DJIA_preprocess.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "import pickle\n",
    "from datetime import datetime\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_raw_time_series(path='datasets/DJIA.csv'):\n",
    "    dates = []\n",
    "    days = []\n",
    "    close = []\n",
    "    \n",
    "    with open(path, newline='') as f:\n",
    "        reader = csv.reader(f, quotechar='|')\n",
    "        \n",
    "        for i, row in enumerate(reader):\n",
    "            if i == 0:\n",
    "                continue\n",
    "            if i == 1:\n",
    "                init_date = datetime.strptime(row[0], \"%Y-%m-%d\")\n",
    "            \n",
    "            dates.append(row[0])\n",
    "            days.append((datetime.strptime(row[0], \"%Y-%m-%d\") - init_date).days)\n",
    "            close.append(float(row[5]))\n",
    "            \n",
    "    return init_date, np.array(days), np.array(close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "130\n"
     ]
    }
   ],
   "source": [
    "start_date, raw_days, raw_close = get_raw_time_series()\n",
    "print((datetime(2009, 9, 1) - start_date).days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'days' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-68a7a0b1af2a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"DJIA\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'days' is not defined"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(days, close)\n",
    "plt.title(\"DJIA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_time_series(days, close, SMOOTHING=3):\n",
    "    N = 1 + max(days)\n",
    "    new_days = list(range(N))\n",
    "\n",
    "    new_close = [-1] * N\n",
    "    for i, day in enumerate(days):\n",
    "        new_close[day] = close[i]\n",
    "        \n",
    "    #Linear interpolation to add values for non-weekdays\n",
    "    for i in new_days:\n",
    "        if new_close[i] > 0:\n",
    "            continue\n",
    "        \n",
    "        d = 0\n",
    "        while new_close[i+d] < 0:\n",
    "            d += 1\n",
    "            \n",
    "        slope = (new_close[i+d] - new_close[i-1]) / (d + 1)\n",
    "            \n",
    "        for j in range(d):\n",
    "            new_close[i+j] = new_close[i-1] + (j + 1) * slope\n",
    "            \n",
    "    #Fourier smoothing to remove extremely volatile data points\n",
    "    dfft = np.fft.fft(new_close)\n",
    "    dfft[:SMOOTHING] = 0\n",
    "    new_close = np.real(np.fft.ifft(dfft))\n",
    "        \n",
    "    #Convert closing values to z-scores so we're not working with numbers like 10000\n",
    "    mean = np.mean(new_close)\n",
    "    sigma = np.std(new_close)\n",
    "    #new_close = (new_close - mean) / sigma\n",
    "    \n",
    "    return new_days, new_close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "days, close = preprocess_time_series(raw_days, raw_close, SMOOTHING=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25136873828>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(days, close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_djia(path=\"djia_ts.txt\"):\n",
    "    with open(path, \"rb\") as f:\n",
    "        return pickle.load(f)\n",
    "\n",
    "def save_djia(ts, path=\"djia_ts.txt\"):\n",
    "    with open(path, \"wb\") as f:\n",
    "        pickle.dump(ts, f, pickle.HIGHEST_PROTOCOL)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_djia(close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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