make_dataset_eurusd-1.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tradesys'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-cb947885162d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'pylab'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m#sys.path.insert(0, '/home/peter/code/projects/tradesys/')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtradesys\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtradesys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtesting\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtalib\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mta\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tradesys'"
     ]
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   "source": [
    "%pylab inline\n",
    "#sys.path.insert(0, '/home/peter/code/projects/tradesys/')\n",
    "from tradesys import *\n",
    "from tradesys.testing import *\n",
    "import talib as ta\n",
    "from sklearn.preprocessing import scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datamatrix = DataMatrix()\n",
    "datamatrix.load_multicharts('../../deep_learning_trader/data/EURUSD_1_Minute_01_01_2010-10_31_2016.txt')\n",
    "datamatrix.clip_dt(DateTime(2015,1,1), DateTime(2015,7,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dmat2df(datamatrix).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(datamatrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#datamatrix = fill_incomplete_datamatrix(datamatrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dmat2df(datamatrix).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(datamatrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot(datamatrix.close);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "datalen = len(datamatrix)\n",
    "\n",
    "d_open = np.array(datamatrix.open)\n",
    "d_high = np.array(datamatrix.high)\n",
    "d_low = np.array(datamatrix.low)\n",
    "d_close = np.array(datamatrix.close)\n",
    "d_volume = np.array(datamatrix.volume)\n",
    "d_year = np.array([x.year for x in datamatrix.get_dt_list()])\n",
    "d_month = np.array([x.month for x in datamatrix.get_dt_list()])\n",
    "d_day = np.array([x.day for x in datamatrix.get_dt_list()])\n",
    "d_hour = np.array([x.hour for x in datamatrix.get_dt_list()])\n",
    "d_minute = np.array([x.minute for x in datamatrix.get_dt_list()])\n",
    "d_second = np.array([x.second for x in datamatrix.get_dt_list()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datamodel_super as dm\n",
    "dmodel = dm.DataModel()\n",
    "dmodel.input(np.vstack([d_open, d_high, d_low, d_close, d_volume, \n",
    "                        d_year, d_month, d_day, d_hour, d_minute, d_second]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmodel.process()\n",
    "nc = dmodel.cut_nans()\n",
    "nc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmodel.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmodel.data[0:10, 0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tgt = np.vstack([d_open[nc:], \n",
    "                 d_high[nc:], \n",
    "                 d_low[nc:], \n",
    "                 d_close[nc:]])\n",
    "tgt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tgt[:, 0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imshow(dmodel.data[:, 0:100]); colorbar();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(open(\"data_eurusd.npy\",'wb'), dmodel.data)\n",
    "np.save(open(\"data_eurusd_targets.npy\",'wb'), tgt)"
   ]
  },
  {
   "cell_type": "code",
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot(dmodel.data.T[0:200, 0:10]);"
   ]
  },
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