nlp_on_financial_statements.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 [00:04<00:00,  4.45filling/s]\n",
      "Downloading BMY Fillings: 100%|██████████| 27/27 [00:07<00:00,  3.80filling/s]\n",
      "Downloading CNP Fillings: 100%|██████████| 19/19 [00:04<00:00,  4.18filling/s]\n",
      "Downloading CVX Fillings: 100%|██████████| 25/25 [00:07<00:00,  3.44filling/s]\n",
      "Downloading FL Fillings: 100%|██████████| 22/22 [00:04<00:00,  5.02filling/s]\n",
      "Downloading FRT Fillings: 100%|██████████| 29/29 [00:06<00:00,  4.82filling/s]\n",
      "Downloading HON Fillings: 100%|██████████| 25/25 [00:06<00:00,  3.97filling/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example Document:\n",
      "\n",
      "<SEC-DOCUMENT>0001018724-17-000011.txt : 20170210\n",
      "<SEC-HEADER>0001018724-17-000011.hdr.sgml : 20170210\n",
      "<ACCEPTANCE-DATETIME>20170209175636\n",
      "ACCESSION NUMBER:\t\t0001018724-17-000011\n",
      "CONFORMED SUBMISSION TYPE:\t10-K\n",
      "PUBLIC DOCUMENT COUNT:\t\t92\n",
      "CONFORMED PERIOD OF REPORT:\t20161231\n",
      "FILED AS OF DATE:\t\t20170210\n",
      "DATE AS OF CHANGE:\t\t20170209\n",
      "\n",
      "FILER:\n",
      "\n",
      "\tCOMPANY DATA:\t\n",
      "\t\tCOMPANY CONFORMED NAME:\t\t\tAMAZON COM INC\n",
      "\t\tCENTRAL INDEX KEY:\t\t\t0001018724\n",
      "\t\tSTANDARD INDUSTRIAL CLASSIFICATION:\tRETAIL-CATALOG & MAIL-ORDER HOUSES [5961]\n",
      "\t\tIRS NUMBER:\t\t\t\t911646860\n",
      "\t\tSTATE OF INCORPORATION:\t\t\tDE\n",
      "\t\tFISCAL YEAR END:\t\t\t1231\n",
      "\n",
      "\tFILING VALUES:\n",
      "\t\tFORM TYPE:\t\t10-K\n",
      "\t\tSEC ACT:\t\t1934 Act\n",
      "\t\tSEC FILE NUMBER:\t000-22513\n",
      "\t\tFILM NUMBER:\t\t17588807\n",
      "\n",
      "\tBUSINESS ADDRESS:\t\n",
      "\t\tSTREET 1:\t\t410 TERRY AVENUE NORTH\n",
      "\t\tCITY:\t\t\tSEATTLE\n",
      "\t\tSTATE:\t\t\tWA\n",
      "\t\tZIP:\t\t\t98109\n",
      "\t\tBUSINESS PHONE:\t\t2062661000\n",
      "\n",
      "\tMAIL ADDRESS:\t\n",
      "\t\tSTREET 1:\t\t410 TERRY AVENUE NORTH\n",
      "\t\tCITY:\t\t\tSEATTLE\n",
      "\t\tSTATE:\t\t\tWA\n",
      "\t\tZIP:\t\t\t98109\n",
      "</SEC-HEADER>\n",
      "<DOCUMENT>\n",
      "<TYPE>10-K\n",
      "<SEQUENCE>1\n",
      "<FILENAME...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "raw_fillings_by_ticker = {}\n",
    "\n",
    "for ticker, data in sec_data.items():\n",
    "    raw_fillings_by_ticker[ticker] = {}\n",
    "    for index_url, file_type, file_date in tqdm(data, desc='Downloading {} Fillings'.format(ticker), unit='filling'):\n",
    "        if (file_type == '10-K'):\n",
    "            file_url = index_url.replace('-index.htm', '.txt').replace('.txtl', '.txt')            \n",
    "            \n",
    "            raw_fillings_by_ticker[ticker][file_date] = sec_api.get(file_url)\n",
    "\n",
    "\n",
    "print('Example Document:\\n\\n{}...'.format(next(iter(raw_fillings_by_ticker[example_ticker].values()))[:1000]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Documents\n",
    "With theses fillings downloaded, we want to break them into their associated documents. These documents are sectioned off in the fillings with the tags `<DOCUMENT>` for the start of each document and `</DOCUMENT>` for the end of each document. There's no overlap with these documents, so each `</DOCUMENT>` tag should come after the `<DOCUMENT>` with no `<DOCUMENT>` tag in between.\n",
    "\n",
    "Implement `get_documents` to return a list of these documents from a filling. Make sure not to include the tag in the returned document text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "\n",
    "def get_documents(text):\n",
    "    \"\"\"\n",
    "    Extract the documents from the text\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    text : str\n",
    "        The text with the document strings inside\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    extracted_docs : list of str\n",
    "        The document strings found in `text`\n",
    "    \"\"\"\n",
    "    \n",
    "    # DONE: Implement\n",
    "    \n",
    "    doc_list = []\n",
    "    \n",
    "    d_start_pattern = re.compile (r'<DOCUMENT>')\n",
    "    d_end_pattern = re.compile (r'</DOCUMENT>')\n",
    "\n",
    "    doc_start = [d.end() for d in d_start_pattern.finditer(text)]\n",
    "    doc_end = [d.start() for d in d_end_pattern.finditer(text)]\n",
    "    \n",
    "    for start,end in zip(doc_start, doc_end):\n",
    "        doc_list.append(text[start:end]) \n",
    "    \n",
    "    return doc_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": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Getting Documents from AMZN Fillings: 100%|██████████| 17/17 [00:00<00:00, 91.55filling/s]\n",
      "Getting Documents from BMY Fillings: 100%|██████████| 23/23 [00:00<00:00, 41.06filling/s]\n",
      "Getting Documents from CNP Fillings: 100%|██████████| 15/15 [00:00<00:00, 32.21filling/s]\n",
      "Getting Documents from CVX Fillings: 100%|██████████| 21/21 [00:00<00:00, 35.22filling/s]\n",
      "Getting Documents from FL Fillings: 100%|██████████| 16/16 [00:00<00:00, 50.36filling/s]\n",
      "Getting Documents from FRT Fillings: 100%|██████████| 19/19 [00:00<00:00, 47.39filling/s]\n",
      "Getting Documents from HON Fillings: 100%|██████████| 20/20 [00:00<00:00, 33.63filling/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Document 0 Filed on 2017-02-10:\n",
      "\n",
      "<TYPE>10-K\n",
      "<SEQUENCE>1\n",
      "<FILENAME>amzn-20161231x10k.htm\n",
      "<DESCRIPTION>FORM 10-K\n",
      "<TEXT>\n",
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http://www.w3.org/TR/html4/loose.dtd\">\n",
      "<html>\n",
      "\t<he...\n",
      "\n",
      "Document 1 Filed on 2017-02-10:\n",
      "\n",
      "<TYPE>EX-12.1\n",
      "<SEQUENCE>2\n",
      "<FILENAME>amzn-20161231xex121.htm\n",
      "<DESCRIPTION>COMPUTATION OF RATIO OF EARNINGS TO FIXED CHARGES\n",
      "<TEXT>\n",
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http:...\n",
      "\n",
      "Document 2 Filed on 2017-02-10:\n",
      "\n",
      "<TYPE>EX-21.1\n",
      "<SEQUENCE>3\n",
      "<FILENAME>amzn-20161231xex211.htm\n",
      "<DESCRIPTION>LIST OF SIGNIFICANT SUBSIDIARIES\n",
      "<TEXT>\n",
      "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http://www.w3.org/TR/h...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "filling_documents_by_ticker = {}\n",
    "\n",
    "for ticker, raw_fillings in raw_fillings_by_ticker.items():\n",
    "    filling_documents_by_ticker[ticker] = {}\n",
    "    for file_date, filling in tqdm(raw_fillings.items(), desc='Getting Documents from {} Fillings'.format(ticker), unit='filling'):\n",
    "        filling_documents_by_ticker[ticker][file_date] = get_documents(filling)\n",
    "\n",
    "\n",
    "print('\\n\\n'.join([\n",
    "    'Document {} Filed on {}:\\n{}...'.format(doc_i, file_date, doc[:200])\n",
    "    for file_date, docs in filling_documents_by_ticker[example_ticker].items()\n",
    "    for doc_i, doc in enumerate(docs)][:3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Document Types\n",
    "Now that we have all the documents, we want to find the 10-k form in this 10-k filing. Implement the `get_document_type` function to return the type of document given. The document type is located on a line with the `<TYPE>` tag. For example, a form of type \"TEST\" would have the line `<TYPE>TEST`. Make sure to return the type as lowercase, so this example would be returned as \"test\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def get_document_type(doc):\n",
    "    \"\"\"\n",
    "    Return the document type lowercased\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    doc : str\n",
    "        The document string\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    doc_type : str\n",
    "        The document type lowercased\n",
    "    \"\"\"\n",
    "    \n",
    "    # DONE: Implement\n",
    "    \n",
    "    regex = re.compile(r'<TYPE>10-K')\n",
    "   \n",
    "    matches = regex.finditer(doc)\n",
    "    \n",
    "    for match in matches:\n",
    "        #print(match)\n",
    "        doc_type = match\n",
    "        doc_type = doc_type[0]\n",
    "        #print(doc_type)\n",
    "        doc_type = re.sub(r\"<TYPE>\", \"\", doc_type).lower()\n",
    "        return doc_type\n",
    "\n",
    "\n",
    "project_tests.test_get_document_type(get_document_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `get_document_type` function, we'll filter out all non 10-k documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2016123...\n",
      "    file_date: '2017-02-10'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2015123...\n",
      "    file_date: '2016-01-29'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2014123...\n",
      "    file_date: '2015-01-30'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>amzn-2013123...\n",
      "    file_date: '2014-01-31'},\n",
      "  {\n",
      "    cik: '0001018724'\n",
      "    file: '\\n<TYPE>10-K\\n<SEQUENCE>1\\n<FILENAME>d445434d10k....\n",
      "    file_date: '2013-01-30'},\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "ten_ks_by_ticker = {}\n",
    "\n",
    "for ticker, filling_documents in filling_documents_by_ticker.items():\n",
    "    ten_ks_by_ticker[ticker] = []\n",
    "    for file_date, documents in filling_documents.items():\n",
    "        for document in documents:\n",
    "            if get_document_type(document) == '10-k':\n",
    "                ten_ks_by_ticker[ticker].append({\n",
    "                    'cik': cik_lookup[ticker],\n",
    "                    'file': document,\n",
    "                    'file_date': file_date})\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data(ten_ks_by_ticker[example_ticker][:5], ['cik', 'file', 'file_date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess the Data\n",
    "### Clean Up\n",
    "As you can see, the text for the documents are very messy. To clean this up, we'll remove the html and lowercase all the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_html_tags(text):\n",
    "    text = BeautifulSoup(text, 'html.parser').get_text()\n",
    "    \n",
    "    return text\n",
    "\n",
    "\n",
    "def clean_text(text):\n",
    "    text = text.lower()\n",
    "    text = remove_html_tags(text)\n",
    "    \n",
    "    return text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `clean_text` function, we'll clean up all the documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cleaning AMZN 10-Ks: 100%|██████████| 17/17 [00:32<00:00,  1.91s/10-K]\n",
      "Cleaning BMY 10-Ks: 100%|██████████| 23/23 [01:08<00:00,  2.96s/10-K]\n",
      "Cleaning CNP 10-Ks: 100%|██████████| 15/15 [00:49<00:00,  3.33s/10-K]\n",
      "Cleaning CVX 10-Ks: 100%|██████████| 21/21 [01:41<00:00,  4.84s/10-K]\n",
      "Cleaning FL 10-Ks: 100%|██████████| 16/16 [00:24<00:00,  1.51s/10-K]\n",
      "Cleaning FRT 10-Ks: 100%|██████████| 19/19 [00:49<00:00,  2.62s/10-K]\n",
      "Cleaning HON 10-Ks: 100%|██████████| 20/20 [00:55<00:00,  2.78s/10-K]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20161231x10k.htm\\nform 10-k\\n\\n\\n...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20151231x10k.htm\\nform 10-k\\n\\n\\n...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20141231x10k.htm\\nform 10-k\\n\\n\\n...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\namzn-20131231x10k.htm\\nform 10-k\\n\\n\\n...},\n",
      "  {\n",
      "    file_clean: '\\n10-k\\n1\\nd445434d10k.htm\\nform 10-k\\n\\n\\nform 1...},\n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    for ten_k in tqdm(ten_ks, desc='Cleaning {} 10-Ks'.format(ticker), unit='10-K'):\n",
    "        ten_k['file_clean'] = clean_text(ten_k['file'])\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data(ten_ks_by_ticker[example_ticker][:5], ['file_clean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lemmatize\n",
    "With the text cleaned up, it's time to distill the verbs down. Implement the `lemmatize_words` function to lemmatize verbs in the list of words provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import wordnet\n",
    "\n",
    "\n",
    "def lemmatize_words(words):\n",
    "    \"\"\"\n",
    "    Lemmatize words \n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    words : list of str\n",
    "        List of words\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    lemmatized_words : list of str\n",
    "        List of lemmatized words\n",
    "    \"\"\"\n",
    "    \n",
    "    # DONE: Implement\n",
    "    \n",
    "    lem_words = list()\n",
    "    for word in words:\n",
    "        words = word.split()\n",
    "        lem_words.extend([WordNetLemmatizer().lemmatize(w,pos='v') for w in words])\n",
    "\n",
    "    return lem_words\n",
    "\n",
    "\n",
    "project_tests.test_lemmatize_words(lemmatize_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the `lemmatize_words` function implemented, let's lemmatize all the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lemmatize AMZN 10-Ks: 100%|██████████| 17/17 [00:05<00:00,  3.2610-K/s]\n",
      "Lemmatize BMY 10-Ks: 100%|██████████| 23/23 [00:11<00:00,  2.0210-K/s]\n",
      "Lemmatize CNP 10-Ks: 100%|██████████| 15/15 [00:09<00:00,  1.6010-K/s]\n",
      "Lemmatize CVX 10-Ks: 100%|██████████| 21/21 [00:10<00:00,  1.9210-K/s]\n",
      "Lemmatize FL 10-Ks: 100%|██████████| 16/16 [00:04<00:00,  3.6810-K/s]\n",
      "Lemmatize FRT 10-Ks: 100%|██████████| 19/19 [00:06<00:00,  2.7410-K/s]\n",
      "Lemmatize HON 10-Ks: 100%|██████████| 20/20 [00:06<00:00,  3.0810-K/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20161231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20151231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20141231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'amzn', '20131231x10k', 'htm', '...},\n",
      "  {\n",
      "    file_lemma: '['10', 'k', '1', 'd445434d10k', 'htm', 'form', '1...},\n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word_pattern = re.compile('\\w+')\n",
    "\n",
    "for ticker, ten_ks in ten_ks_by_ticker.items():\n",
    "    for ten_k in tqdm(ten_ks, desc='Lemmatize {} 10-Ks'.format(ticker), unit='10-K'):\n",
    "        ten_k['file_lemma'] = lemmatize_words(word_pattern.findall(ten_k['file_clean']))\n",
    "\n",
    "\n",
    "project_helper.print_ten_k_data(ten_ks_by_ticker[example_ticker][:5], ['file_lemma'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove Stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Remove Stop Words for AMZN 10-Ks: 100%|██████████| 17/17 [00:02<00:00,  7.3110-K/s]\n",
      "Remove Stop Words for BMY 10-Ks: 100%|██████████| 23/23 [00:04<00:00,  4.6310-K/s]\n",
      "Remove Stop Words for CNP 10-Ks: 100%|██████████| 15/15 [00:04<00:00,  3.6610-K/s]\n",
      "Remove Stop Words for CVX 10-Ks: 100%|██████████| 21/21 [00:04<00:00,  4.3210-K/s]\n",
      "Remove Stop Words for FL 10-Ks: 100%|██████████| 16/16 [00:01<00:00,  8.2910-K/s]\n",
      "Remove Stop Words for FRT 10-Ks: 100%|██████████| 19/19 [00:03<00:00,  5.8710-K/s]\n",
      "Remove Stop Words for HON 10-Ks: 100%|██████████| 20/20 [00:02<00:00,  7.0410-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",
    "import pandas as pd\n",
    "import shutil   \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",
    "#shutil.copytree('../../data/project_5_loughran_mcdonald/', '/home/workspace/data')\n",
    "sentiment_df.columns = [column.lower() for column in sentiment_df.columns] # Lowercase the columns for ease of use\n",
    "\n",
    "# Remove unused information\n",
    "sentiment_df = sentiment_df[sentiments + ['word']]\n",
    "sentiment_df[sentiments] = sentiment_df[sentiments].astype(bool)\n",
    "sentiment_df = sentiment_df[(sentiment_df[sentiments]).any(1)]\n",
    "\n",
    "# Apply the same preprocessing to these words as the 10-k words\n",
    "sentiment_df['word'] = lemmatize_words(sentiment_df['word'].str.lower())\n",
    "sentiment_df = sentiment_df.drop_duplicates('word')\n",
    "\n",
    "\n",
    "sentiment_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bag of Words\n",
    "using the sentiment word lists, let's generate sentiment bag of words from the 10-k documents. Implement `get_bag_of_words` to generate a bag of words that counts the number of sentiment words in each doc. You can ignore words that are not in `sentiment_words`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict, Counter\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "\n",
    "def get_bag_of_words(sentiment_words, docs):\n",
    "    \"\"\"\n",
    "    Generate a bag of words from documents for a certain sentiment\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    sentiment_words: Pandas Series\n",
    "        Words that signify a certain sentiment\n",
    "    docs : list of str\n",
    "        List of documents used to generate bag of words\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    bag_of_words : 2-d Numpy Ndarray of int\n",
    "        Bag of words sentiment for each document\n",
    "        The first dimension is the document.\n",
    "        The second dimension is the word.\n",
    "    \"\"\"\n",
    "    \n",
    "    # DONE: Implement\n",
    "    \n",
    "    bag = np.empty([0,len(sentiment_words)],dtype=int)\n",
    "    vectorizer = CountVectorizer()\n",
    "    vectorizer.fit(sentiment_words)\n",
    "    for doc in docs:\n",
    "        doc=[doc]\n",
    "        vector = vectorizer.transform(doc)\n",
    "        vector = vector.toarray()\n",
    "        bag = np.append(bag, vector, axis=0)\n",
    "\n",
    "    return bag\n",
    "\n",
    "\n",
    "project_tests.test_get_bag_of_words(get_bag_of_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `get_bag_of_words` function, we'll generate a bag of words for all the documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  {\n",
      "    negative: '[[0 0 0 ..., 0 0 0]\\n [0 0 0 ..., 0 0 0]\\n [0 0 0...\n",
      "    positive: '[[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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import jaccard_similarity_score\n",
    "\n",
    "\n",
    "def get_jaccard_similarity(bag_of_words_matrix):\n",
    "    \"\"\"\n",
    "    Get jaccard similarities for neighboring documents\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    bag_of_words : 2-d Numpy Ndarray of int\n",
    "        Bag of words sentiment for each document\n",
    "        The first dimension is the document.\n",
    "        The second dimension is the word.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    jaccard_similarities : list of float\n",
    "        Jaccard similarities for neighboring documents\n",
    "    \"\"\"\n",
    "    \n",
    "    # DONE: Implement\n",
    "    \n",
    "    bag_of_words = bag_of_words_matrix.astype(bool)\n",
    "    jaccard_similarities = [jaccard_similarity_score(u,v) for u, v in zip(bag_of_words,bag_of_words[1:])]\n",
    "    \n",
    "    return jaccard_similarities\n",
    "\n",
    "\n",
    "project_tests.test_get_jaccard_similarity(get_jaccard_similarity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `get_jaccard_similarity` function, let's plot the similarities over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ee8103f98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get dates for the universe\n",
    "file_dates = {\n",
    "    ticker: [ten_k['file_date'] for ten_k in ten_ks]\n",
    "    for ticker, ten_ks in ten_ks_by_ticker.items()}  \n",
    "\n",
    "jaccard_similarities = {\n",
    "    ticker: {\n",
    "        sentiment_name: get_jaccard_similarity(sentiment_values)\n",
    "        for sentiment_name, sentiment_values in ten_k_sentiments.items()}\n",
    "    for ticker, ten_k_sentiments in sentiment_bow_ten_ks.items()}\n",
    "\n",
    "\n",
    "project_helper.plot_similarities(\n",
    "    [jaccard_similarities[example_ticker][sentiment] for sentiment in sentiments],\n",
    "    file_dates[example_ticker][1:],\n",
    "    'Jaccard Similarities for {} Sentiment'.format(example_ticker),\n",
    "    sentiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TFIDF\n",
    "using the sentiment word lists, let's generate sentiment TFIDF from the 10-k documents. Implement `get_tfidf` to generate TFIDF from each document, using sentiment words as the terms. You can ignore words that are not in `sentiment_words`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "\n",
    "def get_tfidf(sentiment_words, docs):\n",
    "    \"\"\"\n",
    "    Generate TFIDF values from documents for a certain sentiment\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    sentiment_words: Pandas Series\n",
    "        Words that signify a certain sentiment\n",
    "    docs : list of str\n",
    "        List of documents used to generate bag of words\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    tfidf : 2-d Numpy Ndarray of float\n",
    "        TFIDF sentiment for each document\n",
    "        The first dimension is the document.\n",
    "        The second dimension is the word.\n",
    "    \"\"\"\n",
    "    \n",
    "    # DONE: Implement\n",
    "    \n",
    "    vectorizer = TfidfVectorizer(vocabulary = sentiment_words)\n",
    "    tfidf = vectorizer.fit_transform(docs).toarray()\n",
    "    \n",
    "    return tfidf\n",
    "\n",
    "\n",
    "project_tests.test_get_tfidf(get_tfidf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `get_tfidf` function, let's generate the TFIDF values for all the documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "\n",
    "def get_cosine_similarity(tfidf_matrix):\n",
    "    \"\"\"\n",
    "    Get cosine similarities for each neighboring TFIDF vector/document\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    tfidf : 2-d Numpy Ndarray of float\n",
    "        TFIDF sentiment for each document\n",
    "        The first dimension is the document.\n",
    "        The second dimension is the word.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    cosine_similarities : list of float\n",
    "        Cosine similarities for neighboring documents\n",
    "    \"\"\"\n",
    "    \n",
    "    # DONE: Implement\n",
    "    \n",
    "    cos_similarity= cosine_similarity(tfidf_matrix[0:], tfidf_matrix[1:])\n",
    "    \n",
    "    return cos_similarity[0].tolist()\n",
    "\n",
    "\n",
    "project_tests.test_get_cosine_similarity(get_cosine_similarity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the cosine similarities over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ee80d9cf8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cosine_similarities = {\n",
    "    ticker: {\n",
    "        sentiment_name: get_cosine_similarity(sentiment_values)\n",
    "        for sentiment_name, sentiment_values in ten_k_sentiments.items()}\n",
    "    for ticker, ten_k_sentiments in sentiment_tfidf_ten_ks.items()}\n",
    "\n",
    "\n",
    "project_helper.plot_similarities(\n",
    "    [cosine_similarities[example_ticker][sentiment] for sentiment in sentiments],\n",
    "    file_dates[example_ticker][1:],\n",
    "    'Cosine Similarities for {} Sentiment'.format(example_ticker),\n",
    "    sentiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate Alpha Factors\n",
    "Just like we did in project 4, let's evaluate the alpha factors. For this section, we'll just be looking at the cosine similarities, but it can be applied to the jaccard similarities as well.\n",
    "### Price Data\n",
    "Let's get yearly pricing to run the factor against, since 10-Ks are produced annually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pricing = pd.read_csv('../../data/project_5_yr/yr-quotemedia.csv', parse_dates=['date'])\n",
    "pricing = pricing.pivot(index='date', columns='ticker', values='adj_close')\n",
    "\n",
    "\n",
    "pricing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dict to DataFrame\n",
    "The alphalens library uses dataframes, so we we'll need to turn our dictionary into a dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style 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>0.95001565</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>0.93176312</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>0.89701433</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>0.85838968</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 0.95001565\n",
       "2 2014-01-01   AMZN  negative 0.93176312\n",
       "3 2013-01-01   AMZN  negative 0.89701433\n",
       "4 2012-01-01   AMZN  negative 0.85838968"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_similarities_df_dict = {'date': [], 'ticker': [], 'sentiment': [], 'value': []}\n",
    "\n",
    "\n",
    "for ticker, ten_k_sentiments in cosine_similarities.items():\n",
    "    for sentiment_name, sentiment_values in ten_k_sentiments.items():\n",
    "        for sentiment_values, sentiment_value in enumerate(sentiment_values):\n",
    "            cosine_similarities_df_dict['ticker'].append(ticker)\n",
    "            cosine_similarities_df_dict['sentiment'].append(sentiment_name)\n",
    "            cosine_similarities_df_dict['value'].append(sentiment_value)\n",
    "            cosine_similarities_df_dict['date'].append(file_dates[ticker][1:][sentiment_values])\n",
    "\n",
    "cosine_similarities_df = pd.DataFrame(cosine_similarities_df_dict)\n",
    "cosine_similarities_df['date'] = pd.DatetimeIndex(cosine_similarities_df['date']).year\n",
    "cosine_similarities_df['date'] = pd.to_datetime(cosine_similarities_df['date'], format='%Y')\n",
    "\n",
    "\n",
    "cosine_similarities_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alphalens Format\n",
    "In order to use a lot of the alphalens functions, we need to aligned the indices and convert the time to unix timestamp. In this next cell, we'll do just that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n",
      "Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions).\n",
      "max_loss is 35.0%, not exceeded: OK!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>1D</th>\n",
       "      <th>factor</th>\n",
       "      <th>factor_quantile</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>asset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1994-01-01</th>\n",
       "      <th>BMY</th>\n",
       "      <td>0.53264104</td>\n",
       "      <td>0.44665176</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CVX</th>\n",
       "      <td>0.22211880</td>\n",
       "      <td>0.64730294</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRT</th>\n",
       "      <td>0.17159556</td>\n",
       "      <td>0.46696523</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1995-01-01</th>\n",
       "      <th>BMY</th>\n",
       "      <td>0.32152919</td>\n",
       "      <td>0.43571094</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CVX</th>\n",
       "      <td>0.28478156</td>\n",
       "      <td>0.62523346</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         1D     factor  factor_quantile\n",
       "date       asset                                       \n",
       "1994-01-01 BMY   0.53264104 0.44665176                1\n",
       "           CVX   0.22211880 0.64730294                5\n",
       "           FRT   0.17159556 0.46696523                3\n",
       "1995-01-01 BMY   0.32152919 0.43571094                3\n",
       "           CVX   0.28478156 0.62523346                5"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import alphalens as al\n",
    "\n",
    "\n",
    "factor_data = {}\n",
    "skipped_sentiments = []\n",
    "\n",
    "for sentiment in sentiments:\n",
    "    cs_df = cosine_similarities_df[(cosine_similarities_df['sentiment'] == sentiment)]\n",
    "    cs_df = cs_df.pivot(index='date', columns='ticker', values='value')\n",
    "\n",
    "    try:\n",
    "        data = al.utils.get_clean_factor_and_forward_returns(cs_df.stack(), pricing, quantiles=5, bins=None, periods=[1])\n",
    "        factor_data[sentiment] = data\n",
    "    except:\n",
    "        skipped_sentiments.append(sentiment)\n",
    "\n",
    "if skipped_sentiments:\n",
    "    print('\\nSkipped the following sentiments:\\n{}'.format('\\n'.join(skipped_sentiments)))\n",
    "factor_data[sentiments[0]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alphalens Format with Unix Time\n",
    "Alphalen's `factor_rank_autocorrelation` and `mean_return_by_quantile` functions require unix timestamps to work, so we'll also create factor dataframes with unix time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "unixt_factor_data = {\n",
    "    factor: data.set_index(pd.MultiIndex.from_tuples(\n",
    "        [(x.timestamp(), y) for x, y in data.index.values],\n",
    "        names=['date', 'asset']))\n",
    "    for factor, data in factor_data.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Factor Returns\n",
    "Let's view the factor returns over time. We should be seeing it generally move up and to the right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ee7b84a90>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ee80b3c88>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls_factor_returns = pd.DataFrame()\n",
    "\n",
    "for factor_name, data in factor_data.items():\n",
    "    ls_factor_returns[factor_name] = al.performance.factor_returns(data).iloc[:, 0]\n",
    "\n",
    "(1 + ls_factor_returns).cumprod().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basis Points Per Day per Quantile\n",
    "It is not enough to look just at the factor weighted return. A good alpha is also monotonic in quantiles. Let's looks the basis points for the factor returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f6ee80dbf28>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6ee80861d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6ee772de48>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f6ee7714898>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6ee78ea9b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6ee78eab00>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f6ee774de80>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6ee76cc710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6ee779f358>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f6ee77919b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6ee7786208>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6ee77f0cc0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f6ee76e8390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6ee77d3160>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6ee783d2b0>]], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ee80ce0b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "qr_factor_returns = pd.DataFrame()\n",
    "\n",
    "for factor_name, data in unixt_factor_data.items():\n",
    "    qr_factor_returns[factor_name] = al.performance.mean_return_by_quantile(data)[0].iloc[:, 0]\n",
    "\n",
    "(10000*qr_factor_returns).plot.bar(\n",
    "    subplots=True,\n",
    "    sharey=True,\n",
    "    layout=(5,3),\n",
    "    figsize=(14, 14),\n",
    "    legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Turnover Analysis\n",
    "Without doing a full and formal backtest, we can analyze how stable the alphas are over time. Stability in this sense means that from period to period, the alpha ranks do not change much. Since trading is costly, we always prefer, all other things being equal, that the ranks do not change significantly per period. We can measure this with the **Factor Rank Autocorrelation (FRA)**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ee7510438>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OsWXxSbJOuzF8RiBKk+5/kFwJB3IO8MK+F5i9qgS7Ghk+3/5MKkd45+hnfG/+JwujF9LXqW+3lpmVWMr27xNpbNIw8vZAclPKOXeimCilCalFCcQVxBFXEEd8YTxVjVUAuFu6M8JjBAOdBxLQ5MKOrQsxtVVTU1ZKqasdfY+U8rTn0/hZ+3F/7/uZ4DsBhaz146q6rIG0hGJc/a3JO1vB4U1pRE31B6BJ18QXx75gyYkl+Fr7smjsIoLtgi/kTciuoFGjA+BIqo4XBr/A3H1z+SHxB+4Jv6db2wjgyIY1JO3fzXCnLEobTDiw6mccvLwJjBzaYb6q7dvR1dRgPeXmbrepPYwrn7uAXC6nylSgqK6+1qb0OJcrDPn5xyiUCyLsQnvQqrYRMhl2cjllwpT5QwX51Q2c8lYSOMiZg2vPcuS3tL90/+Lsala9dYTEA3n0v96bqU/3R+1ghtrBjKlP96f/9d4k7s9l1duxlOR0/Nmora7mu+9XcvBU+oWHUXfSpG3ig9gPeGj7Q4xIVTL8pBbHRx7BvHcE9/W+j6UTliKE4O7Nd/N1wtdoddq/XKZWq+Pg2lQ2fHIMUwsl058fiE1fOVuVu2ms13LvZ09xx+938EHcByQUpOKtiuJ6xye5x+sbbnX6Al/pPopzwtj++ffolGaIGx4EoHHAQEJSG/gg/CVkQsbcfXOZvG4ya1PW0qRtamFD4r4cJElizN2hhAx15ejWDPLOVpBdlc3dm+/mmxPfcEvgLSy/aXkLUQA4nKZ3TnjambEmPpuJfhMZ4zWGT49+SkpZyl9un+acjYth7/KlBA8ZiolrLkEeabi42vLHZx9QlNHx57Ri3TqUHh6YDxzYrTZ1xD9yP4ZrQbWZAlVt3bU2o8eR29igrei6KykhZRMAfbxH95RJHWLv4ERqdTlO+UeY1n8sS/ans/nfI5DJBYc3pqHTSkRO8r2s2SaSJHFydw77V6eiMlcw+Ym+eIbYtUgjl8uImuqPR7At275PZNWCWIZPDyRshFursvLPprDy3bdoKiti9+ZlLLIJQwofxYBePkT62tHPywZT5ZX3OLKqsnhuz3OcKD7BbOeJTFm0C5OwMBwefuhCmgjHCFZNWsUbB9/g06OfEpMXw9sj3sbJ/MpcYZXFdWxdcoqCtEpCR7gReKOaRWc+ZtWZNWilJqabvkhw9nXENniirfOlSmvFxcnMxYYDBpcdJrI8h01ON5B2qIJ7LZwoVMpw1Wrpf6yGNfeu4c/MP/kq4StePvAyXx7/kvvC72Nq4FTkKEjcl4tXqD3WjmYMnx5IdlIZGxbH8mPYa+jkGt4d+S43+LS99jYmrZRgZytu6e/O238kkVZcw8tRLzN1/VTm7pvLshuXoZQrr6h9mlOclcFvn7yHs68/TSMcmHPSGb+mJn7wESw/YM66d9/gjrc+xFxt3SpvU14eNQcP4fCvfyFkV+893thj6CK1ZkrMa+uvtRk9jtza+rJ6DAn5sZhIEsH+N/agVe3j7B8CQMGpWJ6bEIyJQsb8308z5q4QQoe5Evt7Ogd/Patff9IF6mua+GPRCfasSMY92JaZL0W2EoXmeIbaMeulSNwDbdi97Aybvz5JfY3+rVbS6YjdtJbl856hoaGRna7X4xA+kN7lJwjf9xnxvyzhgS+3E/HqVqYvOsC7W5LYnVxEdYOm3fIu5Y+0P5ixcQbpFem8H/0et2+sQqqpxW3hAoSy5UPNysSKhdELeX3o65woPsG0DdPYnbW7y2WdJyW2gF/ePExZfi0DZ7uw23cFkzbdxOqU1Wir+hPBAiZMjMKpwY5t0x8iYd40Trw6vtWx404fBlceJWj4aDa8+xAPRvuRrHQjLyMNZZ8IKtatQyAY4z2GXyb+wudjPsfR3JE3Y95kwpoJ/PDbr9RUNBI+Ur/0SaNoJGtgDJpywaicGayavKpdUdBodcSllxLpa8fUfu7IBPwan4OdqR2vRL1CUmkSixIWXXbbXEpdVSXr3n0DpUqF4pZ+vHLqM1w1Ws6amHCsLIabn36RmvIyNn7wNlpNU6v8Fes3gCRdVTcSGIWhy9SZq7Coa/2P+1/jcl1JCTVZhGKCUnV5K6W7C4fQMORaHXnncnCyMuXfYwLZkVTIruQiRt3Ri/CR7hzdlsm+VSmdikNuShm/vHmYjJMlDLs1gImPRmCuNunUBnO1CRMf68PQWwJIP17ML/MPk3Ysg7ULX2P3j0vw6z+Q3aF3Ydc7kvtfeon7P/qKvqPH0KfmNPfmLON+cQRZdSmLdp9jzreH6fPaVm7+bB9v/X6a7YkFVNS2/tzVNtXy8v6XeXbPswTYBLB68moij9ZSvWMHjv/5D6qAticCCCGYGjiVFRNX4GzuzGM7H2Ph4YU0ahs7rWdTg5adP55m6zenMHdSkHX9bh46dzsbz25kWuA0pjp8SlX2VF4cP4I+w9xRquScO5SPlamy1WEq07H7m0+xtLVn/H0PY2WqZESgAxmmnui0GmqjBtOQkkL9qcQLdkd7RPPThJ/4etzXeKo9Sd5fRI2qnJ1s4FjhMWb9Notfqr5D17sY1/RQtJntz2pKzKukplFLpK8dTmpTooMc+TU+G51O4jqv65jsP5klJ5ZwouhEp+3SHlqNhk0fLaC6pBjz6ZHMT3yPoTbB/JqTh4tQ8b1Kh4ta4vqHniD79En+/P7rFvklSaJi7VrMBw7ExLPnZkq1hdGV1EUaLMyxzL68Qdl/InIbG3SVlUgaDULR8cejqaGGRKmBmZb+V8m61pj6+6Oub6CwUkBlHnOG+rD8SCavb0pk2H+iiZ4VhFwu4/jOLLQaiZGzghCylq4enVZH7O/pxP6ejtrBjGnPDsDJW91OiW0jZIJ+471wC7Rh06cb+XXBJwjRwOh7HsZ3xDj+8+Z2nuqv73nYuLgy/qEnGDJtFofXr+Hkzi0M1sUyO2okJgPGcaJaxbHkEvbvzCStMZNz2jKcVUnUWJ8hXoRyWGlLmeNaUBQhqxzLyczrmbNnLwt+W0CGkz+zs9yQ3tgGgJ2FCV/fOQA/x5bTOP2s/fj5pp/5IPYDfjr9E3EFcbwT/Q4+1j5t1q84u5qt35ykrKCW8tCzLLb6HGWxkttDbufusLtRSDaMWLiD+V6xhK54BjT1BJnMJunQUIblzsRUXtPifvuynCkrtOfWwAxUn+lDYAyX4E7LIVTKFRSZm+BuYkLFunWYhYddbGchiHKLIkTeh5/XHSIvNIEfjy4BwNHMkcXjFzPAfiAr3zrCzh+SmDUvElOL1u4g/fiCRN+3Z3GuRMdDvUfyabEVh456MXRAIM9HPs/h/MPM3TeXVZNWYapoKTLnjhZxaP1ZgiKd6T3aE5VZ6+/K7h+XkHkyAaubI3kvdzGjPEfxvu1gTI5uZbbPBN5LW8epxFWEjZpHUVYGR9avxsHLl77j9b3vumPHaMzIwP7BB9v72PUYRmHoIhoLSyzrJLRaLfJujK30d+PCIrfKyhZzztsi+dxmGmSCCJerNyh2KSY+PqjrGsmxsETKPIRJ+FRenhjK3d8d4dv9aTw80p9h0wOQKwXxWzLRaXSMmt0LmUEcqkrr2fbtKfJSKwge4kL0rCBMTK/sa6HVaEg+tJby3NWozB2QZLeQedqNQrsiJAkifVu2p9rBibH3PcKQqTPYv2oliXu2odu/C1OrEAaLgcjkDijkGixpILs2molmO8FuD3/a2WIvCe6pDcDcypM0JyWDflyNUkDy3U9yo93F0A7rjubw6sZElt4zqNXYh0qu4oXBLxDlFsW8/fOYsWkGcwfP5Wb/my+kPT/esm91Mo2Kev4IWUKZfTZzes3hrtC7sDezB+CDjYd5R/qQmwpjwGsoOIcSXinj1G4Tkiweoq//xQHWzPxa4uOy6NfLBu/IiwPCoiyDB+t+4x3LyWQmnSJ4zHVUbtqE87PPIExa9txO7slBJhO8MOcRpjeOZm/2Xm4PuR07U30bj70nlNUL49j7SzLj7g3jUmLSSnm17leqjxVhYq/EdufvzKuvhzuWcjbAH/OBA1kQcBNPln/Dx/Ef81zkcxfyphwpYNt3iZhZKYnZkMbRbVlEjPagz3WemFrqRShhx2aObt6IanAAnzatYpz3OBaOWIjysL5XMK3vwyw6t47vsrbwHvMYPutOSrIy+PP7r7Bz88ArPIKKtesQZmZYXX99Fz+B3YdRGLqIpLZGoYPCwjxcXf934/JcEIby8k6F4XjGTgD6BEzscbvaQ25lhY1MSQZyyk7txS58KqOCnRgb4synO1KY2s8dZ7UpQ6b4I1PIiP0tHZ1W4rq7epGeUMLOH0+j00qMvSeU4MEuV2xHRWEBv33yDnkpZwgfPZ7Rcx7g7NEy9qxIpjGtgmCVgj6e+raVJImKwjpyU8rJTSknJ6WM6tJglBaeoD1KU+1RdNpEPL29GCE24DDsTn464Mdq7X9Yav8qwy3cebPBFLuCWCjcSWmyBQVnrXG5JZjn++WDtx/Y+oAQ+DhY8MamRLafLmRcaNuxgEZ5jmL1pNU8v/d55u2fx8Hcg8wbMg9Fk4p138RQcrqRTJtEDvdaz7Q+NzM7ZDY2pjYX8pee2cvM2LtxkZfB2Ndg6BMgk+EAuGTFcbJ4AH0eexQhEzTU1rL5mUexdXVnxNyPQdXsTVzTSMGHwxmsOklsjjuKux5G+8dmqnbvRt1sYZemUUvSwTz8+jliYa2iD31aBcFz8lYz8EYfjmxKw7ePY4v1JjqdRMG5k4w8uocmUxm+08wRjx3koy82ULT/EA+oK6jcuAnzmhq+AvJtvidhyFm8Rt5IjiqI3RvzcQ2w4aZHI6goqiPO0Ns8viOL8JHuOHlVs2PJIuR+jnxtu4MJPhN4a8Rb+um2tSUg5FhauTFd5cHShiyyKjLxtPbixsefYdlL/2XjRwu47eW3qfzjD9TjxyG3vPpuWqMwdBG5jX5xdk52+v+4MOhnRnRlkVtCSSJOWgln52sbmdLRyRk0VRQkn+C8lM2bGMK4D/aw8I8kPpjZFyEEgyf5IZcLYjakUZRVRWluDY5eVoy/PwwbpytfOHTm4D62ff0pkiRx07+fpdfQaABChprh4mfNVwsOM7lKyf5lZ9A06shNKae2Uu/TN7NS4hZoQ79xtrgF2mDvdhP1NVXEr1jE0Z07Wabri4N5A3Hm39Mr51aeMHuFe2+dql/0pdPScGQrhb8+i0WAOTbqY7D+T71RanfwHsoc7+Gsc3ThjU2JjAh0aHf2k7OFM9+M/4bFJxbz5fEvyUwpJvLkFOT1KhL8tzNofAAvhfyKlYnVxUw6Hez/EJsd86nBjoJb1+MWPqLFfcNHurP9u0Syz5ThGWLHn0u/prqkhFmvv4NSdckYgMKEgus+InDVDGJxp0glR+XoQMXadS2EISW2kIZaDeHRbcbbvMCACd6kJxSze9kZXAOssbDWL+5LyS/ntbyPqS9UohrrSVFFJs4mJoy8ZQy35JvRd1oE0/u5Up90horDBzi5aRFWu/cTd0xDUvDt2NVm0D8vlbotWThMmsQND/WmJLeauD8yiN98ksbKn8HUnF89UpkUOJnXh75+ccFcbQmY24MQ3BFwCz+e+pif4j/mhdHvozI3Z8qz81g29ynWzZ/HoJpqrKdMuVCfkuoGfj+Rx9T+HliqevbRbRSGLqKy079xFOVldZLyn03zHkNnJDQUE6G0uarT6NrCwTcAWXI8hXklhDTVgdIMb3sLHoj25fM/z3LHEC8GeOslY+CNvsgUMg6tPUvfsZ4MmeKPXHFl9jc11LNr6Tck7NiMS0AQNz3xLDbOLXsdJrYqvjOr4xE3e5IO5mNpq8Kjl14E3AJtsHE2b+XiMdNVMax8MQMGWnLM/Ql2/bYG30Zo4As0G53ZWV2CT0QEbgFBFHzwPcLMAtclGxCODlCUBBn79UfaHhQnVvGrypaPqsfxw582PDi+X7v1kcvkPNznYfpZDOLAR/k0Kuqwv7WST6JfxVx5iXBW5cOvD0Labv7QRRETNo/Xw4e1uqd/f0f2rVRyck8OjbUpnNq1ncFTZ+AW1KtVWoDgPoP5eO0k1IpU0vdtInLyZEqX/oCmpASFvd5tdXJPDrYu5rgF2bR5jwv1kcsYe08oK+cfYdfPZ7htf9ftAAAgAElEQVTxkd4IIajd+T62Jyupt3Nge60lVUUBeL/5EkOmzcLPwYLV8dnMGOSJWXgYZuFhBE2KZMG3ixieNh0X23oGmiXScOgwuZvWITU1YXPrrdi7WTL6Dn9yTn1BQ7UGpXI6M0/ZEW7rQU1II2p7Q/iU88IAOAffxI0xb7E2ayeP1JdjY2qDrYsbE598njVvvkRCkBdhgwZdqM/K2GwWbk5isJ89Qc5WbVW52zAKQxdRO+i74RWFedfYkp6lq8JQWppKlhym2wZ3mO5qYOrnh9Xxg+TXmENOPPjoH1D/GhXAmrgcXt2QyLpHhyE3jCv0H+9NxCgPFH9htXJxZjqbPn6HkuxMBk2exrCZdyJvY7D+aGY5DZJEvyl+DPWxQ6GUdbymorEWVtwOTXWY3r+RILUTD9d+yMMOtxFQ6MyJPw+TsP13jm/dAIBVXQOe14+Cc8l4qJRYOIeCcyhEPqAP35F1GOXe93gmZSWV+zdRpX0Iq5FPgHnbbkJJkij8Q46ZsGD2s8NxdGs9t56U7bD2IWisYa3n8zx7LoKd49oWHIVSrl94tu0MabHLcfT27TAMhEoh54T3ndxY9DKZyWcZ+9+ZlC75lspNm7CbM4eizCoK0ysZPiOwS2tT7FwtGDLFj/2rUzl9II9Q/xL89iwht8QG8fgdVO3ZSqBVMdnpZ/nl1eeZ6OrPr1IoGcUReDsYXDgJdgxPm0667UlCHuyNt+/7SJJExuw7KfzgQ6zGj0dmackfn39ASW4G2wcUEN03g6iC3pzen8fpfXkER7nQ/3pvbGouCgNqN+4WNmyQNPxy5hce6qNfd+Lm5EZIbgmJbvYcWL2c4bPuRKeTWHY4g8G+dj0uCmAUhi5j66J3H9WVFF5jS3qWrgrDiWT9gynCM7rHbeoME18f1HUNFDToB6CFQRgsVApeuLEX/15xjFWxWcyKvBg/6UpFQZIkErZvZtfSxZiYmzNt7uv49OnfbvrDaSXIBAzwtu08bIYkwYbHIO843LYCnHpxNGMHWrnEkCET6OvUFxOLYZzYncGw0fVkLXqHCk83UtJTSfxoAQC2bh54hIThERKOR0g4aq/BcMcq8pMOkbDsJcbHfAhHF8Og+yDqcbBsueHLqb25ZJ4qZcTMoNaioGmEHa/Bwc/AKYy8cZ/z7Pd5zBjoiadd+6640BGuxKz9HElbxfSX3kCu6HjR2JAAJzYkjGCg9ggV+9/HNDyc8nXrsZszh5O7s1GYyOg1pOvjQX2u8yTteDH7Vibj5rOAohNqqmwdqDJVolDIud41GdkDi0g4kU7M+jVMrdjI6tdOcMu9d1Na6MChtefw6WvPny57ef3wr/Rz7YutqS0uL84lbdqtFH/+OakBXqQcPkBsr1JGj7yVZwc9jRCCQTf5Er81k8R9uSQdyCNQPYYBvTIuuDwDvUcxPOcPliUt4+7wu1HJVVRu3IB3UTncPImYtb/g4OVNnn0vskrrePb6tnta3Y1xHUMXcXXXx91pKiu5xpb0LDJLS5DLOxWGhLwY5JJEaOCkq2RZ+6h8fbGubaRRUlB5JqbFtcl93BjkY8s7W85Q0Q3rUPb8/B3bv/kc95Aw7nrn0w5FAeBweilhbtZd8wnv+wBOroGxr0CwfmFWbEEsKrmKMHv9zJrIyX6ozE05vbmMII1g1sdf8ei3K7j9zfeJvuMebF1cST64jz8+e5/Fj97Dt/95kIJzqbj0GkJi9BeMb1hIsdtoOPApfNQb/ngeKnMBqCiqZf+aVDx62dJ75CX++5Kz8O14vSgMuh8e2MGHx/W9n8eu6zh4Yl7yEXRNKZhZj8Deo/P4VVH+9hwz1YdYSU8+i3U/RxpOn6byeCLJRwoIHOSMyrzrK5KFTDBmTghoG9l+eiJNZXKKp80hOWY/vr38Ucm1KLWVDLhpCg9+toSsXhOoLSvh1wWvsnvpGzh5FXH9fWHMj36TisYK3jj0BpIkYRoais2MGZzasJZDq5eT6l7NoEnTeHbQsxd6M5a2pkTPDOLON6PoM8aTc5VhLI+ZQtzmdL1xvtHcXVZKaX0pG89uRJIkytetw7xfP8b/5xnce4Wy5cuPWbPlIA6WKq4Pu/IJEpeDURi6iLu74W2z8n97LYMQQr/6uZOwGAmVaQRJCszacUlcTZQeHqib9LF/Cs5ejIAK+vq8OjmM8tpGPtqe/JfKkXQ6Tv65jYBBUUx74TUsbDrema9Bo+VoZnmraaptkvQ77HgDek+HYf+5cDquII4+jn0uhGYwtVASZpFGqcqTxgdeR2Fri1yhwDUwmEGTpzH1uVf415Jl3LnwE0bPeQCtRsOqN14kNzmJh0f6U2MdxOzyB9E8EgPht8Dhr+HjPug2PsmOb44hkwmuuyuk5VqPhJXwVTSUpsHMn+Cm90mr0LEmPoc7Bnvhat1++PGq0mJ2fPsldu7+aKW+pCd0/mIV4W6N3FyNxsaNDK0v6vq1oFBwYkUMmkZdp4PObaGuTWC4+VcUyEI54T8Rm0Eh1FaU02tQpD5BjT5Eh8LEhJE338wZuztRmI9DadJE5vEfWTb3P3CmkH9F/IttGdv4Pe13AHS3TOa4hwMmmnpCZ03hqQFPtenisrBWMewWf+5yeggftzKObEqnuqwefEYQWd9AiIk9S08tpfZEAo2pZ7GeOgW5Qsnkp+ZiYmmFw6FlzAxTY3KF42GXi1EYuoiZmTnVKpBXVV1rU3qczlY/azWNnNDVEmH299gxTSgU2Ds6I4DCSqAktcX1MDdrbov04oeDGSQXXPn/rygznfrqKgIjo7o04H4iu4IGja5zYSg8Db8+AG59YfKnYHiwVDVWcabsDAOcL24sVRt/FOs172ItryL+tAlNja2D4clkcpx8/Oh/483MfHUBZlZqVs+fR3HqaeZNDCEpv4plZ01gyhfwRDz0vYNje8vJy6gnOuAgVlKO/kYN1bD2Eb1tLr3h4X0Qou8hfrw9GaVc8Mio9hc3SpLE1q8+RavRMPmpZ7CyM+PknuxO200hlxHpa0e6qQe5FQKdqQpLXyUpeWY4eVle9uJDfT0ewq06CYfiBAo9xlMSH4NSZYrvIMOAeW3JBZsdztUR1WhKnc9AHln8LRMefQqNRsOmjxYi/z6OUZW9eOvgfM7lJvHzJ2/QpNAx/Ew+cyrCOh73qC/HTFQwYmgZkiQR+0cGWDggnMK4p1FOemU6p3/6EqFSoZ4wAQBzaxuqR9yJqa4Bx8Mr0DRdnegLRmG4DKrN5JjU1l5rM3qczoQhLWMXNTJBhFP7M1yuNma+vlhpdRTUW0JWTKvr/x0fjKVKwWsbT3U5btKlZJ1K0Jfl17Vd4mIM0TsH+XQgDLWlsHwWmFjArGWgvPj2fazwGDpJR39nvbtKV1tL7vPPY+LizKiHIqkuayB+S0aHNqgdnJj56gIs7exZ8/YrhEgFDAuw5/2tyZTWNIKtDyX93ySm5i78XAsIKv0APhsIq++Fr0fC8eUw8jmYswls9GEZUgqqWH88lzlDfXCyaj/sRML2zaQfiyP6jnuw9/AgdLgbWafLKC/o/DsU5WfPCZzRaXVkBT9OvYsVNaZOBDhfQYTjbS8jlaRTcsYRdcFOtApIPXIQvwGDUdoYXm5qipEkif2rU0nYnkWVuyk/aKpo0EFo9HXc/f7n3PTvZ5HL5fjsq2PcdjXfvvofpNoGbOaMwdrXj8IFC9HVdxBPzSA+aidrQoe5cXp/LpXFdeAbzbisU3iqXJBt34fV2LHIrfQDzI0aHb+c05Hfdyol55LZvvjzK/78Xg5GYbgMaswUmNb8P4mw2oEwJKRtBSDC7+qvyGwPla8P6qpaChrUSBkHW123szDhv+OD2J9awpZT+VdUxsn4vVRZaHnx+OtdSn84rZQgZ0vsLNqJt6RtgpV3QWWeXhTULQUnvjAehVAQ4RABQOF779GUlYXrgrfxiHAlcJAzR7dk6h8uHWBpZ8/MV97GxsmFdQtf55EADdUNGt7begatRsf27xNRmSsY9dQ0xJMnIOoxOLMZGmtgzkYYPRfkF8dIPtqegrlSzkPR7fcWyvPz2P3jErwj+tF3nD7EQ+hwN2Qywam9OZ22XZS/PbmmrgiFkvRKC8653IlCU4ttzM+d5m1B6naIXUKF/CYacwr4wXcYJgEV6LR1yE2C9fUys0WqKWbvimSO79CvYr5udjC1TVo2n9R/VmQyOb2GRnPXO58y+ekXsbN2wrZCgd2UoTx4wzM4v/QSTTk5lHzbwdYyBmHA3J4BE3wQQhD7ezr4jkChqeNfBT6Y1WopGR1xIcuWU/kUVzdy85QJRN16G6d2bycv5czltcEV0C3CIIS4QQhxRgiRKoR4vo3rHwohjhmOZCFEebNr2mbXNnSHPT1FrZkK87rOg4390+lUGIoSUOskvL1GtJvmamPi44O6po46jYLqs3Ftprk90oteLla8sek09U2Xtx/BkdzD5CWdJte+jhPFJyit73ijQa1OIi6jrOPewuYXIH0vTPoYPFqHFYkriCPUIRRzpTnV+/dTtmw5dnfdhUWk3i8+9BZ/hAz2r0ltlfdSLGxsmf7yW9i6exC7+D3u9axl+eFMfl+RRHFWNaPu6IWZlQlYOsH4N+C/SfBYLPi2/B+fyq3gtxN53Dvct13B0+m0bP7yQ2RyOdc//O8LbjcLaxW+fR04fTAPTRsusOaEuqqxMjel3sGXtGPxnCv1x6fxIHX7DqAp7uLMwLoyWP8Ykl0wxXvyqfcL4qBrGLaaVOQKM84lmFGYUYlk5siu+EBO7M6h3zgvhs8IZJCPHd725qyJb+n6EjIZgYOiePzDpdz+6Rc8MPMlfd0GR2I14QZKvl5MU047wtdMGCxtVYRHu5N0KJ9y8wEgZITvPUeZlWCp+dELWX46lIGHrRnRQY5ETbuNWa+/2+4akO7kLwuDEEIOfA5MAEKB24QQLXZtkSTpSUmS+kqS1Bf4FPi12eW689ckSbq6mwZfJvXmpljWdz0k8j+VzvZkOF5fQITM8povbGuOia8v6lq9aBfmFuhdNJegkMt4dXIYOeV1fLX7XJfvHZMXw7xf/4NSI2N8tH6z+EO5hzrMczqvkuoGTfvjC7HfwpHFMPRx6Nt6Xn+9pp4TxScY4DwAbWUleXNfxMTfH8cnLw5MW9qaMmCCD+eOFpF1uvMdcc3V1kyfNx8HL1/M9v7IiLpMMvbl0yvKBb++LaetYqoGVev9kz/cloLaVMH9I/zaLSfut/XkJCVy3T0PYWXfcme/8Gh3Gmo0pMZ3/HCXyQRD/Ow4o3CjoiAXTWM5fSYGgQ4qP36q07oC8PszUFNEucl0mnJy2R99KzYmguJTsQRFDcXC2pzt3yWyo+BOEnODGHijD1G3+COEQAjBtP4eHDhbQnZZa9eXEAJXp5bbxzo/8wwABe++17Y9zYQBoP8N3sgVgiPbS9Cow6k7lUv5qL5sy9pBZmUmKQVVxKSVcvtgL+QygZDJcA8O6Vrd/yLd8c2OBFIlSTonSVIjsALoKHj4bcDybij3qtNoYYFlXffvvvV3Q25tjVRf36a/tLoqj7NCS4T1tYuo2hYmvr6o6xsA9OMM2UfaTDfEz56JEa58sSuV/IrO99fYn7OfR3c8SmCl/gF3/fCZqE3UHMg90GG+8+ML54WhqaCA9NmzSZt2K2mTbiDtibdI2+1H2len9ecuOVJvmcobS+oZ+9oW0qbegqa4GLcFC5CZtvTp9x3ridrBlL0rU9BqO/9smllaMX3emzj7BtAn73eqG09THty1WDzHs8rZfrqAB0b4YW3WerqoprGRY1t+Y/+KHwgYFEXIiNabN7kH22LjbM7J3Z27k4b6O3BCp19Yam1fjMuMOahcLanYcQiy2v7/XuDUWjixCl3UUxQv/w2zfv1Yp/Qk2qKExro6QkeMZMxdIZTl13KmJJxIp20MnuzXYvB4aj/97Ke18Z3bCvqtZu0ffICqzZupOdR6nOtSYTBXmxAx2oPkIwVk5oaCBP3u+BcKmYIfEn/g55hMlHLBjIFXN+Q2dI8wuAPN40RkG861QgjhDfgCO5udNhVCxAohDgkhprSVz5D3QUO62KKiom4w+/LRWVlh3gjV/+NbfHa0yO1k8gYkIejj3vE+tVcbua0tJlZq1CYqCuut2hyAPs/T44Np0OhYf6zjL/yurF08vvNxfK19GaoJwcHLBytbe4a4DuFg7sEOBwEPp5XgZWd+YSpn9Z+7qIuNQ26pQlF/DoWVKYqA/igcHFE4OLQ6yi2h3EJg6eKJKiAAt7fmY9Y7vFU5CqWc4dMDKcur4eSurj3AVOYWuIXMQSjcUNRs5pdV67u0OdD725KxNVdyz3DfFuebGuqJ+20d3zxxPzu+/RKXgCDGPfhYmzN0hBCER7tTkFZJUVbHM8SG+ttjK+xBWKI00dfN5o77qS9V0rDkQf0q8baoKoBNT4Fbf8pz3NAUFGDy0L9ILarBryoFMys1XuF98Ay1Y8TMIEb1PsYgq1WtbuNpZ06Unz1r4rO7POBrf++9KN3dKZg/H0lzSZvWFIPCDEwuLgbsN84bpUrO0fwITO0acbVqYpL/JNalrmPN0SQmhLviYKnqUtndSXcIQ1vzs9prxVnAakmSmjsYvSRJGgjcDnwkhGjzVVSSpK8lSRooSdJAR0fHtpL0OMJaP289K7vrboh/Ih0JQ0LOPgDCg/5eXj8hBCofH2w0EgVNNpDZvjD4OFjQx9OGjQm57abZlrGNJ/98kmDbYL4a/SWFySl4hekHBYe6DaWwrpCz5WfbzCtJEkfSy1q4keoSEpDb2uA5OA3PMfV4/rwOz8VL8PxqUZvH0rs9WPdQKH5ff4PnV4uwvrn9TrhPhANeoXYc3pR2IThfR2QllZK4r5j+Nz2OXWAokdlb+XxRx4O6seml7Eku4uGR/hcW6zXW1XJ4/WoWP3Yfu374Bjs3D6bPe4uZry5sc5vK8wQPcUGhlHFyT8dCFuBkSaROhVD5UJJ1Gp1Oi3rqrSCXUX6sGLa/2jqTJMHGJ6CpFt0NH1O8eAnmQ4aQ4OCPQteElHGKoCHDkRlC50eM9iCsV43e9ahr3eOaNsCD9JJa4jLKOrT1PDJTU5yef46GlBTKVvzS8mJtKVi0dK2ZWioJizAl3yIEXYAzpO1hTugcGrQNNFjsY/aQzhcE9gTdIQzZQPO+jgfQ3jduFpe4kSRJyjX8PAfsAv4+cyAvQWmr7wIW5GR2kvKfTYfCUJ6Kn1agtr763dvOMPH1xaq8kuoGGbUZx/WzftphUoQrJ3MqSSuuaXXt93O/88zuZwh3COfr8V9Tm5mPpqkRT4MwRLlFAbTrTjpbVE1pTSORzQae644dw8xRQhSdhlu/A4f2Vws36Zo4XnS8xfqFjhBCMHxGIJoGLTHr2xar8zTUNrFz6WlsnM0ZfmsId738Bg0uQSgOrGL76jXt5nt/azIOliruivKhvqaag2uWs/ix+9i77Hucff2Z9do7zHj5LbzCIzqNYWRqoSRwkDPJhwtorGu/p1JVUo97vSDb0ouG2hoKzqaisLfHcuQoKnLskQ59Bed2tcx09EdI3gxjX6V0SwzakhIcn3iCmLRSghoy0TU1Xoh+ewELB5C0UN/68z4h3AVzE3mrQeiOsBo7FouhURR98gmasmaCUlvSZowqz9xdKDS1JDnfBWl78LX2xaypN2b2hwj3aH86cE/SHcJwBAgUQvgKIUzQP/xbzS4SQgQDtsDBZudshRAqw+8OwDAgsRts6hHM7fX+zrKCrnXZ/6lcFIaWA9CSTkeCtooI0yvbQL6nMfH1xbJQ78ctrJZDfvvbMt4UoZ+/vul4y3eY9anreWHfC/R16suicYuwMrEi89RxhJDhEap35bhZuuGj9uFAXtvCcOn4graqisa0c5iaZMH4+RAwpsN6nC45TZ2mrsvCAGDrYkHEGE8SD+RRmFHZbrq9K1OoqWhk7N2hKEzkKExMuO+V18m09OX4qu+I3bS2VZ4DqcUcPFfCw4Odift1GYsfvZcDK3/GvVcot89/n2lzX8e9V2gbpbVP+Eh3NA1azsS0P3X41N4chIDdpk4gBOkJ8QBYT52CtqqBmlpfWPco1Bs+p2Xp+plePiPQht5B6TdLsIgegXn/fhxOK6W/Jh1LW7vWtpob3uINq5+bY6FSMCHclU3H87o8k00IgfPcuehqaij66OOLF5pFVj2P1NhI/W/rCVBlkF4VREF6FSfOZVOSE4VOVs3Gsxu7VGZ385eFQZIkDfAYsAU4DayUJOmUEOJ1IURzf8NtwAqppbMuBIgVQhwH/gQWSJL0txUGayf9w6S6qOAaW9KzXNyToeUbVHbOQcpkggiH3tfCrE7RB9PTu1LaW+h2HldrMyJ97Fq4k1Ynr2be/nlEukTy5dgvsVDqB2UzTybg5OuPqcXFWTpD3YYSlx9Hg7ah1b0Pp5XiZKXC217vS67f+BVIYBY5AoY80mk94gr0023PL2zrKoNu9MHMyoQ9K5KRdK29ueeOFnHmUD4DbvDG2ffi6mEXO0vC73yCFAt/dv+4hJi1Ky9ckySJT36LZ1z1YWp+fI2YdSvxiejHnQs/Ycoz83ANuLLouk7eapy8rTi5J6dN/722SUfi/jyce9lSojRD4eRJRoJ+GqfVyJHIbWwor+oLVbn6eE86nV4kEDDlC0p//BFtRQWOT/ybyvomUrOLsC09S1DUiNaz6SwMD+va1sIAMG2AO1UNmsta/6IKCMBu9h2Ur1xJfaLhkdaGMFTv3Yu2rIx+U0IxNYPDVTM4svs3VNoAQu3C+SHxB7S6y5ta3R10y3xDSZJ+lyQpSJIkf0mS5hvOvSxJ0oZmaV6VJOn5S/IdkCSptyRJfQw/l3SHPT2Fk5t+elpDWdsfoP8V2nMlJZzdAkCET8dvvNcKla8vSp0OtaWaQq1Th8IAMKmPK8kF1ZzJr2J50nJeO/gaw9yH8dmYzzBT6AeNm+rryUs5g1d4RIu8Q92GUq+t52jh0RbnJUnicFopkb52epdKXgJ1Gz4DwOz+i+EuOiKuIA4ftQ8OZg6dpm2OiZmCoVP9KUir5Mzhlg+x2spGdi1LwtHLioE3+rTKe/cIf86GTSHbLoR9K37gwKqfqSot5qePPqbfwc/pVRxP4KAh3P3e50x66gWcfNqfrtpVwqLdKc2tIS+19dTos0cLqa9uYtBYL9ysTSlSe5ObnERDbS3CxAT1xIlUHzyKtv9jcHwZrLwTMvbBhAVoUVP63fdYjRuLWXgYcRll+NakgU7b2o0EHfYYAIb42uNuY8bquK67kwAcHn0Uua0t+W/O14tfG8JQvnYtcgcHbEePoN94bzIb+6NLPsPNfT24r/c9ZFZl8mfWn5dVbnfw95mI/g/A00s/G0NX0bWBqH8qMpUKYWbWWhgK4zHTSfj7jr1GlnWM0tsbZDJsVWYUNFhB1uEO00/o7YpMwIIDX/FWzFuM9hzNx6M/RiW/OAsk50wiOq3mwsDzeQa5DEIhU7QaZ8guqyOvop7BvnZQXQQrbqeu1AwTHy/kdp1PmtBJOuIL4y/LjdSc4MEuOPuqOfjr2Qv+e0mS2PVzEo11WsbcHdLmxkRKuYyXJ4ezTh2NPDiSg6uXs/jRe8mP2UmOXS/ufO9Lbnz8aew9vFrlvVL0UVIVbQ5Cn9yTg9rRDK8QO6L8HTjS5Iik010IS2I9ZQpSYyOVlcH6OE5JmyD4Ruh7ByXffoeupgaHxx4H9D244JqzWDk44RIQ1NqQ8wPC7fQYZDLBtP7u7E8t7tIU5/PI1WqcnnqSuvh4Kjesh4bKiyIEaMrKqN69B+tJkxAKBb3H+KCSV2NW04vZQ7wY4zUGD0sPvjv13VUJg9EcozBcBrY29jTKQVR2vu3lP522Vj8n1OQQLkxRKK/NgFhnyExMULq7Y92kpaJGR31ZAZS3v+Oeg6UK/8DDxFYtZZz3ON4f9T4m8pareTNPJSCTy3Hv1XJDeXOlOX0d+3Iwt2X4jcPn4yN5WcIvs5Gqi6mrVGPWt2tuoZSyFKoaq65YGIRMMGJmELWVjfpwC8CZQ/mkHS9m8M1+2Lu1XrR2nhGBjowLc+Ur3SDCJkzFdsBIfnC/jeH3PoaTR/dvZ6s0kRM8xIWz8YUtZlOV5FSTl1pB+Ah3hEwQ5W9PCg7ITVSkG9xJpmGhqAIDqdiwCaYtgT63waSP0ZSWUvrTT6gnTMA0WC8C8Wey8azLImRYdNsD4//H3pnHR1lf+//9nX2SSTLJZCEBQsK+JiirKIobaovigku1V2hrra16bXvpT3trvVZtL7eX2va2LrXu1gpWK+LSWvcNEAImIWyyJISQfbInM8ks398fz0zIvs1MZiY879crL2b5Ps+cAfKc53zPOZ/jv4tv7V/59ZoFE/BK+PuXw4saEq6+GtPcuVRv/A1el+iWfG568y1wuTrHd+r0Grzxh2l0TSOhpgGtRsvNc26msKaQ/Jr8YX1uoKiOYRhotVpazAJ969juY4De3c9ORz0HhYucuKzwGTUEDFlZWOyKQ6t2xva7nSSl5NH8R6nU/h1X43xunvwz9JreTVsn9hUybuoM9KbeznBZxjIO1h2k1nHqTnNncR0JJh0z8u6HEztwLXsQT0MT5tycXsf3hT+/MFLHAJCWFc+sZekUfHCCE/vr+HTzV2RMs5J74eCVZD9fNRsP8KbhDDbrFpGcns7VZwxf5nqozD13PF6P5MC2U7meok9OotVpmLVMyemdNcWGV2gRGdM47ktACyFIuPJKHAUFtDcb4KrHwZKK/c9PIp1Oku+4AwBHhwfH4S8RSGb0tY0EoDOCMb7fiAFgki2WRVmJvLp76D0NoEhopP3sP3HX1FK739JtK6nxtdcwzZ7d6cC2HbXzrtRg0dTyxZavkFJy5dQrsRqtPFP0zJA/MxiojmGYtJh0GE8HIb2EhG4Rw8Ejb+MWgpz0xWG0anAM2VnElCp3dfr19VsAACAASURBVNWuxD63k6SU/H7P73ms4DEuy7ocT9X1vF3UW6LBXyLZM7/gZ1mG0uS3o+KUPMbOkjruTvoE8eULsHw9DocijGfOzR2S/burdpMem06GZWgKrv2x9Mop6PQa3vhDPlLChWtnodEMnt+YmBTDbedO5s3CCg5WNvPDi6ah04buMpE4LpbxM6zs+7Qcr1fS4XRz6ItKpi5IxWRRHPV4q5ksWwxlMRNoqKygoUrJn8Rfvgq0Whq3bAHAVVVN/UsvkXDFFRgnK9u+X56oZ3LzYYzJ40iZlN23EaBcsPvJMfhZs2ACR2tayT8xvJksMWecQcKFS6k7ZKGjQUkkOw99hXP//s5oARRdpGLzdBYkbKWyQkvpvjrMOjM3zLyBj058RHFj8bA+NxBUxzBMWmP0mB29K1HGGj23kgpKPwYgJwImtg2EMTsbQ0srFmsSVUyEE701jfJr8nmq6CmumXYNG859iHOnpfFmgXJh6krZgSKk9PbKL/iZmTQTq9HauZ1U3ewko24HN9Q/BjO+Duf/DEdhAcJkwji9j73tHkgp2V21e9jVSH0RE29g8eWTkRLOXjOV+OT+h+n05PsrpjLeamZGWhyrcgJzUENh7rkTaLY7Kd1n56udVbicHub2mCB31hQb2xzK/ry/OkmfmkrsOWfT+PrrSI8H+5/+hPR4SL79B53HfbGvhAnOcuacc97A/RWxydA6sKLC1+alY9JrhtXT4CflhvMQGknVk0o5cOOWLaDXK84NqGpy8q/9VVy9aDKzZjqJ09ex841jSCm5YcYNGLQGnt///LA/d6SojmGYOMwmYoMwIjLS0Vq7RwyF9QcY74HklNER8RophmzlrjA5KZlqhwkqi5RBLV0oa1Z+sdfNWYdGaLg8N4PyRid7SrsXFZQWFaLTG0if1reapVaj5az0s9hWvg0pJfv25vOI/v9oT5gCV/8JNBqcBYWY5sxB6AYf7VnaXIrdaQ9oG6krORdM4Mb7lzBn+fC2gswGLW/ceQ6bv7cU7RCijEDJnp9MTLyBfZ+cpOiTk9gmWLqV0wKcNSWZk9KCyWrrdAwA1iuvxF1VRcPf/0793/6G9ZprMEw8tWVWsmsbAshZvmJgI2KST2kZ9UOcSc+lc8axNb982Oq8eqOL5DnNtGzfTfMHH9L4xhtYzjsXXaKiprBp5wk8XsmNizPRTjmHReYXqT7eTHFBLTazjSumXMHWI1u7bVuGEtUxDJP22BgsjtGvKx5t/DkG6ZMJKOyoJ8cQ/jGeg+F3DIkGI3WNTjo8Ek52l+G2O5QLgM2s7PdeNDsNo07Dm4UV3dadKCogY8YsdIZ+5imgdEHXOmo5XJXPnE++Bwh039wMxji8HR04DxzAnDN6+YWuCCFIHDc0gbyeJMUasMb0/72DiVarYfY5GZTstWMva2HuueN73d2fNdkGQuBKn05pUQFej/I7aLngAjTx8VT+4gEEkHzb9zqP6XB7MZYW4klMxzZhkPxK7OBbSaAkoZucbt4/METpbz9tdSRNb8WQNYny9evx1NZiveoqANweLy/tLGX5tGSykmMh+1xmmD8iIcGjRA1eyc2zb8bldfHSwdHRH1UdwzDxWCxYnBKPZ2w7B63VCl4v3uZmqqoKqdQKcmyRHS0A6FJT0cTEEO90gYQap6VXnsHutGPQGLDolQodi1HHBTNTebOwAo9vO6mtqZGa0hIy5w6cG/DLY2x/dz02ZymPpt6HPlmp8W8/eBDZ0TGs/EKSKYns+AH2wscos8/JQAjQm7RMX5zW6/2UOCPTUi0cNYynva2VyqPK/G6N0Uj81y4DtxvrDTegTz81bnZX0VHSnJWkz186uAH+iGGQxPKyKcmkJ5h4ZXf/1W590mZHxCYoHdFtbWgTE7EsV2ZdvH+wmsom5yldpPRcNKY4Fmd9if1kK0f2VJOVkMX5E89n86HNtLlCP0VSdQzDRMYnoPNCdXXF4IujGG2Cr8mtsZG9h98CICezt4xypCGEwJCVRVyNUjZapZvSqzLJ7rBjM9u63ZVenptBbUs7XxxToomy/YqcxsQ5A3d5j4sdxxRdHNtajnO/ay1xMy/ofM9R4BsFOoyKpAVpCwbVGhqLxCWZOPPSSSy5fDIGU9/bbsum2Pi0xarIYxSc2k5KuvlmYs85h+Tv3dpt/e4Plcaw5ZcMoe8mNhm8rlPyGv2g1QiuOmM8nxyupbpp6D0N/uY2y7nnkrT2ZpLvvAPhi0T/suM46QkmLpzpk5rRaCHrbKa2v0Rieiy73izG65V8a+63SDQmcrIl9JI8qmMYJtoEZTvlZFlJeA0JMV1lMQord6KXkplTvxZmq4aGITsbzfFSYhKsVMtxULazm3Km3WknydR9W+z8GanEGrSdEhmlRYXoTWbSJk8b+MMK/8ZZ9pPkxcTygve8XoqqupQUdOPGDWpzZWslJ1tOcmZq4InnaGXp6ikDltSeNSWZBq+euAnZ3fIMxsmTyXzyz+iSu3eKN+3fRUNsOlnZQ1Ao9TeeDZJnAGU7yeOVbBlEtr0bbbWdpappP/0pSTfeCEBJbSufHq7lhkWZ3au/ss9F03CMxRfEUV/ZxuGdlcxPnc/rV77OtMRB/k8GAdUxDBOTTelerakYZigZZXSVxShoKWWWNGAwxoXZqqFhyM7GXV5O6qRsqpt1yl1g7ak5uXXOus78gh+zQctFs9P4R1ElLo+X0n2FTJg1B+1ASeOTu2HrHSyLm0wHXoyWUnInWjvfdhQUYJ6fO6QIINj5hbHI0slJCAGtyVOoOHII5wD9RLUnyzA3VaCfPkRHGzuwLEZXpqRYOCPTyqu7+9Z56pM2e7euZz9/3VmKViO4YXEPh5ilbDNNseSTPNHCzrdK8Hi8aMToXLJVxzBM4lKUu7/Gsb6V5HMMHbXV7Jft5FiC3/kaKgzZWSAlNquNWnsTbq/otp1kd9ixmWy9jrs8J4OGNhcf7DlMfXlZv2WqADRXwqabIDaVBVc/D1LHuLTjmPSKzr+7vh5XaSmmYSSeLXoL0xMHL2s9XbHGGJidHs8+Ma6bPEZffP6v95DAnLOHOJc8ZmAhvZ5cu2Aih6qaWfvMLvJKBh+rSltdL50kp8vD3/JOsHJ2GmnxPRooU2dDjA1R8gmLL59MU42DQzuGLuIXKKpjGCa2cUrpn8M+zKqEKMPvGCqPfoFTI8iNojtZo78ySatHer3UivGdg3u80ttnxACwfHoy8SYdn3ys9CVM7C/x7HLCphvB2QTfeAlpSsfTNglp/qpzibPQl1/IGXri+YzUM9BqtEP+nqcjZ0228VljLHqTqdt2Uk+O7fyccmM6Z80b4gjaYUQMANcvmsjdl86k6GQjax7fzjee2MG2o7V9RxCdAnrdty/f3ltBfZur72E8Go0SNRR/StbcJFKz4tn1VjEe1+iMFlYdwzAZN175R3TVD74XGc1o4+NBCKrLFMngeVMuC7NFQ8cwSfk3SvA1IlYZZ3VGDI3tjXikp8+IwajTcunccdR+tQ9jrIXUvjplpYQ37lK2ka56HMbN5cvSBtyt06h3H6emTWmSchQUgEaDee6c3ufoQZ2zjmONx9RtpCGwbKoNp1dgyZrVqZvUk9rSErx1FdSmziLDOsTGvpiBhfR6otUIvr9iCp/dfT73fn0WR2pauPHPX3Dt49v56FB1dwfhagO3s1fE8Jcdx5mcHMuyKb3/LwKQvRyayhD1xSy5PJuWunb2f97/1MFgojqGYTJhfBZegKbhtcVHG0KrRRMfT0NdJTaPJCN9YbhNGjKa2Fh0aWnoKyoxxVqodtug7ii01vbqYejJqpwM0lrLMEyY1lu3H2DbH6BwE6z4T5itjBvZWVKHt1VJCPrlMRwFhRinTUMTO3gfwZdVygVOdQyDsygrCa1GUGfNorGqkobK3lu6B7d9ghdBWs6SoZ/YEAP6mAGF9PoixqDjluWT+fT/nc8Dq+dQ3uBg3TO7uPKRz3l3f5XiIPxRSBfHsK+8kT2lDdy4JLP/HFT2ecqfxZ8wcXYS6VMT2P2PEtwdoS+VVx3DMDEajbSZQNs88CDzsYDWmoCj2UGOLqHvi2QEY8jOpqPkOKnZU6hq9IXfJ77A7lR+8XtWJfmZE+ciwd1MsaEPKYiv/gXv3gezV8N5/6/z5Z3FdmbaZpJkSlK6oL1eHHv3DrmxLa8qD6PWyBzb4NHF6U6cSc+88Qns8SilnT2jBiklRZ9+TJlpPItmDVMiPCZ5yBFDT0x6LTeflcVHPzmfDVfPo77NxXefz+Oy33/KZ4W+wocu857/sqMUo07DmgUD5O5sUyEuHUo+RQjBksuV/piG6ijpYxBCXCqEOCSEOCKEuKeP99cJIWqEEPm+n1u6vLdWCHHY97M2GPaEmmaTFkNb6P9xwo7FjHBCziiUxwUbQ3YWHcXFpGZPobayBo8wwIkvqHMqicK+tpIAyg8UAfBRUwJtHV3mEdccgle/A+PmwpWPdQ7caXd7+LK0gSXZySxNX8r28u20lxTjbRqeompuSi56bW91V5XeLJtiY6ddQ1xyaqfaqp+qY0dora3isGVqt9LhITHE7ueBMOg03LA4kw/+4zx+c20uHW4vf34nD4CPy7y4PV6anS5ezz/J5bkZA3eXC+HLM3wCUjJ+RiL/9tAykieEvjowYMcghNACjwCXAbOBbwgh+hoAu1lKOd/386Tv2CTgv4AlwGLgv4QQiYHaFGrazDpMp4HCaqvOQZxTkjthiJUdEYQxOxtvczPJKWl43G7s8fOh9ItBt5JKiwrQWxKoEPG855c9aKuDl25Q5JlveAkMp7aH9pY10u72sjg7iWUZy7A77ZRs+xcwNEXV5o5mDtUfUreRhsGyKcm4JRizZlFaVIjHfcqBH9r+KVJoaUqd2TladcgEEDH0RKfVcM2CCbz74/O46yzFQf3XexVc9PDH/OdrRbR1ePpOOvck+1xF3K/mIABaffSUqy4Gjkgpj0kpO4BNwOohHnsJ8K6Usk5KWQ+8C1waBJtCSpvZSIyjY/CFUY5d04zFAXMiXFG1Lzo1k4RS5VOtmwrlX2Jvq0YrtCQYE3odI6XkxL5CJufkkpZg4o2CcvC44ZVvKQN/rv8LWLvXm3/hH8yTldQpj1Gx6xM0sbEYJg8+/jK/Oh+v9KqOYRgsmJSIXiuosGTS4Wij8ohSDSa9Xg5t+4SKuEzmT8sYfgd5bPKwcwyDodUIzkxWtjLvvXY5sUYdbxSUM3d8PLkTev8f7EW2b4ZE8adBtWswguEYxgNdu73KfK/15BohRKEQ4hUhhP+3a6jHIoS4VQiRJ4TIq6kZWB431DhjTFic7sEXRjmV2hbiHRBjSQ23KcPG7xhMDU3oTWaq2uPB04697jBJpqQ+G4XqTpbR2lBP5txcvj4vg48P1dD+9k/h2Eew6reQ2VtzZ1dJHdPTLCTFGkiNSWWqdSpi32FM8+YhtIOXnu6p3oNO6MhJGdq2k4rSjHhGZiJfOG0IoenMM5R/dZBmey17jZOV0arDJcamRAzBHqPZVgtCy0Xzp/Hmnefw1+8u4ZEbzxya40qcBNZMKP44uDYNQjAcQ1/fruff7BtAlpQyB3gPeG4YxyovSvmElHKhlHJhSsrgs3NDSUdsLBbH6NQThwuvx02JyY2pA2RH9EVH+vR0hMGAq+Q4qVmTqa5TdG3sjaX9byPtKwAgc04Ol+emcxXvY9z9BCz9AZz5b73We7ySvJJ6FmWdugidY1tM8slW9PP62k3tze6q3cxOno1ZN/R5CSpKniG/up2UyVM78wwHt32C0OkpjslicXY/JaADEZuslJV2tAbXWJ9OEhoNQgiWTUlmkm0YqrfZ50LJZ91kXUJNMBxDGdA1vp4AdCu2lVLapZT+6TZ/BhYM9dhIxBsXR0wHtLSM3RGfJaUfY49R/HbXEZ/RgtBqMUyaREdxMWnZU6guK8ObMAm7o6bfiqQT+wqJS04hIW0c8737eUj/DIWmhXDxg32uP1DRREu7u1uS85yWdLQSTmQOfqF3up3srd2rbiONgLMm25AS5PgZVB45TFtTI1/t+AxH2nRiLbFMS+1/tnW/DLOXYcj4HcNIyT4PnA1QtTd4Ng1CMBzDLmCaECJbCGEAbgC2dl0ghEjv8vQK4IDv8TvASiFEoi/pvNL3WkQjEpT8+ImyY2G2JHQUHHuHZt+1revAnmjCkJ3dWZnkbm+n3rqAOncbtj4cgyKxsJfMObmIxhOIl2+myZTBzU23Uefs+07Nn1/o6hgmlSnR1fbEwfeq99buxe11szAtenpEIoX5mVZMeg3HjROQ0su2v/2VtsYGioyTWZSVNKQxpr3o7H4OcvNqH3IYw8Knm0TxJ8GxZwgE7BiklG7gDpQL+gHgZSnlPiHEA0KIK3zL/l0IsU8IUQD8O7DOd2wd8CCKc9kFPOB7LaLRJyr/yFUnS8NsSegorCnA45NvicaIAcCQlUVHWRmpE5Xqj0oxEbsAm+hdFlpTWoKzpZnMmdPhpW+Ax0XdFc/R4I3hH0V962LtLLaTmRRDesKp6MBddIDGJCMft+YPal9eVR4CwfzU+SP8hqcvRp2WRVlJfN4Yg8EcQ8G7b6MzmtjlSWNx1ggHSoU0YghgyFV8OtimjWoCOii1T1LKt6WU06WUU6SUv/S9dp+Ucqvv8U+llHOklLlSyvOllAe7HPu0lHKq7+eZYNgTamJsyiCR+qrQ66KHi0JHJRkxygUvmiMG3G4sHtDpDZxs1dOhEdgcTb3WlhYp+YWJx1+A6v1w7dNMnXUGk1NileqkHkgp2VVS36tW3lFQgGtmNkcajlDVWjWgfXuq9jA9cTrxhvgB16n0zdLJNg7WtJE2cy5IiXlqLm6Nfvj9C35ifXf1AfYy9KK1NrCIARR5jOOfg2d0xgpHVztrhJCQquyMtdQM/IsfrbS1VHNEeMhKUe60o9UxGLOzAHCfKCVlUjYVlb4ehqbeKpUn9hWSmGAi7vjbsPIhmHoRQgguz8ngi+I6qnoMZTla00Jda0e3u1NXdTXuigpSFi4DYHvF9n5tc3ldFNQUqPmFAPBrDLWnTQWgwjaDGIOWORkjdLShiBi8XnDUdet6HhHZ50JHC5QPHokGA9UxjIDUDKXVvr1+dAZzjzb7Dr+BVwimT1UucNHqGPwlq/48Q33pCZBgqy3uts7r8VBWlM9EUQzzb1KqkHxcnpuOlIoSZlf6yi/4FVUnLr0Am8nGtvJt/dp2wH4Ah9uhOoYAmDc+AYtRx4G4mVx2x3+wvSONBZMSuw+8GQ7GONAaghsxOBtAegOPGPx5hpLRyTOojmEETMxULjjexvowWxIaCsqUvcy5c69C6PVR6xi0CQlok5LoKCkhNXsKHmc7cW06bPZjimS2j6qd/6Cjw0VmZorSr9Clvnxqahyz0uN7bSftLK4jNc7YrbvWUVAAej3m2XM4K+MsdpTvwCv7Tlz7B/OcmXb6TmwLFJ1Ww+LsJLaXNJGx4GwOVrWMrH/BjxCnZj8HizZfyjRQxxCbDKlzRi0BrTqGEZBotdGhBdEUnUnZwSisP0yWBxKTJqO1WnFHqWMAJWpo95WsAtiaDCR53HBS0a+hpZrSV34FwMRv/VGRvejBqpx09pQ2cKJO0ceSUrKzuI7F2UndmpQcBYWYZsxAYzKxLGMZ9e31HKw72Ot8oDiGrPgsks0BbjGc5iybYqO4trXTcY+of6ErQdBL6obfyQSSfPaTfS6U7gB3++BrA0R1DCNAq9XSYhboBxgtGK1Ir5dCdyM5JqWJUGtNwBulVUngF9MrwTZxEmgEtkYDiR6pDO5xt8Ommyht0JGcnkZMxtQ+z3F5jqK0+pZvO6ms3kFFo7Pb3an0eHAWFXUqqi5NV7qk+9pO8kove6r3qNtIQeAsX57h0Y+OYtBpyBmKzMRABFEvCTh1rkAjBlAcg5SKoGOIUR3DCGkx6TCOQSG98oo87FpBjm0uANoEK5766I0YjNnZeOx2hMOBx2YmrSUWXdocOLED3vwR7tJdlDsTyZzfv3Z/pi2G3InWzrvSnX59pC6Oof3IUbxtbZjnK8J5KTEpTE+czvby3gnow/WHae5oVh1DEJg1Lh5rjJ6KRifzJ1o7R6uOmNjkEEUMQYgMp14E9xyH9NDLp6iOYYS0xugxO0If0o02hUfeBiAn60IAtIlWPI3R6xi6JqAdNi2JjTrkhMVw7GPIf5GKGd/D7fb0P8bTx+U56ewrb+JYTQs7i+tIMOuZnnpK/thRoFSLdJ3BsCxjGXuq99Dm6i7R7s8vqI4hcDQawVLf9lFA+QU/Qc8x+B1DECIGnQH0oyOdojqGEeIwm4h1jE5N8WhSWLUbk1cybfIlANGfY8hSHEN7cTH1CW707dCcMA+QMOtySnXzEELDhFkDD8n5eo5SovxmYQU7S+p6ddc6CgvRJiSgn3RKSvmsjLNwe93kVeV1O9fuqt2kx6aTYeljGJDKsFk2VbnoLhppY1tXYm1KWajLOfjaodBmB51ZmRAXRaiOYYS0x8ZgcYR+xN5oU9haxhxhQqdX2p61Viuehsa+h5xHAYaJE0CrpaO4hAqLkhOq0k2GSzfAlY9zYv9eUrOnYIodWFsnPcHM4qwkXtpZSnFta6+7U2dBIabcnG7J6DNTz8SoNXbbTpJSsrtqtxotBJFrzpzAA6vncPbUIGzXBLuXIVA5jDChOoYR4rFYsDglHs/YcQ4d7c0cEC5y4k6NRNQmJIDLhYzSiXVCr8cwYQIdxcWUGu1IAdVlZbD0+7ikjorDh8icO7Q928tz06loVO4ku/YveFpaaD9yBHNO9+0ok87EgrQF3RLQpc2l2J12tUw1iMQaddx8Vhbakegj9TqZXy8pSI6htTY4FUmjjOoYRoiMT0DnhcoxJItx4PBbuIQgd9zizte0VisQvU1uoOQZnMeO0oIDbXI81cVHATh5aD9ej5vMOUNzDJfNS0cj6NVd6ywqUiQZ+hjluSxjGccaj1HZqnRbq/mFCCfoEYM98K7nMKA6hhGiTVDuAk6eOB5mS4JHcuJkfpCQQ+6MKztf8zuGqM4zZGfjKi1FeCUx41Op8jmG0n2FaLRaxs8cOL/gJ9li5NK547hwVlq37lpHgdLxbJ43r9cx/qlu/u2k3VW7STIlkR2fHdB3UgkRwVZYDVRyO0zowm1AtGKyKXX+tZUnBlkZPYwfv5jvj3+x22tjI2LIgg4XyU1aEidlciL/CC31dZwoKiB92gz0JtOQz9XX5C1HQQGGrKzOv6uuTLNOI9mczLbybVw17arO/MKwx06qjA7+i7iaY1AZCXEp4wBoqo74uUIBMRYcg9FXsppRJ0mfPB2AE/v3UnXsKBOHuI3kp+cFXUqJo7Cwz20k//plGcvYXrGd8pZyTracVLeRIhmTFYQ2ODkGjwvaG1XHcDphG6eMpnbYwzt/OtR0Ooao7n72OQY7ZE1VGvf2vLUFKb1Dzi/0h7u8HE9tLaac/s9zVsZZNLY38pcDfwGUaiWVCEWjOTX7OVCCKYcxygTFMQghLhVCHBJCHBFC3NPH+z8WQuwXQhQKId4XQkzq8p5HCJHv+9na89hIZdx45Su4GoI87SnC0MYrSdZojhi0NhvuGAMZdZLUxHQS08dTefQwOr2B9GkzAzq3w6eoas7tf9jOWelKnmHzwc1Y9BamJ04P6DNVQkxscnByDMHseh5lAnYMQggt8AhwGTAb+IYQouck9C+BhVLKHOAV4Ndd3nNIKef7fq4gSpgwPgsvQBTfSQ8FYTCgiY2NascghKBpXBwT67UYtUZSfYJ6GTNmoTMYAjq3I78AYTRimtH/xd5mtjEraRYd3g7OSD0DrSZA2QaV0BL0iOH03EpaDByRUh6TUnYAm4DVXRdIKT+UUvoL4XcAE4LwuWHFaDTSZgJtS3O4TQk5SpNb9DoGAHuKiQyfArJfaTVzEBmMoeAoLMQ0ezZC33tcaFf81UlqfiEKCJZe0mnuGMYDXUtzynyv9cd3gH90eW4SQuQJIXYIIa7s7yAhxK2+dXk1NZGxr99s0mJobQ23GSFnLDiGCpvA2ujG29bGxNnz0Gh1TD5zUUDnlB0dOPfvx5w7uIO5MPNCdBod54w/J6DPVBkFgqWwGsWOIRjlqn3V3fWpnyCE+CawEDivy8uZUspyIcRk4AMhxF4p5dFeJ5TyCeAJgIULF0aEPkOrWY+5LUiaKhGM1mqN6uQzwHGrG4COkhLGzZ7Nnc++HPA2kvPQV8j29n4rkrqSk5LD9m9sx6QbemmsSpiITQZno1JVpB04EhyQ1tM7+VwGTOzyfALQq4ZTCHER8DPgCillpyyplLLc9+cx4CPgjCDYNCq0xRiIcXSE24yQo01IiPqI4Ui8Etm1FytjPQN1CgCOwgKgu6LqQKhOIUro7GUIMAHdZgdTQmDOJUwEwzHsAqYJIbKFEAbgBqBbdZEQ4gzgTyhOobrL64lCCKPvcTJwNrA/CDaNCs4YM7EOd7jNCDl+Ib1opcPTwRFLG1JAR3FJ0M7rLCxEm5yMLkNVSR1TBEsvKUq7niEIW0lSSrcQ4g7gHUALPC2l3CeEeADIk1JuBf4XsAB/8zUIlfoqkGYBfxJCeFGc1AYpZdQ4BldsLHHOqnCbEXK0Vivepiakx4PQRl9FTZ2zDpde4Eqx0lFSErTzOgoKMefmql3MY41g6SWdzo4BQEr5NvB2j9fu6/L4on6O2wb0FpiJErxxcZg7oKWlBYtlYNnmaEZrtYKUeJqa0CUmhtucYWN3KlsC3onj6PBtJQWKp6GBjpISEq66KijnU4kgghkxxEdnNKl2PgeASFAukidKe+XKxxTaxOiWxbA7FMegy5pER3FxUGZLOPbuBRhS4lklyuiMGIKQY4jSiEF1DAFgSFT+0SvLS8NsSWjRJigD1qPdMcRMnoa3rQ13deDlzo6CQhAC09yoDXhV+iMmCRCBRQxSqo7hdCXWWJsKIgAAIABJREFUlgZA/RiaydAX0S6k599Ksk5TGvKDsZ3kKCjAOHUqWktswOdSiTA0WjAnBpZjcLWB26k6htORhDRl/7C1dmwnoE85huisTLI77Jh1ZuJ9ukgdJYE5BiklzkJllKfKGCXQ7ucobm4D1TEEREqGMgKzvS5I2u0RSrRHDHXOOpJMSejS0hBmc8ARg+v4cTyNjUPuX1CJQmKSA8sxqI7h9CVzgiLn7G2sC7MloUUTFwdabdQ6BrvTjs1sQ2g0GLKyOpvcRspQFFVVopxYW2ARQ6vqGE5bkmzJdOhANDeF25SQIoRAGx+PpzFKHYPDjs2k/IIas7MCbnJz5BegiYnBOHVK4MapRCaB6iX5I4YonPcMqmMImGaTBn3L6SKkF505hjpnHTaz4hgMWVm4Tp7E2zFyKRNHYSGmuXOjstlPZYjEJitjOb2ekR0fxUN6QHUMAdNi1mJsc4TbjJATrQqrbq+bemd9Z8RgyM4GrxfX8eMjOp/X6cR58OCQFFVVopiYZECCo35kx7fZlRGhxoSgmjVaqI4hQNrMBmLa2gdfGOVEq2NoaG9AIkkyKXduhiwlL9Q+QmkM5/4D4HarjW1jnUC7n9vsSrSgic5LbHRaHUE4YozEOlzhNiPkRKtj8De3dW4lZWcBIxfT8yuqDjTjWWUM0KmwOlLHUBu1iWcIklbS6Ux7bAwW5wjDzShCm5AQlTMZ/M1t/q0krcWCLiVlWCWrUkpcx4/TumsXjX9/DV1GOvrU1JDYqxIhBBwx1EXlrGc/qmMIEI/FgsUh8Xg8aMdwMlJrtSIdDrzt7WiMxnCbM2R6Rgyg5BkGcgzS66X98BHa8nbRlpdHW14enhrlAqFNSsL23e+G1miV8BOowmqbHZL7nwMe6aiOIUBknBWthPKKMiZOmBRuc0JG1yY3TVpamK0ZOnVOpcekp2No/uc/O59LtxvngYOdTsCRl9cZHenGjSN26VnELFxIzKKFGLKzVZnt0wH/NlDrCJvcolgnCVTHEDBaq5LUrCgrPm0cgz6KHIPdYUev0ROnj+t8zZCdhaexkZr/+wOOvXtx7NmD1ze7Wz8pE8tFFxKzcBExixaiHz9edQSnIzqDUlE0kojB6/VtJamO4bTFnJQCQE1lWZgtCS2djqE+uhLQnV3PXS7uphkzAKh99FGM06YSf8XlSkSwcBH6NDV3oOJjpN3PzgaQHtUxCCEuBX6PMsHtSSnlhh7vG4HngQWAHbheSlnie++nwHcAD/DvUsp3gmHTaBGXOg6ApuqKMFsSWjpnMkRZAtrutHeWqvqJWbqUSS/9FUNWVlQOHlIZJUba/dzmk8iJ0q5nCEK5qhBCCzwCXAbMBr4hhJjdY9l3gHop5VTgt8D/+I6djTIjeg5wKfCo73xRQ1LaBACc9sA1/iOZaJ3JUOeo66xI8iOEIOaMM1SnoDIwsckjyzFEedczBKePYTFwREp5TErZAWwCVvdYsxp4zvf4FeBCocT2q4FNUsp2KWUxcMR3vqghw5dXcDUEOO0pwolWhVW7w94t8ayiMmRibCOMGKJbQA+C4xjGAye6PC/zvdbnGimlG2gEbEM8FgAhxK1CiDwhRF5NTeTcnWdkZOIFaIquLZbhojGZECZTVDkGr/QqOkmm6P0FVQkjsT7p7eGOglUdAwB9lWz0/Jvsb81QjlVelPIJKeVCKeXClJSUYZoYOoxGI60mgbalJdymhJxo635uam/CLd1qxKAyMmKSwetWksnDwR9lnOaOoQyY2OX5BKC8vzVCCB2QANQN8diIp8WswdB6miisRlHyuWfXs4rKsOjsfh7mNnGbHXRmMETv2NdgOIZdwDQhRLYQwoCSTN7aY81WYK3v8RrgAyml9L1+gxDCKITIBqYBO4Ng06jSYtZjbnOG24yQo01IiKqIwd/clmSO3iSgShiJHWH3c5T3MEAQylWllG4hxB3AOyjlqk9LKfcJIR4A8qSUW4GngBeEEEdQIoUbfMfuE0K8DOwH3MDtUsoRCqCHD4fZQFzLaeAYrFbaDx8OtxlDplMOQ40YVEZCzAj1kvzKqlFMUPoYpJRvA2/3eO2+Lo+dwLX9HPtL4JfBsCNcOGPMjKtRcwyRRudWkppjUBkJI44YolsOA1TZ7aDgio0lzukNtxkhx59jkMOt0ggTdocdrdBiNVrDbYpKNDLSiKE1uiW3QXUMQcEbF4e5A1rGeGWS1moFjwdvlHxPu9NOoikRjVD/m6uMAL0JDJZT5adDpa0uqrueQXUMQUEkKB20J0qPhtmS0BJt3c92h13NL6gERsww9ZI8LmhvVCMGFTAkKv8JKstLw2xJaIm27uc6Z10vnSQVlWERO0y9JL9OUpQnn1XHEARibYoMdX3VyTBbElqizTGochgqAROTPLyIYQx0PYMqux0UEtIyAGitrQqzJaElmhyDlFKR3Fa3kkYdl8tFWVkZTucYKOGe+e/gbocDB4a23t0Ol7wM3tShHxMCTCYTEyZMQK/Xj+h41TEEgZSMTADa60Y4BjBK6JTejoKZDK2uVto97WrEEAbKysqIi4sjKysr+occNZ6E1hpInwlD+S6OeqgHUmaC3hxy8/pCSondbqesrIzs7OwRnUPdSgoCmROUv3xvY12YLQkt2jhlClo0yGKoPQzhw+l0YrPZot8pAGh1gAQ5xHJ0r1v5UxO+e24hBDabLaCITXUMQSDJlkyHDkRzU7hNCSlCp0MTHx8VW0lq13N4GRNOAU5d4P0X/MHw+oQbNOEdKxPo37/qGIJEs0mDvuU0EdKLAsfQqZOkViWpBMKwHYMbhBaivHcmuq2PIFrMWoxtjnCbEXKixTF0RgzqVpJKIPThGBoaGnj00Uc7n5eXl7NmzRrliccd9mghGKiOIUi0mQ3EtLWH24yQo7VGh8KqP8eQaFLHd6oEwBAcQ0ZGBq+88sqpdWHMLwSL6P8GEYIjxkha9djOMYASMXQcKw63GYNid9ixGq3oNSMr11MJDr94Yx/7y4P7ezE7I57/unxOv++XlJRw2WWXcc4557Bt2zbGjx/P66+/Tnl5Obfffjs1NTXExMTw5z//mZkzZ3L06FFuuukmPB4Pl112GQ8//DAtLS20tLSwevUV1FdX4PLCQ7/awOrVq7nnnns4evQo8+fP5+KLL+b2229n1apVFBUVsWTlNTz9hw3MSZkBwIoVK/jNb37DzJkzufPOO9m7dy9ut5v777+f1at7TkCOHNSIIUi0x8ZgcUadYviw0SZEyVaS2sNwWnP48GFuv/129u3bh9Vq5dVXX+XWW2/lD3/4A7t372bjxo384Ac/AOCuu+7irrvuYteuXWRkZHSew2Qy8drfX2PPOy/x4Rsv8x//8R9IKdmwYQNTpkwhPz+f//3f/+32uTesvoSXX/8nABUVFZSXl7NgwQJ++ctfcsEFF7Br1y4+/PBDfvKTn9AawcO91IghSHgsFiwOicfjQauN/j3G/tBaE/C2tCBdLsQIm2dGA7XrOTIY6M4+lGRnZzN//nwAFixYQElJCdu2bePaa0+p/7e3K1u/27dvZ8uWLQDceOONrF+/HlD6Af7zZz/jkw/+hUar4+TJk1RVDdzEet2qi7j4pjv4xa9/z8svv9z5ef/617/YunUrGzduBJSS3tLSUmbNmhXcLx4kVMcQJGScFa2E8ooyJk6YFG5zQkZn93NjI7rkyFWQrHPWMds2O9xmqIQJo9HY+Vir1VJVVYXVaiU/P3/I53jxxRepqalh93uvojfGkLXgwoF7A7wexqenYEtKorCwkM2bN/OnP/0JUJzMq6++yowZM0b8nUaTgLaShBBJQoh3hRCHfX/2yvQJIeYLIbYLIfYJIQqFENd3ee9ZIUSxECLf9zM/EHvCidaqlEVWlEX+/nsgRIssht2pRgwqp4iPjyc7O5u//e1vgHKhLigoAGDp0qW8+uqrAGzatKnzmMbGRlJTU9EbzXz4yWccP34cgLi4OJqbm3t/iC9BfcOaq/n1r39NY2Mj8+bNA+CSSy7hD3/4Q+csky+//DI0XzRIBJpjuAd4X0o5DXjf97wnbcDNUso5wKXA74QQXSen/ERKOd/3M3R3HmGYk1IAqKksC7MloaVrxBCpON1OWl2tao5BpRsvvvgiTz31FLm5ucyZM4fXX38dgN/97nc8/PDDLF68mIqKChJ88vI33XQTeXl5LLzoal58ZSszZ84EwGazcfbZZzN37lx+8pOfnPoAX3PbmquvYtOmTVx33XWdb/385z/H5XKRk5PD3Llz+fnPfz5K33pkBLqVtBpY4Xv8HPARcHfXBVLKr7o8LhdCVAMpQGTfcg6TuNRxADRVV4TZktCiTYj8iEGVwzi9ycrKoqioqPO5P2cA8M9//rPX+vHjx7Njxw6EEGzatImFCxcCkJyczPbt26GxTFFNTc/tPOavf/1rt3MUFRWBU6m+SktPx+3u3hBnNps7t5WigUAdQ5qUsgJASlkhhEgdaLEQYjFgALpOtPmlEOI+fBGHlLLPZgAhxK3ArQCZmZkBmh18ktImAOC014TZktASDVtJqhyGynDYvXs3d9xxB1JKrFYrTz/9dPcFGp2ileT1gmaATZYI0EkKFoN+AyHEe8C4Pt762XA+SAiRDrwArJWyU5Hqp0AlirN4AiXaeKCv46WUT/jWsHDhwogbOpwxYRJOwNUwzDGAUUZUOQY1YlAZAsuXL+/MN/RJ1yY3jaH/daeTY5BSXtTfe0KIKiFEui9aSAeq+1kXD7wF3Cul3NHl3P59l3YhxDPA+r6OjwYyMjI5AtAUuXvvwUATGwN6fWQ7Bt9WkqqTpBIUunU/D8UxRH+5eqDJ563AWt/jtcDrPRcIIQzAa8DzUsq/9Xgv3fenAK4EinoeHy0YjUZaTQJtX9UKYwghhE8WI3IdoF9AT40YVILCUIX0/HIYY0BZNlDHsAG4WAhxGLjY9xwhxEIhxJO+NdcB5wLr+ihLfVEIsRfYCyQDDwVoT1hpMWswtLaF24yQo02IbL0ku8OORW/BqDUOvlhFZTC0w3QMY4CAvoWU0g5c2MfrecAtvsd/Af7Sz/EXBPL5kUaLWY+5bQyMMxyESFdYVXsYVILKkCMGz5hxDKpWUhBxmA3EODrCbUbIiXjH4FB1klQC4/HHH+f5558H4NnnXqC8sqbTMdxyyy3s37+/90HesSG5DapjCCrOGDMW5xAHekQxEe8Y1IhBJUBuu+02br75ZgCefe45yqvrwesC4Mknn2T27D7kVtStJJW+cFksxDkGFtkaC+h8jkFKGZEjHO0OO4vHLQ63GSoA/7gHKvcG95zj5sFlG/p9u6SkhEsvvZQlS5bw5ZdfMn36dJ5//nm2b9/O+vXrcbvdLFq0iMceewyj0cg999zD1q1b0el0rFy5ko0bN3L//fdjsVjIysoiLy+Pm+64B7PZzPadu7nsssvYuHEju3btori4mF//+tcgJc++9Hd2HzzOH/70NH/5y1/4v//7Pzo6OliyZAmPPvpoVIlrqhFDEPHGxWFyQVNz5FbsBANNQgLS5UI6Im9incvjoqmjSd1KOs05dOgQt956K4WFhcTHx/Pwww+zbt06Nm/e3DkT4bHHHqOuro7XXnuNffv2UVhYyL333tvtPGvWrGHhwoW8+PhvyP/gNcxmc7f3/v73vytPpIfNb/yL66+5igMHDrB582Y+//xz8vPz0Wq1vPjii6P59QNGjRiCiEhQNARPlBYzZ07U6gEOStcmN01MTJit6Y5aqhphDHBnH0omTpzI2WefDcA3v/lNHnzwQbKzs5k+fToAa9eu5ZFHHuGOO+7AZDJxyy238PWvf51Vq1b1fUKh65V8TklJYfLkyezYsYNp2RM5dPQ4Z5+9jEeeeYndu3ezaNEiABwOB6mpA4pCRByqYwgihkRFhrqq/Php4xj0XQabRAKdOklqxHBaM9QtTp1Ox86dO3n//ffZtGkTf/zjH/nggw96L9RqO0XyunL99dfz8ssvM3NKFlddej5Cq0dKydq1a/nv//7vQL9G2FC3koJIrE25K6ivLA+zJaFFF8GyGKochgpAaWmpIoAHvPTSS1x00UWUlJRw5MgRAF544QXOO+88WlpaaGxs5Gtf+xq/+93v+pzXEBcXR3OrE6RH0UzqwtVXX82WLVt46eW/cf0VK0Gj48ILL+SVV16huloRgqirq+uU7I4W1IghiFjTlLvnttqxnYCOZL0kNWJQAZg1axbPPfcc3/ve95g2bRq///3vWbp0Kddee21n8vm2226jrq6O1atX43Q6kVLy29/+tte51q1bx20/uhuzXsv2L3Z2ey8xMZHZs2ezf18Ri8+YCxots2fP5qGHHmLlypV4vV70ej2PPPIIkyZFzwAv1TEEkdSMiQC0N9SG2ZLQovHp1UfiTAY1YlAB0Gg0PP74491eu/DCC3sNyElPT2fnzu4Xe4D777+/8/E111zDNV+7EOqLwaDno48+6rb2zTffhOYqaC7vLFe9/vrruf7664lW1K2kIDJx4mQAPA31YbYktETyVlKdsw6T1oRZZx58sYrKUBms+9nrBgSIsXFJHRvfIkJITLTRrgNNc1O4TQkpwmBAExMTkY7B39wWif0VKqNDz0E9QWEojmGMCOiB6hiCTrNZg76lJdxmhJxI7X5W5TBUQsJQHcMYQXUMQabVrMXUFnmNX8FGa7XijkTH4LSTZFbnMKgEGb8GkuoYVEZCq9mA2dHndNIxhdZqxRuBMxnUiEElJAihXPj7dQxjR1kVVMcQdBxmE7GO00FIL/JmMni8HhraG9SKJJXQMKBjcJ+a2zAGCMgxCCGShBDvCiEO+/5M7Gedp8uQnq1dXs8WQnzhO36zb9pbVNMRG0Oco3eH5FgjEnMMDe0NeKVXHempEhr6cQxbXvs7+w8dHlRyu6uUd3/k5+fz9ttvB2RmMAg0YrgHeF9KOQ143/e8LxxSyvm+nyu6vP4/wG99x9cD3wnQnrDjtsRhcUpcLle4TQkpWqsVT1MT0hM5TrCzuU2NGFRCgUYHnu6Owe12s2XLFvZ/dWzQraSuUt79ESmOIdDYZzWwwvf4OeAj4O6hHOib83wBcGOX4+8HHgvQpvASH49GQkVFGZmZ2eG2JmRorVaQEk9TE7rEPgPFUaezuU3NMUQM/7PzfzhYdzCo55yZNJO7F/d/mSkpKWHVqlWdJasbN26kpaWFjz76iCVLlvDhhx/S0NDAU089xfLly/F4PNx999288847CCH47ne/y5133snu3bv58Y9/TEtLC8nJyTz7+1+SnmBgxYoVLFu2jM8//5yVK1ey9Y03+fjD93noj8/z6mtb+OCDD3jiiSfo6Ohg6tSpvPDCC8TExHRKea9fv54VK1b0smXJkiXcd999OBwOPvvsM376059y7733sm3bNlJSUvB6vUyfPp0dO3aQnJwc1L/TngTqGNKklBUAUsoKIUR/EoImIUQe4AY2SCm3ADagQUrpd8FlwPj+PkgIcStwK0BmZmaAZocOnVW5KJWXlYx9xwB4GxshUhyDGjGoDILb7Wbnzp28/fbb/OIXv+C9997jiSeeoLi4mC+//BKdTkddXR0ul4s777yT119/nZSUFDZv3szPfrmRp/9HcUgNDQ18/PHHABw+uJ9Vy+ezZu1tYIzDarXy3e9+F4B7772Xp556ijvvvHNItjzwwAPk5eXxxz/+EYCDBw/y4osv8sMf/pD33nuP3NzckDsFGIJjEEK8B4zr462fDeNzMqWU5UKIycAHQoi9QF9dYLK/E0gpnwCeAFi4cGG/68KNyZYCgL2yLMyWhBatXxYjgvIMasQQeQx0Zx8Orr76agAWLFhASUkJAO+99x633XYbOp1yOUxKSqKoqIiioiIuvvhiADweD+mpvv9XUvaQu/BdjnxbSUVFRdx77700NDTQ0tLCJZdcMmRbevLtb3+b1atX88Mf/pCnn36ab33rWyP85sNjUMcgpbyov/eEEFVCiHRftJAOVPdzjnLfn8eEEB8BZwCvAlYhhM4XNUwAol6WNC5F8aFNNRVhtiS0+COGSOplsDvt6DQ64g3x4TZFJYzodDq83lMqqE6ns/Ox0WgEQKvV4nYrmxV9TSKUUjJnzpxOhVYAHPVQXwJIYmNjuyz2fZbPMaxbt44tW7aQm5vLs88+20tbaSBbejJx4kTS0tL44IMP+OKLL0Zt4E+gyeetwFrf47XA6z0XCCEShRBG3+Nk4Gxgv5RSAh8CawY6PtpITp8AgNNeE2ZLQkskKqzWOepIMiWpchinOWlpaVRXV2O322lvb1dE7gZg5cqVPP74450X57q6OmbMmEFNTU2nY3C5XOw7eFg5QHbfsIiLjaW5tbWzKqm5uZn09HRcLtewL+RxcXE0Nzd3e+2WW27hm9/8Jtddd92ojQcN1DFsAC4WQhwGLvY9RwixUAjxpG/NLCBPCFGA4gg2SCn3+967G/ixEOIISs7hqQDtCTsZ47MAcDXYw2tIiIlEx2B3qs1tKqDX67nvvvtYsmQJq1atYubMmQOuv+WWW8jMzCQnJ4fc3Fz++te/YjAYeOWVV7j77rvJzc1l/vz5bPtil++I7o7hhqtX8b+PvcAZZy7g6NGjPPjggyxZsoSLL7540M/uyfnnn8/+/fuZP38+mzdvBuCKK66gpaVl1LaRAISUEbtd3y8LFy6UeXl54TajT1wuF1/l5PD5ebO59fFXw21OyJBeLwfnzsP2vVtJveuucJsDwHVvXIfNbOOxi6K7sC3aOXDgALNmzQq3GcHH44KqIkiYALEpp16vL4GOVkibE5KPzcvL40c/+hGffvrpsI7r699BCLFbSrlwsGPVzucgo9fraTEJtC3Ngy+OYoRGgzY+Xo0YVE4f/A1sPXoZQqmTtGHDBq655ppRHxOqOoYQ0GLSYGxtC7cZISeSup+llNQ569RSVZXQITQgtL27nz2hcwz33HMPx48f55xzzgnJ+ftDdQwhoNWsx9zmHHxhlBNJjqGpowm3161GDCqhpS9ZjDGmrAqqYwgJbWYjMY6xLYkBfscQGQqr/uY2VXJbJaT06RjGlrIqqI4hJLTHmrGcFgqrkRMxqM1tKqOCtodj8HoAr+oYVAanIzYWi9M7+MIoR5uQgKcxsiIGNcegElJ6Rgxen4jkIMqq0YbqGEKAjI/H5IKGxvpwmxJStIlWZFsb3o6OcJuiRgwq3bBYLACUl5ezZo3SQ9tTuXTr1q1s2LBhwPN0PR445Rj8Zf5+J6FGDCqDIeIVUbmy0uIwWxJaOpvc6sO/nWR32NEIDVajNdymqEQQGRkZvPLKK0Bvx3DFFVdwzz39TQrofTzQZfazL1IYo45hbH2bCMGQqNy1VleegHlnhtma4dHe1ooxJnbwhXTvftan9SesOzrUOetINCaiHWMhfbRT+atf0X4guLLbxlkzGfef/zmktX4J7j179vSStHY4HJ1KpkePHuWmm27C4/Fw2WWX8fDDD9PS0tJNwtvpdPL97/+QvLxd6IyxPPzb33L+klye3byVvMMV/PHRxwFYtWoV69evZ/ny5XznO98hLy8PIQTf/va3+dGPfhTUv4tQoUYMISDWplwk6yujSxPweGE+j9/6b1QdOzKk9ZEki2F32tWKJJV+MRgMPPDAA1x//fXk5+f3UEeFu+66i7vuuotdu3aRkZHR5zkeeeQREBr2vv8yLz3/FGvXrsXZ1qK8KXpfSvPz8zl58iRFRUXs3bt3VCUtAkWNGEKANU35j9VWWxVmS4bHuKnTEBoNX/7zTS79wQ8HXd/pGBrD7xjqHHVqfiECGeqdfbjZvn07W7ZsAeDGG29k/fr1vdZ89tln3HmbMmdh5rQpTJo0ia+++sr3bm/hxsmTJ3Ps2DHuvPNOvv71r7Ny5cqQ2R9s1IghBKRmTASgvaE2zJYMD2NMLLPPvYCD2z6mrWnwaqNImslgd9rViiSVkCKlPFV95M8teD3o9Aa8XTTn/DLfiYmJFBQUsGLFCh555BFuueWW0TZ5xKiOIQRMnDgZAE9D9FUlnXHpKjwuF3vff2fQtae2ksJbsiqlxO5QdZJUBqYvSWs/S5cu5dVXFdHLTZs29bnm3HPP5cWXFMXTrw4dorS0lBlTMsmaNJH8/Hy8Xi8nTpxg586dANTW1uL1ernmmmt48MEH2bNnTwi+VWhQHUMISEy00a4DTXNfQ+oiG9uETDLn5lLw7j/wejwDrtWYzQijMewRQ5u7DafHqUYMKgPSl6S1n9/97nc8/PDDLF68mIqKChJ80XBXfvCDH+Dxepl34XVcv+57PPvssxj1Os5eupjs7GzmzZvH+vXrOfNMpeDk5MmTrFixgvnz57Nu3bpRF8ILBDXHECKazRr0LS3hNmNEnHHp5by+8SGO5O1g+pKzB1wbCd3Pag+DSk9afL97WVlZFBUVAcrIzl27dnVbt27dOgDGjx/Pjh07EEKwadMmFi5c2Ot4k8nEs88+C1X7QB8LSVlQfQChM/Y7kCeaooSuqI4hRLSatZjaHOE2Y0RMXrCI+JQ0vvznG0NzDGHufq5z1gFq17PKyNm9ezd33HEHUkqsVitPP/10/4u7dj973aAZWnl3NBGQYxBCJAGbgSygBLhOSlnfY835wG+7vDQTuEFKuUUI8SxwHuC/sqyTUuYHYlOk0Go2YHa0h9uMEaHRaJm/8mt88uIz1BwvJmVSdr9rtQkJERMxJJnUclWVkbF8+XIKCgqGtlijU4b2SDkmlVUh8BzDPcD7UsppwPu+592QUn4opZwvpZwPXAC0Af/qsuQn/vfHilMAcJhNxEaxkN7cC1aiMxj58p2B5+VGxFaSU91KUhlFOmUxPKeejzECdQyrged8j58Drhxk/RrgH1LKMT/FpiM2hjjHwMnbSMZsiWPWOedx4NOPcAwwjS4iHINDldxWGUX8CquesSmHAYE7hjQpZQWA78/BdBEFeandAAAU4UlEQVRuAF7q8dovhRCFQojfCiGM/R0ohLhVCJEnhMirqakJzOpRwG2Jw+KUuFzRO5fhjEsvx93RTtGH7/a7xp9jCOfscLvTToIxAb1GHzYbVE4jNDpAgqejy/OxxaCOQQjxnhCiqI+f1cP5ICFEOjAP6Fog/1OUnMMiIAm4u7/jpZRPSCkXSikXpqSk9LcscoiPRyOhoqIs3JaMmJRJ2UyYNZf8d97C6+07+tFareB2421tHWXrTqH2MKiMKn5H4PZNaRyD+lyDOgYp5UVSyrl9/LwOVPku+P4Lf/UAp7oOeE1K2XkLLaWskArtwDPA4sC+TuSgsyoXqvKykvAaEiDzL1lFU00Vx/bk9fl+JHQ/q7OeVUaLX/3qV304hsEjhltuuYX9+/cPuObxxx/n+eefD9TEoBDoVtJWYK3v8Vrg9QHWfoMe20hdnIpAyU8UBWhPxGCyKVGNvTJ6IwaAqYuWYkmy8eU/3+jzfW1i+KW37U67WpGkMip0cwyuU45BSonX2/9wrieffJLZs2cPeO7bbruNm2++OVimBkSgm2MbgJeFEN8BSoFrAYQQC4HbpJS3+J5nAROBj3sc/6IQIgVFgSofuC1AeyKGuJRxADTVVITZksDQ6nTkXvw1Pt/8AvayE9gmTOz+fgQorKpbSZHLpy9/Re2J4DZ6Jk+0sPy66QOuef7559m4cSNCCHJycnjooYf49re/TU1NDSkpKTzzzDNkZmaybt064uPjycvLo7Kykl//+tesWbOGiooKrr/+epqamnC73Tz22GO89dZbOBwO5i9axpwp4/nlPXdy2U23c/5Fl7B9xw62bNnChg0b2LVrFw6HgzVr1vCLX/wCgBUrVrBx40YWLlyIxWLhrrvu4s0338RsNvP666+TlpbG/fffj8ViYf369axYsYIlS5bw4Ycf/v/27j04qjLN4/j3SdMhkISARuQSgYisBGIug0CEiAbGEhQRBXQsBmFDiTuYcWZ31NGyRGSlBgvHRUSlQEZ2R7zAwKyU4sAgt+UiEiYXICIDEpSBAQ1GDAm5kHf/OCdNOnSSJp3uk5DnU5VK9zknOb/upPvpc3teiouLWbZsGbfeeiulpaVMmzaNgwcPkpCQQGFhIa+//rrngrzmEtAWgzGmyBgzyhjTz/5+xp6eXVMU7PuFxpiexpjqOj8/0hhzk71r6ufGmNZ5qbAPsd3jADhf1PIPlDcmadSduNq1I3fDpaeuOl0Yyi+UU1JZoruSlMeBAweYO3cumzZtIi8vj1dffZWsrCwefvhh8vPzmTx5Mo8//rhn+ZMnT7J9+3Y++ugjz8A97777LnfeeSe5ubnk5eWRkpLCvHnz6NChA7k5f2PForlgqvnySCEPT51KTk4OvXv3Zu7cuWRnZ5Ofn8/WrVvJz8+/JN+5c+dIS0sjLy+PESNGsHTpUp+Po6qqis8//5wFCxZ4Cswbb7xBly5dyM/P57nnnmPv3r1BeAb1yueg6dGzD6VAZXGR01EC1jGmMzcOG8GBrZtI/9lU2nfs6Jl3sfW2M1c/azuMlq2xT/bBsGnTJiZOnEhsbCxgtcLYtWsXa9asAWDKlCk89dRTnuXHjx9PWFgYAwYM4NQpq1X+4MGDyczMpLKykvHjx5OSknJxBWEuaj5T947rQVpammfWypUrWbJkCVVVVZw8eZKCggKSkpK88oWHhzN27FgABg0axF//6vusv/vvv9+zTGFhIWC1/v7Vr34FQGJi4iW/u7loE70g6dGjF9UC+NG+ujVIHX0PlefLOLB1o9d0V6dOgHNbDJ7CoFsMymaMwTpsWb/a89u3v3iWfM1p1yNGjGDbtm307NmTKVOmXHpQ2GV9po6MvPgh6ejRo7z88st8+umn5Ofnc/fdd3tacNfmdrs963e5XFRV+b4QtiZX7WVCdVq4FoYgcbvdlEQIrgYuDmtNuvXtR/d+N5K7/iNMrYNs0q4dYdHRjhUGT58k3WJQtlGjRrFy5UqKiqwPDWfOnGHYsGGedtorVqwgPT29wd9x7NgxunbtyiOPPML06dM9zfDcbrd1bZLnFNWLBebs2bNERkYSExPDqVOn+OSTT5r9saWnp7Ny5UoACgoK2LdvX7OvA3RXUlCVRITR/tyVc5F36uh7WPfayxTm5xCfMsgz3cmrn2vaYehVz6rGwIEDefbZZ7nttttwuVykpqaycOFCMjMzmT9/vufgc0O2bNnC/PnzcbvdREVFebYYZsyYQVJSEj8Z2I+5Tz7qNXBbcnIyqampDBw4kOuvv57hwxtuQNkUM2fOZOrUqSQlJZGamkpSUpLPFuGBEievWG2qm2++2WRn+z6vviVZOzKVSreLCetbflZ/XKiqZOljmXSN78v9T8/2TD866QFcMTH0esv3QbRgWpq/lIU5C9kzeQ8R7SJCvn51qS+++IKEhASnYwTX98eg7AxEd4Po7iFb7YULF6isrCQiIoIjR44watQoDh06RHh4+CXL+vo7iMheY0yjpzDpFkMQlXZoT8yPrbP1ti+udm6SfjqaXavf5/t/nqBLN2tsa1fnzlz43pnR6orOFxHpjtSioEKr5lqGELfDKC0tJSMjg8rKSowxvPnmmz6LQqD0GEMQlUd2IKoVd1j1JemnYwgLCyN3/ceeaa6YGEfPStLjCyrkao4xhLgwREdHk52dTV5eHvn5+YwZMyYo69HCEEQVkZFEna//asjWKKrLVfxLWjr7N/+VivPW1pDTxxj0jCQVcg5tMYSKFoYgMp06EVEJxT84s5slWFJHj6WirJSCbZsBqzBU//gjpp7T7oLpTNkZ3WJQoefuCBIG7eptCN2qaWEIIunUBYDjXx91OEnz6t6vP13j+1qnrhrj6EVu2idJOSK8I3RPBlfz799vCbQwBFH4VdYn2VMnvnY4SfMSEVJH30PR8a/5en+eY20xKqsrKS4v1l1JSjUzLQxBFHX1tQAUn/qHw0maX/9hI+gQ3Ymcv3zk2BbD9+etXXS6K0nVNWzYsEaXWbBgAaWlwb3OKDc3l3Xr1nnur127lnnz5gV1nc1BC0MQdb62JwAlRaccTtL82oWHc9OoO/lq7+ecs8e+DfUWg7bDUPXZuXNno8s0pTBcuHB5w/XWLQzjxo3zNOprya7MQ+otxLU9egFQcab1N9LzJfmOu9izdjUF+3PoRujHZKi56lkLQ8u1efkSTh/7qll/Z9fe15MxbUaDy0RFRVFSUsKWLVuYPXs2sbGx7N+/n0GDBvHOO+/w2muvceLECTIyMoiNjWXz5s1s2LCB559/nvLycvr27cvbb79NVFQUffr0ITMzkw0bNpCVlcXgwYN57LHH+Pbbb+nYsSNLly6lf//+rFq1ihdeeAGXy0VMTAwbN25k1qxZlJWVsX37dp555hnKysrIzs5m0aJF9bb8rq6uJisri61btxIfH091dTWZmZlMnDixWZ/HhmhhCKK4XvGcBMzZK+uspBqdYq/hhsFpHPh8J9eIOLfFoLuSVANycnI4cOAAPXr0YPjw4ezYsYPHH3+cV155hc2bNxMbG8t3333Hiy++yMaNG4mMjOSll17ilVdeYdasWQBERESwfft2wOrFtHjxYvr168fu3buZOXMmmzZtYs6cOaxfv56ePXtSXFxMeHg4c+bM8RQCgOXLl3tlq2n5ffDgQcaNG8fEiRNZs2YNhYWF7Nu3j9OnT5OQkEBmZmZInzMtDEHUOaYLR90gZ886HSVoUkffw9937+TE1TF0DXFhqGmgp2cltVyNfbIPhSFDhhAXZ42PkpKSQmFh4SVN9D777DMKCgo8/Y0qKiq45ZZbPPMffPBBAEpKSti5cyeTJk3yzCsvLwdg+PDhTJs2jQceeMDTMrsxvlp+b9++nUmTJhEWFka3bt3IyMho4iNvuoAKg4hMAmYDCcAQY4zPpkAiMhp4FXABbxlj5tnT44H3gauAvwFTjDEVgWRqaUoiwgg/d87pGEETl5BIbK8+HKu8QKIDWwztXe2JdEeGdL2qdandVru+NtfGGO644w7ee++9S+YBREZa/2PV1dV07tyZ3NzcS5ZZvHgxu3fv5uOPPyYlJcXnMg1lq+lb1xL61wV68Hk/cD+wrb4FRMQFvA6MAQYAD4lIzeCnLwH/ZYzpB3wPTA8wT4tT0qEd7c9dOf2S6rJOXR3LWbeLf377z5Cuu+i81Q6jsd77SvkSHR3Njz9abfHT0tLYsWMHhw8fBqyeRIcOHbrkZzp16kR8fDyrVq0CrDfxvLw8AI4cOcLQoUOZM2cOsbGxfPPNN17r8Fd6ejqrV6+murqaU6dOsWXLlgAeZdMEOrTnF8aYLxtZbAhw2Bjzlb018D5wr1iv5pHAn+zl/hsYH0ielqi0g5uOZeVOxwiqhPTbcSP8/YfvQrreojJth6GabsaMGYwZM4aMjAyuueYali9fzkMPPURSUhJpaWkcPHjQ58+tWLGCZcuWkZyczMCBA/nwww8BePLJJ7nppptITExkxIgRJCcnk5GRQUFBASkpKXzwwQd+5ZowYQJxcXEkJiby6KOPMnTo0KC01m5Is7TdFpEtwBO+diWJyERgdM0Y0CIyBRiKtQvqM2PMDfb064BPjDGJ9axjBjADoFevXoOOHTsWcO5QWPLryUhJCY+89aHTUYJq09O/oaqigjt+vzBkn+AX5SzCJS5+kfKLkKxP+adNtN0OspKSEqKioigqKmLIkCHs2LGDbt26XdbvCGrbbRHZCPhK9Kwxxp93O1/vEqaB6T4ZY5YAS8Aaj8GP9bYIMxascDpCSIyc9/uQrzMrNSvk61QqFMaOHUtxcTEVFRU899xzl10UAtVoYTDG/DTAdRwHrqt1Pw44AXwHdBaRdsaYqlrTlVKqTXPiuEJtobjyeQ/QT0TiRSQc+Bmw1lj7sDYDNVdtTAWu7P0tSrURLeHMmrYs0Oc/oMIgIveJyHHgFuBjEVlvT+8hIuvsgFVAFrAe+AJYaYw5YP+K3wL/ISKHgauBZYHkUUo5LyIigqKiIi0ODjHGUFRURERE00c11DGflVLNqrKykuPHj3P+/Hmno7RZERERxMXF4Xa7vabrmM9KKUe43W7i4+OdjqECoN1VlVJKedHCoJRSyosWBqWUUl5a5cFnEfkWaM5Ln2OxrqtobTR3aGnu0NLcza+3MeaaxhZqlYWhuYlItj9H6lsazR1amju0NLdzdFeSUkopL1oYlFJKedHCYFnidIAm0tyhpblDS3M7RI8xKKWU8qJbDEoppbxoYVBKKeWlTRUGERktIl+KyGERedrH/F4isllEckQkX0TuciJnXX7k7i0in9qZt4hInBM562T6g4icFpH99cwXEVloP6Z8EflJqDP64kfu/iKyS0TKReSJUOerjx+5J9vPc76I7BSR5FBn9MWP3PfamXNFJFtE0kOd0ZfGctdabrCIXLBHsmw9jDFt4gtwAUeA64FwIA8YUGeZJcAv7NsDgMJWknsVMNW+PRL4YwvIPQL4CbC/nvl3AZ9gjeSXBux2OrOfubsCg4G5WMPZOp7Zz9zDgC727TGt6PmO4uKx0CTgoNOZ/cltL+MCNgHrgIlOZ76cr7a0xTAEOGyM+coYUwG8D9xbZxkDdLJvx9AyRpTzJ/cA4FP79mYf80POGLMNONPAIvcC/2Msn2GN5tc9NOnq11huY8xpY8weoDJ0qRrnR+6dxpjv7bufYY2Y6Dg/cpcY+10WiKSB4X9DyY//b4BfAquB08FP1LzaUmHoCXxT6/5xe1pts4Gf24MPrcP6wzrNn9x5wAT79n1AtIhcHYJsgfDncangmI61tdYq2AOCHQQ+BjKdzuMPEemJ9Vpc7HSWpmhLhUF8TKv76eMhYLkxJg5rV8cfRcTp58if3E8At4lIDnAb8A+gKtjBAuTP41LNTEQysArDb53O4i9jzJ+NMf2B8cB/Op3HTwuA3xpjLjgdpCna0kA9x4Hrat2P49JdRdOB0QDGmF0iEoHVEMvJTcFGcxtjTgD3A4hIFDDBGPNDyBI2jT9/D9WMRCQJeAsYY4wpcjrP5TLGbBORviISa4xpqU3qatwMvC8iYL2H3CUiVcaY/3U2ln+c/jQcSnuAfiISLyLhwM+AtXWW+RoYBSAiCUAE8G1IU16q0dwiEltry+YZ4A8hztgUa4GH7bOT0oAfjDEnnQ51pRKRXsAaYIox5pDTefwlIjeI/e5qn7kWDrT4omaMiTfG9DHG9AH+BMxsLUUB2tAWgzGmSkSygPVYZwv8wRhzQETmANnGmLXAb4ClIvLvWLs1ptU68OUIP3PfDvxORAywDXjMscA2EXkPK1esfczmecANYIxZjHUM5y7gMFAK/KszSb01lltEugHZWCcpVIvIr7HOEjvrUGTAr+d7FnA18Ib9PltlWkAHUD9yT8D6AFEJlAEPOv2aBL9yt2raEkMppZSXtrQrSSmllB+0MCillPKihUEppZQXLQxKKaW8aGFQSqkWzt+mffayATfV1MKg1GUSkdkNdVYVkfEiMiCUmdQVbzn2xbd+eBmrD1kSMAf43eWuTAuDUs1vPFZjQ6Waha+mffZV4H8Rkb0i8n8i0t+eFXBTTS0MSvlBRJ61x8TYCNxoT3tERPaISJ6IrBaRjiIyDBgHzLfHEOjbwAtYqUAsAX5pjBmE1S/tDXt6wE0128yVz0o1lYgMwmpFkor1mvkbsBdYY4xZai/zIjDdGPOaiKwFPjLG/Mme9ynwb8aYv4vIUKwX8EgHHoq6Qtg90YYBq+wr2QHa29+fABaJyDSsTgiX3VRTC4NSjbsV+LMxphTAfuMHSLQLQmesAWXW1/3BRl7ASjVVGFBsjEmpO6M5mmrqriSl/OOrd8xyIMsYcxPwAlbTxbo8L+BaXwlBzKnaALs311ERmQSeoXKT7dsBN9XUwqBU47YB94lIBxGJBu6xp0cDJ0XEDUyutfyP9rwGX8BK+ctu2rcLuFFEjovIdKz/uekikgcc4OJB5tuBL0XkEHAt1jC0l7c+baKnVONE5FngYeAY1lgSBcA54Cl72j4g2hgzTUSGA0uBcmAiUA28CXTH6sD5vjFmTsgfhFJ+0sKglFLKi+5KUkop5UULg1JKKS9aGJRSSnnRwqCUUsqLFgallFJetDAopZTyooVBKaWUl/8HtnYJcJ9a4ssAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ee74df080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls_FRA = pd.DataFrame()\n",
    "\n",
    "for factor, data in unixt_factor_data.items():\n",
    "    ls_FRA[factor] = al.performance.factor_rank_autocorrelation(data)\n",
    "\n",
    "ls_FRA.plot(title=\"Factor Rank Autocorrelation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sharpe Ratio of the Alphas\n",
    "The last analysis we'll do on the factors will be sharpe ratio. Let's see what the sharpe ratio for the factors are. Generally, a Sharpe Ratio of near 1.0 or higher is an acceptable single alpha for this universe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "negative       -0.24000000\n",
       "positive       -1.33000000\n",
       "uncertainty     0.82000000\n",
       "litigious       1.34000000\n",
       "constraining   -2.99000000\n",
       "interesting    -2.47000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_annualization_factor = np.sqrt(252)\n",
    "\n",
    "(daily_annualization_factor * ls_factor_returns.mean() / ls_factor_returns.std()).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it! You've successfully done sentiment analysis on 10-ks!\n",
    "## Submission\n",
    "Now that you're done with the project, it's time to submit it. Click the submit button in the bottom right. One of our reviewers will give you feedback on your project with a pass or not passed grade. You can continue to the next section while you wait for feedback."
   ]
  }
 ],
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