stock_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from ipywidgets import interact, interactive, fixed\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Format:  ['Date' 'Open' 'High' 'Low' 'Close' 'Adj Close' 'Volume']\n"
     ]
    }
   ],
   "source": [
    "dateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d')\n",
    "dax = pd.read_csv('DAX.csv',parse_dates=['Date'], date_parser=dateparse) # read in raw data\n",
    "dax = dax[dax.Close != 'null'] # delete 'null' holidays\n",
    "dax[dax.columns[1:7]] =  dax[dax.columns[1:7]].astype(float) # convert index prices to floats\n",
    "print \"Data Format: \", dax.columns.get_values()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fde0c3bac50>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QZRt3ccXrP/mzt4ITHO4+oUfAe91wbJdy+R5SVdVOHmIqFf+IleqlN4dcMLRd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\njDFBLDgYY4wJ8v9/hZNfX26pEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fde0c458d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6EylOTJC15hvChNvIO6k82RmLV7NgRaoZt2tfKNmU48708f2hdxc1+HmmI/Di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\nMAwjZphwG4ZhxIz/D99LsJzubnZ0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fde0c440050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dax.plot(x='Date',y='Close',title='Closing price')\n",
    "dax.plot(x='Date',y='Volume',title='Volume')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Volantility:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fde0c0d35d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OoiFV3RwsKKJGNW/67fk+1LvyDzFnTfCxv/8zbbW7XZJl+hB4OTHL4g272H+o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\nAY4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fde0c713b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dax['Spread'] = abs(dax['High'] - dax['Low'])\n",
    "dax.plot(x='Date',y='Spread',title='Volume')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gap Closures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gap_data = pd.DataFrame()\n",
    "\n",
    "\n",
    "\n",
    "for i in range(dax.shape[0]):\n",
    "    if dax.iloc[i,2] != dax.iloc[i,3]:\n",
    "        #print i\n",
    "        break\n",
    "gap_data = dax.iloc[1489:,:]\n",
    "\n",
    "def f(min_points):\n",
    "    gap_closed = []\n",
    "    for i in range(1,gap_data.shape[0]):\n",
    "        last_closing = gap_data.iloc[i-1,4]\n",
    "        opening = gap_data.iloc[i,1]\n",
    "        high = gap_data.iloc[i,2]\n",
    "        low = gap_data.iloc[i,3]\n",
    "        gap_size = abs(opening-last_closing)\n",
    "        if opening > last_closing: # if opening price is larger than last closing price\n",
    "            if low <= last_closing and gap_size > min_points : # if gap has been closed from the top\n",
    "                gap_closed.append(1)\n",
    "            else:\n",
    "                gap_closed.append(0)\n",
    "        else:\n",
    "            if high >= gap_data.iloc[i-1,4] and gap_size > min_points: # if gap has been closed from below\n",
    "                gap_closed.append(1)\n",
    "            else:\n",
    "                gap_closed.append(0)\n",
    "\n",
    "\n",
    "    gap_closed = np.asarray(gap_closed)\n",
    "\n",
    "    total_elems = gap_closed.shape[0]\n",
    "    print \"Gap closed %: \" , float(np.count_nonzero(gap_closed))/float(total_elems)\n",
    "    print \"Gap not closed %: \", float(np.count_nonzero(gap_closed==0))/float(total_elems)\n",
    "\n",
    "    gap_closed_filtered = np.convolve(gap_closed, np.ones((100,))/100, mode='valid')\n",
    "    plt.plot(gap_closed_filtered)\n",
    "    plt.title('Gap closures over time')\n",
    "    plt.show\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5e589ea21b8548b780c353d7691c4b34",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(f, min_points=(0,20));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avrg points in gap direction:  44.0768150665\n"
     ]
    },
    {
     "data": {
      "image/png": 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/AENeUk+W/MDUuWxy/3d56Y5PLxj8fspW0kbhFf2Qyf3fHcvVvP8ikPRuYtAvYanQHn5z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\nb1gaWIAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fde0c076590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "points_in_gap_dir = []\n",
    "for i in range(1,gap_data.shape[0]):\n",
    "    last_closing = gap_data.iloc[i-1,4]\n",
    "    opening = gap_data.iloc[i,1]\n",
    "    high = gap_data.iloc[i,2]\n",
    "    low = gap_data.iloc[i,3]\n",
    "    gap_size = abs(opening-last_closing)\n",
    "    if opening > last_closing: # if opening price is larger than last closing price\n",
    "        if low < opening : # if gap has been closed from the top\n",
    "            points_in_gap_dir.append(abs(low-opening))\n",
    "        else:\n",
    "            points_in_gap_dir.append(0)\n",
    "    else:\n",
    "        if high > opening: # if gap has been closed from below\n",
    "            points_in_gap_dir.append(abs(high-opening))\n",
    "        else:\n",
    "            points_in_gap_dir.append(0)\n",
    "\n",
    "\n",
    "points_in_gap_dir = np.asarray(points_in_gap_dir)\n",
    "plt.hist(points_in_gap_dir,500)\n",
    "plt.show\n",
    "\n",
    "total_elems = points_in_gap_dir.shape[0]\n",
    "print \"Avrg points in gap direction: \" , float(sum(points_in_gap_dir))/float(total_elems)\n",
    "\n",
    "#filter = 10\n",
    "#gap_closed_filtered = np.convolve(points_in_gap_dir, np.ones((filter,))/filter, mode='valid')\n",
    "#plt.plot(gap_closed_filtered)\n",
    "#plt.title('Points in gap direction')\n",
    "#plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Let the deep learning magic happend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "dax['Date'] = pd.to_datetime(dax['Date'])\n",
    "dax =  dax.set_index('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume data starts at 1993-12-14 00:00:00\n",
      "We have 5826 train data and 176 test data. That makes up a ratio of 33.1022727273 :1\n"
     ]
    }
   ],
   "source": [
    "#train_data = pd.DataFrame()\n",
    "#test_data = pd.DataFrame()\n",
    "\n",
    "volume_start = -1\n",
    "for i in range(dax.shape[0]):\n",
    "    if dax.iloc[i,6] != 0:\n",
    "        volume_start = i\n",
    "        break\n",
    "print \"Volume data starts at\", dax.index[volume_start]\n",
    "\n",
    "#print type(dax.iloc[volume_start,:])\n",
    "\n",
    "train_data = dax['1994-01-01':'2016-12-31']\n",
    "test_data = dax['2017-01-01':]\n",
    "\n",
    "#print type(train_data)\n",
    "\n",
    "print \"We have\", train_data.shape[0], \"train data and\", test_data.shape[0], \"test data. That makes up a ratio of\", float(train_data.shape[0])/float(test_data.shape[0]), \":1\"\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sample_batch(batch_size, feature_length):\n",
    "    rand_idx = np.random.randint(feature_length,5826, size=batch_size) # these are the indizes of the values we want to predict\n",
    "    labels = train_data.iloc[rand_idx,3] # closing prices to predict\n",
    "    \n",
    "    features = [train_data.iloc[rand_idx[i]-feature_length:rand_idx[i],[0,1,2,3,5]].values for i in range(batch_size)]\n",
    "    \n",
    "    return features, labels\n",
    "    \n",
    "def make_regression_network():\n",
    "    features = tf.placeholder(tf.float32, shape=(None, 25))\n",
    "    hidden1 = tf.layers.dense(features,1000,tf.nn.elu)\n",
    "    hidden2 = tf.layers.dense(hidden1,1000,tf.nn.elu)\n",
    "    prediction = tf.layers.dense(hidden2,1,activation=None)\n",
    "    \n",
    "    label = tf.placeholder(tf.float32, shape=(None, 25))\n",
    "    loss = tf.reduce_mean(tf.square(prediction-label))\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate=0.0001)\n",
    "    optimize = optimizer.minimize(loss)\n",
    "    return features, label, loss, optimize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n"
     ]
    }
   ],
   "source": [
    "features, labels = sample_batch(32,5)\n",
    "print len(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Cannot feed value of shape (32,) for Tensor u'Placeholder_26:0', which has shape '(?, 25)'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-46-5dc5fb0bbbde>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0;32mprint\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mfeature_pl\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel_pl\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/martin/.local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    893\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 895\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    896\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/martin/.local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1098\u001b[0m                 \u001b[0;34m'Cannot feed value of shape %r for Tensor %r, '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1099\u001b[0m                 \u001b[0;34m'which has shape %r'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1100\u001b[0;31m                 % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))\n\u001b[0m\u001b[1;32m   1101\u001b[0m           \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_feedable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubfeed_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1102\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Tensor %s may not be fed.'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0msubfeed_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Cannot feed value of shape (32,) for Tensor u'Placeholder_26:0', which has shape '(?, 25)'"
     ]
    }
   ],
   "source": [
    "training_steps = 1000\n",
    "feature_pl, label_pl, loss, optimize = make_regression_network()\n",
    "\n",
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "for i in range(training_steps):\n",
    "    print i\n",
    "    features, labels = sample_batch(32,5)\n",
    "    features = np.reshape(features,(32,25))\n",
    "    print \n",
    "    sess.run([optimize,loss], feed_dict={feature_pl:features, label_pl:labels})\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
   "outputs": [],
   "source": []
  }
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