project5/project_5_starter.ipynb

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    "# 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,
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   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import pprint\n",
    "import project_helper\n",
    "import project_tests\n",
    "\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download NLP Corpora\n",
    "You'll need two corpora to run this project: the stopwords corpus for removing stopwords and wordnet for lemmatizing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get 10ks\n",
    "We'll be running NLP analysis on 10-k documents. To do that, we first need to download the documents. For this project, we'll download 10-ks for a few companies. To lookup documents for these companies, we'll use their CIK. If you would like to run this against other stocks, we've provided the dict `additional_cik` for more stocks. However, the more stocks you try, the long it will take to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cik_lookup = {\n",
    "    'AMZN': '0001018724',\n",
    "    'BMY': '0000014272',   \n",
    "    'CNP': '0001130310',\n",
    "    'CVX': '0000093410',\n",
    "    'FL': '0000850209',\n",
    "    'FRT': '0000034903',\n",
    "    'HON': '0000773840'}\n",
    "\n",
    "additional_cik = {\n",
    "    'AEP': '0000004904',\n",
    "    'AXP': '0000004962',\n",
    "    'BA': '0000012927', \n",
    "    'BK': '0001390777',\n",
    "    'CAT': '0000018230',\n",
    "    'DE': '0000315189',\n",
    "    'DIS': '0001001039',\n",
    "    'DTE': '0000936340',\n",
    "    'ED': '0001047862',\n",
    "    'EMR': '0000032604',\n",
    "    'ETN': '0001551182',\n",
    "    'GE': '0000040545',\n",
    "    'IBM': '0000051143',\n",
    "    'IP': '0000051434',\n",
    "    'JNJ': '0000200406',\n",
    "    'KO': '0000021344',\n",
    "    'LLY': '0000059478',\n",
    "    'MCD': '0000063908',\n",
    "    'MO': '0000764180',\n",
    "    'MRK': '0000310158',\n",
    "    'MRO': '0000101778',\n",
    "    'PCG': '0001004980',\n",
    "    'PEP': '0000077476',\n",
    "    'PFE': '0000078003',\n",
    "    'PG': '0000080424',\n",
    "    'PNR': '0000077360',\n",
    "    'SYY': '0000096021',\n",
    "    'TXN': '0000097476',\n",
    "    'UTX': '0000101829',\n",
    "    'WFC': '0000072971',\n",
    "    'WMT': '0000104169',\n",
    "    'WY': '0000106535',\n",
    "    'XOM': '0000034088'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get list of 10-ks\n",
    "The SEC has a limit on the number of calls you can make to the website per second. In order to avoid hiding that limit, we've created the `SecAPI` class. This will cache data from the SEC and prevent you from going over the limit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sec_api = project_helper.SecAPI()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the class constructed, let's pull a list of filled 10-ks from the SEC for each company."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "\n",
    "def get_sec_data(cik, doc_type, start=0, count=60):\n",
    "    newest_pricing_data = pd.to_datetime('2018-01-01')\n",
    "    rss_url = 'https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany' \\\n",
    "        '&CIK={}&type={}&start={}&count={}&owner=exclude&output=atom' \\\n",
    "        .format(cik, doc_type, start, count)\n",
    "    sec_data = sec_api.get(rss_url)\n",
    "    feed = BeautifulSoup(sec_data.encode('ascii'), 'xml').feed\n",
    "    entries = [\n",
    "        (\n",
    "            entry.content.find('filing-href').getText(),\n",
    "            entry.content.find('filing-type').getText(),\n",
    "            entry.content.find('filing-date').getText())\n",
    "        for entry in feed.find_all('entry', recursive=False)\n",
    "        if pd.to_datetime(entry.content.find('filing-date').getText()) <= newest_pricing_data]\n",
    "\n",
    "    return entries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's pull the list using the `get_sec_data` function, then display some of the results. For displaying some of the data, we'll use Amazon as an example. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('https://www.sec.gov/Archives/edgar/data/1018724/000101872417000011/0001018724-17-000011-index.htm',\n",
      "  '10-K',\n",
      "  '2017-02-10'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000101872416000172/0001018724-16-000172-index.htm',\n",
      "  '10-K',\n",
      "  '2016-01-29'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000101872415000006/0001018724-15-000006-index.htm',\n",
      "  '10-K',\n",
      "  '2015-01-30'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000101872414000006/0001018724-14-000006-index.htm',\n",
      "  '10-K',\n",
      "  '2014-01-31'),\n",
      " ('https://www.sec.gov/Archives/edgar/data/1018724/000119312513028520/0001193125-13-028520-index.htm',\n",
      "  '10-K',\n",
      "  '2013-01-30')]\n"
     ]
    }
   ],
   "source": [
    "example_ticker = 'AMZN'\n",
    "sec_data = {}\n",
    "\n",
    "for ticker, cik in cik_lookup.items():\n",
    "    sec_data[ticker] = get_sec_data(cik, '10-K')\n",
    "\n",
    "pprint.pprint(sec_data[example_ticker][:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download 10-ks\n",
    "As you see, this is a list of urls. These urls point to a file that contains metadata related to each filling. Since we don't care about the metadata, we'll pull the filling by replacing the url with the filling url."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading AMZN Fillings: 100%|██████████| 22/22 [00:06<00:00,  3.58filling/s]\n",
      "Downloading BMY Fillings: 100%|██████████| 27/27 [00:09<00:00,  2.98filling/s]\n",
      "Downloading CNP Fillings: 100%|██████████| 19/19 [00:06<00:00,  2.73filling/s]\n",
      "Downloading CVX Fillings: 100%|██████████| 25/25 [00:08<00:00,  2.81filling/s]\n",
      "Downloading FL Fillings: 100%|██████████| 22/22 [00:06<00:00,  3.50filling/s]\n",
      "Downloading FRT Fillings: 100%|██████████| 29/29 [00:06<00:00,  4.79filling/s]\n",
      "Downloading HON Fillings: 100%|██████████| 25/25 [00:08<00:00,  3.09filling/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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "def get_documents(text):\n",
    "    \"\"\"\n",
    "    Extract the documents from the text\n",
    "    Parameters\n",
    "    ----------\n",
    "    text : str\n",
    "        The text with the document strings inside\n",
    "    Returns\n",
    "    -------\n",
    "    extracted_docs : list of str\n",
    "        The document strings found in `text`\n",
    "    \"\"\"\n",
    "    \n",
    "    filings_list = []\n",
    "    doc_start_pattern = re.compile(r'<DOCUMENT>')\n",
    "    doc_end_pattern = re.compile(r'</DOCUMENT>')\n",
    "    \n",
    "    doc_start_is = [x.end() for x in doc_start_pattern.finditer(text)]\n",
    "    doc_end_is = [x.start() for x in doc_end_pattern.finditer(text)]\n",
    "    \n",
    "    for doc_start_i, doc_end_i in zip(doc_start_is, doc_end_is): \n",
    "        filings_list.append(text[doc_start_i : doc_end_i])\n",
    "    \n",
    "    return filings_list\n",
    "\n",
    "\n",
    "project_tests.test_get_documents(get_documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `get_documents` function implemented, let's extract all the documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Getting Documents from AMZN Fillings: 100%|██████████| 17/17 [00:00<00:00, 90.14filling/s]\n",
      "Getting Documents from BMY Fillings: 100%|██████████| 23/23 [00:00<00:00, 36.50filling/s]\n",
      "Getting Documents from CNP Fillings: 100%|██████████| 15/15 [00:00<00:00, 29.85filling/s]\n",
      "Getting Documents from CVX Fillings: 100%|██████████| 21/21 [00:00<00:00, 31.79filling/s]\n",
      "Getting Documents from FL Fillings: 100%|██████████| 16/16 [00:00<00:00, 38.65filling/s]\n",
      "Getting Documents from FRT Fillings: 100%|██████████| 19/19 [00:00<00:00, 46.24filling/s]\n",
      "Getting Documents from HON Fillings: 100%|██████████| 20/20 [00:00<00:00, 35.85filling/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Document 0 Filed on 2017-02-10:\n",
      "\n",
      "<TYPE>10-K\n",
      "<SEQUENCE>1\n",
      "<FILENAME>amzn-20161231x10k.htm\n",
      "<DESCRIPTION>FORM 10-K\n",
      "<TEXT>\n",
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http://www.w3.org/TR/html4/loose.dtd\">\n",
      "<html>\n",
      "\t<he...\n",
      "\n",
      "Document 1 Filed on 2017-02-10:\n",
      "\n",
      "<TYPE>EX-12.1\n",
      "<SEQUENCE>2\n",
      "<FILENAME>amzn-20161231xex121.htm\n",
      "<DESCRIPTION>COMPUTATION OF RATIO OF EARNINGS TO FIXED CHARGES\n",
      "<TEXT>\n",
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http:...\n",
      "\n",
      "Document 2 Filed on 2017-02-10:\n",
      "\n",
      "<TYPE>EX-21.1\n",
      "<SEQUENCE>3\n",
      "<FILENAME>amzn-20161231xex211.htm\n",
      "<DESCRIPTION>LIST OF SIGNIFICANT SUBSIDIARIES\n",
      "<TEXT>\n",
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http://www.w3.org/TR/h...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "filling_documents_by_ticker = {}\n",
    "\n",
    "for ticker, raw_fillings in raw_fillings_by_ticker.items():\n",
    "    filling_documents_by_ticker[ticker] = {}\n",
    "    for file_date, filling in tqdm(raw_fillings.items(), desc='Getting Documents from {} Fillings'.format(ticker), unit='filling'):\n",
    "        filling_documents_by_ticker[ticker][file_date] = get_documents(filling)\n",
    "\n",
    "\n",
    "print('\\n\\n'.join([\n",
    "    'Document {} Filed on {}:\\n{}...'.format(doc_i, file_date, doc[:200])\n",
    "    for file_date, docs in filling_documents_by_ticker[example_ticker].items()\n",
    "    for doc_i, doc in enumerate(docs)][:3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Document Types\n",
    "Now that we have all the documents, we want to find the 10-k form in this 10-k filing. Implement the `get_document_type` function to return the type of document given. The document type is located on a line with the `<TYPE>` tag. For example, a form of type \"TEST\" would have the line `<TYPE>TEST`. Make sure to return the type as lowercase, so this example would be returned as \"test\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
    "    regex = re.compile(r'<TYPE>.+')\n",
    "    matches = regex.findall(doc)\n",
    "    doc_type_list = [match [ len('<TYPE>'): ] for match in matches]\n",
    "    doc_type_str = ' '.join(doc_type_list).lower()\n",
    "  \n",
    "    return doc_type_str\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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2016123...\n",
      "    file_date: '2017-02-10'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2015123...\n",
      "    file_date: '2016-01-29'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2014123...\n",
      "    file_date: '2015-01-30'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2013123...\n",
      "    file_date: '2014-01-31'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>d445434d10k....\n",
      "    file_date: '2013-01-30'},\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "ten_ks_by_ticker = {}\n",
    "\n",
    "for ticker, filling_documents in filling_documents_by_ticker.items():\n",
    "    ten_ks_by_ticker[ticker] = []\n",
    "    for file_date, documents in filling_documents.items():\n",
    "        for document in documents:\n",
    "            if get_document_type(document) == '10-k':\n",
    "                ten_ks_by_ticker[ticker].append({\n",
    "                    'cik': cik_lookup[ticker],\n",
    "                    'file': document,\n",
    "                    'file_date': file_date})\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data(ten_ks_by_ticker[example_ticker][:5], ['cik', 'file', 'file_date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess the Data\n",
    "### Clean Up\n",
    "As you can see, the text for the documents are very messy. To clean this up, we'll remove the html and lowercase all the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cleaning AMZN 10-Ks: 100%|██████████| 17/17 [00:33<00:00,  1.98s/10-K]\n",
      "Cleaning BMY 10-Ks: 100%|██████████| 23/23 [01:11<00:00,  3.10s/10-K]\n",
      "Cleaning CNP 10-Ks: 100%|██████████| 15/15 [00:52<00:00,  3.50s/10-K]\n",
      "Cleaning CVX 10-Ks: 100%|██████████| 21/21 [01:48<00:00,  5.18s/10-K]\n",
      "Cleaning FL 10-Ks: 100%|██████████| 16/16 [00:25<00:00,  1.57s/10-K]\n",
      "Cleaning FRT 10-Ks: 100%|██████████| 19/19 [00:52<00:00,  2.77s/10-K]\n",
      "Cleaning HON 10-Ks: 100%|██████████| 20/20 [00:57<00:00,  2.89s/10-K]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20161231x10k.htm\\nform 10-k\\n\\n\\n...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20151231x10k.htm\\nform 10-k\\n\\n\\n...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20141231x10k.htm\\nform 10-k\\n\\n\\n...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20131231x10k.htm\\nform 10-k\\n\\n\\n...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\nd445434d10k.htm\\nform 10-k\\n\\n\\nform 1...},\n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    for ten_k in tqdm(ten_ks, desc='Cleaning {} 10-Ks'.format(ticker), unit='10-K'):\n",
    "        ten_k['file_clean'] = clean_text(ten_k['file'])\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data(ten_ks_by_ticker[example_ticker][:5], ['file_clean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lemmatize\n",
    "With the text cleaned up, it's time to distill the verbs down. Implement the `lemmatize_words` function to lemmatize verbs in the list of words provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import wordnet\n",
    "\n",
    "\n",
    "def lemmatize_words(words):\n",
    "    \"\"\"\n",
    "    Lemmatize words \n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    words : list of str\n",
    "        List of words\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    lemmatized_words : list of str\n",
    "        List of lemmatized words\n",
    "    \"\"\"\n",
    "    \n",
    "    wnl = WordNetLemmatizer()\n",
    "    [wnl.lemmatize(w, pos='v') for w in  words]\n",
    "    \n",
    "    lemmatized_words = [wnl.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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lemmatize AMZN 10-Ks: 100%|██████████| 17/17 [00:08<00:00,  1.9810-K/s]\n",
      "Lemmatize BMY 10-Ks: 100%|██████████| 23/23 [00:18<00:00,  1.2310-K/s]\n",
      "Lemmatize CNP 10-Ks: 100%|██████████| 15/15 [00:15<00:00,  1.04s/10-K]\n",
      "Lemmatize CVX 10-Ks: 100%|██████████| 21/21 [00:17<00:00,  1.1810-K/s]\n",
      "Lemmatize FL 10-Ks: 100%|██████████| 16/16 [00:07<00:00,  2.2310-K/s]\n",
      "Lemmatize FRT 10-Ks: 100%|██████████| 19/19 [00:11<00:00,  1.6510-K/s]\n",
      "Lemmatize HON 10-Ks: 100%|██████████| 20/20 [00:10<00:00,  1.8610-K/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20161231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20151231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20141231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20131231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'd445434d10k', 'htm', 'form', '1...},\n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word_pattern = re.compile('\\w+')\n",
    "\n",
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    for ten_k in tqdm(ten_ks, desc='Lemmatize {} 10-Ks'.format(ticker), unit='10-K'):\n",
    "        ten_k['file_lemma'] = lemmatize_words(word_pattern.findall(ten_k['file_clean']))\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data(ten_ks_by_ticker[example_ticker][:5], ['file_lemma'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove Stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Remove Stop Words for AMZN 10-Ks: 100%|██████████| 17/17 [00:02<00:00,  8.2310-K/s]\n",
      "Remove Stop Words for BMY 10-Ks: 100%|██████████| 23/23 [00:04<00:00,  5.1110-K/s]\n",
      "Remove Stop Words for CNP 10-Ks: 100%|██████████| 15/15 [00:03<00:00,  3.9410-K/s]\n",
      "Remove Stop Words for CVX 10-Ks: 100%|██████████| 21/21 [00:04<00:00,  4.7310-K/s]\n",
      "Remove Stop Words for FL 10-Ks: 100%|██████████| 16/16 [00:01<00:00,  9.1210-K/s]\n",
      "Remove Stop Words for FRT 10-Ks: 100%|██████████| 19/19 [00:02<00:00,  6.6810-K/s]\n",
      "Remove Stop Words for HON 10-Ks: 100%|██████████| 20/20 [00:02<00:00,  7.7210-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": 19,
   "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": 19,
     "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict, Counter\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "\n",
    "def get_bag_of_words(sentiment_words, docs):\n",
    "    \"\"\"\n",
    "    Generate a bag of words from documents for a certain sentiment\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    sentiment_words: Pandas Series\n",
    "        Words that signify a certain sentiment\n",
    "    docs : list of str\n",
    "        List of documents used to generate bag of words\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    bag_of_words : 2-d Numpy Ndarray of int\n",
    "        Bag of words sentiment for each document\n",
    "        The first dimension is the document.\n",
    "        The second dimension is the word.\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Implement\n",
    "    vectorizer = CountVectorizer(vocabulary=sentiment_words)\n",
    "    X = vectorizer.fit_transform(docs)\n",
    "        \n",
    "    bag_of_words = X.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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    negative: '[[0 0 0 ..., 0 0 0]\\n [0 0 0 ..., 0 0 0]\\n [0 0 0...\n",
      "    positive: '[[16  0  0 ...,  0  0  0]\\n [16  0  0 ...,  0  0 ...\n",
      "    uncertainty: '[[0 0 0 ..., 1 1 3]\\n [0 0 0 ..., 1 1 3]\\n [0 0 0...\n",
      "    litigious: '[[0 0 0 ..., 0 0 0]\\n [0 0 0 ..., 0 0 0]\\n [0 0 0...\n",
      "    constraining: '[[0 0 0 ..., 0 0 2]\\n [0 0 0 ..., 0 0 2]\\n [0 0 0...\n",
      "    interesting: '[[2 0 0 ..., 0 0 0]\\n [2 0 0 ..., 0 0 0]\\n [2 0 0...},\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "sentiment_bow_ten_ks = {}\n",
    "\n",
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    lemma_docs = [' '.join(ten_k['file_lemma']) for ten_k in ten_ks]\n",
    "    \n",
    "    sentiment_bow_ten_ks[ticker] = {\n",
    "        sentiment: get_bag_of_words(sentiment_df[sentiment_df[sentiment]]['word'], lemma_docs)\n",
    "        for sentiment in sentiments}\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data([sentiment_bow_ten_ks[example_ticker]], sentiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jaccard Similarity\n",
    "Using the bag of words, let's calculate the jaccard similarity on the bag of words and plot it over time. Implement `get_jaccard_similarity` to return the jaccard similarities between each tick in time. Since the input, `bag_of_words_matrix`, is a bag of words for each time period in order, you just need to compute the jaccard similarities for each neighboring bag of words. Make sure to turn the bag of words into a boolean array when calculating the jaccard similarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import jaccard_similarity_score\n",
    "\n",
    "\n",
    "def get_jaccard_similarity(bag_of_words_matrix):\n",
    "    \"\"\"\n",
    "    Get jaccard similarities for neighboring documents\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    bag_of_words : 2-d Numpy Ndarray of int\n",
    "        Bag of words sentiment for each document\n",
    "        The first dimension is the document.\n",
    "        The second dimension is the word.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    jaccard_similarities : list of float\n",
    "        Jaccard similarities for neighboring documents\n",
    "    \"\"\"\n",
    "    \n",
    "    bag = bag_of_words_matrix.astype(bool)\n",
    "    jaccard_similarities = [jaccard_similarity_score(u,v) for u, v in zip(bag, bag[1:])]\n",
    "    \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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f63eb652c18>"
      ]
     },
     "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": 25,
   "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",
    "    X = vectorizer.fit_transform(docs)\n",
    "    \n",
    "    tfidf = X.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": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    negative: '[[ 0.          0.          0.         ...,  0.   ...\n",
      "    positive: '[[ 0.22288432  0.          0.         ...,  0.   ...\n",
      "    uncertainty: '[[ 0.          0.          0.         ...,  0.005...\n",
      "    litigious: '[[ 0.  0.  0. ...,  0.  0.  0.]\\n [ 0.  0.  0. .....\n",
      "    constraining: '[[ 0.          0.          0.         ...,  0.   ...\n",
      "    interesting: '[[ 0.01673784  0.          0.         ...,  0.   ...},\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "sentiment_tfidf_ten_ks = {}\n",
    "\n",
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    lemma_docs = [' '.join(ten_k['file_lemma']) for ten_k in ten_ks]\n",
    "    \n",
    "    sentiment_tfidf_ten_ks[ticker] = {\n",
    "        sentiment: get_tfidf(sentiment_df[sentiment_df[sentiment]]['word'], lemma_docs)\n",
    "        for sentiment in sentiments}\n",
    "\n",
    "    \n",
    "project_helper.print_ten_k_data([sentiment_tfidf_ten_ks[example_ticker]], sentiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cosine Similarity\n",
    "Using the TFIDF values, we'll calculate the cosine similarity and plot it over time. Implement `get_cosine_similarity` to return the cosine similarities between each tick in time. Since the input, `tfidf_matrix`, is a TFIDF vector for each time period in order, you just need to computer the cosine similarities for each neighboring vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "\n",
    "def get_cosine_similarity(tfidf_matrix):\n",
    "    \"\"\"\n",
    "    Get cosine similarities for each neighboring TFIDF vector/document\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    tfidf : 2-d Numpy Ndarray of float\n",
    "        TFIDF sentiment for each document\n",
    "        The first dimension is the document.\n",
    "        The second dimension is the word.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    cosine_similarities : list of float\n",
    "        Cosine similarities for neighboring documents\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Implement\n",
    "    cosine_similarities = list(np.diag(cosine_similarity(tfidf_matrix, tfidf_matrix), k=1))\n",
    "    \n",
    "    return cosine_similarities\n",
    "\n",
    "\n",
    "project_tests.test_get_cosine_similarity(get_cosine_similarity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the cosine similarities over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f63eb629eb8>"
      ]
     },
     "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": [
    "## 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": 29,
   "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": 29,
     "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>ticker</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.98065125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.95951741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.98838551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.97472377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.97585100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date ticker sentiment      value\n",
       "0 2016-01-01   AMZN  negative 0.98065125\n",
       "1 2015-01-01   AMZN  negative 0.95951741\n",
       "2 2014-01-01   AMZN  negative 0.98838551\n",
       "3 2013-01-01   AMZN  negative 0.97472377\n",
       "4 2012-01-01   AMZN  negative 0.97585100"
      ]
     },
     "execution_count": 30,
     "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": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    }
   ],
   "source": [
    "import alphalens as al\n",
    "\n",
    "\n",
    "factor_data = {}\n",
    "skipped_sentiments = []\n",
    "\n",
    "for sentiment in sentiments:\n",
    "    cs_df = cosine_similarities_df[(cosine_similarities_df['sentiment'] == sentiment)]\n",
    "    cs_df = cs_df.pivot(index='date', columns='ticker', values='value')\n",
    "\n",
    "    try:\n",
    "        data = al.utils.get_clean_factor_and_forward_returns(cs_df.stack(), pricing, quantiles=5, bins=None, periods=[1])\n",
    "        factor_data[sentiment] = data\n",
    "    except:\n",
    "        skipped_sentiments.append(sentiment)\n",
    "\n",
    "if skipped_sentiments:\n",
    "    print('\\nSkipped the following sentiments:\\n{}'.format('\\n'.join(skipped_sentiments)))\n",
    "factor_data[sentiments[0]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alphalens Format with Unix Time\n",
    "Alphalen's `factor_rank_autocorrelation` and `mean_return_by_quantile` functions require unix timestamps to work, so we'll also create factor dataframes with unix time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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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