PickStocks_Xinrow.ipynb

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   "source": [
    "### ------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {},
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   "source": [
    "import os\n",
    "\n",
    "if 'COLAB_GPU' in os.environ:\n",
    "    !git clone https://github.com/impulsecorp/PickStocks.git\n",
    "    !mv PickStocks/*.py .\n",
    "    !mv PickStocks/data .\n",
    "    !pip install -U -qq -r PickStocks/requirements.txt"
   ]
  },
  {
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   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import system\n",
    "from system import *\n",
    "# small hack to prevent Colab error\n",
    "try:\n",
    "    from datablock import *\n",
    "except:\n",
    "    from datablock import *\n",
    "seed"
   ]
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   "cell_type": "code",
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   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "data_timeperiod = 'D'\n",
    "data = get_data('SPY', period=data_timeperiod, nrows=None)\n",
    "data = procdata_lite(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# for inspectiion\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "system.train_set_end = 0.5 # percentage point specifying the training set end point (1.0 means all data is training set)\n",
    "system.val_set_end = 0.75 # percentage point specifying the validation set end point (1.0 means no test set)\n",
    "# basically this is the data with the values above, which are like sliders determining the layout\n",
    "# [|0.0| ...... train ............. |0.5| ............ val ............ |0.75| .............. test ............... |1.0|]\n",
    "system.balance_data = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Base test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_name = 'times in row'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train ensemble on train data\n",
    "clf, scaler = train_clf_ensemble(LogisticRegression, data, ensemble_size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "equity, pf, trades = qbacktest(clf, scaler, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trades.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Search for best value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute the profit factor for every candidate value\n",
    "levels = [0,1,2,3,4,5,6,7,8,9,10]\n",
    "pfs = []\n",
    "nts = []\n",
    "for l in tqdm(levels):\n",
    "    pf, ntrades = compute_stats(data, filter_trades_by_feature(trades, data, featformat(feature_name), exact_value=l))\n",
    "    pfs.append(pf)\n",
    "    nts.append(len(ntrades))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the optimization/search results\n",
    "plt.plot(levels, pfs);\n",
    "plt.xlabel(feature_name);\n",
    "plt.ylabel('Profit Factor');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = pd.DataFrame(data = np.hstack([ np.array(nts).reshape(-1,1),\n",
    "                                      np.array(pfs).reshape(-1,1)]),\n",
    "             index=np.array(levels),\n",
    "             columns=['num trades', 'profit factor'])\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "min_best_x = 7\n",
    "max_best_x = 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test on unseen data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Base test without the filter\n",
    "equity, pf, trades = qbacktest(clf, scaler, data, skip_val=1, skip_test=0)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# Test with the filter\n",
    "equity, pf, trades = qbacktest(clf, scaler, data, quiet=1, skip_val=1, skip_test=0)\n",
    "# filter stats\n",
    "pf, ntrades = compute_stats(data, filter_trades_by_feature(trades, data, featformat(feature_name), min_value=min_best_x, max_value=max_best_x))\n",
    "print(f'Profit factor: {get_profit_factor(ntrades):.5f}, Winners: {get_winner_pct(ntrades):.2f}%, Trades: {len(ntrades)}')"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "plot(ntrades['profit'].cumsum());"
   ]
  },
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   "source": [
    "ntrades[0:20]"
   ]
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   "source": [
    "### ------------------------------------------------------------------------------------------------------------"
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