regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'close': 895,\n",
       " 'date': 1484870400,\n",
       " 'high': '902.42999999',\n",
       " 'low': '882.5432',\n",
       " 'open': '900.30000001',\n",
       " 'quoteVolume': '940.82405676',\n",
       " 'volume': '841765.1320593',\n",
       " 'weightedAverage': '894.71046792'}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from poloniex import Poloniex\n",
    "from time import time\n",
    "import json\n",
    "\n",
    "polo = Poloniex()\n",
    "start=time() - polo.YEAR\n",
    "\n",
    "if False:\n",
    "  data = polo.returnChartData('USDT_BTC', period=86400, start=start)\n",
    "  with open('data.json', 'w') as outfile:\n",
    "    json.dump(data, outfile)\n",
    "\n",
    "with open('data.json') as infile:\n",
    "  market = json.load(infile)\n",
    "\n",
    "market[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>close</th>\n",
       "      <th>date</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>quoteVolume</th>\n",
       "      <th>volume</th>\n",
       "      <th>weightedAverage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1212.5</td>\n",
       "      <td>1492300800</td>\n",
       "      <td>1217.00000001</td>\n",
       "      <td>1190.00000009</td>\n",
       "      <td>1193.94839339</td>\n",
       "      <td>2691.61774504</td>\n",
       "      <td>3253026.4592128</td>\n",
       "      <td>1208.57668783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     close        date           high            low           open  \\\n",
       "86  1212.5  1492300800  1217.00000001  1190.00000009  1193.94839339   \n",
       "\n",
       "      quoteVolume           volume weightedAverage  \n",
       "86  2691.61774504  3253026.4592128   1208.57668783  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.io.json.json_normalize(market)\n",
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[list(df)] = df[list(df)].apply(pd.to_numeric)\n",
    "df['date'] = df['date'].apply(pd.to_datetime, unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10882b240>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10880db00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "%matplotlib inline\n",
    "df.describe()\n",
    "df['low'].plot()"
   ]
  },
  {
   "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>close</th>\n",
       "      <th>date</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>quoteVolume</th>\n",
       "      <th>volume</th>\n",
       "      <th>weightedAverage</th>\n",
       "      <th>up</th>\n",
       "      <th>high_rate</th>\n",
       "      <th>low_rate</th>\n",
       "      <th>close_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>895.00000</td>\n",
       "      <td>2017-01-20</td>\n",
       "      <td>902.430000</td>\n",
       "      <td>882.54320</td>\n",
       "      <td>900.300000</td>\n",
       "      <td>940.824057</td>\n",
       "      <td>8.417651e+05</td>\n",
       "      <td>894.710468</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>925.70600</td>\n",
       "      <td>2017-01-21</td>\n",
       "      <td>928.518494</td>\n",
       "      <td>894.00000</td>\n",
       "      <td>895.099711</td>\n",
       "      <td>1522.358253</td>\n",
       "      <td>1.397884e+06</td>\n",
       "      <td>918.235895</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>924.11940</td>\n",
       "      <td>2017-01-22</td>\n",
       "      <td>939.060710</td>\n",
       "      <td>894.10010</td>\n",
       "      <td>925.706000</td>\n",
       "      <td>1706.434429</td>\n",
       "      <td>1.567170e+06</td>\n",
       "      <td>918.388676</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>912.22165</td>\n",
       "      <td>2017-01-23</td>\n",
       "      <td>927.235100</td>\n",
       "      <td>912.22165</td>\n",
       "      <td>923.990000</td>\n",
       "      <td>1550.690618</td>\n",
       "      <td>1.425305e+06</td>\n",
       "      <td>919.142208</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>885.00000</td>\n",
       "      <td>2017-01-24</td>\n",
       "      <td>923.000000</td>\n",
       "      <td>884.28100</td>\n",
       "      <td>912.221650</td>\n",
       "      <td>2382.927366</td>\n",
       "      <td>2.149635e+06</td>\n",
       "      <td>902.098438</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       close       date        high        low        open  quoteVolume  \\\n",
       "0  895.00000 2017-01-20  902.430000  882.54320  900.300000   940.824057   \n",
       "1  925.70600 2017-01-21  928.518494  894.00000  895.099711  1522.358253   \n",
       "2  924.11940 2017-01-22  939.060710  894.10010  925.706000  1706.434429   \n",
       "3  912.22165 2017-01-23  927.235100  912.22165  923.990000  1550.690618   \n",
       "4  885.00000 2017-01-24  923.000000  884.28100  912.221650  2382.927366   \n",
       "\n",
       "         volume  weightedAverage  up  high_rate  low_rate  close_rate  \n",
       "0  8.417651e+05       894.710468   0          0         0           0  \n",
       "1  1.397884e+06       918.235895   0          0         0           0  \n",
       "2  1.567170e+06       918.388676   0          0         0           0  \n",
       "3  1.425305e+06       919.142208   0          0         0           0  \n",
       "4  2.149635e+06       902.098438   0          0         0           0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['up'] = 0\n",
    "df['high_rate'] = 0\n",
    "df['low_rate'] = 0\n",
    "df['close_rate'] = 0\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>date</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>quoteVolume</th>\n",
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       "      <th>low_rate</th>\n",
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       "      <th>prev_up</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>895.000000</td>\n",
       "      <td>2017-01-20</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>924.119400</td>\n",
       "      <td>2017-01-22</td>\n",
       "      <td>939.060710</td>\n",
       "      <td>894.100100</td>\n",
       "      <td>925.706000</td>\n",
       "      <td>1706.434429</td>\n",
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       "      <th>3</th>\n",
       "      <td>912.221650</td>\n",
       "      <td>2017-01-23</td>\n",
       "      <td>927.235100</td>\n",
       "      <td>912.221650</td>\n",
       "      <td>923.990000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>885.000000</td>\n",
       "      <td>2017-01-24</td>\n",
       "      <td>923.000000</td>\n",
       "      <td>884.281000</td>\n",
       "      <td>912.221650</td>\n",
       "      <td>2382.927366</td>\n",
       "      <td>2.149635e+06</td>\n",
       "      <td>902.098438</td>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>-27</td>\n",
       "      <td>-27</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>892.667773</td>\n",
       "      <td>2017-01-25</td>\n",
       "      <td>899.990000</td>\n",
       "      <td>882.080000</td>\n",
       "      <td>885.100000</td>\n",
       "      <td>1064.256635</td>\n",
       "      <td>9.488559e+05</td>\n",
       "      <td>891.566831</td>\n",
       "      <td>1</td>\n",
       "      <td>-23</td>\n",
       "      <td>-2</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>914.097671</td>\n",
       "      <td>2017-01-26</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>892.667773</td>\n",
       "      <td>892.667773</td>\n",
       "      <td>1099.568637</td>\n",
       "      <td>9.975648e+05</td>\n",
       "      <td>907.232818</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>917.027451</td>\n",
       "      <td>2017-01-27</td>\n",
       "      <td>921.650000</td>\n",
       "      <td>907.800000</td>\n",
       "      <td>916.000000</td>\n",
       "      <td>852.670935</td>\n",
       "      <td>7.812089e+05</td>\n",
       "      <td>916.190376</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>918.586004</td>\n",
       "      <td>2017-01-28</td>\n",
       "      <td>926.000000</td>\n",
       "      <td>909.987300</td>\n",
       "      <td>917.436775</td>\n",
       "      <td>900.645563</td>\n",
       "      <td>8.283338e+05</td>\n",
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       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>914.999800</td>\n",
       "      <td>2017-01-29</td>\n",
       "      <td>923.000000</td>\n",
       "      <td>911.860406</td>\n",
       "      <td>918.990000</td>\n",
       "      <td>406.000777</td>\n",
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       "      <td>917.644336</td>\n",
       "      <td>1</td>\n",
       "      <td>-3</td>\n",
       "      <td>1</td>\n",
       "      <td>-3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        close       date        high         low        open  quoteVolume  \\\n",
       "0  895.000000 2017-01-20  902.430000  882.543200  900.300000   940.824057   \n",
       "1  925.706000 2017-01-21  928.518494  894.000000  895.099711  1522.358253   \n",
       "2  924.119400 2017-01-22  939.060710  894.100100  925.706000  1706.434429   \n",
       "3  912.221650 2017-01-23  927.235100  912.221650  923.990000  1550.690618   \n",
       "4  885.000000 2017-01-24  923.000000  884.281000  912.221650  2382.927366   \n",
       "5  892.667773 2017-01-25  899.990000  882.080000  885.100000  1064.256635   \n",
       "6  914.097671 2017-01-26  918.000000  892.667773  892.667773  1099.568637   \n",
       "7  917.027451 2017-01-27  921.650000  907.800000  916.000000   852.670935   \n",
       "8  918.586004 2017-01-28  926.000000  909.987300  917.436775   900.645563   \n",
       "9  914.999800 2017-01-29  923.000000  911.860406  918.990000   406.000777   \n",
       "\n",
       "         volume  weightedAverage  up  high_rate  low_rate  close_rate  prev_up  \n",
       "0  8.417651e+05       894.710468   0          0         0           0      0.0  \n",
       "1  1.397884e+06       918.235895   0         26        11          30      0.0  \n",
       "2  1.567170e+06       918.388676   0         10         0          -1      0.0  \n",
       "3  1.425305e+06       919.142208   0        -11        18         -11      0.0  \n",
       "4  2.149635e+06       902.098438   1         -4       -27         -27      0.0  \n",
       "5  9.488559e+05       891.566831   1        -23        -2           7      1.0  \n",
       "6  9.975648e+05       907.232818   1         18        10          21      1.0  \n",
       "7  7.812089e+05       916.190376   1          3        15           2      1.0  \n",
       "8  8.283338e+05       919.711219   0          4         2           1      1.0  \n",
       "9  3.725643e+05       917.644336   1         -3         1          -3      0.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prev_up = 0\n",
    "for index, row in df.iterrows():\n",
    "    try:\n",
    "        df.at[index, 'prev_up'] = prev_up\n",
    "        df.at[index, 'close_rate'] = row['close']-df.loc[index-1]['close']\n",
    "        df.at[index, 'high_rate'] = row['high']-df.loc[index-1]['high']\n",
    "        df.at[index, 'low_rate'] = row['low']-df.loc[index-1]['low']\n",
    "        if (df.loc[index+1]['close']>row['close']):\n",
    "            df.at[index, 'up'] = 1\n",
    "            prev_up = 1\n",
    "        else:\n",
    "            df.at[index, 'up'] = 0\n",
    "            prev_up = 0\n",
    "    except KeyError as e:\n",
    "        pass\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hild/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "#X = df[['close', 'high', 'low', 'prev_up']]\n",
    "X = df[['prev_up','close_rate','high_rate','low_rate']]\n",
    "y = df['up']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size=0.2, \n",
    "                                                    random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>quoteVolume</th>\n",
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       "      <th>low_rate</th>\n",
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       "      <th>prev_up</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998374</td>\n",
       "      <td>0.997848</td>\n",
       "      <td>0.995246</td>\n",
       "      <td>-0.032855</td>\n",
       "      <td>0.762353</td>\n",
       "      <td>0.999163</td>\n",
       "      <td>-0.095933</td>\n",
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       "      <td>0.073194</td>\n",
       "      <td>-0.042211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>0.998374</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996194</td>\n",
       "      <td>0.997824</td>\n",
       "      <td>-0.015969</td>\n",
       "      <td>0.783253</td>\n",
       "      <td>0.998947</td>\n",
       "      <td>-0.097323</td>\n",
       "      <td>0.065011</td>\n",
       "      <td>0.031921</td>\n",
       "      <td>0.029995</td>\n",
       "      <td>-0.065496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>0.997848</td>\n",
       "      <td>0.996194</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995683</td>\n",
       "      <td>-0.050931</td>\n",
       "      <td>0.741268</td>\n",
       "      <td>0.999003</td>\n",
       "      <td>-0.097113</td>\n",
       "      <td>0.069447</td>\n",
       "      <td>0.077024</td>\n",
       "      <td>0.046886</td>\n",
       "      <td>-0.052739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>0.995246</td>\n",
       "      <td>0.997824</td>\n",
       "      <td>0.995683</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.023455</td>\n",
       "      <td>0.775373</td>\n",
       "      <td>0.997226</td>\n",
       "      <td>-0.095656</td>\n",
       "      <td>0.017741</td>\n",
       "      <td>0.000773</td>\n",
       "      <td>-0.024245</td>\n",
       "      <td>-0.088127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quoteVolume</th>\n",
       "      <td>-0.032855</td>\n",
       "      <td>-0.015969</td>\n",
       "      <td>-0.050931</td>\n",
       "      <td>-0.023455</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.461377</td>\n",
       "      <td>-0.033002</td>\n",
       "      <td>-0.017155</td>\n",
       "      <td>-0.046691</td>\n",
       "      <td>-0.204879</td>\n",
       "      <td>-0.097004</td>\n",
       "      <td>-0.187497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volume</th>\n",
       "      <td>0.762353</td>\n",
       "      <td>0.783253</td>\n",
       "      <td>0.741268</td>\n",
       "      <td>0.775373</td>\n",
       "      <td>0.461377</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.762836</td>\n",
       "      <td>-0.070965</td>\n",
       "      <td>-0.016730</td>\n",
       "      <td>-0.238179</td>\n",
       "      <td>-0.117113</td>\n",
       "      <td>-0.178210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weightedAverage</th>\n",
       "      <td>0.999163</td>\n",
       "      <td>0.998947</td>\n",
       "      <td>0.999003</td>\n",
       "      <td>0.997226</td>\n",
       "      <td>-0.033002</td>\n",
       "      <td>0.762836</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.096908</td>\n",
       "      <td>0.071132</td>\n",
       "      <td>0.058674</td>\n",
       "      <td>0.044412</td>\n",
       "      <td>-0.056536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>up</th>\n",
       "      <td>-0.095933</td>\n",
       "      <td>-0.097323</td>\n",
       "      <td>-0.097113</td>\n",
       "      <td>-0.095656</td>\n",
       "      <td>-0.017155</td>\n",
       "      <td>-0.070965</td>\n",
       "      <td>-0.096908</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004585</td>\n",
       "      <td>-0.027919</td>\n",
       "      <td>-0.003410</td>\n",
       "      <td>0.049302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high_rate</th>\n",
       "      <td>0.088154</td>\n",
       "      <td>0.065011</td>\n",
       "      <td>0.069447</td>\n",
       "      <td>0.017741</td>\n",
       "      <td>-0.046691</td>\n",
       "      <td>-0.016730</td>\n",
       "      <td>0.071132</td>\n",
       "      <td>0.004585</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.548163</td>\n",
       "      <td>0.727657</td>\n",
       "      <td>0.295809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low_rate</th>\n",
       "      <td>0.067477</td>\n",
       "      <td>0.031921</td>\n",
       "      <td>0.077024</td>\n",
       "      <td>0.000773</td>\n",
       "      <td>-0.204879</td>\n",
       "      <td>-0.238179</td>\n",
       "      <td>0.058674</td>\n",
       "      <td>-0.027919</td>\n",
       "      <td>0.548163</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.687635</td>\n",
       "      <td>0.317147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close_rate</th>\n",
       "      <td>0.073194</td>\n",
       "      <td>0.029995</td>\n",
       "      <td>0.046886</td>\n",
       "      <td>-0.024245</td>\n",
       "      <td>-0.097004</td>\n",
       "      <td>-0.117113</td>\n",
       "      <td>0.044412</td>\n",
       "      <td>-0.003410</td>\n",
       "      <td>0.727657</td>\n",
       "      <td>0.687635</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.467078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prev_up</th>\n",
       "      <td>-0.042211</td>\n",
       "      <td>-0.065496</td>\n",
       "      <td>-0.052739</td>\n",
       "      <td>-0.088127</td>\n",
       "      <td>-0.187497</td>\n",
       "      <td>-0.178210</td>\n",
       "      <td>-0.056536</td>\n",
       "      <td>0.049302</td>\n",
       "      <td>0.295809</td>\n",
       "      <td>0.317147</td>\n",
       "      <td>0.467078</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    close      high       low      open  quoteVolume  \\\n",
       "close            1.000000  0.998374  0.997848  0.995246    -0.032855   \n",
       "high             0.998374  1.000000  0.996194  0.997824    -0.015969   \n",
       "low              0.997848  0.996194  1.000000  0.995683    -0.050931   \n",
       "open             0.995246  0.997824  0.995683  1.000000    -0.023455   \n",
       "quoteVolume     -0.032855 -0.015969 -0.050931 -0.023455     1.000000   \n",
       "volume           0.762353  0.783253  0.741268  0.775373     0.461377   \n",
       "weightedAverage  0.999163  0.998947  0.999003  0.997226    -0.033002   \n",
       "up              -0.095933 -0.097323 -0.097113 -0.095656    -0.017155   \n",
       "high_rate        0.088154  0.065011  0.069447  0.017741    -0.046691   \n",
       "low_rate         0.067477  0.031921  0.077024  0.000773    -0.204879   \n",
       "close_rate       0.073194  0.029995  0.046886 -0.024245    -0.097004   \n",
       "prev_up         -0.042211 -0.065496 -0.052739 -0.088127    -0.187497   \n",
       "\n",
       "                   volume  weightedAverage        up  high_rate  low_rate  \\\n",
       "close            0.762353         0.999163 -0.095933   0.088154  0.067477   \n",
       "high             0.783253         0.998947 -0.097323   0.065011  0.031921   \n",
       "low              0.741268         0.999003 -0.097113   0.069447  0.077024   \n",
       "open             0.775373         0.997226 -0.095656   0.017741  0.000773   \n",
       "quoteVolume      0.461377        -0.033002 -0.017155  -0.046691 -0.204879   \n",
       "volume           1.000000         0.762836 -0.070965  -0.016730 -0.238179   \n",
       "weightedAverage  0.762836         1.000000 -0.096908   0.071132  0.058674   \n",
       "up              -0.070965        -0.096908  1.000000   0.004585 -0.027919   \n",
       "high_rate       -0.016730         0.071132  0.004585   1.000000  0.548163   \n",
       "low_rate        -0.238179         0.058674 -0.027919   0.548163  1.000000   \n",
       "close_rate      -0.117113         0.044412 -0.003410   0.727657  0.687635   \n",
       "prev_up         -0.178210        -0.056536  0.049302   0.295809  0.317147   \n",
       "\n",
       "                 close_rate   prev_up  \n",
       "close              0.073194 -0.042211  \n",
       "high               0.029995 -0.065496  \n",
       "low                0.046886 -0.052739  \n",
       "open              -0.024245 -0.088127  \n",
       "quoteVolume       -0.097004 -0.187497  \n",
       "volume            -0.117113 -0.178210  \n",
       "weightedAverage    0.044412 -0.056536  \n",
       "up                -0.003410  0.049302  \n",
       "high_rate          0.727657  0.295809  \n",
       "low_rate           0.687635  0.317147  \n",
       "close_rate         1.000000  0.467078  \n",
       "prev_up            0.467078  1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression()\n",
    "model = model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a13177be0>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0oP2XRxKvSomEOyZheAQe3ee4m9xV/VK/Z0sDzwBGW0tLXyeRGR+y72sSqIwx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\n/wOD62PTiInnqQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a13078da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sJtUP7wB5LGUshaw0X8wLKUZbIbGMNb4ux9To0rWV1watvwcDBu3Pk8KsHG0Y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a130f9390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "plt.figure(0)\n",
    "plt.plot(X)\n",
    "plt.figure(1)\n",
    "plt.plot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.59016393,  0.59016393,  0.52066116])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(model, X, y, scoring='accuracy') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1.0,\n",
       " 'class_weight': None,\n",
       " 'dual': False,\n",
       " 'fit_intercept': True,\n",
       " 'intercept_scaling': 1,\n",
       " 'max_iter': 100,\n",
       " 'multi_class': 'ovr',\n",
       " 'n_jobs': 1,\n",
       " 'penalty': 'l2',\n",
       " 'random_state': None,\n",
       " 'solver': 'liblinear',\n",
       " 'tol': 0.0001,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(5, 2), 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=1, shuffle=True,\n",
       "       solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "       warm_start=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "clf = MLPClassifier(solver='lbfgs', alpha=1e-5,\n",
    "                    hidden_layer_sizes=(5, 2), random_state=1)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.4111219  0.5888781]] up    0\n",
      "Name: 1, dtype: object\n",
      "[[ 0.4111219  0.5888781]] up    0\n",
      "Name: 2, dtype: object\n",
      "[[ 0.4111219  0.5888781]] up    0\n",
      "Name: 3, dtype: object\n",
      "[[ 0.4111219  0.5888781]] up    1\n",
      "Name: 4, dtype: object\n",
      "[[ 0.4111219  0.5888781]] up    1\n",
      "Name: 5, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#print(clf.predict_proba([[924, 939, 894]])) \n",
    "#print(model.predict_proba([[924, 939, 894]]))\n",
    "for index in range(5):\n",
    "    #print(df.loc[index+1,['close','high','low', 'prev_up']].values)\n",
    "    #print(df.loc[index+1,['prev_up']].values)\n",
    "    #print(clf.predict_proba([df.loc[index+1,['close','high','low', 'prev_up']].values]), df.loc[index+1,['up']])    \n",
    "    print(clf.predict_proba([df.loc[index+1,['prev_up','close_rate','high_rate', 'low_rate']].values]), df.loc[index+1,['up']])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly = PolynomialFeatures(degree=7)\n",
    "X_ = poly.fit_transform(X)\n",
    "\n",
    "X_train_, X_test_, y_train_, y_test_ = train_test_split(X_, y, \n",
    "                                                    test_size=0.2, \n",
    "                                                    random_state=0)\n",
    "\n",
    "model2 = LinearRegression()\n",
    "model2 = model.fit(X_train, y_train)\n",
    "predict = [[924, 939, 894]]\n",
    "predict_ = poly.fit_transform(predict)\n",
    "\n",
    "model2.predict(predict_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(model2, X_train, y_train, scoring='accuracy') \n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, model2.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}