/O0Yeq6l96G5LsDBwMPB7YFvhakke3zccCfwmsBs5LsqyqftLnGCVJkiRpKD8Cuz9wSlXdAfw8yQpg97ZtRVVdCZDklNbX4kiSJElS3/X7mqMjklyU5PgkW7a27YBVPX1Wt7bJ2u8lyWFJrkiyduXKlf2IW9I8YC6RNFvMJ9LGYYOKoyRfS3LJBLf9gY8BjwR2Aa4BPji22wRD1RTt926sOq6qllTVwkWLFm3IS5A0j5lLJM0W84m0cdigaXVVtfd0+iX5N+CM9nA1sEPP5u2BNe3+ZO2SJEmS1Fd9m1aX5BE9Dw8ELmn3lwEHJ7lfkp2AJcAPgfOAJUl2SrIp3aINy/oVnyRJkiT16ueCDB9Isgvd1LirgNcCVNWlSU6jW2jhTuDwqroLIMkRwFl0S3kfX1WX9jE+SZIkSfqDvhVHVfXyKbYdDRw9QftyYHm/YpIkSZKkyfR7tTpJkiRJGgkWR5IkSZKExZEkSZIkARZHkiRJkgRYHEmSJEkSYHEkSZIkSYDFkSRJkiQBFkeSJEmSBFgcSZIkSRJgcSRJkiRJgMWRJEmSJAEWR5IkSZIEwIJhByBJkiRp4/PGU88Ydggz5pkjSZIkScLiSJIkSZIAiyNJkiRJAjawOEpyUJJLk9ydZLdx296WZEWSy5M8u6d939a2IsmRPe07JTk3yRVJTk2y6YbEJkmSJEkzsaFnji4BXgB8p7cxyc7AwcDjgX2BjybZJMkmwLHAfsDOwCGtL8D7gQ9V1RLgJuDQDYxNkiRJkqZtg4qjqrqsqi6fYNP+wClVdUdV/RxYAezebiuq6sqq+h1wCrB/kgDPBD7f9j8ROGBDYpMkSZKkmejXNUfbAat6Hq9ubZO1PxS4uaruHNc+oSSHtel3a1euXDmrgUuaP8wlkmaL+UTaOKyzOErytSSXTHDbf6rdJmir9WifUFUdV1VLqmrhokWLpn4BkjQJc4mk2WI+kTYO6/wR2Kraez3GXQ3s0PN4e2BNuz9R+/XAFkkWtLNHvf0lSZIkqe/6Na1uGXBwkvsl2QlYAvwQOA9Y0lam25Ru0YZlVVXAN4EXtv2XAl/qU2ySJEmSdC8bupT3gUlWA3sCZyY5C6CqLgVOA34CfAU4vKruameFjgDOAi4DTmt9Ad4K/F2SFXTXIP37hsQmSZIkSTOxzml1U6mq04HTJ9l2NHD0BO3LgeUTtF9Jt5qdJEmSJA1cv6bVSZIkSdJIsTiSJEmSJCyOJEmSJAmwOJIkSZIkwOJIkiRJkgCLI0mSJEkCIN3vr46uJGuBq/s0/CJgZZ/G7ifjHqxRjLufMe9YVQv7NHbfmEsmZNyDZdz3Zj65N/+dDJZxD9bQ88nIF0f9lGTtqCZl4x6cUYx7FGMeZaP6fhv3YBm3pmNU32/jHizjXn9Oq5vazcMOYD0Z92CNYtyjGPMoG9X327gHy7g1HaP6fhv3YBn3erI4mtotww5gPRn3YI1i3KMY8ygb1ffbuAfLuDUdo/p+G/dgGfd6sjia2nHDDmA9GfdgjWLcoxjzKBvV99u4B8u4NR2j+n4b92AZ93rymiNJkiRJwjNHkiRJkgRYHEmSJEkSYHE08pI8NsmzkjxoXPu+w4ppOpLsnuTJ7f7OSf4uyXOGHddMJfn0sGOYqSR/3t7vfYYdi+YW88lwmU+0MTGfDM8o5hKYO/nEa46mIcmrqupTw45jvCSvBw4HLgN2Ad5QVV9q235UVbsOM77JJHknsB+wADgbeArwLWBv4KyqOnp40U0uybLxTcAzgG8AVNVfDTyoaUjyw6ravd1/Dd2/mdOBfYAvV9X7hhnffGM+mV3mk8Eyn8wdczWXgPlkkEY1l8DczScWR9OQZGVVLRp2HOMluRjYs6puS7IY+Dzwmar6cJL/rqr/OdQAJ9Hi3gW4H/BLYPuqujXJ/YFzq+qJQw1wEkl+BPwE+CRQdAnoZOBggKr69vCim1zvv4Uk5wHPqaq1SR4InFNVTxhuhPOL+WR2mU8Gy3wyd8zVXALmk0Ea1VwCczefLBjGk85FSS6abBOwzSBjmYFNquo2gKq6KslewOeT7EgX91x1Z1XdBdye5GdVdStAVf0myd1Djm0quwFvAP4eeHNV/TjJb+Zy4mnuk2RLumm0qaq1AFX16yR3Dje0jZP5ZKDMJ4NlPhmgEc0lYD4ZpFHNJTBH84nF0R9tAzwbuGlce4DvDz6cafllkl2q6scA7Rua5wHHA3P527vfJXlAVd0O/OlYY5LNgbmafKiqu4EPJflc+3sto/EZ2hy4gO7fciV5eFX9ss0Dn8v/kxpl5pPBMZ8MlvlksEYxl4D5ZGBGOJfAHM0no/LmDcIZwIPGPsi9knxr8OFMyyuAe1TWVXUn8IoknxhOSNPytKq6A/7woR5zX2DpcEKavqpaDRyU5LnArcOOZ12qavEkm+4GDhxgKPOJ+WRwzCcDZD4ZuFHMJWA+GbhRyyUwd/OJ1xxJkiRJEi7lLUmSJEmAxZEkSZIkARZHkiRJkgRYHM1bSV6f5LIk/zHD/f42yQP6FddsS3JAkp17Hr87yd7t/reS7Da86KSNg/nEfCLNFvOJ+WTYLI7mr/+L7se2XjrD/f4WmFHySbLJDJ9jNh0A/CH5VNU7quprQ4xH2hiZTyTNFvOJhsriaB5K8nHgT4BlSd6a5PtJ/rv9fUzrs0mSf0lycZKLkvxNktcD2wLfTPLN1u+Q1ueSJO/veY7b2rcg5wJ7ThLHvkl+muS7SY5JckZrPyrJm3r6XZLuF7ZJ8p9JLkhyaZLDxj3f0UkuTHJOkm2SPBX4K+D/SfLjJI9MckKSF04Qyz5JfpDkR0k+19bYl7QO5hPziTRbzCfmkzmhqrzNwxtwFbA18BBgQWvbG/hCu//XwBd6tm3Vu1+7vy2wElhI95tZ3wAOaNsKeNEUz78ZsApYQvdDX6cBZ7RtRwFv6ul7CbB4XBz3b+0P7Xm+57f7HwD+od0/AXhhz1h/eAx8i+6XpbcGvgM8sLW/FXjHsP8befM2KjfzifnEm7fZuplPzCfDvvkjsNocODHJEroP8H1b+97Ax6v70Taq6sYJ9n0y8K2qWguQbn7w04D/BO6iS16TeSzw86q6ou37WeCwKfqPeX2SsR8G24Eued0A/I7ux/Kg+7Xlv5zGWGP2oDu1/b0kAJsCP5jB/pI65hPziTRbzCfmk6GwONI/Ad+sqgPbqeFvtfbQJaOpZIptv62qu9ax/2Tj38k9p3xuBpBkL7qkuGdV3Z7u18E3a31+X+1rFbrEN5N/2wHOrqpDZrCPpHszn5hPpNliPjGfDIXXHGlz4Bft/it72r8KvC7JAoAkW7X2XwEPbvfPBZ6eZOt0FzUeAnx7ms/7U2CnJI9sj3s/+FcBu7bn3RXYqSfWm1rieSzdNyrr0hvvZM4B/izJo9pzPiDJo6f1KiT1Mp+YT6TZYj4xnwyFxZE+ALw3yfeA3lVbPkk3X/eiJBcCL2ntxwH/leSbVXUN8Dbgm8CFwI+q6kvTedKq+i3daeozk3wXuLpn8xeArZL8mG5u8f9p7V8BFiS5iO4bpXOm8VSnAG9uF3Q+cqIO7bT7K4GT29jn0J1WlzQz5hPziTRbzCfmk6HIH8/0ScPTTkm/qaqeN+xYJI0284mk2WI+mX88cyRJkiRJeOZIA5DkdP44L3fMW6vqrGHEI2l0mU8kzRbziSZicSRJkiRJOK1OkiRJkgCLI0mSJEkCLI4kSZIkCbA4kiRJkiTA4kiSJEmSAPj/AbXUn8zGNcWlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5aa319b978>"
      ]
     },
     "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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5aa36a6ba8>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5aa38e5320>"
      ]
     },
     "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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "negative       -1.05000000\n",
       "positive       -2.14000000\n",
       "uncertainty    -0.29000000\n",
       "litigious      -1.05000000\n",
       "constraining   -1.05000000\n",
       "interesting    -6.19000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "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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