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    "# Developing Trading Strategies with Genetic Algorithm\n",
    "## Annex : In-Sample vs. Out-of-sample Regressions \n",
    "### Introduction\n",
    "This notebook is an annex to the [Lean](https://github.com/QuantConnect/Lean) Project ***Trading Rules based in Genetic Algorithms***. The main post can be found [here](https://www.quantconnect.com/forum)."
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\")\n",
    "import matplotlib.pylab as pylab\n",
    "pylab.rcParams['figure.figsize'] = 16, 8\n",
    "%matplotlib inline\n",
    "\n",
    "base_path = '/run/media/jjd/Win7/Users/jjd/Desktop/GAExperiment_2017-05-28_0211/'"
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    "### Fitness by Generations\n",
    "The genetic algorithm ran for 32 generations. The session was ending after a  fitness stagnation for 9 generation.\n",
    "\n",
    "The paper uses Sterling ratio as fitness:\n",
    "$$SterlingRatio = \\frac{NetProfit}{MaxDrawdown}$$\n",
    "\n",
    "The Net Profit instead the Annualized return because the training period was of six months.\n",
    "\n",
    "In order to avoid negative fitness issues, this study used:\n",
    "$$fitness = 2^{SterlingRatio}$$"
   ]
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6BZVK5fuSfDzJl6rV6o894/7XJPlyknOjd5WS1JP8RLVa/fsG1AoAAMAMd/TEuQv9BLes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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e7224c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitness_by_generation_file = base_path + 'FitnessByGeneration.csv' \n",
    "fitness_by_generation = pd.read_csv(fitness_by_generation_file)\n",
    "fitness_by_generation.plot(x='Generation', y='Fitness', figsize=(16,6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fitness shows a typical pattern in genetic algorithms, that is, many generations of fitness stagnation until a new innovation reaches new highs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitness by individuals\n",
    "\n",
    "The number of individuals tested in the learning session was **1873**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total individuals: 1873\n"
     ]
    }
   ],
   "source": [
    "print('Total individuals:', ga_results.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2dNkPeeVtrvv4nVhlErSWP1DB/V8/dp/832+7X+4zGVCtQ2WeZ+/U+ld5gKBu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ayCx8E6Cy1xNCv30CHXb1qjTeEjxYt+Bf5MVar1SP0tGrbrti6aZ9vk3HndLynEB/Us1ekOFW8z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\nH6gFkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e716ae828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ga_results['SterlingRatio'] = ga_results.NetProfit / ga_results.Drawdown\n",
    "ga_results['Fitness'] = 2 ** ga_results.SterlingRatio\n",
    "ga_results.Fitness.plot(figsize=(12,6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cases with zero Total Trades: 249\n"
     ]
    }
   ],
   "source": [
    "print('Cases with zero Total Trades:', ga_results[ga_results.TotalTrades==0].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cases with zero Drawdown: 456\n"
     ]
    }
   ],
   "source": [
    "print('Cases with zero Drawdown:', ga_results[ga_results.Drawdown==0].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cases with trades made but zero Drawdown: 207\n"
     ]
    }
   ],
   "source": [
    "print('Cases with trades made but zero Drawdown:', ga_results[(ga_results.Drawdown==0) & (ga_results.TotalTrades!=0)].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ga_results = ga_results[ga_results.Drawdown!=0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The blanks in the plot corresponds to the 456 cases where the **`MaxDrawdown`** is equal to zero. \n",
    "\n",
    "From those cases 249 corresponds to cases where the agent was not able to generate signals (**`TotalTrades==0`**), the remaining 207 cases are consequence of some flaws in the experiment design and implementation.\n",
    "\n",
    "First, the algorithm used to run the evaluations had a very low exposure. The reason for that decision was trying to keep the portfolio in positives values, even when many bad trades were made by the individuals (some individuals made more than 10000 trades!). *The problem with the low exposure is that the draw down is very small*. Second, the **`MaxDrawdown`** value is parsed from a string in percentage format with two decimals; thus, the small draw downs are rounded to zero. \n",
    "\n",
    "Another side-effect of this problem is that as the genetic algorithm only checks the fitness when considering the next generation, some of those individuals were completely ignored for the recombination, event when they had a good behavior (the best one has a Sharpe of 1.9)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IIg8AAAAdUeQBAACgI4o8AAAAdESRBwAAgI4o8gAAANARRR4AAAA6osgDAABARxR5AAAA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\nRxR5AAAA6IgiDwAAAB35/zphz5QkNbCrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f206f0eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ga_results.TotalTrades.hist(bins=50, figsize=(12,6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "163"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oos_results = ga_results[ga_results.SterlingRatio>1]\n",
    "cols = ['ID']\n",
    "cols.extend(ga_results.columns[:28])\n",
    "# Save file to be used by LeanSTP in the OOS analysis \n",
    "#oos_results[cols].to_csv(base_path+'SelectedIndividualsForOOS.csv', index=False)\n",
    "oos_results.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In Sample vs Out of Sample\n",
    "\n",
    "The training session period was 6 months from July 1st 2016 to December 31th 2016. The out sample period was January 2017.\n",
    "\n",
    "Only the 40 individuals with Sterling ratio greater than one were considered to run the out of sample analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
    "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.ix[:, 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)\n",
    "    \n",
    "oos_vs_is.to_csv(base_path + \"FullOosResults.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>TotalTrades_OOS</th>\n",
       "      <th>AverageWin_OOS</th>\n",
       "      <th>AverageLoss_OOS</th>\n",
       "      <th>CompoundingAnnualReturn_OOS</th>\n",
       "      <th>Drawdown_OOS</th>\n",
       "      <th>Expectancy_OOS</th>\n",
       "      <th>NetProfit_OOS</th>\n",
       "      <th>SharpeRatio_OOS</th>\n",
       "      <th>LossRate_OOS</th>\n",
       "      <th>...</th>\n",
       "      <th>Alpha_IS</th>\n",
       "      <th>Beta_IS</th>\n",
       "      <th>AnnualStandardDeviation_IS</th>\n",
       "      <th>AnnualVariance_IS</th>\n",
       "      <th>InformationRatio_IS</th>\n",
       "      <th>TrackingError_IS</th>\n",
       "      <th>TreynorRatio_IS</th>\n",
       "      <th>TotalFees_IS</th>\n",
       "      <th>ID_IS</th>\n",
       "      <th>SterlingRatio_IS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1005</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>-0.00284</td>\n",
       "      <td>0.002</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-0.00025</td>\n",
       "      <td>-0.386</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.02</td>\n",
       "      <td>1005</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1014</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152.06</td>\n",
       "      <td>1014</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1025</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>156.07</td>\n",
       "      <td>1025</td>\n",
       "      <td>3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1026</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>0.01705</td>\n",
       "      <td>0.003</td>\n",
       "      <td>1.271</td>\n",
       "      <td>0.00148</td>\n",
       "      <td>1.616</td>\n",
       "      <td>0.67</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60.02</td>\n",
       "      <td>1026</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1031</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>156.06</td>\n",
       "      <td>1031</td>\n",
       "      <td>2.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1032</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>0.01705</td>\n",
       "      <td>0.003</td>\n",
       "      <td>1.271</td>\n",
       "      <td>0.00148</td>\n",
       "      <td>1.616</td>\n",
       "      <td>0.67</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72.02</td>\n",
       "      <td>1032</td>\n",
       "      <td>1.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1039</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>156.07</td>\n",
       "      <td>1039</td>\n",
       "      <td>3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1044</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152.06</td>\n",
       "      <td>1044</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1059</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>156.07</td>\n",
       "      <td>1059</td>\n",
       "      <td>3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1063</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>-0.00284</td>\n",
       "      <td>0.002</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-0.00025</td>\n",
       "      <td>-0.386</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.02</td>\n",
       "      <td>1063</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1068</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152.06</td>\n",
       "      <td>1068</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1077</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152.06</td>\n",
       "      <td>1077</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1084</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>156.07</td>\n",
       "      <td>1084</td>\n",
       "      <td>3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1086</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152.06</td>\n",
       "      <td>1086</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1091</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>168.08</td>\n",
       "      <td>1091</td>\n",
       "      <td>3.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1093</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>-0.00018</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.027</td>\n",
       "      <td>-0.00002</td>\n",
       "      <td>-0.185</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172.02</td>\n",
       "      <td>1093</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1103</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>156.06</td>\n",
       "      <td>1103</td>\n",
       "      <td>2.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1107</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>0.00554</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1.066</td>\n",
       "      <td>0.00048</td>\n",
       "      <td>2.694</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>164.02</td>\n",
       "      <td>1107</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1108</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>0.01705</td>\n",
       "      <td>0.003</td>\n",
       "      <td>1.271</td>\n",
       "      <td>0.00148</td>\n",
       "      <td>1.616</td>\n",
       "      <td>0.67</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60.02</td>\n",
       "      <td>1108</td>\n",
       "      <td>1.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1111</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>156.07</td>\n",
       "      <td>1111</td>\n",
       "      <td>3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1122</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152.06</td>\n",
       "      <td>1122</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1136</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152.06</td>\n",
       "      <td>1136</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1139</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>156.07</td>\n",
       "      <td>1139</td>\n",
       "      <td>3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1146</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152.06</td>\n",
       "      <td>1146</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1152</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>156.07</td>\n",
       "      <td>1152</td>\n",
       "      <td>3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1156</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>-0.00018</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.027</td>\n",
       "      <td>-0.00002</td>\n",
       "      <td>-0.185</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>168.02</td>\n",
       "      <td>1156</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1172</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>0.01705</td>\n",
       "      <td>0.003</td>\n",
       "      <td>1.271</td>\n",
       "      <td>0.00148</td>\n",
       "      <td>1.616</td>\n",
       "      <td>0.67</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60.02</td>\n",
       "      <td>1172</td>\n",
       "      <td>1.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1177</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07651</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.00648</td>\n",
       "      <td>1.761</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152.05</td>\n",
       "      <td>1177</td>\n",
       "      <td>2.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1180</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>156.07</td>\n",
       "      <td>1180</td>\n",
       "      <td>3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1184</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>0.07099</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.00603</td>\n",
       "      <td>1.701</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>164.06</td>\n",
       "      <td>1184</td>\n",
       "      <td>3.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>842</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.03166</td>\n",
       "      <td>0.013</td>\n",
       "      <td>-0.737</td>\n",
       "      <td>-0.01061</td>\n",
       "      <td>-2.750</td>\n",
       "      <td>0.80</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.02</td>\n",
       "      <td>842</td>\n",
       "      <td>1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>845</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.00541</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.00179</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.57</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72.02</td>\n",
       "      <td>845</td>\n",
       "      <td>1.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>850</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.03131</td>\n",
       "      <td>0.012</td>\n",
       "      <td>-0.736</td>\n",
       "      <td>-0.01049</td>\n",
       "      <td>-2.615</td>\n",
       "      <td>0.80</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.02</td>\n",
       "      <td>850</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>855</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.03131</td>\n",
       "      <td>0.012</td>\n",
       "      <td>-0.736</td>\n",
       "      <td>-0.01049</td>\n",
       "      <td>-2.615</td>\n",
       "      <td>0.80</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.02</td>\n",
       "      <td>855</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>857</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.03131</td>\n",
       "      <td>0.012</td>\n",
       "      <td>-0.736</td>\n",
       "      <td>-0.01049</td>\n",
       "      <td>-2.615</td>\n",
       "      <td>0.80</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.02</td>\n",
       "      <td>857</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>867</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.03166</td>\n",
       "      <td>0.013</td>\n",
       "      <td>-0.737</td>\n",
       "      <td>-0.01061</td>\n",
       "      <td>-2.750</td>\n",
       "      <td>0.80</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80.02</td>\n",
       "      <td>867</td>\n",
       "      <td>1.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>869</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.03131</td>\n",
       "      <td>0.012</td>\n",
       "      <td>-0.736</td>\n",
       "      <td>-0.01049</td>\n",
       "      <td>-2.615</td>\n",
       "      <td>0.80</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.02</td>\n",
       "      <td>869</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>872</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.03131</td>\n",
       "      <td>0.012</td>\n",
       "      <td>-0.736</td>\n",
       "      <td>-0.01049</td>\n",
       "      <td>-2.615</td>\n",
       "      <td>0.80</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.02</td>\n",
       "      <td>872</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>883</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.03131</td>\n",
       "      <td>0.012</td>\n",
       "      <td>-0.736</td>\n",
       "      <td>-0.01049</td>\n",
       "      <td>-2.615</td>\n",
       "      <td>0.80</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.02</td>\n",
       "      <td>883</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>892</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.00541</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.00179</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.57</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60.02</td>\n",
       "      <td>892</td>\n",
       "      <td>1.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>901</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.03131</td>\n",
       "      <td>0.012</td>\n",
       "      <td>-0.736</td>\n",
       "      <td>-0.01049</td>\n",
       "      <td>-2.615</td>\n",
       "      <td>0.80</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.02</td>\n",
       "      <td>901</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>904</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.00541</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.00179</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.57</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60.02</td>\n",
       "      <td>904</td>\n",
       "      <td>1.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>906</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.00541</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.00179</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.57</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72.02</td>\n",
       "      <td>906</td>\n",
       "      <td>1.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>920</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.03131</td>\n",
       "      <td>0.012</td>\n",
       "      <td>-0.736</td>\n",
       "      <td>-0.01049</td>\n",
       "      <td>-2.615</td>\n",
       "      <td>0.80</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.02</td>\n",
       "      <td>920</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>921</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.03166</td>\n",
       "      <td>0.013</td>\n",
       "      <td>-0.737</td>\n",
       "      <td>-0.01061</td>\n",
       "      <td>-2.750</td>\n",
       "      <td>0.80</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.02</td>\n",
       "      <td>921</td>\n",
       "      <td>1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>928</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.00541</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.00179</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.57</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60.02</td>\n",
       "      <td>928</td>\n",
       "      <td>1.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>929</td>\n",
       "      <td>42</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>-0.0021</td>\n",
       "      <td>0.00158</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.00052</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.57</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152.06</td>\n",
       "      <td>929</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>936</td>\n",
       "      <td>44</td>\n",
       "      <td>0.0027</td>\n",
       "      <td>-0.0021</td>\n",
       "      <td>0.00524</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.00173</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.55</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152.06</td>\n",
       "      <td>936</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>938</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.03131</td>\n",
       "      <td>0.012</td>\n",
       "      <td>-0.736</td>\n",
       "      <td>-0.01049</td>\n",
       "      <td>-2.615</td>\n",
       "      <td>0.80</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.02</td>\n",
       "      <td>938</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>944</td>\n",
       "      <td>42</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>-0.0021</td>\n",
       "      <td>0.00158</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.00052</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.57</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>152.06</td>\n",
       "      <td>944</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>946</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.00541</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.00179</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.57</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72.02</td>\n",
       "      <td>946</td>\n",
       "      <td>1.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>947</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.03131</td>\n",
       "      <td>0.012</td>\n",
       "      <td>-0.736</td>\n",
       "      <td>-0.01049</td>\n",
       "      <td>-2.615</td>\n",
       "      <td>0.80</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.02</td>\n",
       "      <td>947</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>948</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.03131</td>\n",
       "      <td>0.012</td>\n",
       "      <td>-0.736</td>\n",
       "      <td>-0.01049</td>\n",
       "      <td>-2.615</td>\n",
       "      <td>0.80</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.02</td>\n",
       "      <td>948</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>956</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.00541</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.00179</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.57</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60.02</td>\n",
       "      <td>956</td>\n",
       "      <td>1.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>960</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>0.00337</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.00111</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.62</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.02</td>\n",
       "      <td>960</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>964</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.03131</td>\n",
       "      <td>0.012</td>\n",
       "      <td>-0.736</td>\n",
       "      <td>-0.01049</td>\n",
       "      <td>-2.615</td>\n",
       "      <td>0.80</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.02</td>\n",
       "      <td>964</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>981</td>\n",
       "      <td>42</td>\n",
       "      <td>0.0027</td>\n",
       "      <td>-0.0016</td>\n",
       "      <td>0.01366</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.00451</td>\n",
       "      <td>0.431</td>\n",
       "      <td>0.57</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>156.06</td>\n",
       "      <td>981</td>\n",
       "      <td>2.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>983</td>\n",
       "      <td>42</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>-0.0021</td>\n",
       "      <td>0.00920</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.00304</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.57</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>156.07</td>\n",
       "      <td>983</td>\n",
       "      <td>3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>986</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.03131</td>\n",
       "      <td>0.012</td>\n",
       "      <td>-0.736</td>\n",
       "      <td>-0.01049</td>\n",
       "      <td>-2.615</td>\n",
       "      <td>0.80</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80.02</td>\n",
       "      <td>986</td>\n",
       "      <td>1.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>994</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.0009</td>\n",
       "      <td>-0.03431</td>\n",
       "      <td>0.013</td>\n",
       "      <td>-0.518</td>\n",
       "      <td>-0.01150</td>\n",
       "      <td>-3.254</td>\n",
       "      <td>0.68</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172.02</td>\n",
       "      <td>994</td>\n",
       "      <td>1.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>652 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID  TotalTrades_OOS  AverageWin_OOS  AverageLoss_OOS  \\\n",
       "0    1005                4          0.0000          -0.0001   \n",
       "1    1014                6          0.0054          -0.0048   \n",
       "2    1025                6          0.0054          -0.0048   \n",
       "3    1026                6          0.0023          -0.0004   \n",
       "4    1031                6          0.0054          -0.0048   \n",
       "5    1032                6          0.0023          -0.0004   \n",
       "6    1039                6          0.0054          -0.0048   \n",
       "7    1044                6          0.0054          -0.0048   \n",
       "8    1059                6          0.0054          -0.0048   \n",
       "9    1063                4          0.0000          -0.0001   \n",
       "10   1068                6          0.0054          -0.0048   \n",
       "11   1077                6          0.0054          -0.0048   \n",
       "12   1084                6          0.0054          -0.0048   \n",
       "13   1086                6          0.0054          -0.0048   \n",
       "14   1091                6          0.0054          -0.0048   \n",
       "15   1093                6          0.0001          -0.0002   \n",
       "16   1103                6          0.0054          -0.0048   \n",
       "17   1107                6          0.0003          -0.0002   \n",
       "18   1108                6          0.0023          -0.0004   \n",
       "19   1111                6          0.0054          -0.0048   \n",
       "20   1122                6          0.0054          -0.0048   \n",
       "21   1136                6          0.0054          -0.0048   \n",
       "22   1139                6          0.0054          -0.0048   \n",
       "23   1146                6          0.0054          -0.0048   \n",
       "24   1152                6          0.0054          -0.0048   \n",
       "25   1156                6          0.0001          -0.0002   \n",
       "26   1172                6          0.0023          -0.0004   \n",
       "27   1177                6          0.0057          -0.0048   \n",
       "28   1180                6          0.0054          -0.0048   \n",
       "29   1184                6          0.0054          -0.0048   \n",
       "..    ...              ...             ...              ...   \n",
       "133   842               20          0.0005          -0.0014   \n",
       "134   845               14          0.0010          -0.0003   \n",
       "135   850               20          0.0005          -0.0014   \n",
       "136   855               20          0.0005          -0.0014   \n",
       "137   857               20          0.0005          -0.0014   \n",
       "138   867               20          0.0005          -0.0014   \n",
       "139   869               20          0.0005          -0.0014   \n",
       "140   872               20          0.0005          -0.0014   \n",
       "141   883               20          0.0005          -0.0014   \n",
       "142   892               14          0.0010          -0.0003   \n",
       "143   901               20          0.0005          -0.0014   \n",
       "144   904               14          0.0010          -0.0003   \n",
       "145   906               14          0.0010          -0.0003   \n",
       "146   920               20          0.0005          -0.0014   \n",
       "147   921               20          0.0005          -0.0014   \n",
       "148   928               14          0.0010          -0.0003   \n",
       "149   929               42          0.0028          -0.0021   \n",
       "150   936               44          0.0027          -0.0021   \n",
       "151   938               20          0.0005          -0.0014   \n",
       "152   944               42          0.0028          -0.0021   \n",
       "153   946               14          0.0010          -0.0003   \n",
       "154   947               20          0.0005          -0.0014   \n",
       "155   948               20          0.0005          -0.0014   \n",
       "156   956               14          0.0010          -0.0003   \n",
       "157   960               16          0.0010          -0.0004   \n",
       "158   964               20          0.0005          -0.0014   \n",
       "159   981               42          0.0027          -0.0016   \n",
       "160   983               42          0.0031          -0.0021   \n",
       "161   986               20          0.0005          -0.0014   \n",
       "162   994               50          0.0005          -0.0009   \n",
       "\n",
       "     CompoundingAnnualReturn_OOS  Drawdown_OOS  Expectancy_OOS  NetProfit_OOS  \\\n",
       "0                       -0.00284         0.002          -1.000       -0.00025   \n",
       "1                        0.07099         0.006           0.422        0.00603   \n",
       "2                        0.07099         0.006           0.422        0.00603   \n",
       "3                        0.01705         0.003           1.271        0.00148   \n",
       "4                        0.07099         0.006           0.422        0.00603   \n",
       "5                        0.01705         0.003           1.271        0.00148   \n",
       "6                        0.07099         0.006           0.422        0.00603   \n",
       "7                        0.07099         0.006           0.422        0.00603   \n",
       "8                        0.07099         0.006           0.422        0.00603   \n",
       "9                       -0.00284         0.002          -1.000       -0.00025   \n",
       "10                       0.07099         0.006           0.422        0.00603   \n",
       "11                       0.07099         0.006           0.422        0.00603   \n",
       "12                       0.07099         0.006           0.422        0.00603   \n",
       "13                       0.07099         0.006           0.422        0.00603   \n",
       "14                       0.07099         0.006           0.422        0.00603   \n",
       "15                      -0.00018         0.000          -0.027       -0.00002   \n",
       "16                       0.07099         0.006           0.422        0.00603   \n",
       "17                       0.00554         0.001           1.066        0.00048   \n",
       "18                       0.01705         0.003           1.271        0.00148   \n",
       "19                       0.07099         0.006           0.422        0.00603   \n",
       "20                       0.07099         0.006           0.422        0.00603   \n",
       "21                       0.07099         0.006           0.422        0.00603   \n",
       "22                       0.07099         0.006           0.422        0.00603   \n",
       "23                       0.07099         0.006           0.422        0.00603   \n",
       "24                       0.07099         0.006           0.422        0.00603   \n",
       "25                      -0.00018         0.000          -0.027       -0.00002   \n",
       "26                       0.01705         0.003           1.271        0.00148   \n",
       "27                       0.07651         0.006           0.454        0.00648   \n",
       "28                       0.07099         0.006           0.422        0.00603   \n",
       "29                       0.07099         0.006           0.422        0.00603   \n",
       "..                           ...           ...             ...            ...   \n",
       "133                     -0.03166         0.013          -0.737       -0.01061   \n",
       "134                      0.00541         0.003           0.824        0.00179   \n",
       "135                     -0.03131         0.012          -0.736       -0.01049   \n",
       "136                     -0.03131         0.012          -0.736       -0.01049   \n",
       "137                     -0.03131         0.012          -0.736       -0.01049   \n",
       "138                     -0.03166         0.013          -0.737       -0.01061   \n",
       "139                     -0.03131         0.012          -0.736       -0.01049   \n",
       "140                     -0.03131         0.012          -0.736       -0.01049   \n",
       "141                     -0.03131         0.012          -0.736       -0.01049   \n",
       "142                      0.00541         0.003           0.824        0.00179   \n",
       "143                     -0.03131         0.012          -0.736       -0.01049   \n",
       "144                      0.00541         0.003           0.824        0.00179   \n",
       "145                      0.00541         0.003           0.824        0.00179   \n",
       "146                     -0.03131         0.012          -0.736       -0.01049   \n",
       "147                     -0.03166         0.013          -0.737       -0.01061   \n",
       "148                      0.00541         0.003           0.824        0.00179   \n",
       "149                      0.00158         0.014           0.015        0.00052   \n",
       "150                      0.00524         0.015           0.041        0.00173   \n",
       "151                     -0.03131         0.012          -0.736       -0.01049   \n",
       "152                      0.00158         0.014           0.015        0.00052   \n",
       "153                      0.00541         0.003           0.824        0.00179   \n",
       "154                     -0.03131         0.012          -0.736       -0.01049   \n",
       "155                     -0.03131         0.012          -0.736       -0.01049   \n",
       "156                      0.00541         0.003           0.824        0.00179   \n",
       "157                      0.00337         0.004           0.365        0.00111   \n",
       "158                     -0.03131         0.012          -0.736       -0.01049   \n",
       "159                      0.01366         0.012           0.133        0.00451   \n",
       "160                      0.00920         0.014           0.073        0.00304   \n",
       "161                     -0.03131         0.012          -0.736       -0.01049   \n",
       "162                     -0.03431         0.013          -0.518       -0.01150   \n",
       "\n",
       "     SharpeRatio_OOS  LossRate_OOS        ...         Alpha_IS  Beta_IS  \\\n",
       "0             -0.386          1.00        ...                0        0   \n",
       "1              1.701          0.33        ...                0        0   \n",
       "2              1.701          0.33        ...                0        0   \n",
       "3              1.616          0.67        ...                0        0   \n",
       "4              1.701          0.33        ...                0        0   \n",
       "5              1.616          0.67        ...                0        0   \n",
       "6              1.701          0.33        ...                0        0   \n",
       "7              1.701          0.33        ...                0        0   \n",
       "8              1.701          0.33        ...                0        0   \n",
       "9             -0.386          1.00        ...                0        0   \n",
       "10             1.701          0.33        ...                0        0   \n",
       "11             1.701          0.33        ...                0        0   \n",
       "12             1.701          0.33        ...                0        0   \n",
       "13             1.701          0.33        ...                0        0   \n",
       "14             1.701          0.33        ...                0        0   \n",
       "15            -0.185          0.33        ...                0        0   \n",
       "16             1.701          0.33        ...                0        0   \n",
       "17             2.694          0.33        ...                0        0   \n",
       "18             1.616          0.67        ...                0        0   \n",
       "19             1.701          0.33        ...                0        0   \n",
       "20             1.701          0.33        ...                0        0   \n",
       "21             1.701          0.33        ...                0        0   \n",
       "22             1.701          0.33        ...                0        0   \n",
       "23             1.701          0.33        ...                0        0   \n",
       "24             1.701          0.33        ...                0        0   \n",
       "25            -0.185          0.33        ...                0        0   \n",
       "26             1.616          0.67        ...                0        0   \n",
       "27             1.761          0.33        ...                0        0   \n",
       "28             1.701          0.33        ...                0        0   \n",
       "29             1.701          0.33        ...                0        0   \n",
       "..               ...           ...        ...              ...      ...   \n",
       "133           -2.750          0.80        ...                0        0   \n",
       "134            0.958          0.57        ...                0        0   \n",
       "135           -2.615          0.80        ...                0        0   \n",
       "136           -2.615          0.80        ...                0        0   \n",
       "137           -2.615          0.80        ...                0        0   \n",
       "138           -2.750          0.80        ...                0        0   \n",
       "139           -2.615          0.80        ...                0        0   \n",
       "140           -2.615          0.80        ...                0        0   \n",
       "141           -2.615          0.80        ...                0        0   \n",
       "142            0.958          0.57        ...                0        0   \n",
       "143           -2.615          0.80        ...                0        0   \n",
       "144            0.958          0.57        ...                0        0   \n",
       "145            0.958          0.57        ...                0        0   \n",
       "146           -2.615          0.80        ...                0        0   \n",
       "147           -2.750          0.80        ...                0        0   \n",
       "148            0.958          0.57        ...                0        0   \n",
       "149            0.060          0.57        ...                0        0   \n",
       "150            0.165          0.55        ...                0        0   \n",
       "151           -2.615          0.80        ...                0        0   \n",
       "152            0.060          0.57        ...                0        0   \n",
       "153            0.958          0.57        ...                0        0   \n",
       "154           -2.615          0.80        ...                0        0   \n",
       "155           -2.615          0.80        ...                0        0   \n",
       "156            0.958          0.57        ...                0        0   \n",
       "157            0.582          0.62        ...                0        0   \n",
       "158           -2.615          0.80        ...                0        0   \n",
       "159            0.431          0.57        ...                0        0   \n",
       "160            0.288          0.57        ...                0        0   \n",
       "161           -2.615          0.80        ...                0        0   \n",
       "162           -3.254          0.68        ...                0        0   \n",
       "\n",
       "     AnnualStandardDeviation_IS  AnnualVariance_IS  InformationRatio_IS  \\\n",
       "0                         0.002                  0                    0   \n",
       "1                         0.003                  0                    0   \n",
       "2                         0.003                  0                    0   \n",
       "3                         0.002                  0                    0   \n",
       "4                         0.003                  0                    0   \n",
       "5                         0.002                  0                    0   \n",
       "6                         0.003                  0                    0   \n",
       "7                         0.003                  0                    0   \n",
       "8                         0.003                  0                    0   \n",
       "9                         0.002                  0                    0   \n",
       "10                        0.003                  0                    0   \n",
       "11                        0.003                  0                    0   \n",
       "12                        0.003                  0                    0   \n",
       "13                        0.003                  0                    0   \n",
       "14                        0.003                  0                    0   \n",
       "15                        0.002                  0                    0   \n",
       "16                        0.003                  0                    0   \n",
       "17                        0.001                  0                    0   \n",
       "18                        0.002                  0                    0   \n",
       "19                        0.003                  0                    0   \n",
       "20                        0.003                  0                    0   \n",
       "21                        0.003                  0                    0   \n",
       "22                        0.003                  0                    0   \n",
       "23                        0.003                  0                    0   \n",
       "24                        0.003                  0                    0   \n",
       "25                        0.002                  0                    0   \n",
       "26                        0.002                  0                    0   \n",
       "27                        0.003                  0                    0   \n",
       "28                        0.003                  0                    0   \n",
       "29                        0.003                  0                    0   \n",
       "..                          ...                ...                  ...   \n",
       "133                       0.002                  0                    0   \n",
       "134                       0.002                  0                    0   \n",
       "135                       0.002                  0                    0   \n",
       "136                       0.002                  0                    0   \n",
       "137                       0.002                  0                    0   \n",
       "138                       0.002                  0                    0   \n",
       "139                       0.002                  0                    0   \n",
       "140                       0.002                  0                    0   \n",
       "141                       0.002                  0                    0   \n",
       "142                       0.002                  0                    0   \n",
       "143                       0.002                  0                    0   \n",
       "144                       0.002                  0                    0   \n",
       "145                       0.002                  0                    0   \n",
       "146                       0.002                  0                    0   \n",
       "147                       0.002                  0                    0   \n",
       "148                       0.002                  0                    0   \n",
       "149                       0.003                  0                    0   \n",
       "150                       0.003                  0                    0   \n",
       "151                       0.002                  0                    0   \n",
       "152                       0.003                  0                    0   \n",
       "153                       0.002                  0                    0   \n",
       "154                       0.002                  0                    0   \n",
       "155                       0.002                  0                    0   \n",
       "156                       0.002                  0                    0   \n",
       "157                       0.002                  0                    0   \n",
       "158                       0.002                  0                    0   \n",
       "159                       0.003                  0                    0   \n",
       "160                       0.003                  0                    0   \n",
       "161                       0.002                  0                    0   \n",
       "162                       0.002                  0                    0   \n",
       "\n",
       "     TrackingError_IS  TreynorRatio_IS  TotalFees_IS  ID_IS  SterlingRatio_IS  \n",
       "0                   0                0         68.02   1005              1.35  \n",
       "1                   0                0        152.06   1014              3.16  \n",
       "2                   0                0        156.07   1025              3.40  \n",
       "3                   0                0         60.02   1026              1.24  \n",
       "4                   0                0        156.06   1031              2.79  \n",
       "5                   0                0         72.02   1032              1.46  \n",
       "6                   0                0        156.07   1039              3.40  \n",
       "7                   0                0        152.06   1044              3.16  \n",
       "8                   0                0        156.07   1059              3.40  \n",
       "9                   0                0         68.02   1063              1.35  \n",
       "10                  0                0        152.06   1068              3.16  \n",
       "11                  0                0        152.06   1077              3.16  \n",
       "12                  0                0        156.07   1084              3.40  \n",
       "13                  0                0        152.06   1086              3.16  \n",
       "14                  0                0        168.08   1091              3.47  \n",
       "15                  0                0        172.02   1093              1.23  \n",
       "16                  0                0        156.06   1103              2.79  \n",
       "17                  0                0        164.02   1107              1.23  \n",
       "18                  0                0         60.02   1108              1.39  \n",
       "19                  0                0        156.07   1111              3.40  \n",
       "20                  0                0        152.06   1122              3.16  \n",
       "21                  0                0        152.06   1136              3.16  \n",
       "22                  0                0        156.07   1139              3.40  \n",
       "23                  0                0        152.06   1146              3.16  \n",
       "24                  0                0        156.07   1152              3.40  \n",
       "25                  0                0        168.02   1156              1.09  \n",
       "26                  0                0         60.02   1172              1.39  \n",
       "27                  0                0        152.05   1177              2.46  \n",
       "28                  0                0        156.07   1180              3.40  \n",
       "29                  0                0        164.06   1184              3.23  \n",
       "..                ...              ...           ...    ...               ...  \n",
       "133                 0                0         68.02    842              1.34  \n",
       "134                 0                0         72.02    845              1.46  \n",
       "135                 0                0         68.02    850              1.35  \n",
       "136                 0                0         68.02    855              1.35  \n",
       "137                 0                0         68.02    857              1.35  \n",
       "138                 0                0         80.02    867              1.41  \n",
       "139                 0                0         68.02    869              1.35  \n",
       "140                 0                0         68.02    872              1.35  \n",
       "141                 0                0         68.02    883              1.35  \n",
       "142                 0                0         60.02    892              1.39  \n",
       "143                 0                0         68.02    901              1.35  \n",
       "144                 0                0         60.02    904              1.39  \n",
       "145                 0                0         72.02    906              1.46  \n",
       "146                 0                0         68.02    920              1.35  \n",
       "147                 0                0         68.02    921              1.34  \n",
       "148                 0                0         60.02    928              1.39  \n",
       "149                 0                0        152.06    929              3.16  \n",
       "150                 0                0        152.06    936              3.16  \n",
       "151                 0                0         68.02    938              1.35  \n",
       "152                 0                0        152.06    944              3.16  \n",
       "153                 0                0         72.02    946              1.46  \n",
       "154                 0                0         68.02    947              1.35  \n",
       "155                 0                0         68.02    948              1.35  \n",
       "156                 0                0         60.02    956              1.39  \n",
       "157                 0                0         68.02    960              1.35  \n",
       "158                 0                0         68.02    964              1.35  \n",
       "159                 0                0        156.06    981              2.79  \n",
       "160                 0                0        156.07    983              3.40  \n",
       "161                 0                0         80.02    986              1.42  \n",
       "162                 0                0        172.02    994              1.26  \n",
       "\n",
       "[652 rows x 44 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oos_vs_is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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:                Wed, 07 Jun 2017   Prob (F-statistic):           7.98e-29\n",
      "Time:                        14:12:21   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": [
    "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": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Out of Sample lenght: 1_month\n",
      "                            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:                Wed, 07 Jun 2017   Prob (F-statistic):           4.53e-17\n",
      "Time:                        14:12:38   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",
      "==========================================================================================\n",
      "\n",
      "\n",
      "Out of Sample lenght: 2_month\n",
      "                            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:                Wed, 07 Jun 2017   Prob (F-statistic):           1.90e-05\n",
      "Time:                        14:12:38   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",
      "==========================================================================================\n",
      "\n",
      "\n",
      "Out of Sample lenght: 3_month\n",
      "                            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:                Wed, 07 Jun 2017   Prob (F-statistic):           3.34e-18\n",
      "Time:                        14:12:38   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",
      "==========================================================================================\n",
      "\n",
      "\n",
      "Out of Sample lenght: 4_month\n",
      "                            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:                Wed, 07 Jun 2017   Prob (F-statistic):           2.39e-12\n",
      "Time:                        14:12:38   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",
      "==========================================================================================\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",
    "    print (\"\\n\\nOut of Sample lenght:\", sample )\n",
    "    print(res.summary())\n",
    "    print ('='*90)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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t2wbz1FMDqFDhUttJSUkhKupN4uP3cPZsGp6enrRs2Yrhw0dRrVpgsWN7//13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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f205552e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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28847K4mJ2Ulk5IeYzdcYN+75Asc2fvxofvjhAKVLl85xv79/ANHRm7Nt37v3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PAPcu59o12bIOvAD4U611GUAp9QXgq+GGJEL0LuCjWusn6vt/HWYwoqF8Crgf+MuwAxGhOw78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\nDUaSdSGEEEIIIYQQosH8P1tITlACz7WfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9f2050c240>"
      ]
     },
     "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=(12,6), stacked=True, alpha = 0.3, colormap='gnuplot', title='Sharpe ratio in-sample vs. out of sample');\n",
    "df_for_kde.plot.kde(figsize=(12,6), title='Sharpe ratio stacked frequencies');\n",
    "df_for_kde.to_csv(base_path + 'sharpeForKDE.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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:                Wed, 07 Jun 2017   Prob (F-statistic):           7.98e-29\n",
      "Time:                        14:13:17   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": [
    "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": 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
}