data preparation for time warp test

warp_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas_datareader.data as web"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "pd.set_option('display.max_colwidth', 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "start = pd.to_datetime('2010-01-01')\n",
    "end = pd.to_datetime('2017-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "spy = web.get_data_yahoo('SPY', start, end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "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>Volume</th>\n",
       "      <th>Adj Close</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>2010-01-04</th>\n",
       "      <td>112.370003</td>\n",
       "      <td>113.389999</td>\n",
       "      <td>111.510002</td>\n",
       "      <td>113.330002</td>\n",
       "      <td>118944600</td>\n",
       "      <td>98.214371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>113.260002</td>\n",
       "      <td>113.680000</td>\n",
       "      <td>112.849998</td>\n",
       "      <td>113.629997</td>\n",
       "      <td>111579900</td>\n",
       "      <td>98.474354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>113.519997</td>\n",
       "      <td>113.989998</td>\n",
       "      <td>113.430000</td>\n",
       "      <td>113.709999</td>\n",
       "      <td>116074400</td>\n",
       "      <td>98.543685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>113.500000</td>\n",
       "      <td>114.330002</td>\n",
       "      <td>113.180000</td>\n",
       "      <td>114.190002</td>\n",
       "      <td>131091100</td>\n",
       "      <td>98.959667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>113.889999</td>\n",
       "      <td>114.620003</td>\n",
       "      <td>113.660004</td>\n",
       "      <td>114.570000</td>\n",
       "      <td>126402800</td>\n",
       "      <td>99.288981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close     Volume  \\\n",
       "Date                                                                    \n",
       "2010-01-04  112.370003  113.389999  111.510002  113.330002  118944600   \n",
       "2010-01-05  113.260002  113.680000  112.849998  113.629997  111579900   \n",
       "2010-01-06  113.519997  113.989998  113.430000  113.709999  116074400   \n",
       "2010-01-07  113.500000  114.330002  113.180000  114.190002  131091100   \n",
       "2010-01-08  113.889999  114.620003  113.660004  114.570000  126402800   \n",
       "\n",
       "            Adj Close  \n",
       "Date                   \n",
       "2010-01-04  98.214371  \n",
       "2010-01-05  98.474354  \n",
       "2010-01-06  98.543685  \n",
       "2010-01-07  98.959667  \n",
       "2010-01-08  99.288981  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "spy_adj_close = spy['Adj Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x119876390>], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yjWi5q127NtHR0Wzfvp2EhAQuu+wyrz/3YqI1WiIiF7H33nuPXr160blzZ06c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\n16J3EZGCUqAlIiXGihUrALj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1Hufh4eH06NGD2bNne/3ZIiJSuPIdaBlj7gduA/rZP4cz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1198a6fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# adj close plotted\n",
    "spy_adj_close.plot(subplots=True ,figsize=(10, 5), title='SPY', color='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "112.370003"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_open = spy['Open'].iloc[0]\n",
    "first_open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "223.52999900000003"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_close = spy['Close'].iloc[-1]\n",
    "last_close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111.15999600000004"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total return between 2010-1-1 <> 2017-1-1\n",
    "last_close - first_open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Lets look at daily returns\n",
    "spy['Daily Change'] = pd.Series(spy['Close'] - spy['Open'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73.64011700000037"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spy['Daily Change'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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