notebooks/Market-Data-EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Market Data EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As part of this notebook we are going to take a look at the following Kaggle dataset:\n",
    "\n",
    "https://www.kaggle.com/borismarjanovic/price-volume-data-for-all-us-stocks-etfs\n",
    "\n",
    "The plan is to analyze this data and perform some quality checks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "from tqdm.notebook import tqdm, trange\n",
    "import matplotlib.pyplot as plt\n",
    "import dask.dataframe as dd\n",
    "import pandas_datareader.data as web\n",
    "\n",
    "pd.set_option('display.min_rows', 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to data location\n",
    "DATA_DIR = '../data/price-volume-data-for-all-us-stocks-etfs/Data'\n",
    "STOCK_DIR = 'Stocks'\n",
    "ETF_DIR = 'ETFs'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some basic functions to get stock & etf tickers and extract data from csv files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EquityData:\n",
    "    \"\"\"\n",
    "    Provides an access layer to market data files made available\n",
    "    as part of the following Kaggle dataset:\n",
    "\n",
    "        https://www.kaggle.com/borismarjanovic/price-volume-data-for-all-us-stocks-etfs\n",
    "\n",
    "    Each file contains market data for one ticker (either stock or ETF). Also, each filename\n",
    "    has a specific naming convention: it consists of a ticker and a suffix (common to all files).\n",
    "\n",
    "    Attributes:\n",
    "        stock_dir (str): Directory location for stock files.\n",
    "        etf_dir (str): Directory location for ETF files.\n",
    "        file_suffix (str): A file suffix used by stock & ETF files.\n",
    "    \"\"\"\n",
    "\n",
    "    FILE_SUFFIX = '.us.txt'\n",
    "\n",
    "    def __init__(self, data_dir, stock_dir, etf_dir, file_suffix=FILE_SUFFIX):\n",
    "\n",
    "        self.stock_dir = os.path.join(data_dir, stock_dir)\n",
    "        self.etf_dir = os.path.join(data_dir, etf_dir)\n",
    "        self.file_suffix = file_suffix\n",
    "\n",
    "    @staticmethod\n",
    "    def _get_tickers(dirname, file_suffix):\n",
    "        \"\"\"\n",
    "        Creates a list of available tickers in a given directory.\n",
    "\n",
    "        Only non-empty files are considered.\n",
    "\n",
    "        Args:\n",
    "            file_suffix (str): A file suffix used by each data file.\n",
    "\n",
    "        Returns:\n",
    "            (List[str]): A list of available tickers.\n",
    "        \"\"\"\n",
    "\n",
    "        tickers = []\n",
    "        for file in os.listdir(dirname):\n",
    "            # This dataset includes empty files which we exclude here.\n",
    "            if os.stat(os.path.join(dirname, file)).st_size > 0:\n",
    "                tickers.append(file.replace(file_suffix, ''))\n",
    "\n",
    "        return tickers\n",
    "\n",
    "    def get_etf_tickers(self):\n",
    "        \"\"\"\n",
    "        Returns a list of available ETF tickers.\n",
    "\n",
    "        Returns:\n",
    "            (List[str]): A sorted list of available ETF tickers.\n",
    "        \"\"\"\n",
    "\n",
    "        return sorted(self._get_tickers(self.etf_dir, self.file_suffix))\n",
    "\n",
    "    def get_stock_tickers(self):\n",
    "        \"\"\"\n",
    "        Returns a list of available stock tickers.\n",
    "\n",
    "        Returns:\n",
    "            (List[str]): A sorted list of available stock tickers.\n",
    "        \"\"\"\n",
    "        return sorted(self._get_tickers(self.stock_dir, self.file_suffix))\n",
    "\n",
    "    def get_all_tickers(self):\n",
    "        \"\"\"\n",
    "        Returns a list of available stock and ETF tickers.\n",
    "\n",
    "        Returns:\n",
    "            (List[str]): A sorted list of available stock and ETF tickers.\n",
    "        \"\"\"\n",
    "        return sorted(self.get_etf_tickers() + self.get_stock_tickers())\n",
    "\n",
    "    def get_csv_data(self, ticker, start_date=None, end_date=None):\n",
    "        \"\"\"\n",
    "        Returns a list of available stock and ETF tickers.\n",
    "\n",
    "        Args:\n",
    "            ticker (str): A ticker name.\n",
    "            start_date (str): A start date\n",
    "            end_date (str): An end date\n",
    "\n",
    "        Returns:\n",
    "            (pd.DataFrame): Parsed market data for a given ticker.\n",
    "        \"\"\"\n",
    "        ticker = ticker.lower()\n",
    "\n",
    "        # Check ETFs first since the number of potential hits is much smaller.\n",
    "        if ticker in self.get_etf_tickers():\n",
    "            dirname = self.etf_dir\n",
    "        else:\n",
    "            dirname = self.stock_dir\n",
    "\n",
    "        result = pd.DataFrame()\n",
    "        csv = pd.read_csv(os.path.join(dirname, ticker + self.file_suffix))\n",
    "        result = pd.DataFrame({\n",
    "            'date': csv['Date'],\n",
    "            'ticker': ticker,\n",
    "            'price': csv['Close'],\n",
    "            'volume': csv['Volume']\n",
    "        })\n",
    "        result['date'] = result['date'].map(\n",
    "            lambda t: datetime.strptime(t, '%Y-%m-%d')).to_numpy()\n",
    "\n",
    "        if start_date is not None:\n",
    "            result = result[result['date'] >= start_date]\n",
    "\n",
    "        if end_date is not None:\n",
    "            result = result[result['date'] <= end_date]\n",
    "\n",
    "        result.index = result.date\n",
    "\n",
    "        # Only including weekdays\n",
    "        result['dayofweek'] = pd.DatetimeIndex(result.index).dayofweek\n",
    "        result = result[(result.dayofweek != 5) & (result.dayofweek != 6)]\n",
    "\n",
    "        return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "equity_data = EquityData(DATA_DIR, STOCK_DIR, ETF_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try out this API and load market data for 'SPY' (ETF tracking S&P 500 index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>price</th>\n",
       "      <th>volume</th>\n",
       "      <th>dayofweek</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-02-25</th>\n",
       "      <td>2005-02-25</td>\n",
       "      <td>spy</td>\n",
       "      <td>105.79</td>\n",
       "      <td>70221808</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-28</th>\n",
       "      <td>2005-02-28</td>\n",
       "      <td>spy</td>\n",
       "      <td>105.08</td>\n",
       "      <td>79695344</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-01</th>\n",
       "      <td>2005-03-01</td>\n",
       "      <td>spy</td>\n",
       "      <td>105.62</td>\n",
       "      <td>54607412</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-02</th>\n",
       "      <td>2005-03-02</td>\n",
       "      <td>spy</td>\n",
       "      <td>105.57</td>\n",
       "      <td>73733090</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-03</th>\n",
       "      <td>2005-03-03</td>\n",
       "      <td>spy</td>\n",
       "      <td>105.61</td>\n",
       "      <td>71286823</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date ticker   price    volume  dayofweek\n",
       "date                                                     \n",
       "2005-02-25 2005-02-25    spy  105.79  70221808          4\n",
       "2005-02-28 2005-02-28    spy  105.08  79695344          0\n",
       "2005-03-01 2005-03-01    spy  105.62  54607412          1\n",
       "2005-03-02 2005-03-02    spy  105.57  73733090          2\n",
       "2005-03-03 2005-03-03    spy  105.61  71286823          3"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_ticker = 'SPY'\n",
    "\n",
    "data = equity_data.get_csv_data(example_ticker)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make sure there is no overlap between between stock and ETF tickers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No overlap - good\n"
     ]
    }
   ],
   "source": [
    "etf_tickers = equity_data.get_etf_tickers()\n",
    "stock_tickers = equity_data.get_stock_tickers()\n",
    "\n",
    "if len(np.intersect1d(etf_tickers, stock_tickers)) == 0:\n",
    "    print('No overlap - good')\n",
    "else:\n",
    "    print('There is some overlap')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's calculate some basic stats for each ticker. Given limited resources of my laptop I am going to analyze each file separately and aggregate results. Another approach would be to use Spark or Dask (something that we will try to do in the following section)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "model_id": "52ddd7c03883468b8a272bc52fa9de23",
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       "version_minor": 0
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       "HBox(children=(FloatProgress(value=0.0, max=8507.0), HTML(value='')))"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "stats = []\n",
    "\n",
    "for ticker in tqdm(equity_data.get_all_tickers()):\n",
    "    data = equity_data.get_csv_data(ticker)\n",
    "    stats.append({\n",
    "        'ticker':\n",
    "        ticker,\n",
    "        'min_date':\n",
    "        data.index.min(),\n",
    "        'max_date':\n",
    "        data.index.max(),\n",
    "        'dollar_volume':\n",
    "        np.median(data['price'][-10:] * data['volume'][-10:])\n",
    "    })\n",
    "\n",
    "stats = pd.DataFrame(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt_min_date = stats.groupby(\n",
    "    ['min_date'])['ticker'].count().reset_index().rename({'ticker': 'count'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's calculate the number of data points for each date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def count_data_points(ticker_list):\n",
    "    cnt = None\n",
    "    for ticker in tqdm(ticker_list):\n",
    "        data = equity_data.get_csv_data(ticker)\n",
    "        data['count'] = 1\n",
    "\n",
    "        if cnt is None:\n",
    "            cnt = data['count']\n",
    "        else:\n",
    "            cnt = cnt.add(data['count'], fill_value=0)\n",
    "\n",
    "    return cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "cnt_etf = count_data_points(equity_data.get_etf_tickers())\n",
    "cnt_stock = count_data_points(equity_data.get_stock_tickers())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "plt.plot(cnt_etf.index, cnt_etf.values)\n",
    "plt.plot(cnt_stock.index, cnt_stock.values)\n",
    "plt.plot(cnt_min_date.min_date, np.cumsum(cnt_min_date.ticker))\n",
    "plt.title('Number of Daily OHLC Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The latest date available is 2017-11-10 00:00:00 and it contains 6,764 stocks and 1,261 ETFs\n"
     ]
    }
   ],
   "source": [
    "print('The latest date available is {} and it contains {:,.0f} stocks and {:,.0f} ETFs'.format(\n",
    "    cnt_stock.index[-1], cnt_stock[-1], cnt_etf[-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the amount of data points is generally growing and it contains a couple of extreme jumps. Given that the overall number of listed companies has been going down this most likely implies that the original download process was being actively extended to include additional tickers.\n",
    "\n",
    "In particular there is a huge jump taking place sometime in 2005 -- such a sharp jump would indicate that a process collecting data was extended to include a much larger universe of stocks and ETFs.\n",
    "\n",
    "Let's calculate daily percentage change in the number of per day tickers and check for anomalies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt_total = cnt_etf.add(cnt_stock, fill_value=0)\n",
    "cnt = pd.DataFrame({\n",
    "    'date': cnt_total.index,\n",
    "    'total': cnt_total.values\n",
    "},\n",
    "                   index=cnt_total.index)\n",
    "\n",
    "cnt['etf'] = cnt_etf\n",
    "cnt['etf'] = cnt['etf'].fillna(0)\n",
    "\n",
    "cnt['stock'] = cnt_stock\n",
    "cnt['stock'] = cnt['stock'].fillna(0)\n",
    "\n",
    "cnt['stock_cnt_pct_chg'] = np.abs(cnt['stock'].pct_change())\n",
    "cnt['etf_cnt_pct_chg'] = np.abs(cnt['etf'].pct_change())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>date</th>\n",
       "      <th>total</th>\n",
       "      <th>etf</th>\n",
       "      <th>stock</th>\n",
       "      <th>stock_cnt_pct_chg</th>\n",
       "      <th>etf_cnt_pct_chg</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",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-11-12</th>\n",
       "      <td>1962-11-12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-02</th>\n",
       "      <td>1970-01-02</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-03-24</th>\n",
       "      <td>1978-03-24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.976744</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-03-27</th>\n",
       "      <td>1978-03-27</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-09-27</th>\n",
       "      <td>1985-09-27</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.932099</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-09-30</th>\n",
       "      <td>1985-09-30</td>\n",
       "      <td>162.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>13.727273</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-11-16</th>\n",
       "      <td>1999-11-16</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.958944</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-11-17</th>\n",
       "      <td>1999-11-17</td>\n",
       "      <td>344.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-25</th>\n",
       "      <td>2005-02-25</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>2823.0</td>\n",
       "      <td>6.734247</td>\n",
       "      <td>156.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02-18</th>\n",
       "      <td>2011-02-18</td>\n",
       "      <td>4534.0</td>\n",
       "      <td>781.0</td>\n",
       "      <td>3753.0</td>\n",
       "      <td>3.691250</td>\n",
       "      <td>7.582418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date   total    etf   stock  stock_cnt_pct_chg  \\\n",
       "date                                                              \n",
       "1962-11-12 1962-11-12     2.0    0.0     2.0           1.000000   \n",
       "1970-01-02 1970-01-02    28.0    0.0    28.0          13.000000   \n",
       "1978-03-24 1978-03-24     1.0    0.0     1.0           0.976744   \n",
       "1978-03-27 1978-03-27    43.0    0.0    43.0          42.000000   \n",
       "1985-09-27 1985-09-27    11.0    0.0    11.0           0.932099   \n",
       "1985-09-30 1985-09-30   162.0    0.0   162.0          13.727273   \n",
       "1999-11-16 1999-11-16    14.0    0.0    14.0           0.958944   \n",
       "1999-11-17 1999-11-17   344.0    1.0   343.0          23.500000   \n",
       "2005-02-25 2005-02-25  2980.0  157.0  2823.0           6.734247   \n",
       "2011-02-18 2011-02-18  4534.0  781.0  3753.0           3.691250   \n",
       "\n",
       "            etf_cnt_pct_chg  \n",
       "date                         \n",
       "1962-11-12              NaN  \n",
       "1970-01-02              NaN  \n",
       "1978-03-24              NaN  \n",
       "1978-03-27              NaN  \n",
       "1985-09-27              NaN  \n",
       "1985-09-30              NaN  \n",
       "1999-11-16         1.000000  \n",
       "1999-11-17              inf  \n",
       "2005-02-25       156.000000  \n",
       "2011-02-18         7.582418  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt[cnt.stock_cnt_pct_chg > 0.8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1999-03-10</th>\n",
       "      <td>1999-03-10</td>\n",
       "      <td>338.0</td>\n",
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       "      <td>337.0</td>\n",
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       "      <th>1999-11-16</th>\n",
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       "      <td>1.000000</td>\n",
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       "      <th>1999-11-17</th>\n",
       "      <td>1999-11-17</td>\n",
       "      <td>344.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>inf</td>\n",
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       "    <tr>\n",
       "      <th>2005-02-25</th>\n",
       "      <td>2005-02-25</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>2823.0</td>\n",
       "      <td>6.734247</td>\n",
       "      <td>156.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02-17</th>\n",
       "      <td>2011-02-17</td>\n",
       "      <td>891.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>0.787629</td>\n",
       "      <td>0.882883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02-18</th>\n",
       "      <td>2011-02-18</td>\n",
       "      <td>4534.0</td>\n",
       "      <td>781.0</td>\n",
       "      <td>3753.0</td>\n",
       "      <td>3.691250</td>\n",
       "      <td>7.582418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                 date   total    etf   stock  stock_cnt_pct_chg  \\\n",
       "date                                                              \n",
       "1999-03-10 1999-03-10   338.0    1.0   337.0           0.005900   \n",
       "1999-11-16 1999-11-16    14.0    0.0    14.0           0.958944   \n",
       "1999-11-17 1999-11-17   344.0    1.0   343.0          23.500000   \n",
       "2005-02-25 2005-02-25  2980.0  157.0  2823.0           6.734247   \n",
       "2011-02-17 2011-02-17   891.0   91.0   800.0           0.787629   \n",
       "2011-02-18 2011-02-18  4534.0  781.0  3753.0           3.691250   \n",
       "\n",
       "            etf_cnt_pct_chg  \n",
       "date                         \n",
       "1999-03-10              inf  \n",
       "1999-11-16         1.000000  \n",
       "1999-11-17              inf  \n",
       "2005-02-25       156.000000  \n",
       "2011-02-17         0.882883  \n",
       "2011-02-18         7.582418  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt[cnt.etf_cnt_pct_chg > 0.8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the two huge jumps observed on the chart took place on 2005-02-25 and 2011-02-17. Let's take a look at them in detail below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>date</th>\n",
       "      <th>total</th>\n",
       "      <th>etf</th>\n",
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       "      <th>2005-02-23</th>\n",
       "      <td>2005-02-23</td>\n",
       "      <td>364.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>0.002747</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-24</th>\n",
       "      <td>2005-02-24</td>\n",
       "      <td>366.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>0.005510</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-25</th>\n",
       "      <td>2005-02-25</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>2823.0</td>\n",
       "      <td>6.734247</td>\n",
       "      <td>156.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-28</th>\n",
       "      <td>2005-02-28</td>\n",
       "      <td>2977.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>2821.0</td>\n",
       "      <td>0.000708</td>\n",
       "      <td>0.006369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date   total    etf   stock  stock_cnt_pct_chg  \\\n",
       "date                                                              \n",
       "2005-02-23 2005-02-23   364.0    1.0   363.0           0.002747   \n",
       "2005-02-24 2005-02-24   366.0    1.0   365.0           0.005510   \n",
       "2005-02-25 2005-02-25  2980.0  157.0  2823.0           6.734247   \n",
       "2005-02-28 2005-02-28  2977.0  156.0  2821.0           0.000708   \n",
       "\n",
       "            etf_cnt_pct_chg  \n",
       "date                         \n",
       "2005-02-23         0.000000  \n",
       "2005-02-24         0.000000  \n",
       "2005-02-25       156.000000  \n",
       "2005-02-28         0.006369  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt['2005-02-23':'2005-02-28']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This jump is probably due to the fact that the collection process was extended to include a much \n",
    "bigger universe of stocks and ETFs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2011-02-16</th>\n",
       "      <td>2011-02-16</td>\n",
       "      <td>4544.0</td>\n",
       "      <td>777.0</td>\n",
       "      <td>3767.0</td>\n",
       "      <td>0.005069</td>\n",
       "      <td>0.002567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02-17</th>\n",
       "      <td>2011-02-17</td>\n",
       "      <td>891.0</td>\n",
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       "      <td>800.0</td>\n",
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       "      <td>0.882883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02-18</th>\n",
       "      <td>2011-02-18</td>\n",
       "      <td>4534.0</td>\n",
       "      <td>781.0</td>\n",
       "      <td>3753.0</td>\n",
       "      <td>3.691250</td>\n",
       "      <td>7.582418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02-22</th>\n",
       "      <td>2011-02-22</td>\n",
       "      <td>4553.0</td>\n",
       "      <td>781.0</td>\n",
       "      <td>3772.0</td>\n",
       "      <td>0.005063</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date   total    etf   stock  stock_cnt_pct_chg  \\\n",
       "date                                                              \n",
       "2011-02-16 2011-02-16  4544.0  777.0  3767.0           0.005069   \n",
       "2011-02-17 2011-02-17   891.0   91.0   800.0           0.787629   \n",
       "2011-02-18 2011-02-18  4534.0  781.0  3753.0           3.691250   \n",
       "2011-02-22 2011-02-22  4553.0  781.0  3772.0           0.005063   \n",
       "\n",
       "            etf_cnt_pct_chg  \n",
       "date                         \n",
       "2011-02-16         0.002567  \n",
       "2011-02-17         0.882883  \n",
       "2011-02-18         7.582418  \n",
       "2011-02-22         0.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt['2011-02-16':'2011-02-22']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There does not seem to be a significant trading event taking place on this date (that would somehow affect trading for a large number of stocks and ETFs), therefore this drop is likely due to a one-day issue with the data collection process. In fact let's use Yahoo data to test this theory out."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison with Yahoo Finance Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date = '2010-01-01'\n",
    "end_date = '2011-12-31'\n",
    "example_ticker = 'SPY'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>113.389999</td>\n",
       "      <td>111.510002</td>\n",
       "      <td>112.370003</td>\n",
       "      <td>113.330002</td>\n",
       "      <td>118944600.0</td>\n",
       "      <td>92.246048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>113.680000</td>\n",
       "      <td>112.849998</td>\n",
       "      <td>113.260002</td>\n",
       "      <td>113.629997</td>\n",
       "      <td>111579900.0</td>\n",
       "      <td>92.490204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>113.989998</td>\n",
       "      <td>113.430000</td>\n",
       "      <td>113.519997</td>\n",
       "      <td>113.709999</td>\n",
       "      <td>116074400.0</td>\n",
       "      <td>92.555328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>114.330002</td>\n",
       "      <td>113.180000</td>\n",
       "      <td>113.500000</td>\n",
       "      <td>114.190002</td>\n",
       "      <td>131091100.0</td>\n",
       "      <td>92.946060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>114.620003</td>\n",
       "      <td>113.660004</td>\n",
       "      <td>113.889999</td>\n",
       "      <td>114.570000</td>\n",
       "      <td>126402800.0</td>\n",
       "      <td>93.255348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close       Volume  \\\n",
       "Date                                                                      \n",
       "2010-01-04  113.389999  111.510002  112.370003  113.330002  118944600.0   \n",
       "2010-01-05  113.680000  112.849998  113.260002  113.629997  111579900.0   \n",
       "2010-01-06  113.989998  113.430000  113.519997  113.709999  116074400.0   \n",
       "2010-01-07  114.330002  113.180000  113.500000  114.190002  131091100.0   \n",
       "2010-01-08  114.620003  113.660004  113.889999  114.570000  126402800.0   \n",
       "\n",
       "            Adj Close  \n",
       "Date                   \n",
       "2010-01-04  92.246048  \n",
       "2010-01-05  92.490204  \n",
       "2010-01-06  92.555328  \n",
       "2010-01-07  92.946060  \n",
       "2010-01-08  93.255348  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yh_data = web.DataReader(example_ticker, 'yahoo', start_date, end_date)\n",
    "yh_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>price</th>\n",
       "      <th>volume</th>\n",
       "      <th>dayofweek</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>spy</td>\n",
       "      <td>98.728</td>\n",
       "      <td>136535215</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>spy</td>\n",
       "      <td>98.986</td>\n",
       "      <td>128081381</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>spy</td>\n",
       "      <td>99.040</td>\n",
       "      <td>133240614</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>spy</td>\n",
       "      <td>99.478</td>\n",
       "      <td>150478068</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>spy</td>\n",
       "      <td>99.810</td>\n",
       "      <td>145096435</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date ticker   price     volume  dayofweek\n",
       "date                                                      \n",
       "2010-01-04 2010-01-04    spy  98.728  136535215          0\n",
       "2010-01-05 2010-01-05    spy  98.986  128081381          1\n",
       "2010-01-06 2010-01-06    spy  99.040  133240614          2\n",
       "2010-01-07 2010-01-07    spy  99.478  150478068          3\n",
       "2010-01-08 2010-01-08    spy  99.810  145096435          4"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_data = equity_data.get_csv_data(example_ticker, start_date, end_date)\n",
    "csv_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make sure that calls return two identical sets of tradning dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is a list of dates returned by Yahoo but not found in csv: ['2011-02-17']\n"
     ]
    }
   ],
   "source": [
    "yh_dates_not_csv = yh_data.index[~yh_data.index.isin(csv_data.index)]\n",
    "if len(yh_dates_not_csv):\n",
    "    print('Here is a list of dates returned by Yahoo but not found in csv: {}'.\n",
    "          format(list(yh_dates_not_csv.strftime('%Y-%m-%d'))))\n",
    "\n",
    "csv_dates_not_yh = csv_data.index[~csv_data.index.isin(yh_data.index)]\n",
    "if len(csv_dates_not_yh):\n",
    "    print('Here is a list of dates returned by Yahoo but not found in csv: {}'.\n",
    "          format(list(csv_dates_not_yh.strftime('%Y-%m-%d'))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This confirms our earlier theory that the csv data was not fully downloaded on '2011-02-17'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The csv data is supposed to be split & dividend adjusted as of '2017-11-10' vs how Yahoo adjusts its data based on the the latest available date. Let's download a new set of dates and compare csv prices vs unadjusted Yahoo prices. Let's use ratios instead of absolute differences to perform this comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc8991d36d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_date = '2016-01-01'\n",
    "end_date = '2017-11-10'\n",
    "\n",
    "yh_data = web.DataReader(example_ticker, 'yahoo', start_date, end_date)\n",
    "csv_data = equity_data.get_csv_data(example_ticker, start_date, end_date)\n",
    "\n",
    "abs(yh_data['Close'] / csv_data['price']).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These steps (for the most part) are very smooth and imply that csv data was dividend adjusted (SPY does not have any splits).\n",
    "\n",
    "There are more checks that should be performed to ensure data quality of csv files, but let's stop here for now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utilizing Dask for Analyzing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given that each ticker is saved into its own file, a more efficient (from usability perspective) would be to calculate basic statistics utilizing a tool like Dask. Here is an example how we could go about in case of analyzing stock data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "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>ticker</th>\n",
       "      <th>price</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999-11-18</th>\n",
       "      <td>a</td>\n",
       "      <td>29.702</td>\n",
       "      <td>66277506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-11-19</th>\n",
       "      <td>a</td>\n",
       "      <td>27.257</td>\n",
       "      <td>16142920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-11-22</th>\n",
       "      <td>a</td>\n",
       "      <td>29.702</td>\n",
       "      <td>6970266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-11-23</th>\n",
       "      <td>a</td>\n",
       "      <td>27.002</td>\n",
       "      <td>6332082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-11-24</th>\n",
       "      <td>a</td>\n",
       "      <td>27.717</td>\n",
       "      <td>5132147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ticker   price    volume\n",
       "date                               \n",
       "1999-11-18      a  29.702  66277506\n",
       "1999-11-19      a  27.257  16142920\n",
       "1999-11-22      a  29.702   6970266\n",
       "1999-11-23      a  27.002   6332082\n",
       "1999-11-24      a  27.717   5132147"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We map stock files to one virtual dataframe. A new column ('path') is created\n",
    "# which contains a source filename for each data row.\n",
    "\n",
    "STOCK_DATA_DIR = os.path.join(DATA_DIR, STOCK_DIR)\n",
    "stocks = dd.read_csv(os.path.join(STOCK_DATA_DIR,\n",
    "                                  '*' + EquityData.FILE_SUFFIX),\n",
    "                     include_path_column=True)\n",
    "\n",
    "# We extract a ticker using the path variable\n",
    "stocks['ticker'] = stocks['path'].str.replace('.*' + STOCK_DATA_DIR + '/',\n",
    "                                              '',\n",
    "                                              regex=True)\n",
    "stocks['ticker'] = stocks['ticker'].str.replace('.us.txt', '')\n",
    "\n",
    "stocks = stocks.drop(columns=['Open', 'High', 'Low', 'OpenInt', 'path'])\n",
    "stocks = stocks.rename(columns={\n",
    "    'Date': 'date',\n",
    "    'Close': 'price',\n",
    "    'Volume': 'volume'\n",
    "})\n",
    "stocks.index = stocks.date\n",
    "stocks = stocks[['ticker', 'price', 'volume']]\n",
    "stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cnt_stock_dask = stocks.groupby(['date'])['price'].count().compute()\n",
    "\n",
    "# We need to make sure that index values are sorted\n",
    "cnt_stock_dask = cnt_stock_dask.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for both approaches (Dask & Pandas) are matching up - good\n"
     ]
    }
   ],
   "source": [
    "if all(cnt_stock == cnt_stock):\n",
    "    print('Results for both approaches (Dask & Pandas) are matching up - good')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:** In general, using Dask is much more convenient compared to writing custom functions to process data for each ticker and than aggregate results. One should run some performance checks to further examine pros and cons of using Dask."
   ]
  }
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