GA_rules_experiment_annex.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "<script>\n",
    "  jQuery(document).ready(function($) {  \n",
    "  \n",
    "  $(window).on('load', function(){\n",
    "    $('#preloader').fadeOut('slow',function(){$(this).remove();});\n",
    "  });\n",
    "  \n",
    "  });\n",
    "</script>\n",
    "\n",
    "<style type=\"text/css\">\n",
    "  div#preloader { position: fixed; \n",
    "      left: 0; \n",
    "      top: 0; \n",
    "      z-index: 999; \n",
    "      width: 100%; \n",
    "      height: 100%; \n",
    "      overflow: visible; \n",
    "      background: #fff url('http://preloaders.net/preloaders/720/Moving%20line.gif') no-repeat center center; \n",
    "  }\n",
    "\n",
    "</style>\n",
    "\n",
    "<div id=\"preloader\">\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "<script>\n",
    "  function code_toggle() {\n",
    "    if (code_shown){\n",
    "      $('div.input').hide('500');\n",
    "      $('#toggleButton').val('Show Code')\n",
    "    } else {\n",
    "      $('div.input').show('500');\n",
    "      $('#toggleButton').val('Hide Code')\n",
    "    }\n",
    "    code_shown = !code_shown\n",
    "  } \n",
    "  \n",
    "  $( document ).ready(function(){\n",
    "    code_shown=false; \n",
    "    $('div.input').hide()\n",
    "  });\n",
    "</script>\n",
    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" id=\"toggleButton\" value=\"Show Code\"></form>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developing Trading Strategies with Genetic Algorithm\n",
    "## Annex : In-Sample vs. Out-of-sample Regressions \n",
    "This notebook is an statistical annex to the Lean-based project ***Developing Trading Strategies with Genetic Algorithm***. The main post can be found [here](https://www.quantconnect.com/forum)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\")\n",
    "import matplotlib.pylab as pylab\n",
    "%matplotlib inline\n",
    "\n",
    "from IPython.display import display, Markdown, Latex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "base_path = 'C:/Users/jjd/Desktop/GAExperiment_2017-05-28_0211/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read the data of all individual generated by the genetic algorithm and apply CamleCase format for the column names.\n",
    "optimization_resutls = base_path + 'ResultsNoUnits.csv'\n",
    "ga_results = pd.read_csv(optimization_resutls, )\n",
    "camel_column_name = [n.replace(\" \", \"\") for n in ga_results.columns]\n",
    "ga_results.columns = camel_column_name\n",
    "\n",
    "# The simple order in which the backtest results were saved will be used as individuals identification for further comparison.\n",
    "ga_results['ID'] = ga_results.index\n",
    "\n",
    "# Drop values with Drawdown equal to zero\n",
    "ga_results = ga_results[ga_results.Drawdown!=0]\n",
    "ga_results['SterlingRatio'] = ga_results.NetProfit / ga_results.Drawdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "statistics_cols = ['TotalTrades', 'AverageWin', 'AverageLoss', 'CompoundingAnnualReturn',\n",
    "                   'Drawdown', 'Expectancy', 'NetProfit', 'SharpeRatio', 'LossRate',\n",
    "                   'WinRate', 'Profit-LossRatio', 'Alpha', 'Beta', 'AnnualStandardDeviation',\n",
    "                   'AnnualVariance', 'InformationRatio', 'TrackingError', 'TreynorRatio',\n",
    "                   'TotalFees', 'ID', 'SterlingRatio']\n",
    "\n",
    "\n",
    "# Read and join the 4 out-of-sample results\n",
    "for i in range(1,5):\n",
    "    out_of_sample_results = base_path + 'OutOfSample' + str(i) + 'Month/FullResutls.csv'\n",
    "    oos_results = pd.read_csv(out_of_sample_results)\n",
    "    oos_results['SterlingRatio'] = oos_results.NetProfit / oos_results.Drawdown\n",
    "    oos_results['OosPeriod'] = str(i) + '_month'\n",
    "    \n",
    "    oos_results = oos_results.join(ga_results.loc[:, statistics_cols], on='ID', how='inner',\n",
    "                                   lsuffix='_OOS', rsuffix='_IS')\n",
    "    if(i==1):\n",
    "        oos_vs_is = oos_results\n",
    "    else:\n",
    "        oos_vs_is = pd.concat([oos_vs_is, oos_results], axis=0)\n",
    "    oos_vs_is = oos_vs_is.replace([np.inf, -np.inf], np.nan).fillna(value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Regression with 4 out-of-sample periods."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        SharpeRatio_OOS   R-squared:                       0.174\n",
      "Model:                            OLS   Adj. R-squared:                  0.173\n",
      "Method:                 Least Squares   F-statistic:                     136.9\n",
      "Date:                Thu, 03 Aug 2017   Prob (F-statistic):           7.98e-29\n",
      "Time:                        09:24:19   Log-Likelihood:                -1146.7\n",
      "No. Observations:                 652   AIC:                             2297.\n",
      "Df Residuals:                     650   BIC:                             2306.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "SterlingRatio_IS     0.6282      0.054     11.699      0.000       0.523       0.734\n",
      "const               -1.7429      0.147    -11.875      0.000      -2.031      -1.455\n",
      "==============================================================================\n",
      "Omnibus:                       43.457   Durbin-Watson:                   1.362\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               15.818\n",
      "Skew:                          -0.002   Prob(JB):                     0.000367\n",
      "Kurtosis:                       2.237   Cond. No.                         8.13\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "display(Markdown('### Regression with 4 out-of-sample periods.'))\n",
    "x = sm.add_constant(oos_vs_is.SterlingRatio_IS, prepend=False)\n",
    "y = oos_vs_is.SharpeRatio_OOS\n",
    "mod = sm.OLS(y, x)\n",
    "res = mod.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "### Regression with 1 month out-of-sample period."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        SharpeRatio_OOS   R-squared:                       0.356\n",
      "Model:                            OLS   Adj. R-squared:                  0.352\n",
      "Method:                 Least Squares   F-statistic:                     88.83\n",
      "Date:                Thu, 03 Aug 2017   Prob (F-statistic):           4.53e-17\n",
      "Time:                        09:24:19   Log-Likelihood:                -172.60\n",
      "No. Observations:                 163   AIC:                             349.2\n",
      "Df Residuals:                     161   BIC:                             355.4\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "SterlingRatio_IS     0.5051      0.054      9.425      0.000       0.399       0.611\n",
      "const               -0.0173      0.146     -0.118      0.906      -0.307       0.272\n",
      "==============================================================================\n",
      "Omnibus:                        8.941   Durbin-Watson:                   2.035\n",
      "Prob(Omnibus):                  0.011   Jarque-Bera (JB):                9.169\n",
      "Skew:                           0.473   Prob(JB):                       0.0102\n",
      "Kurtosis:                       3.674   Cond. No.                         8.13\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "### Regression with 2 months out-of-sample period."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        SharpeRatio_OOS   R-squared:                       0.108\n",
      "Model:                            OLS   Adj. R-squared:                  0.102\n",
      "Method:                 Least Squares   F-statistic:                     19.43\n",
      "Date:                Thu, 03 Aug 2017   Prob (F-statistic):           1.90e-05\n",
      "Time:                        09:24:19   Log-Likelihood:                -268.28\n",
      "No. Observations:                 163   AIC:                             540.6\n",
      "Df Residuals:                     161   BIC:                             546.7\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "SterlingRatio_IS     0.4248      0.096      4.408      0.000       0.235       0.615\n",
      "const               -2.1024      0.263     -7.981      0.000      -2.623      -1.582\n",
      "==============================================================================\n",
      "Omnibus:                        6.672   Durbin-Watson:                   2.219\n",
      "Prob(Omnibus):                  0.036   Jarque-Bera (JB):                6.907\n",
      "Skew:                           0.486   Prob(JB):                       0.0316\n",
      "Kurtosis:                       2.735   Cond. No.                         8.13\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "### Regression with 3 months out-of-sample period."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        SharpeRatio_OOS   R-squared:                       0.376\n",
      "Model:                            OLS   Adj. R-squared:                  0.372\n",
      "Method:                 Least Squares   F-statistic:                     96.97\n",
      "Date:                Thu, 03 Aug 2017   Prob (F-statistic):           3.34e-18\n",
      "Time:                        09:24:19   Log-Likelihood:                -262.02\n",
      "No. Observations:                 163   AIC:                             528.0\n",
      "Df Residuals:                     161   BIC:                             534.2\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "SterlingRatio_IS     0.9133      0.093      9.848      0.000       0.730       1.097\n",
      "const               -2.7345      0.253    -10.787      0.000      -3.235      -2.234\n",
      "==============================================================================\n",
      "Omnibus:                        5.332   Durbin-Watson:                   2.127\n",
      "Prob(Omnibus):                  0.070   Jarque-Bera (JB):                5.453\n",
      "Skew:                           0.439   Prob(JB):                       0.0655\n",
      "Kurtosis:                       2.824   Cond. No.                         8.13\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "### Regression with 4 months out-of-sample period."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        SharpeRatio_OOS   R-squared:                       0.264\n",
      "Model:                            OLS   Adj. R-squared:                  0.259\n",
      "Method:                 Least Squares   F-statistic:                     57.66\n",
      "Date:                Thu, 03 Aug 2017   Prob (F-statistic):           2.39e-12\n",
      "Time:                        09:24:19   Log-Likelihood:                -253.78\n",
      "No. Observations:                 163   AIC:                             511.6\n",
      "Df Residuals:                     161   BIC:                             517.8\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "SterlingRatio_IS     0.6696      0.088      7.594      0.000       0.495       0.844\n",
      "const               -2.1174      0.241     -8.786      0.000      -2.593      -1.641\n",
      "==============================================================================\n",
      "Omnibus:                        6.316   Durbin-Watson:                   2.160\n",
      "Prob(Omnibus):                  0.043   Jarque-Bera (JB):                6.081\n",
      "Skew:                           0.467   Prob(JB):                       0.0478\n",
      "Kurtosis:                       3.148   Cond. No.                         8.13\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,5):\n",
    "    sample = str(i) + '_month'\n",
    "    x = sm.add_constant(oos_vs_is[oos_vs_is.OosPeriod==sample].SterlingRatio_IS, prepend=False)\n",
    "    y = oos_vs_is[oos_vs_is.OosPeriod==sample].SharpeRatio_OOS\n",
    "    mod = sm.OLS(y, x)\n",
    "    res = mod.fit()\n",
    "    title = '### Regression with {0} month{1} out-of-sample period.'.format(i, '' if i==1 else 's')\n",
    "    display(Markdown(title))\n",
    "    print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sharpe Ratio In-Sample vs. Out-of-sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7EAAAHfCAYAAACGQl9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAADdUGIBAADoxv8H+UGoMIclSJIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc1c2f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA64AAAHfCAYAAABQ54U/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNDQJXIUQQgghhBBCNLT/Hx1pd3gszqp+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3a178d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "oos_vs_is.OosPeriod.unique()\n",
    "df_for_kde = pd.DataFrame({\"SharpeRatio_IS\":oos_vs_is[oos_vs_is.OosPeriod=='1_month'].SharpeRatio_IS,\n",
    "                           \"SharpeRatio_OOS_1Month\":oos_vs_is[oos_vs_is.OosPeriod=='1_month'].SharpeRatio_OOS,\n",
    "                           \"SharpeRatio_OOS_2Month\":oos_vs_is[oos_vs_is.OosPeriod=='2_month'].SharpeRatio_OOS,\n",
    "                           \"SharpeRatio_OOS_3Month\":oos_vs_is[oos_vs_is.OosPeriod=='3_month'].SharpeRatio_OOS,\n",
    "                           \"SharpeRatio_OOS_4Month\":oos_vs_is[oos_vs_is.OosPeriod=='4_month'].SharpeRatio_OOS,\n",
    "                          })\n",
    "df_for_kde.plot.hist(figsize=(16,8), stacked=True, alpha = 0.3, colormap='gnuplot',\n",
    "                     title='Sharpe ratio in-sample vs. out of sample stacked frequencies');\n",
    "df_for_kde.plot.kde(figsize=(16,8), title='Sharpe ratio in-sample vs. out of sample kernel densities');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}