yahoofinancials.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from yahoofinancials import YahooFinancials\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ticker = 'HDFCBANK.NS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "yahoo_financials = YahooFinancials(ticker)\n",
    "data = yahoo_financials.get_historical_price_data(\"2019-05-01\",\"2020-05-01\",\"daily\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting stock data (historical close price) for the stocks identified\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_tickers = [\"AAPL\",\"MSFT\",\"CSCO\",\"AMZN\",\"HDFCBANK.NS\",\"BAJFINANCE.NS\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "close_prices = pd.DataFrame()\n",
    "end_date = (datetime.date.today()).strftime('%Y-%m-%d')\n",
    "beg_date = (datetime.date.today()-datetime.timedelta(1825)).strftime('%Y-%m-%d')\n",
    "for ticker in all_tickers:\n",
    "    yahoo_financials = YahooFinancials(ticker)\n",
    "    json_obj = yahoo_financials.get_historical_price_data(beg_date,end_date,\"daily\")\n",
    "    ohlv = json_obj[ticker]['prices']\n",
    "    temp = pd.DataFrame(ohlv)[[\"formatted_date\",\"adjclose\"]]\n",
    "    temp.set_index(\"formatted_date\",inplace=True)\n",
    "    temp.dropna(inplace=True)\n",
    "    close_prices[ticker] = temp[\"adjclose\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>CSCO</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>HDFCBANK.NS</th>\n",
       "      <th>BAJFINANCE.NS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>formatted_date</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>2015-08-25</th>\n",
       "      <td>95.867607</td>\n",
       "      <td>36.778946</td>\n",
       "      <td>20.985384</td>\n",
       "      <td>466.369995</td>\n",
       "      <td>490.323517</td>\n",
       "      <td>470.049957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-26</th>\n",
       "      <td>101.366074</td>\n",
       "      <td>38.814651</td>\n",
       "      <td>21.888901</td>\n",
       "      <td>500.769989</td>\n",
       "      <td>487.940399</td>\n",
       "      <td>474.993805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-27</th>\n",
       "      <td>104.350983</td>\n",
       "      <td>39.896111</td>\n",
       "      <td>22.306561</td>\n",
       "      <td>518.369995</td>\n",
       "      <td>492.080780</td>\n",
       "      <td>487.683228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-28</th>\n",
       "      <td>104.692871</td>\n",
       "      <td>39.923386</td>\n",
       "      <td>22.161655</td>\n",
       "      <td>518.010010</td>\n",
       "      <td>494.897186</td>\n",
       "      <td>483.145752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td>104.203110</td>\n",
       "      <td>39.550766</td>\n",
       "      <td>22.059370</td>\n",
       "      <td>512.890015</td>\n",
       "      <td>494.656464</td>\n",
       "      <td>480.152740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-17</th>\n",
       "      <td>458.429993</td>\n",
       "      <td>209.772919</td>\n",
       "      <td>42.090000</td>\n",
       "      <td>3182.409912</td>\n",
       "      <td>1032.750000</td>\n",
       "      <td>3384.800049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-18</th>\n",
       "      <td>462.250000</td>\n",
       "      <td>210.979996</td>\n",
       "      <td>41.980000</td>\n",
       "      <td>3312.489990</td>\n",
       "      <td>1056.500000</td>\n",
       "      <td>3419.899902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-19</th>\n",
       "      <td>462.829987</td>\n",
       "      <td>209.699997</td>\n",
       "      <td>41.869999</td>\n",
       "      <td>3260.479980</td>\n",
       "      <td>1066.599976</td>\n",
       "      <td>3409.949951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-20</th>\n",
       "      <td>473.100006</td>\n",
       "      <td>214.580002</td>\n",
       "      <td>42.310001</td>\n",
       "      <td>3297.370117</td>\n",
       "      <td>1059.000000</td>\n",
       "      <td>3393.800049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-21</th>\n",
       "      <td>497.480011</td>\n",
       "      <td>213.020004</td>\n",
       "      <td>42.250000</td>\n",
       "      <td>3284.719971</td>\n",
       "      <td>1085.650024</td>\n",
       "      <td>3389.300049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1258 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      AAPL        MSFT       CSCO         AMZN  HDFCBANK.NS  \\\n",
       "formatted_date                                                                \n",
       "2015-08-25       95.867607   36.778946  20.985384   466.369995   490.323517   \n",
       "2015-08-26      101.366074   38.814651  21.888901   500.769989   487.940399   \n",
       "2015-08-27      104.350983   39.896111  22.306561   518.369995   492.080780   \n",
       "2015-08-28      104.692871   39.923386  22.161655   518.010010   494.897186   \n",
       "2015-08-31      104.203110   39.550766  22.059370   512.890015   494.656464   \n",
       "...                    ...         ...        ...          ...          ...   \n",
       "2020-08-17      458.429993  209.772919  42.090000  3182.409912  1032.750000   \n",
       "2020-08-18      462.250000  210.979996  41.980000  3312.489990  1056.500000   \n",
       "2020-08-19      462.829987  209.699997  41.869999  3260.479980  1066.599976   \n",
       "2020-08-20      473.100006  214.580002  42.310001  3297.370117  1059.000000   \n",
       "2020-08-21      497.480011  213.020004  42.250000  3284.719971  1085.650024   \n",
       "\n",
       "                BAJFINANCE.NS  \n",
       "formatted_date                 \n",
       "2015-08-25         470.049957  \n",
       "2015-08-26         474.993805  \n",
       "2015-08-27         487.683228  \n",
       "2015-08-28         483.145752  \n",
       "2015-08-31         480.152740  \n",
       "...                       ...  \n",
       "2020-08-17        3384.800049  \n",
       "2020-08-18        3419.899902  \n",
       "2020-08-19        3409.949951  \n",
       "2020-08-20        3393.800049  \n",
       "2020-08-21        3389.300049  \n",
       "\n",
       "[1258 rows x 6 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting stock data (OHLC) for the stocks identified\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "ohlv_dict = {}\n",
    "end_date = (datetime.date.today()).strftime('%Y-%m-%d')\n",
    "beg_date = (datetime.date.today()-datetime.timedelta(1825)).strftime('%Y-%m-%d')\n",
    "for ticker in all_tickers:\n",
    "    yahoo_financials = YahooFinancials(ticker)\n",
    "    json_obj = yahoo_financials.get_historical_price_data(beg_date,end_date,\"daily\")\n",
    "    ohlv = json_obj[ticker]['prices']\n",
    "    temp = pd.DataFrame(ohlv)[[\"formatted_date\",\"adjclose\",\"open\",\"low\",\"high\",\"volume\"]]\n",
    "    temp.set_index(\"formatted_date\",inplace=True)\n",
    "    temp.dropna(inplace=True)\n",
    "    ohlv_dict[ticker] = temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adjclose</th>\n",
       "      <th>open</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>formatted_date</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-08-25</th>\n",
       "      <td>95.867607</td>\n",
       "      <td>111.110001</td>\n",
       "      <td>103.500000</td>\n",
       "      <td>111.110001</td>\n",
       "      <td>103601600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-26</th>\n",
       "      <td>101.366074</td>\n",
       "      <td>107.089996</td>\n",
       "      <td>105.050003</td>\n",
       "      <td>109.889999</td>\n",
       "      <td>96774600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-27</th>\n",
       "      <td>104.350983</td>\n",
       "      <td>112.230003</td>\n",
       "      <td>110.019997</td>\n",
       "      <td>113.239998</td>\n",
       "      <td>84616100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-28</th>\n",
       "      <td>104.692871</td>\n",
       "      <td>112.169998</td>\n",
       "      <td>111.540001</td>\n",
       "      <td>113.309998</td>\n",
       "      <td>53164400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td>104.203110</td>\n",
       "      <td>112.029999</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>114.529999</td>\n",
       "      <td>56229300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-17</th>\n",
       "      <td>458.429993</td>\n",
       "      <td>464.250000</td>\n",
       "      <td>455.850006</td>\n",
       "      <td>464.350006</td>\n",
       "      <td>29890400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-18</th>\n",
       "      <td>462.250000</td>\n",
       "      <td>457.410004</td>\n",
       "      <td>456.029999</td>\n",
       "      <td>464.000000</td>\n",
       "      <td>26408400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-19</th>\n",
       "      <td>462.829987</td>\n",
       "      <td>463.929993</td>\n",
       "      <td>462.440002</td>\n",
       "      <td>468.649994</td>\n",
       "      <td>36384500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-20</th>\n",
       "      <td>473.100006</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>462.929993</td>\n",
       "      <td>473.570007</td>\n",
       "      <td>31726800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-21</th>\n",
       "      <td>497.480011</td>\n",
       "      <td>477.049988</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>499.470001</td>\n",
       "      <td>84299500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1258 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  adjclose        open         low        high     volume\n",
       "formatted_date                                                           \n",
       "2015-08-25       95.867607  111.110001  103.500000  111.110001  103601600\n",
       "2015-08-26      101.366074  107.089996  105.050003  109.889999   96774600\n",
       "2015-08-27      104.350983  112.230003  110.019997  113.239998   84616100\n",
       "2015-08-28      104.692871  112.169998  111.540001  113.309998   53164400\n",
       "2015-08-31      104.203110  112.029999  112.000000  114.529999   56229300\n",
       "...                    ...         ...         ...         ...        ...\n",
       "2020-08-17      458.429993  464.250000  455.850006  464.350006   29890400\n",
       "2020-08-18      462.250000  457.410004  456.029999  464.000000   26408400\n",
       "2020-08-19      462.829987  463.929993  462.440002  468.649994   36384500\n",
       "2020-08-20      473.100006  463.000000  462.929993  473.570007   31726800\n",
       "2020-08-21      497.480011  477.049988  477.000000  499.470001   84299500\n",
       "\n",
       "[1258 rows x 5 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohlv_dict['AAPL']"
   ]
  }
 ],
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