scratch/get_yahoo_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get data from yahoo, after the ichart interface is gone.\n",
    "\n",
    "\n",
    "**Author: Jev Kuznetsov**\n",
    "\n",
    "On 18 May 2017 the `ichart` data api of yahoo finance went down, without any notice. And it does not seem like it is coming back. This has left many (including me) without a descent free end-of-day data source. \n",
    "\n",
    "It appears that Yahoo now tries to block automatic requests by requiring a combination of a *cookie* and a *crumb* token. \n",
    "\n",
    "There are ways around it, pointers to how to do it can be found on [stackoverflow](http://stackoverflow.com/questions/44030983/yahoo-finance-url-not-working)\n",
    "\n",
    "The script below executes the steps needed to get the data and at the same time demonstrates the use of a couple very handy python libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import requests # interaction with the web\n",
    "import os  #  file system operations\n",
    "import yaml # human-friendly data format\n",
    "import re  # regular expressions\n",
    "import pandas as pd # pandas... the best time series library out there\n",
    "import datetime as dt # date and time functions\n",
    "import io "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get cookie and token\n",
    "\n",
    "The cookie and token can be used for about a year, so it's better to download them once and save to file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cookie:  ctdg6u1ci1a3o&b=3&s=u5\n",
      "Crumb= YoBpll1PYIi\n"
     ]
    }
   ],
   "source": [
    "# search with regular expressions\n",
    "\n",
    "# \"CrumbStore\":\\{\"crumb\":\"(?<crumb>[^\"]+)\"\\}\n",
    "\n",
    "url = 'https://uk.finance.yahoo.com/quote/AAPL/history' # url for a ticker symbol, with a download link\n",
    "r = requests.get(url)  # download page\n",
    "\n",
    "txt = r.text # extract html\n",
    "\n",
    "\n",
    "cookie = r.cookies['B'] # the cooke we're looking for is named 'B'\n",
    "print('Cookie: ', cookie)\n",
    "\n",
    "# Now we need to extract the token from html. \n",
    "# the string we need looks like this: \"CrumbStore\":{\"crumb\":\"lQHxbbYOBCq\"}\n",
    "# regular expressions will do the trick!\n",
    "\n",
    "pattern = re.compile('.*\"CrumbStore\":\\{\"crumb\":\"(?P<crumb>[^\"]+)\"\\}')\n",
    "\n",
    "for line in txt.splitlines():\n",
    "    m = pattern.match(line)\n",
    "    if m is not None:\n",
    "        crumb = m.groupdict()['crumb']\n",
    "        \n",
    "        \n",
    "print('Crumb=',crumb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create data directory in the user folder\n",
    "dataDir = os.path.expanduser('~')+'/twpData'\n",
    "\n",
    "if not os.path.exists(dataDir):\n",
    "    os.mkdir(dataDir)\n",
    "\n",
    "\n",
    "# save data to YAML file\n",
    "data = {'cookie':cookie,'crumb':crumb}\n",
    "\n",
    "dataFile = os.path.join(dataDir,'yahoo_cookie.yml')\n",
    "\n",
    "with open(dataFile,'w') as fid:\n",
    "    yaml.dump(data,fid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define start and end times\n",
    "\n",
    "start and end times of the requested data are in seconds since epoch (1 Jan 1970)\n",
    "these are easey to get with `timestamp()` "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`https://query1.finance.yahoo.com/v7/finance/download/SPY?period1=1463754366&period2=1495290366&interval=1d&events=history&crumb=DB/mJy8XKWr`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1230764400.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# start with tuples ...\n",
    "sDate = (2009,1,1)\n",
    "eDate = (2017,5,20)\n",
    " \n",
    "dt.datetime(*sDate).timestamp() # convert to seconds since epoch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get data\n",
    "\n",
    "now it's time to get data from yahoo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://query1.finance.yahoo.com/v7/finance/download/VXX?period1=1230764400&period2=1495231200&interval=1d&events=history&crumb=YoBpll1PYIi\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# prepare input data as a tuple\n",
    "data = (int(dt.datetime(*sDate).timestamp()),\n",
    "        int(dt.datetime(*eDate).timestamp()), \n",
    "        crumb)\n",
    "\n",
    "\n",
    "url = \"https://query1.finance.yahoo.com/v7/finance/download/VXX?period1={0}&period2={1}&interval=1d&events=history&crumb={2}\".format(*data)\n",
    "\n",
    "\n",
    "print(url)\n",
    "data = requests.get(url, cookies={'B':cookie})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-01-30</th>\n",
       "      <td>25628.160156</td>\n",
       "      <td>26890.240234</td>\n",
       "      <td>25536.000000</td>\n",
       "      <td>104.580002</td>\n",
       "      <td>26772.480469</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-02-02</th>\n",
       "      <td>27673.599609</td>\n",
       "      <td>27673.599609</td>\n",
       "      <td>26608.640625</td>\n",
       "      <td>104.250000</td>\n",
       "      <td>26688.000000</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-02-03</th>\n",
       "      <td>26659.839844</td>\n",
       "      <td>26659.839844</td>\n",
       "      <td>25423.359375</td>\n",
       "      <td>99.370003</td>\n",
       "      <td>25438.720703</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-02-04</th>\n",
       "      <td>25152.000000</td>\n",
       "      <td>25699.839844</td>\n",
       "      <td>24832.000000</td>\n",
       "      <td>99.739998</td>\n",
       "      <td>25533.439453</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-02-05</th>\n",
       "      <td>26009.599609</td>\n",
       "      <td>26570.240234</td>\n",
       "      <td>25126.400391</td>\n",
       "      <td>99.129997</td>\n",
       "      <td>25377.279297</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Open          High           Low       Close  \\\n",
       "Date                                                               \n",
       "2009-01-30  25628.160156  26890.240234  25536.000000  104.580002   \n",
       "2009-02-02  27673.599609  27673.599609  26608.640625  104.250000   \n",
       "2009-02-03  26659.839844  26659.839844  25423.359375   99.370003   \n",
       "2009-02-04  25152.000000  25699.839844  24832.000000   99.739998   \n",
       "2009-02-05  26009.599609  26570.240234  25126.400391   99.129997   \n",
       "\n",
       "               Adj Close  Volume  \n",
       "Date                              \n",
       "2009-01-30  26772.480469     800  \n",
       "2009-02-02  26688.000000    1200  \n",
       "2009-02-03  25438.720703     600  \n",
       "2009-02-04  25533.439453     600  \n",
       "2009-02-05  25377.279297     900  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buf = io.StringIO(data.text) # create a buffer\n",
    "df = pd.read_csv(buf,index_col=0) # convert to pandas DataFrame\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8306e79390>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KV764gPd+3MbR/AImDGwfsP9TuzbjuStTWZaexYAOjWnVMIG/vrOcL37axeT3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\ns+YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8306e722b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot \n",
    "df['Adj Close'].plot(logy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8303ddf198>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EraMOA+C9HwyTU534+DcaWbiOD+1aZAHQJlcCGIXo0+yURM9iI4X4tv1GSVOx6zaOqto6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\n2/mavYLFpQSjsQ9vx1aG6cn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whtleCgwAXrb9iIHkO8aSD/glxo39IIZiWwOcgfEg78Nb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\nESUhCIIgBEWUhCAIghAUURKCIAhCUP4f1UaTS9L8Hj8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8303e0c2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the closing price is not adjustes for splits ...\n",
    "df.Close.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}