Stock Analysis Ensemble Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We are going to Predict  Tomorrow's close Price is going to lower or higher than Today's Close Price\n",
    "\n",
    "### After that based on Such Results We will develop a Trading Strategy and implement it\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "# moving average in pandas http://chrisalbon.com/python/pandas_moving_average.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Problem Defination : \n",
    "\n",
    "#Our Aim is to predict if 'Future Daily-Returns' of S&P 500 are going to be +ve or -ve. This is\n",
    "#binary classification.\n",
    "\n",
    "# What is S&P 500? - Backtesting - Benchmark\n",
    "# S&p is market index which contains list of top 500 companies having stocks in NYSE or or NASDAQ\n",
    "# What is Binary Classification?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Daily Return: \n",
    "\n",
    "# it is just the way that how we want to look for Value in the system. We are using following formula:\n",
    "\n",
    "#Returni=AdjClosei–AdjClosei−1/AdjClosei−1\n",
    "\n",
    "#Return at ith day is equal to return adjacent-close at ith day---adjacentclose at i-1th day // adjacentclose at i-1th day\n",
    "\n",
    "# in other words it's just percent-change/ rate of change w.r.t to time-- What that is called in physics? :D\n",
    "\n",
    "## What is Adjacent Close Price?\n",
    "## Close price of the day modified by taking account into dividends. \n",
    "## What are dividends?\n",
    "## How Adjacent-close price is calculated? \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Adj. Close'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('WIKI-AAPL.csv')\n",
    "df.columns.values[-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['returns']=df['Adj. Close'].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#pd.rolling_mean(df['returns'],4)\n",
    "\n",
    "df['rolling'] = df['returns'].rolling(window = 4,center=False).mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
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   "outputs": [
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     "execution_count": 11,
     "metadata": {},
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   "source": [
    "#pd.rolling_mean(df['returns'],0) # what is the meaning o rolling mean?\n",
    "df[['returns','rolling']].head()\n",
    "#Multiple Day Returns: percentage difference of Adjusted Close Price of i-th day  compared to (i-delta)-th day. \n",
    "#Example: 3-days Return is the percentage difference of Adjusted Close Price of today compared to the one of 3 days ago\n",
    "df['returns']=df['Adj. Close'].pct_change()\n",
    "df['multiple_day']=df['Adj. Close'].pct_change(3)\n",
    "# we are shifting for 2 so we are calculating for last 'two' days\n",
    "df['time_lagged'] = df['Adj. Close']-df['Adj. Close'].shift(-2)\n",
    "\n",
    "new_df = pd.DataFrame(columns=[df['Adj. Close'],df['returns'],df['rolling'],df['time_lagged'],df['multiple_day']])\n",
    "new_df # we need horizontly , columns are only for names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
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    {
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     "text": [
      "/home/metal-machine/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "/home/metal-machine/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>121.6300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.3100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>121.7190</td>\n",
       "      <td>0.000732</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.1310</td>\n",
       "      <td>up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>121.9400</td>\n",
       "      <td>0.001816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.9692</td>\n",
       "      <td>up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>121.8500</td>\n",
       "      <td>-0.000738</td>\n",
       "      <td>0.001809</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7700</td>\n",
       "      <td>down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>119.9708</td>\n",
       "      <td>-0.015422</td>\n",
       "      <td>-0.014363</td>\n",
       "      <td>-0.003403</td>\n",
       "      <td>-0.0292</td>\n",
       "      <td>down</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   close_value  daily-returns  multiple_day   rolling  time-lagged updown\n",
       "0     121.6300            NaN           NaN       NaN      -0.3100    NaN\n",
       "1     121.7190       0.000732           NaN       NaN      -0.1310     up\n",
       "2     121.9400       0.001816           NaN       NaN       1.9692     up\n",
       "3     121.8500      -0.000738      0.001809       NaN       1.7700   down\n",
       "4     119.9708      -0.015422     -0.014363 -0.003403      -0.0292   down"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " #dataset.UpDown[dataset.UpDown >= 0] = 'Up'\n",
    "df['updown'] = df['returns']\n",
    "df.updown[df['returns']>=0]='up'\n",
    "df.updown[df['returns']<0]='down'\n",
    "seris_dict ={'close_value':df['Adj. Close'],'daily-returns':df['returns'],'rolling':df['rolling'],'time-lagged':df['time_lagged'],'multiple_day':df['multiple_day'],'updown':df['updown']}\n",
    "new_df = pd.DataFrame(seris_dict)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "\n",
    "df['encoding']=le.fit(df['updown']).transform(df['updown']) # what is label encoding, ? also read more about preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>close_value</th>\n",
       "      <th>daily-returns</th>\n",
       "      <th>encoding</th>\n",
       "      <th>multiple_day</th>\n",
       "      <th>rolling</th>\n",
       "      <th>time-lagged</th>\n",
       "      <th>updown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>121.6300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.3100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>121.7190</td>\n",
       "      <td>0.000732</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.1310</td>\n",
       "      <td>up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>121.9400</td>\n",
       "      <td>0.001816</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.9692</td>\n",
       "      <td>up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>121.8500</td>\n",
       "      <td>-0.000738</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001809</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7700</td>\n",
       "      <td>down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>119.9708</td>\n",
       "      <td>-0.015422</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.014363</td>\n",
       "      <td>-0.003403</td>\n",
       "      <td>-0.0292</td>\n",
       "      <td>down</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   close_value  daily-returns  encoding  multiple_day   rolling  time-lagged  \\\n",
       "0     121.6300            NaN         0           NaN       NaN      -0.3100   \n",
       "1     121.7190       0.000732         2           NaN       NaN      -0.1310   \n",
       "2     121.9400       0.001816         2           NaN       NaN       1.9692   \n",
       "3     121.8500      -0.000738         1      0.001809       NaN       1.7700   \n",
       "4     119.9708      -0.015422         1     -0.014363 -0.003403      -0.0292   \n",
       "\n",
       "  updown  \n",
       "0    NaN  \n",
       "1     up  \n",
       "2     up  \n",
       "3   down  \n",
       "4   down  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seris_dict ={'close_value':df['Adj. Close'],'daily-returns':df['returns'],'rolling':df['rolling'],'time-lagged':df['time_lagged'],'multiple_day':df['multiple_day'],'updown':df['updown'],'encoding':df['encoding']}\n",
    "new_df = pd.DataFrame(seris_dict)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "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>close_value</th>\n",
       "      <th>daily-returns</th>\n",
       "      <th>multiple_day</th>\n",
       "      <th>rolling</th>\n",
       "      <th>time-lagged</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>121.6300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.3100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>121.7190</td>\n",
       "      <td>0.000732</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.1310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>121.9400</td>\n",
       "      <td>0.001816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.9692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>121.8500</td>\n",
       "      <td>-0.000738</td>\n",
       "      <td>0.001809</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>119.9708</td>\n",
       "      <td>-0.015422</td>\n",
       "      <td>-0.014363</td>\n",
       "      <td>-0.003403</td>\n",
       "      <td>-0.0292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   close_value  daily-returns  multiple_day   rolling  time-lagged\n",
       "0     121.6300            NaN           NaN       NaN      -0.3100\n",
       "1     121.7190       0.000732           NaN       NaN      -0.1310\n",
       "2     121.9400       0.001816           NaN       NaN       1.9692\n",
       "3     121.8500      -0.000738      0.001809       NaN       1.7700\n",
       "4     119.9708      -0.015422     -0.014363 -0.003403      -0.0292"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seris_dict ={'close_value':df['Adj. Close'],'daily-returns':df['returns'],'rolling':df['rolling'],'time-lagged':df['time_lagged'],'multiple_day':df['multiple_day']}\n",
    "new_df = pd.DataFrame(seris_dict)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features = new_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.indexes.base.Index"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'close_value', u'daily-returns', u'multiple_day', u'rolling',\n",
       "       u'time-lagged'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "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>close_value</th>\n",
       "      <th>daily-returns</th>\n",
       "      <th>multiple_day</th>\n",
       "      <th>rolling</th>\n",
       "      <th>time-lagged</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>121.6300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.3100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>121.7190</td>\n",
       "      <td>0.000732</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.1310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>121.9400</td>\n",
       "      <td>0.001816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.9692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>121.8500</td>\n",
       "      <td>-0.000738</td>\n",
       "      <td>0.001809</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>119.9708</td>\n",
       "      <td>-0.015422</td>\n",
       "      <td>-0.014363</td>\n",
       "      <td>-0.003403</td>\n",
       "      <td>-0.0292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   close_value  daily-returns  multiple_day   rolling  time-lagged\n",
       "0     121.6300            NaN           NaN       NaN      -0.3100\n",
       "1     121.7190       0.000732           NaN       NaN      -0.1310\n",
       "2     121.9400       0.001816           NaN       NaN       1.9692\n",
       "3     121.8500      -0.000738      0.001809       NaN       1.7700\n",
       "4     119.9708      -0.015422     -0.014363 -0.003403      -0.0292"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[features].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     NaN\n",
       "1      up\n",
       "2      up\n",
       "3    down\n",
       "4    down\n",
       "Name: updown, dtype: object"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.updown.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = new_df[features].dropna()\n",
    "Y =  df.updown[6:]\n",
    "#new_df[features].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#dir(sklearn.model_selection)\n",
    "from sklearn.model_selection import train_test_split,TimeSeriesSplit\n",
    "# TimeSeries Split Scikit Learn\n",
    "# What is the difference between tain_test_split and cross validation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y)\n",
    "#X_train,X_test,Y_train,Y_test = TimeSeriesSplit(X) - Why it is not working?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "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>close_value</th>\n",
       "      <th>daily-returns</th>\n",
       "      <th>multiple_day</th>\n",
       "      <th>rolling</th>\n",
       "      <th>time-lagged</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6465</th>\n",
       "      <td>1.522772</td>\n",
       "      <td>0.002442</td>\n",
       "      <td>0.056187</td>\n",
       "      <td>0.016551</td>\n",
       "      <td>0.050089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>51.084252</td>\n",
       "      <td>0.001965</td>\n",
       "      <td>-0.010485</td>\n",
       "      <td>0.002438</td>\n",
       "      <td>-0.769016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3001</th>\n",
       "      <td>5.837234</td>\n",
       "      <td>0.008090</td>\n",
       "      <td>0.073463</td>\n",
       "      <td>0.012158</td>\n",
       "      <td>0.051398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1600</th>\n",
       "      <td>36.924471</td>\n",
       "      <td>-0.013831</td>\n",
       "      <td>-0.029249</td>\n",
       "      <td>-0.006385</td>\n",
       "      <td>1.093017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1645</th>\n",
       "      <td>31.955146</td>\n",
       "      <td>-0.025051</td>\n",
       "      <td>-0.051903</td>\n",
       "      <td>-0.014072</td>\n",
       "      <td>-0.763811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      close_value  daily-returns  multiple_day   rolling  time-lagged\n",
       "6465     1.522772       0.002442      0.056187  0.016551     0.050089\n",
       "1386    51.084252       0.001965     -0.010485  0.002438    -0.769016\n",
       "3001     5.837234       0.008090      0.073463  0.012158     0.051398\n",
       "1600    36.924471      -0.013831     -0.029249 -0.006385     1.093017\n",
       "1645    31.955146      -0.025051     -0.051903 -0.014072    -0.763811"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train,Y_train) # how to remove Nan's? from Pandas or somewhereelse use dropna() method or finance.fillna(finance.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['down', 'up', 'down', ..., 'down', 'down', 'down'], dtype=object)"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "accuracy = rf.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.731225296443\n"
     ]
    }
   ],
   "source": [
    "print accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ada = AdaBoostClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
       "          learning_rate=1.0, n_estimators=50, random_state=None)"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75757575757575757"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gbc = GradientBoostingClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "              min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "              n_estimators=100, presort='auto', random_state=None,\n",
       "              subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75669740887132186"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.69257795344751871"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.qda import QDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "des_a = QDA()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "QDA(priors=None, reg_param=0.0, store_covariances=False, tol=0.0001)"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "des_a.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.57619675010979354"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "des_a.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['down', 'up', 'up', ..., 'down', 'down', 'down'], dtype=object)"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "des_a.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "mlp = MLPClassifier(hidden_layer_sizes=(30,30,30),max_iter=200,validation_fraction=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(30, 30, 30), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.9,\n",
       "       verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75537988581466842"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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  "language_info": {
   "codemirror_mode": {
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