practical_ml/practical_ml_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical Machine Learning\n",
    "\n",
    "## Stock Market Data\n",
    "Use quandl to retreive data. \n",
    "- Relationship between open and close price tells you how the shares did that day.\n",
    "- Adjusted prices take into account stock splits.\n",
    "- Volume is how many trades occurred that day. Related to volatility\n",
    "\n",
    "We can create new features to express relationships which can be more useful than putting in the raw features themselves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import quandl\n",
    "import math\n",
    "import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "from sklearn import preprocessing, cross_validation, svm\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "#from pylab import rcParams\n",
    "#rcParams['figure.figsize'] = 20, 16\n",
    "#plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Open    High     Low    Close      Volume  Ex-Dividend  \\\n",
      "Date                                                                   \n",
      "2004-08-19  100.01  104.06   95.96  100.335  44659000.0          0.0   \n",
      "2004-08-20  101.01  109.08  100.50  108.310  22834300.0          0.0   \n",
      "2004-08-23  110.76  113.48  109.05  109.400  18256100.0          0.0   \n",
      "2004-08-24  111.24  111.60  103.57  104.870  15247300.0          0.0   \n",
      "2004-08-25  104.76  108.00  103.88  106.000   9188600.0          0.0   \n",
      "\n",
      "            Split Ratio  Adj. Open  Adj. High   Adj. Low  Adj. Close  \\\n",
      "Date                                                                   \n",
      "2004-08-19          1.0  50.159839  52.191109  48.128568   50.322842   \n",
      "2004-08-20          1.0  50.661387  54.708881  50.405597   54.322689   \n",
      "2004-08-23          1.0  55.551482  56.915693  54.693835   54.869377   \n",
      "2004-08-24          1.0  55.792225  55.972783  51.945350   52.597363   \n",
      "2004-08-25          1.0  52.542193  54.167209  52.100830   53.164113   \n",
      "\n",
      "            Adj. Volume  \n",
      "Date                     \n",
      "2004-08-19   44659000.0  \n",
      "2004-08-20   22834300.0  \n",
      "2004-08-23   18256100.0  \n",
      "2004-08-24   15247300.0  \n",
      "2004-08-25    9188600.0  \n",
      "Open           float64\n",
      "High           float64\n",
      "Low            float64\n",
      "Close          float64\n",
      "Volume         float64\n",
      "Ex-Dividend    float64\n",
      "Split Ratio    float64\n",
      "Adj. Open      float64\n",
      "Adj. High      float64\n",
      "Adj. Low       float64\n",
      "Adj. Close     float64\n",
      "Adj. Volume    float64\n",
      "dtype: object\n",
      "['Open', 'High', 'Low', 'Close', 'Volume', 'Ex-Dividend', 'Split Ratio', 'Adj. Open', 'Adj. High', 'Adj. Low', 'Adj. Close', 'Adj. Volume']\n"
     ]
    }
   ],
   "source": [
    "df = quandl.get(\"WIKI/GOOGL\")\n",
    "print(df.head())\n",
    "print(df.dtypes)\n",
    "print(list(df.columns.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Adj. Close    HL_PCT  PCT_change  Adj. Volume\n",
      "Date                                                     \n",
      "2004-08-19   50.322842  8.072956    0.324968   44659000.0\n",
      "2004-08-20   54.322689  7.921706    7.227007   22834300.0\n",
      "2004-08-23   54.869377  4.049360   -1.227880   18256100.0\n",
      "2004-08-24   52.597363  7.657099   -5.726357   15247300.0\n",
      "2004-08-25   53.164113  3.886792    1.183658    9188600.0\n",
      "            Adj. Close    HL_PCT  PCT_change  Adj. Volume\n",
      "Date                                                     \n",
      "2016-12-02      764.46  1.504330    0.336002    1686840.0\n",
      "2016-12-05      778.22  1.674514    1.067532    1680524.0\n",
      "2016-12-06      776.18  1.540880   -0.513977    1694701.0\n",
      "2016-12-07      791.47  2.333632    1.477018    2014582.0\n",
      "2016-12-08      795.17  1.395299    0.279967    1603167.0\n"
     ]
    }
   ],
   "source": [
    "df = df[['Adj. Open',  'Adj. High',  'Adj. Low',  'Adj. Close', 'Adj. Volume']]\n",
    "\n",
    "# percent difference between high and low price for the day\n",
    "df['HL_PCT'] = (df['Adj. High'] - df['Adj. Low']) / df['Adj. Close'] * 100.0\n",
    "# percent change from start and end of the day\n",
    "df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open']) / df['Adj. Open'] * 100.0\n",
    "\n",
    "df = df[['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']]\n",
    "print(df.head())\n",
    "print(df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "310\n",
      "            Adj. Close    HL_PCT  PCT_change  Adj. Volume       label\n",
      "Date                                                                 \n",
      "2004-08-19   50.322842  8.072956    0.324968   44659000.0  190.162012\n",
      "2004-08-20   54.322689  7.921706    7.227007   22834300.0  196.155513\n",
      "2004-08-23   54.869377  4.049360   -1.227880   18256100.0  195.804430\n",
      "2004-08-24   52.597363  7.657099   -5.726357   15247300.0  199.099601\n",
      "2004-08-25   53.164113  3.886792    1.183658    9188600.0  197.008145\n",
      "            Adj. Close    HL_PCT  PCT_change  Adj. Volume  label\n",
      "Date                                                            \n",
      "2016-12-02      764.46  1.504330    0.336002    1686840.0    NaN\n",
      "2016-12-05      778.22  1.674514    1.067532    1680524.0    NaN\n",
      "2016-12-06      776.18  1.540880   -0.513977    1694701.0    NaN\n",
      "2016-12-07      791.47  2.333632    1.477018    2014582.0    NaN\n",
      "2016-12-08      795.17  1.395299    0.279967    1603167.0    NaN\n"
     ]
    }
   ],
   "source": [
    "forecast_col = 'Adj. Close'\n",
    "# fill missing data, will be treated as an outlier as ML algorithms don't cope well with missing values\n",
    "df.fillna(-99999, inplace=True)\n",
    "# number of days to predict out - 10% of the length of the df\n",
    "forecast_out = int(math.ceil(0.1 * len(df)))\n",
    "print(forecast_out)\n",
    "\n",
    "# shift columns up spreadsheet in label column 10% of the time in the future\n",
    "df['label'] = df[forecast_col].shift(-forecast_out)\n",
    "# remove NAs from end of column after shift up\n",
    "print(df.head())\n",
    "print(df.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing\n",
    "You can scale your data to normalise the data points. This can get good results, but you need to consider that you'll have to scale the new values alongside the original values which can take time. For predictions where time is crucial, most people tend to skip this step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = np.array(df.drop(['label'], 1))\n",
    "X = preprocessing.scale(X)\n",
    "X_lately = X[-forecast_out:]\n",
    "X = X[:-forecast_out]\n",
    "\n",
    "df.dropna(inplace=True)\n",
    "\n",
    "y = np.array(df['label'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training & Testing Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM & Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79452107880892431"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = svm.SVR(kernel='linear')\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.795202847872\n"
     ]
    }
   ],
   "source": [
    "clf = LinearRegression(n_jobs=4) # num threads\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "accuracy = clf.score(X_test, y_test)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
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   "outputs": [
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      "  930.72190592  930.00249439  951.49559095  941.86921671  940.26088176\n",
      "  932.6071271   933.38715591  938.13283882  957.95790193  956.92781376\n",
      "  951.32845873  952.99961126  968.92648016  962.31695839  954.34341388\n",
      "  947.47916647  955.95977189  942.3280311   939.12598224  924.78355657\n",
      "  910.37163156  916.89197912  934.2566626   949.75815882  942.65977097\n",
      "  927.36296158  896.41397581  890.6415672   906.30559527  914.25106452\n",
      "  914.40717078  907.56813932  914.26015706  913.38893705  912.73639993\n",
      "  904.70693592  924.82011459  919.62541277  908.61673955  901.71714115\n",
      "  892.65090461  909.84407705  905.04689003  929.19063175  925.88030527] 0.795202847872 310\n"
     ]
    }
   ],
   "source": [
    "# predict next 30 days of data\n",
    "forecast_set = clf.predict(X_lately)\n",
    "print(forecast_set, accuracy, forecast_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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aZYiIiOqjkgpVn9L877HJMLpa/bAOETIEB16UlpZi2bJl2Lx5MwoKCtCkSRMMHDgQI0eO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f51438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Forecast'] = np.nan\n",
    "last_date = df.iloc[-1].name\n",
    "last_unix = last_date.timestamp()\n",
    "one_day = 86400\n",
    "next_unix = last_unix + one_day\n",
    "\n",
    "for i in forecast_set:\n",
    "    next_date = datetime.datetime.fromtimestamp(next_unix)\n",
    "    next_unix += 86400\n",
    "    df.loc[next_date] = [np.nan for _ in range(len(df.columns)-1)]+[i]\n",
    "    \n",
    "plt.figure()\n",
    "df['Adj. Close'].plot()\n",
    "df['Forecast'].plot()\n",
    "plt.legend(loc=4)\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pickling Classifiers\n",
    "\n",
    "Writing CLF\n",
    "```\n",
    "with open('linearregression.pickle','wb') as f:\n",
    "    pickle.dump(clf, f)\n",
    "```\n",
    "\n",
    "Reading CLF\n",
    "```\n",
    "pickle_in = open('linearregression.pickle','rb')\n",
    "clf = pickle.load(pickle_in)\n",
    "```"
   ]
  },
  {
   "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
}