From 2c1428c254c3496b7a61dfcc001fab2dfd7e292b Mon Sep 17 00:00:00 2001 From: Christopher van Hoecke Date: Fri, 14 Jul 2017 12:18:36 -0400 Subject: [PATCH] Factor Aggregation Lecture Draft --- .../Alpha Factor Aggregation.ipynb | 1770 +++++++++++++++++ 1 file changed, 1770 insertions(+) create mode 100644 notebooks/lectures/alpha_factor_aggregation/Alpha Factor Aggregation.ipynb diff --git a/notebooks/lectures/alpha_factor_aggregation/Alpha Factor Aggregation.ipynb b/notebooks/lectures/alpha_factor_aggregation/Alpha Factor Aggregation.ipynb new file mode 100644 index 00000000..106b6c18 --- /dev/null +++ b/notebooks/lectures/alpha_factor_aggregation/Alpha Factor Aggregation.ipynb @@ -0,0 +1,1770 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Alpha Factor Aggregation\n", + "by Christopher van Hoecke Maxwell Margenot and Delaney Granizo-Mackenzie\n", + "\n", + "Part of the Quantopian Lecture Series:\n", + "\n", + "* [www.quantopian.com/lectures](https://www.quantopian.com/lectures)\n", + "* [github.com/quantopian/research_public](https://github.com/quantopian/research_public)\n", + "\n", + "Notebook released under the Creative Commons Attribution 4.0 License.\n", + "\n", + "---\n", + "#Summary : \n", + "- [Introduction](#intro)\n", + " - **What is Factor Aggregation and why does it matter?**\n", + "- [Normalizing Factor Values](#Normalizing Factor Values.)\n", + " - **The importance of scalling factors for portfolio homogeneity.**\n", + "- [Static Weights](#Static Weights)\n", + " - **Crude solution to assing weights to a factor**\n", + "- [Dynamic Weights](#Dynamic Weighting)\n", + " - **Using Maching Learning to assign varying weights to each factor**\n", + " - [Defining and building the pipeline](#Defining and building the pipeline)\n", + " - [Running pipeline](#running pipeline)\n", + " - [Testing our ML Pipeline](#Testing our ML Pipeline)\n", + " - [Plugging all into Pipeline.](#Plugging all into Pipeline)\n", + " - [Analysing mega-alpha with alphalens](#analyzing mega-alpha with alphalens)\n", + "----" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\"Two is *sometimes* better than one\"
\n", + "----\n", + "\n", + "# Introduction\n", + "When you come across good alpha factors, you may wish to combine them into one algorithm. Once you've decided what factors to include in your long-short equity, you will need to think of ways to combine them. This can be one way of adding flavor to worn-out, alpha drained and familiar factors as combining factors in a novel way could lend a new perspective on the relative movements in the market that you couldn't access from the factors you chose individually.\n", + "\n", + "We begin by importing the necessary libraries to successfully run Pipeline, create our custom factor and run some computations. Using the imported libraries, we can build our Custom Factors using the built-in class MyFactor(CustomFactor) function. You can read more about building factors and analyzing factors in our [tutorial](https://www.quantopian.com/tutorials/pipeline#lesson10), and [lecture](https://www.quantopian.com/lectures/factor-analysis) pages. \n", + "\n", + "We use a simple Momentum Factor, a very common form of alpha factor, that come in many shapes and sizes. They all try to get at the same idea however, that securities in motion will stay in motion. Momentum factors try to quantify trends in financial markets and to \"ride the wave\", so to speak.\n", + "Alongside that, we use a Volatility Factor, another common form of alpha factor, which longs highly volatile stocks and shorts the lesser ones. To those, we add a value factor, which simply ranks the ratio between the cash flow and the market cap. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# ____ Importing Libraries ____ #\n", + "\n", + "from quantopian.pipeline.data.builtin import USEquityPricing\n", + "from quantopian.research import run_pipeline\n", + "from quantopian.pipeline import Pipeline\n", + "from quantopian.pipeline.filters import Q500US, Q1500US\n", + "from quantopian.pipeline.factors import CustomFactor, Returns, AverageDollarVolume\n", + "from quantopian.pipeline.data import morningstar\n", + "import alphalens as al\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import statsmodels.api as sm\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn import linear_model, decomposition, ensemble, preprocessing, isotonic, metrics\n", + "from sklearn.ensemble import GradientBoostingClassifier\n", + "from sklearn import neural_network\n", + "\n", + "bs = morningstar.balance_sheet\n", + "cfs = morningstar.cash_flow_statement\n", + "is_ = morningstar.income_statement\n", + "v = morningstar.valuation\n", + "\n", + "universe = Q500US()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# ____ Defining Factors ____ #\n", + "\n", + "class Momentum(CustomFactor):\n", + " # Momentum Factor\n", + " # Enhanced Price Momentum\n", + " # (12-month momentum - Momentum over last month)/ sigma of past six months\n", + " inputs = [USEquityPricing.close, Returns(window_length=126)]\n", + " window_length = 252\n", + " def compute(self, today, assets, out, prices, returns):\n", + " out[:] = ((prices[-21] - prices[-252])/prices[-252] -\n", + " (prices[-1] - prices[-21])/prices[-21]) / np.nanstd(returns, axis=0)\n", + "\n", + "class Volatility(CustomFactor):\n", + " # Volatility Factor\n", + " # Volatility of price over last 90 days\n", + " inputs = [USEquityPricing.close]\n", + " window_length = 90\n", + " def compute(self, today, assets, out, prices):\n", + " out[:] = np.nanstd(prices, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/dist-packages/numpy/lib/nanfunctions.py:1147: RuntimeWarning: Degrees of freedom <= 0 for slice.\n", + " warnings.warn(\"Degrees of freedom <= 0 for slice.\", RuntimeWarning)\n" + ] + }, + { + "data": { + "text/html": [ + "
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MomentumValueVolatilityhighmomentumhighvalhighvollowmomentumlowvallowvolreturns
2016-01-04 00:00:00+00:00Equity(2 [ARNC])-3.9061000.0116480.609595FalseFalseFalseTrueFalseTrue-0.010040
Equity(24 [AAPL])1.1191940.0164074.228688FalseFalseFalseFalseFalseFalse-0.019474
Equity(62 [ABT])0.3818350.0116101.804147FalseFalseFalseFalseFalseFalse-0.007733
Equity(67 [ADSK])1.0198800.0048777.267074FalseFalseTrueFalseFalseFalse-0.021365
Equity(76 [TAP])2.2598540.0111738.992524TrueFalseTrueFalseFalseFalse-0.006558
\n", + "
" + ], + "text/plain": [ + " Momentum Value Volatility \\\n", + "2016-01-04 00:00:00+00:00 Equity(2 [ARNC]) -3.906100 0.011648 0.609595 \n", + " Equity(24 [AAPL]) 1.119194 0.016407 4.228688 \n", + " Equity(62 [ABT]) 0.381835 0.011610 1.804147 \n", + " Equity(67 [ADSK]) 1.019880 0.004877 7.267074 \n", + " Equity(76 [TAP]) 2.259854 0.011173 8.992524 \n", + "\n", + " highmomentum highval highvol \\\n", + "2016-01-04 00:00:00+00:00 Equity(2 [ARNC]) False False False \n", + " Equity(24 [AAPL]) False False False \n", + " Equity(62 [ABT]) False False False \n", + " Equity(67 [ADSK]) False False True \n", + " Equity(76 [TAP]) True False True \n", + "\n", + " lowmomentum lowval lowvol \\\n", + "2016-01-04 00:00:00+00:00 Equity(2 [ARNC]) True False True \n", + " Equity(24 [AAPL]) False False False \n", + " Equity(62 [ABT]) False False False \n", + " Equity(67 [ADSK]) False False False \n", + " Equity(76 [TAP]) False False False \n", + "\n", + " returns \n", + "2016-01-04 00:00:00+00:00 Equity(2 [ARNC]) -0.010040 \n", + " Equity(24 [AAPL]) -0.019474 \n", + " Equity(62 [ABT]) -0.007733 \n", + " Equity(67 [ADSK]) -0.021365 \n", + " Equity(76 [TAP]) -0.006558 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "NUM_LONG_POSITIONS = 100\n", + "NUM_SHORT_POSITIONS = 100\n", + "\n", + "value = cfs.free_cash_flow.latest / v.market_cap.latest\n", + "momentum = Momentum(mask=universe)\n", + "volatility = Volatility(mask=universe)\n", + "\n", + "value_rank = value.rank()\n", + "momentum_rank = momentum.rank()\n", + "volatility_rank = volatility.rank()\n", + "\n", + "pipe = Pipeline(\n", + " columns = {\n", + " 'Value' : value,\n", + " 'Momentum' : momentum,\n", + " 'Volatility' : volatility,\n", + " 'returns' : Returns(inputs=[USEquityPricing.close], window_length=2),\n", + " 'highval' : value_rank.top(NUM_LONG_POSITIONS),\n", + " 'lowval' : value_rank.bottom(NUM_SHORT_POSITIONS),\n", + " 'highmomentum' : momentum_rank.top(NUM_LONG_POSITIONS),\n", + " 'lowmomentum' : momentum_rank.bottom(NUM_SHORT_POSITIONS),\n", + " 'highvol' : volatility_rank.top(NUM_LONG_POSITIONS),\n", + " 'lowvol' : volatility_rank.bottom(NUM_SHORT_POSITIONS)\n", + " },\n", + " screen = universe)\n", + "\n", + "results = run_pipeline(pipe, '2016-01-01', '2017-01-01')\n", + "results.head() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have our data, we can begin working on the due diligence. Before assigning them their respective weights, we should check the mean returns and volume of each factor. This gives us better insight into how each factor performs with respect to the other, which in turn allows us to assign each factor a certain weight **without compromising the homogeneity of the algorithm.**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Momentum return: -0.00416878739144 || Volatility: 0.0124160894965\n", + "Value return: 0.00255983659939 || Volatility: 0.0398508395849\n", + "Volatility return: -0.000791575314159 || Volatility: 0.00557122197776\n" + ] + } + ], + "source": [ + "# ___ Calculating Returns ___ #\n", + "momentum_returns = results[results.highmomentum]['returns'].groupby(level=0).mean()- \\\n", + " results[results.lowmomentum]['returns'].groupby(level=0).mean()\n", + " \n", + "value_returns = results[results.highval]['returns'].groupby(level=0).mean()- \\\n", + " -results[results.lowval]['returns'].groupby(level=0).fillna(0).mean()\n", + " \n", + "value_returns = value_returns.reindex(momentum_returns.index, fill_value=0)\n", + "\n", + "volatility_returns = results[results.highvol]['returns'].groupby(level=0).mean()- \\\n", + " results[results.lowvol]['returns'].groupby(level=0).mean()\n", + " \n", + "print 'Momentum return: ', momentum_returns.mean(), '||', 'Volatility: ', momentum_returns.std()\n", + "print 'Value return: ', value_returns.mean(),'||' ,' Volatility: ', value_returns.std()\n", + "print 'Volatility return: ', volatility_returns.mean(), '||',' Volatility: ', volatility_returns.std()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have our momentum, value and volatility factor returns and volatility, we can assign them respective weights. As a simple example, we will assign factors with greatest return (relative to 0) to volatility ratio the lowest weights, and those with smallest ratio, greatest weights. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Momentum Ratio: -0.335756873581 || Value Ratio: 0.0642354496431 || Volatility Ratio: -0.142082889053\n" + ] + } + ], + "source": [ + "# Ratios\n", + "momentum_ratio = momentum_returns.mean() / momentum_returns.std()\n", + "value_ratio = value_returns.mean() / value_returns.std()\n", + "volatility_ratio = volatility_returns.mean() / volatility_returns.std()\n", + "print 'Momentum Ratio:', momentum_ratio, '|| Value Ratio:', value_ratio, \\\n", + "'|| Volatility Ratio:', volatility_ratio" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From these ratios, we determine that the Value Factor should get greatest weight, followed by the Volatility Ratio, then the Momentum Ratio. Arbitrarily, we chose to assign 80% of weights to the Value Factor, 15% to the Volatility Factor, and 5% to the Momentum Factor." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Portfolio return: 0.00102676351372 | Volatility: 9.07341197679e-05\n" + ] + } + ], + "source": [ + "factor_returns = np.array([value_returns.mean(), momentum_returns.mean(), volatility_returns.mean()])\n", + "\n", + "factor_portfolio_weights = np.array([0.80, 0.15, 0.5])\n", + "factor_portfolio_return = np.sum(factor_returns * factor_portfolio_weights)\n", + "factor_portfolio_covariance = np.cov([momentum_returns.values, \n", + " value_returns.values, \n", + " volatility_returns.values])\n", + "factor_portfolio_volatility = factor_portfolio_weights.dot(\n", + " factor_portfolio_covariance.dot(factor_portfolio_weights.T)\n", + " )\n", + "\n", + "print \"Portfolio return: \", factor_portfolio_return, '|', ' Volatility: ', factor_portfolio_volatility" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Note these weights were chosen pseudo-randomly, given a more rigorous study, we would try to optimize these values to maximize returns and minimize volatility.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Normalizing Factor Values.\n", + "\n", + "When aggregating factors together, we need to consider their individual scales. Certain calculations of factors may simply yield larger values and if we want to combine many different factors that work on different scales, it helps to normalize them so that they don't unduly influence our composite factor. Bellow we show an example of a calculation that is far overweighed by one factor." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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kgG/8eKCoyPj9S3bcBDjQY0zGONBjdkWzjp6xrpuc0WOMMcdUMqPn6K5do62X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\n4sMfu86Rd6XUwtHXT1rWFSqrNE26T4WKSg3//mYPlVVaHrgtjra+Hs1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ETmggdFOr1ZJ8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Here's an example of a calculation that is far overweighed by one factor #\n", + "fig, ax1 = plt.subplots()\n", + "\n", + "value_factor = results['Value']\n", + "value_factor_daily_mean = value_factor.groupby(level=0).mean()\n", + "\n", + "momentum_factor = results['Momentum']\n", + "momentum_factor_daily_mean = momentum_factor.groupby(level=0).mean()\n", + "\n", + "ax1.plot(value_factor_daily_mean)\n", + "ax1.set_xlabel('Time')\n", + "ax1.set_ylabel('Value Factor Daily Mean')\n", + "ax1.legend('Value Factor Daily Mean')\n", + "\n", + "ax2 = ax1.twinx()\n", + "ax2.plot(momentum_factor_daily_mean, color = 'red')\n", + "ax2.set_ylabel('Momentum Factor Returns')\n", + "ax2.legend('Momentum Factor Returns')\n", + "\n", + "plt.title('Graph of Momentum and Value Factors Daily Mean.')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can clearly see the scale discrepancies in this graph, the Momentum Factor returns are multiple orders of magnitude greater than those of the Value Factor. Assigning weights to these two factors based on the available information would give the momentum factor an unjustified advantage. We can compute the ratio of daily means obtained by the value factor and the means factor. Doing this we clearly see the unwanted discrepancy between the two factors: " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ratio of Value to Momentum factors: 0.00886477539166\n" + ] + } + ], + "source": [ + "# ___ Ratio of Value to Momentum ___ #\n", + "print \"Ratio of Value to Momentum factors: \", \\\n", + " (value_factor_daily_mean / momentum_factor_daily_mean).mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "If we wanted to combine these factors by adding their values, the momentum factor would entirely dominate the value factor, giving us a portfolio that is mostly built on momentum rather than built on both momentum and value equally. To remedy this, we normalize the factors, using the standard normalization function, defined as \n", + "\n", + "$$ \n", + "F_{normalized} = \\frac{X - \\mu}{\\sigma}\n", + "$$\n", + "\n", + "where $\\mu$ and $\\sigma$ are the mean and standard deviation respectively. *An alternative way of normalizing the data would be to use the `.zscore()` function from the backtester, we use this method later on in the lecture.*" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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d/yaNToVEwkEdJ++1oOpoiQ52pwdKhRReLwuYC4sQQggZLIROeHGxMijkUkgk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\n3HXXXZg3bx42btwIwPX+uf/++zF37lw88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kuXLkV0dDT279+PVatWoaCgAEeOHMHq1aux\ne/duPPzww3jiiSewevVqJCYm4sEHH2yz/Zd5fj9MnDgRPXv2xLRp0/DUU09h3759mDdvHvbt24f3\n3nsvIN+TsbGxKCwsRJ8+fRo1fkk0pqCQiNpEWloaVqxYUedqGQHffPMN9u7dW++6bkQdXXl5OY4c\nOYIpU6YgPz8fCxYsqHcOVkeVnZ2Nhx9+GLt3727voRAR+cRMFVE7qq+pRDj7y1/+gkOHDmH16tXt\nPRSidhUVFYV//etfWL9+PYQQAWsuE4z4eUlEwYyZKiIiIiIiohZgowoiIiIiIqIWYFBFRERERETU\nAgyqiIiIiIiIWoBBFRERERERUQswqCIiIiIiImqB/w9RsZMD2orAVQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(momentum_factor_daily_mean + value_factor_daily_mean)\n", + "plt.ylabel('Daily Means')\n", + "plt.title('Graph of aggregated momentum and value factor returns');" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Scalling Momentum and Value factors\n", + "normalized_momentum = (momentum_factor_daily_mean - momentum_factor_daily_mean.mean())\\\n", + " / momentum_factor_daily_mean.std()\n", + " \n", + "normalized_value = (value_factor_daily_mean - value_factor_daily_mean.mean())\\\n", + " / value_factor_daily_mean.std()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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A797SxNgRtWrJF0lBgWvHzsmRY1aoIF8+rhTxXm3IWGXnTtfOEQLoES8nDZQp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\n7/L+ziNe06Z5PDh7jBs3Djt27IDBYMCbb76Jtm3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2Yt2kVMNOSusPq385h4NJZOl+5FwO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\n0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Nmz4eHhgdDQUMHHGxZeS5cC//oXMH58a5vrSZNMGS/D2uGFlyUjXs3XcsjKstw1rY3r\n1yndiKUcmQWpRMzs5BnWCUs1ZPQCVM0LW32lxqstDjIpXntkDNZtT8WpSwX45MdzEIvPIzpUgRHR\nvhge7YOIADdU1zXh298vwdFeinefiYeHa+v+k01KNWoblHCUSWEvk7SLnGXmV2HzwWsorWyAh5sD\njp3PxVtfJWLuaDdkVacj9UYpKmsaUV3XhKpaJWrrm9oZZDjIJJgxJhSxEZ44lVqA6romLJo2oNtS\nJHsrTU1NkMlkuPfee1vuGzduHEpLSzFw4EDB5zEsvL7/nhwNd+/W3icSAUeOGDVgho0gkQAODj0i\nvOyzsqiOsBfatBokMxOIju7pUfQaWKohw2ppm2ooFjPTKobNoW7+fJX20YgXAAR4OePVh8egoLQW\nB89kI+lKEdIyynDpZhk2/JEGd2d7uDnLUFOvxGN3DmonugCKYMk6EUCh/q54YYm2BGP84AC8991p\n/HKsDAAt3EjEIrjIZXB3kSHY1xkuchlc5DLYScXQcBzOpBVix9Eb2HGUsmncne1x+4Rw874YfYCJ\nEydi9uzZWLRoEWJiYgAAvr6+8PX1Neo8hoVXcTFLfepryOWWTTVsvpakvh7IywMCAy13bWugsZFW\nv5mltNmQSpnwYlgpbSNeCgWJLwbDhlBp+IgXe+/6eTrh3lujce+t0aiqbULy1WIkXSnE2ctFyCyo\nRr8gN9w+3jxCZ9xgfyx/YBS27L+AaWOjMGKgLxQu9p3WmKnUGpxIyUNhWR36B7kjMkTRJ1ICzc22\nbduwbds2vPjii5DJZFi4cCHmzp0LVyMXzgwLr0mTKA2KFf33HZyceiTiBQC4erXvCa9q6mPBVr3N\nh51EhLoGdU8Pg8FoT1s7eZZmyLBB+IiXpI+mGurD1UmGicMCMXFYIDiOQ3ZhNTzcHM1aCzdmkD+k\njXkYMUJYXZFUIsakYeat5+qL+Pn54YknnsATTzyBlJQUbN26FXPmzMGoUaOwcOFCjBs3TtB5DL8T\n9uyhFKiAACAkBAgOpi2j99KTwuvKFctd11qoai6uZcLLbLBUQ4bVwke8+FRDZqzBsEH4z1cpi9bq\nRSQSIcTPtUPDDYZtM2TIELz++uvYunUrAODhhx8WfKzhiNfvv5s8MIaNIpcD2cXFcyYAACAASURB\nVNmWu17biFdfgxdeLu3tWRmmIZGIW4q/GQyrgo94lZZSmjETXgwbhHeNZREvRl9Do9Hg6NGj+O23\n33D+/HnMmjUL27ZtE3y8YeEVFAT88ANw+jSZHowdS329GL0XJydajbWU0UVfj3ixVEOzY8ciXgxr\nhY945ebSlqUaMmwQlYaZazD6FleuXMGWLVvw+++/IyoqCnfddRc+/PBD2NkZF9E0LLz+9jegqAiY\nMoUm4r/8AiQmAv/5j4lDZ1g9cjn9rRsatJOE7oRFvGjLhJfZ4M01OI7rtOiYwbA4fMSLF14s4sWw\nQVQqvsaLCS9G3+Cpp57CXXfdhV9++QX+/v4mn8ew8Lp4ETh8WHv7L38BJk40+YIMG8DJiba1tRYV\nXipXV0hv3gSamgBZ1zqz2xRMeJkdqUQEjkNzg08mvBhWRNuIFxNeDBtEzbsaitnnK6NvsH//frMs\n5BpeqmhqAjQ6KTtqNaBSdfnCDCtGV3hZgubr1EVH0/urr7UvYDVeZodPf2FNlBlWBy+8ampoy4QX\nwwZRqVnEi9G3MFf2jOGI1+23A6NGAZMn0+2DB4F77jHLxRlWiq7dsSXghVdUFFxPnaI6r97YTJjj\n6LlFRrbu28NqvMwOL7zUzGCDYW1IpYCdHaBU0m1W48WwQfjPVinLKGAwjMKw8Hr1VWD6dODkSTJa\n+OILYPRoCwyN0WP0VMQrKopu99Y6r2++AR5+GBgxgmokJ0yg+1mqodnhhRcz2GBYJY6OWuHFIl4M\nG4RFvBh9jby8vE4fDwgIEHQe/cIrK0v3bMD8+a0fY728ei+WFl51dYBIhPrISLrdW50N//c/2iYl\nAfHxwPbtwB13MOHVDTDhxbBq5HLt/z0TXgwbpCXixWq8GH2Ee++9FyKRCBzHoaioCM7OzlCr1air\nq0NISAj27Nkj6Dz6hVdYGBAVBfDOHZxOyo5IBBw40IXhM6yankg1lMvRGBhIt3VFf2/h0iXg1Clg\n9mzgwQeBu+8m0xomvLoFqZQmA0oVE14MK0TXtIilGjJsEN5OnkW8GH2Fw81Gg2+//Tbmz5+PmJgY\nAEBycjJ27Ngh+Dz6/2M2bgT69aMGj3feSTbyBw/SDxNdvZueSDV0cgJnb0+1D7wQ6U2sX0/bBx8E\nhg+n30tKaMvXeDFzDbPBIl4Mq4Zf3AJYxIthk7AaL0Zf5dKlSy2iCwDi4uKQnp4u+Hj9Ea8lS+in\nuBj48Udg7lxambv/fko7tITNOKNn6AnhxU9EXF21QqS3oFIBGzbQ/8/cudQfDQBKS2nLIl7/z959\nh0dZ5e0Dv6elkYT0hFAChho6oUpHKSqKgkFAUFlF2V1RYVeF17Uu+/K6/ty1rW1trAZxQbEgViAg\nJZTQSyABQkIq6b3MzPn9cfJMEkgyk2SSaffnurgmTOaZOc+QCXPP95zvsTqdKXixuQbZIVa8yMEp\nH2pxA2VyNWq1Gq+++iqio6OhUqlw9OhRVFVVWX682VsEB8tNlPfvB558Eli7tm76ITknW0w1VMKe\nr6/zVbx+/BHIzpYfZLi7y3PUahsGL51Ofo+sQsOKF9kz5Xeslxdf9+SQlIoXpxqSq3nttdegVqux\nceNGfP7556ipqcFrr71m8fHmuxqWlwNffgmsXw8UFAAPPijfQJLzstFUQwByul1KSsc8bkf55BN5\nuXSpvFSp5PSi+sHL11deT1ahTH9h8CK7pFS8OM2QHJSyxotTDcnVBAYG4oEHHsCVK1cwePBgGI1G\nqNWWfwDRdPD69VfgP/+RDQHmzAFeew0YNMgaYyZ7p4Sgjqh46fVyk+76Fa+SEtnMxRmCSG6u7F44\neHDd2i4ACAyUVTBAni/Xd1mVVltb8WJzDbJHDF7k4AxKO/kWvOEkcgZbt27FG2+8ATc3N2zduhV/\n/etfERUVhZiYGIuOb/oVM2MGcOgQMGYMkJMD/OMfcg8i5Q85L2UaTEdUvJTHqB+8hOi4alt727BB\n7tfzwAMNg2RQkKwgGwx1FS+yGh2nGpI9U37HMniRg9KzuQa5qI8//hjffPMN/GvX5z799NP473//\na/HxTVe8du5s8+DIQXXkVMNrg5dS+SkuBry92//x29snn8j1XIsXN7w+MFAGzIICBq92oGVzDbJn\nSsWLjTXIQRm4gTK5KB8fH3jWa5Dk4eEBnU5n8fFNB6/Jk9s0MHJgHdlco7GKF+AcnQ2PHweOHpWd\nDENCGn4vMFBepqbKAMbgZVWmqYaseJE9YsWLHJzeyIoXuSZ/f39s2bIFVVVVOH36NLZt24aAFvwu\n50cVdL2OrHgp4e7a4OUMnQ2vbapRnxK8Ll2SlwxeVqVVs7kG2TGu8SIHx4oXuaoXX3wRJ0+eRFlZ\nGf7yl7+gqqoKa9eutfh4810NyfXYy1RDR1ZdLTchDwoCbr31+u9fG7zYXMOq2FyD7JpS8eJUQ3JQ\npjVebK5BLsbX1xfPPfdcg+tSUlLg5+dn0fFNv2JSU5v/Q85LeVNQWtr+j6UEr/obKAOOP9Vw2zbZ\n0XDxYsDN7frvBwXJS6V1PiteVqVlcw2yZ6x4kYPTmypenGpIjmXdunVYsGABFi5ciJMnT7b4+KFD\nh2Lz5s0Nrrs2iDWn6YrX+PGyC5sQQEaGfGNoMMg345GRQFJSiwdLDkKjkZ/EKvtMtSdnrXh9/LG8\nbGyaIcCphu1MCV41bK5B9kh5/YeH23YcRK2kBC8tpxqSAzl06BAuX76MjRs34sKFC3jmmWewcePG\nFt1Hnz59sGfPHhw/fhzPP/88tFothLD8vUbTr5i0NFnZmjcPOHy4rvva/v3ALbe0aJDkgEJC5DYC\n7c0Zm2tkZwPffw8MHw4MGdL4bZQ3XhcvyksGL6viVEOyawsXAlu2ALNm2XokRK1iqP1QixUvciT7\n9+/HzTffDACIjIxEcXExylq4rMbLywuvvfYaevbsicWLFyMrKwuqFuw7a/6jiiNH5BtIxZgxwJkz\nLRokOaDgYDlVzmBo38dpKng5csUrNlY+b01Vu4C64KVMNeQaL6tSmmsYjAxeZIe8vIA77wRa0IKY\nyJ7oa3+3co0XOZLc3NwGHQj9/f2Rm5vbovtQqlsPPvggnnjiCTz00ENIS0uz+HiVMFcfmzIFGDcO\nmDABUKuBffvkHl979rRooG2RkJDQYY9FRI4vKaMSsXG5uGmoLyYOZDWRiMiavj9UgENJZVh+SwjC\n/BtZx0xkQ9HR0Y1e/9xzz2HKlCmYNm0aAGDRokVYt24dIiIiLL7vHTt2mI4HgOzsbLz99tt48cUX\nLTrefFfD//4XeP114L335HqvqCh5XQdr6kl0RgkJCbY/39//Hnj3XeDUKWDgwPZ7nLVrgWefBX76\nCQmBgYh2c5PT8/74R+Ctt9rvcdtLQgIwcqSconvN4ssG9PqGn3b/+CMwc2btXdjBv78NWeP8tb5X\ngbhchIaFIzq6n5VG1vH4s+Da56/g81DH1Z8Lezn//RePAUllGDJ4ELqHdvyMDXt5HmzF1c+/vmuf\ni+aKNSEhIQ0qXDk5OQgODrbocXbt2oXJkycjPz//uuYagwcPtni85oNXSAiwapVsAjByJGA0ysoX\nOTdlw9+cnNYHr507ZYOWKVOavo2zTTU011RDodUCfn5AYaH8O9d4WRW7GhIRtR9ljReba5AjGT9+\nPN566y3Mnz8fp0+fRmhoKLyUrtpmnDt3DpMnT24y2N19990W3Y/54PX558BzzwHu7rL6sWIFMGIE\n8OCDFj0AOaj6wau17rtPdkhU1jE1pn7wMhgcu6thZSWwYQMQFmaqXjUrMJDBq53o2FyDiKjdKGu8\n2FyDHMnw4cMxcOBALFiwABqNpkVt4B9++GEAsh19W5gPXv/4B3D8OHDbbfLv/+//yQoGg5dzU4LX\n1autO95oBDIz5dcGgwxgjSkvl5edOsmwpQQvR+xq+N13svvnk0/KipY5gYHAhQvyazbXsCpTxYvN\nNYiIrI4VL3JUq1atatVxkydPbrZ7YVxcnEX3Y/7dYefOdZvbAnLjx8Y2hCXn0taKV2FhXUfEnByg\nS5fGb1e/4lVcLNc9eXiYr3jV1Mi1hnfd1fDn05aUaYYPPGDZ7ZXOhgArXlamfArLihcRkfWZNlBW\ns+JFrmHDhg1Nfq+4BbO0zH9UERQErF8PVFTI1vJPPy1bjZNza2nwMhqB//kf4OjR649LT2/6OCV4\n1Q9Pvr7mg9cbbwCLF8upsPZi505g0CDZgMYSQUF1X7PiZVU60xovbqBMRGRtrHiRq+natavpT0VF\nBTIyMpCRkYGUlJQWVdHMV7zefRf4y1/k1K+HHgImTgQ++KAtYydHoIRrS4PXkSPAunVARgbwyScN\nj8vIaPq4a5trADJ4NTfV0GgE3nlHft3aqZDWVlEh13h17Wr5MUrFq1OnpqdiUquwuQYRUfvhGi9y\nVWvXrsXevXuRm5uLHj16IDU1Fb/73e8sPt588PLzu76td1ISUG8DMnJCAQGye6WlwSs1VV4qIat+\nIDJX8dLpGrZW9/GpWx/WmF9/rVsbZS9rwfLz5WVLXhdK8GK1y+q0bK5BRNRuDLUfarHiRa7m1KlT\n+OGHH7BkyRJ8+umnOHXqFH788UeLjzf/ivHyAj76qOF1jzzS0nGSo1GrZdXL0uCl7NqtBKaWTDWs\nX+0CZMWrrKxujdi1lGoXYD/Bq6BAXvr7W36MEry4vsvqlDcDNax4ERFZnTKNm2u8yNVoamco1dTU\nQAiBQYMG4dixYxYfb77iNWgQ8NNPQHw88Pbbslub4LoJlxASUlfJMkcJXkrFqyVTDRsLXgBQWiqb\nu9R35Qrw7bdAaCiQnW0/bedbE7yUNV4MXlanrZ3+YuAaLyIiqzMYjNCoVc12eSNyRpGRkfjss88w\ncuRILF26FL169UJpaanFx5uveHl7A198AfTtC0yaJN/48oXmGkJCgKIioLra/G2VgJafD1RVta3i\n1dxeXv/+t1zj9cQT8u/2UvFqy1RDBi+r4xovIqL2ozcKaDjNkFzQiy++iNtvvx2rVq3CvHnzEBER\ngXfffdfi482/apTq1p//DPztb8CsWXXra8i5tWQvL6XiBcjphsoxanXrphoC14eqmhoZvDp3Bmo3\nsrOb4MWphnZFw+BFRNRu9HqjaWYBkStRqVTIzMzEkSNHEBYWhoEDByKt/ntgM8xPNazfInHqVDnt\ncO3a1oyVHE39lvLmuvVdG7yUilfv3k1PNRRCbqBsacXr22/lfa9YIStLOp1jB68ePeS59+7dPmNy\nYRq1Cmq1CjVsrkFEZHUGoxEaNSte5HqWL1+OpKQkhIaGmq5TqVSIjY216Pimg9cPPwC33CIrF9c2\n1xg1qlWDJQdj6V5een3DLoQZGfKYwEAgIgI4f162W/f0bHhcZaUMX01VvK4NXkpTjeXL5aWPj/2s\n8WrNVEN/f+DcOXYIbSdajZoVLyKidqA3CFa8yCVdvXoV27dvb/XxTQevEydk8Prtt+u/p1IBLehZ\nTw7K0r28MjLkuisPDxmmMjJkYA8OrquUZWQAkZENj2tsDy+g8amG588D27cDkyfXbVDs4+PYFS+g\nZft+UYvoNCo21yAiagcGg9G0bQeRKxk4cCCuXLmCbt26ter4poPX00/Ly48/btUdkxOwtOKlTDOM\njgb27pV/z8uTASk8XH4vPb3p4OXl1fD6xqYaKgsXf//7uut8fRtOcbSl1gYvajcajZrt5ImI2oHe\nIOCu09h6GEQdLioqCrNmzUJQUBA0Gg2EEFCpVBZXwZoOXt27N9+90NI24+S4Whq8Ro+WwevUKTmF\nMCSkrqLTWIMNcxUvJXhVVACffCJbyN91V93tlKmGQti+06Yy1ZDBy25wqiERUfswGI3QaMy3CSBy\nNh988AE++ugjhIWFter4pl81e/Y0fZTy6T45t5YGrzFj5OXx4/Ly2qmG17J0quEXX8ifuf/5H8DN\nre52Pj5yimNFxfVVs45WUCDHZutxkIlWy+BFRNQe5BovTjUk19OvXz+MHj261cc3HbwiIuq+PnMG\nyM2VX1dVAY89Bpw92+oHJQdhaTt5pfrZt69s9a6ErJCQhlMNr9VU8Lp2quE778iKltJCXlE/oNk6\n8BQUyGqXrStvZKLTqFBeabD1MIiInI7BYISGzTXIBQUFBWHJkiUYPnw4NJq66baPP/64RcebrxM/\n/jjw889AVpZse52cLPf0Iufn7S0bZlha8erRQwatoiL59+amGgoBJCbKr5ubanjkCHDwIDB7dsMP\nA4C6gFZSIqch2lJ+PhAUZNsxUAOcakhE1D70BgEt28mTCwoODkaw0nyuFcwHr0OHZHVr6lRg504g\nIQHYtKnVD0gORKWS4Skrq/nbpaXJVvEBATJ4KdXQ4GAZiNTquipYURHw2WeyWcapU/K6Xr0a3l/9\nQPXWW/Lr+k01rr2drVvKCyErXn372nYc1ICcasiuhkRE1ibXeLHiRa4nNDQUMTExrT7e/McV2tps\nVlUl32BGRwP797f6AcnB9Owpq1VVVU3fJi2trhlLly5114eEABoNEBYGJCUBy5bJYPboo7LaNX8+\nsGMHcO0PsFLx2rdPNtUYMACYOfP6x60f0Dpabi4wZIjc7660FDAY2FjDzmjVrHgREVmbwSggBLjG\ni1zS9u3bUdKG953mK14DBsiqw6RJwPTpQL9+tq8wUMfp0wfYvRu4dAno3//671dWyjVgQ4bIvytr\nuoC6NWJdu8rK6QcfyCD38MPA0qUykDXG21tepqTIy/fflwHuWo3t99VREhKAkyeBL7+s21eMwcuu\nKM01lFavRETUdobaD7Q0av5eJddTWVmJadOmoVevXtDpdKbrY2NjLTrefPB6912gsFA2Tdi4EcjO\nBtasafWAycH07i0vk5IaD15XrsjL7t3lZf3gpcyBXbUK2LoVuPdeYMaMxkNUfRqNXPdVVgYsXw5M\nmND47WxZ8VI6eyYn130dENDx46AmaTUqCAEYjYJTYoiIrESZSaBhxYtc0B/+8Ic2HW8+eKlUcirZ\nqVNAt27yz8WL8pKcnxK8kpMb//6FC/JSCV7KVEO1ui6ILFgg/7REz55yPdj//V/Tt7HlGi9l366k\nJO7hZaeUaTA1BiPfIBARWYnBKNfOavmBFrmg0aNH4/Dhwzh58iRUKhWGDh2K4cOHW3y8+eB1xx0y\ndCnd6QAZxnbvbs14YTAY8MwzzyA1NRVGoxFPPfUURowY0ar7og7Qp4+8bCx4CQGsXSu/njJFXioV\nr+BgGb5a69df5fGdOzd9G3uoeGVk1HVsZPCyK0rwMrDBBhGR1bDiRa7s9ddfx969exEdHQ0AWLt2\nLWbMmIFHHnnEouPNB6/MTFnhspJvvvkGHh4e2LBhA5KTk7FmzRpsYpdE+xUZKS+Tkq7/Xmys3Gj7\nrruAadPkdUrFqw2tNgE0vf6rvvZY43XmjOzQeG2nxWspVS5Arl8DONXQzijBiw02iIisR/kwi+3k\nyRUdOHAAGzduhLr251+v12Px4sVWDF7R0bLJQc+ebRhmnTvuuAO33XYbACAgIABFyp5PZJ+8vWUI\nurbiVVwMPPmkDCn//Gfd9eHhgLu73NOrvVm74iWErNz16AEcPtz8bZWKFyD3GQNY8bIzDF5ERNan\n/E7VajnVkFyP0Wg0hS4A0Gq1LWrgZT54DR8u9ycKC5Ot5YWQUw1bWQXTarXQ1raoX79+PWbPnt2q\n+6EO1KcPsHcvUF0NuLnJ6154Qe7v9de/NtzY2NMT+OWXhk022ou113hduSI7NBYUNDzXxtQPXseO\nyUsGL7uivCmo0TN4ERFZiyl4caohuaBBgwZh+fLluPHGGwEA+/btw+DBgy0+XiWEaH4BRGQk8NFH\n1zfTUKagNWPTpk3YvHkzVCqVqaXzihUrMH78eMTGxiIuLg7vvvsuNGa63CUkJJh9LGo/ES++iKDv\nvsOpzZtR1bMnPJKTEXXvvajq0gVnvvgCwt3dJuPS5uZi6KxZyJ8+HZfWrWvz/fnEx6Pvo48CAE5v\n3IhKpbFII/o+/DB8jhxpcN3pTZtQaW6KInWY7w4WICG5DI/ODkWQr878AUREZFZ2YQ3e2ZaNUX06\n4bZR/MCR7I+y/qo9GI1GbNu2DSdOnDA117jlllssrnqZr3gNGQJMntyqwcXExDS6u/OmTZsQFxeH\nt99+22zoUrTnk2hvEhIS7Ot8x44FvvsOgzw8gBEjgD/9CTAY4PHeexhRm/itocXnXVYGAAjQahFg\njedr717TlwOVzcKbUlMDeHkBFRWyCgxg4IQJlq1NM8Pu/v07mLXO//DlE0DyJfTrH4WeXXytMLKO\nx58F1z5/BZ+HOq7+XNjD+V+4Ughsy0aXsFBER1v+Sb812cPzYEuufv71XftctHexRq1WY/bs2a2e\nsWc+eIWFAVOnAuPGyamGipdeatUDpqWl4YsvvkBsbGyDjcfIjimdDZOS5F5uu3bJbpe33mrbcXl5\nyc6H1lrjdfZs3denTjV/2/x8IDQUMBiA1FR5Haca2hWtlmu8iIisTWknz66G5EqmTZvWaFWruroa\nubm5OFv/PWQzLAteVvgUX7F582YUFRVh2bJlpumHH330kWndF9khZcrd0aOyzbuHB/Daa7YdEyDX\nGnp7W2+NV0uCV0EBMGCAbHefmirXttloyiU1TqOWvyAZvIiIrKdujReba5Dr2LFjx3XX/frrr3j1\n1Vcxb948i+/HfNrp1g148MEWDa45K1euxMqVK612f9QBlOD16adyWt0LL5hvt95RfH2tV/FKTJTn\nVVoKnDzZ9O2qqoDyclnhiowEduxgK3k7ZKp4sbkGEZHVKO3kNWwnTy4qJSUFa9euhU6nw/vvv4/u\n3btbfKz5V83XXwNs+e7afHzktDohZDB56ilbj6iOj491gldBAZCdLatYgwbJrp21a8gavS0gg5cS\nSjnN0O7o2E6eiMjqWPEiV1VeXo5XXnkFjz76KJYsWYJ33nmnRaELsCR4VVTIPbzGjgUmTar7Q65F\nWef1+utyWp29sFbwUqYZKsELkJspN0YJXgEBdc8Lg5fdqdvHq/nGrUREZDmu8SJXtHXrVsydOxed\nO3fGli1bMLmVjQfNTzV89tlW3TE5mb//XU6/u/12W4+kIR8fOfXP3L5b5tQPXgaD/PrUKWDUqOtv\nm58vL1nxsmtsrkFEZH2seJEr+vOf/4yePXvit99+w549e0zXK/0q/vOf/1h0P+aD1+TJwG+/AYcO\nyWYGY8fKDofkWsaNs89/d9/aNuElJUBgYOvvRwle/fvLTolA0w026le8+vcHli2zv0BK0LK5BhGR\n1XGNF7mi7du3W+V+zAev554Dfv4ZmDhRrvF57DFg7lxgzRqrDICoTXx85GVbg1diorwcMKBu2wRz\nwcvfH9BogPffb/3jUrthcw0iIutjxYtcUdeuXa1yP+aD186dwL59dVUAvV6u8WLwIntQP3i1xdmz\nQEhIXXfCHj2AY8fktMNrN/muP9WQ7JayxquGwYuIyGoMRvk7lWu8iFrO/KvGaKwLXYCsBrC8TPZC\nmWrYlr28KiuBS5dktUsxcyaQkwN89dX1t68/1ZDslr+PBwAgr7jSxiMhInIeSsMiVryIWs58goqO\nBu64A3jjDfnn9tsbbzhAZAvWqHjl5spptOHhddc9+aT8gGHdOvm9+ljxcgjhwZ0AAOk5pTYeCRGR\n8zCYphryQ3iiljL/qnntNWDhQlkRSEkBliwB/vGP9h8ZkSWsEbyUY5X7AmSb+JgY4OhR4KefGt6e\nFS+HEOzvBa1GjfSrDF5ERNaiVLw41ZCo5cyv8VKrZfBauLADhkPUQu0VvABg9Wrgiy+A//1fYNas\nuuvrN9cgu6VRq9AlqBMyrpaa2r0SEVHbKGu8lM6xRGS5poNXr16yffy1qqqArKy6vY6IbMkaa7xK\naysi1wavYcOAW28Ftm0D9uwBJkyQ1+fny4Yb3t6tf0zqEF2DOyEtuwRFpdXw83G39XCIiBye0rCI\nFS+ilmv6VXPpEnDxYsM///iHfKP7P//TgUMkakZ7VryAup/1devqrisokNMMWUGxe12DZTjmdEMi\nIuswGNlcg6i1zE81BICkJLl/l5sb8P33wA03tPOwiCykVLyU6X+t0VzwGj9e7mG3bZtsLz9smKx4\ncZqhQ6gfvAbe0IZ93oiICEDdPl6seBG1XPOvmrIy4OmngbvuksHrm28Yusi+9OsnK08JCa2/DyV4\nNTV1sH7VSwgZ8hi8HEJ4bfDKYMWLiMgqDEo7eW4tRNRiTb9qPv9ctpIPCJCd3W65pQOHRWShzp2B\noUOBAwfk+sPWaK7iBcg9vYYPBzZtklWvmhp2NHQQnGpIRGRddRUvTjUkaqmmg9e998rLH3+Ubzyn\nTZN/pk6Vl0T2YuJEGboOH27d8eaCl0oFrFkjq12rV8vrWPFyCJ293dDJQ4v0q2W2HgoRkVOoW+PF\nihdRSzW9xuvSpQ4cBlEbTJwIvPkm8Ntvck1WSzXV1bC+uXOBvn2Bn3+Wf2fFyyGoVCqEB3vjUkYx\nDEYBDdsfExG1CSteRK3XdPCKiOjAYRC1gdLmfc+e1h1vruIFyPbxq1cDv/ud/DsrXg6ja7A3ktIK\ncbWgHGGBnWw9HCIih2Za48WKF1GL8VVDjq9LFyAyEti7F6jd2LFFLAlegJx+262b/JrBy2F0DeE6\nLyIiazFVvDiDgKjFGLzIOUycCBQWAqdOtfxYc10NFW5ucq0XwO6eDqRrkPx3PX0xD0IIG4+GiMix\nseJF1Hp81ZBzmDhRXv72W8uPLSmRDTQ6WTAN7fe/B44fB26/veWPQzbRr6c/3HQabNqehKff2oPE\nlHxbD4mIyGHpjVzjRdRaDF7kHJR1Xrt3t/zY0lJZ7VJZ8J+ISgUMGQJw/xKHEeLvhddWTsbYQWE4\nm5KPJ9/8Df/7yUFk5bHTIRFRSxlqpxrqWPEiajG+asg59Okj13rFxcm2O9oM3wAAIABJREFU7y1R\nUmJ+fRc5tO6hPnhm6Ri8/OgE9I/wx/6TmXj2vX2mtQpERGQZfe1UQw2DF1GL8VVDzkGlkvvL5eQA\np0+37FgGL5cR1SsQf18xEbeM64msvHL8ejDV1kMiInIoSsVLy6mGRC3G4EXO46ab5OWOHS07rqTE\nfGMNchoqlQoLZ/SDm06DL345h+oag62HRETkMJQNlDWcck/UYnzVkPOYNk1etiR46fVARQUrXi7G\n39cDt43vhdyiSvwUf9nWwyFySj/Fp+Av7+7FB9+cwoFTmTAa2VXUGegNRqhVgJrt5IlajMGLnEdE\nhGzzHhcHGCysYpTVNlhg8HI586b2hoebBpu2n0dltd7WwyFyCEII5BVVmL3dVzuT8Nam4zielItv\ndl/A2o8P4pl391rc1KaySm/R41DHMxgE13cRtZLW1gMgsqpp04APPgCOHgVGjjR/e0s3Tyan09nb\nHXdMisR/fz2PH/al4K4pvW09JCK7VVpeja/ikvHbsXRk5ZXjwTsG4c7JkdfdLqegHFvikrF1zyUE\ndfbA88vGobS8Gt/svoD4U1n4/cs74NvJDSoV0LmTO4L8PBHk51F7Kf+cuZSHb3ZdQEl5DUZFhWLh\njH7o052b1tuLGoOR67uIWonBi5yLErx27GDwIrPumhyJ7/dcxOYdSZg5NgJeHjpbD4nILn235xI2\nbU+Ch5sGXh5a/GfbGUT3D0H3UB8IIXD6Yh627rmE/bVTCkMCvLD2kRvRJUjujzjwhkDsOpqOL3ck\noarGAKNRID23FBczihp9PG9PHfp098OhM9lIOJuNFx8eh2F9QzrylKkJBoOR67uIWonBi5zL1Kny\ncvt24KmnzN+ewculeXu5Yc7k3tjwUyK27rmE+Tf3tfWQiOzSpdqA9PZTNyEprQDr1h/C618cxcwx\nEfhuz0VcyigGANwQ3hm3T+yFicO7wV2nMR2vUqkwZUQ3TBnRzXSdEAJlFTW4WliB3No/Vwsr4NvJ\nHTPG9ICnuxaHz2Zj7ccH8a/Nx/Hmn6d27ElTo/QGAS2nGhK1CoMXOZewMKBvXyA+HjAazW90rAQv\ndjV0WXMm3YDvfruALXHJuG18L3TyZNWL6FopGcXw7eSGID8PBPuHY9Kwrth9LB3nLhdArVZh/NBw\n3D7hBkT1CoDKks3oIcOYt5cbvL3c0Cu8c6O3GRUVhjsnReKruGR8/tM5DAm35lnRtQpKKnHmUj56\nhPqga7B3ow00DEYjNJxqSNQqDF7kfEaNAmJjgaQkoF+/5m/LipfL8/LQYe7UPlj//Rl8s/sCFs3s\nb+shEdmViio9MvPKMKR3kClUPXzXYFTrDegW4oNbb+yFYH/Pdnv8hTP7Yd/JDHy9+wJCZ3G6oaVq\n9EYcPpsNN50aXYO9EezvBc01QSolsxgX0wsBAOcuF+DXg6mo1st9urw9degX4Y8BPQPQPyIAfXr4\nwctDBz2baxC1GoMXOR8leB0+bD54lZbKSwYvlzZ7fC98s+sCvt51AbMn3ADfTm62HhKR3bicJacR\n9gz3NV3X2dsdzywd0yGP7+Gmxb2zBuDV2AQkZVR2yGM6OqNR4LXPj2D3sXTTdVqNGl2COqFrcCeE\nB3njXGoBTl/Ma3BcSIAXbhrZHZl5ZUhMyUdCYg4SEnMAAGoV0CPMF0WlVQj2a7+gTeTMGLzI+Ywa\nJS8PHQLuvbf527LiRQA83LWYN60PPvz2FLbEJeP+26JsPSQiu5FSu36rVxdfM7dsP727yamIV4tq\nbDYGRyGEwIffnsLuY+noH+GP6AGhSM8pRfpV+Sctu8R02+F9gzFuSDi0ahV8Orlh5IDQBuu3Ckoq\nce5yARJT8pF4uQBJqQWo0Rvh7+thi1MjcngMXuR8hg0DNBpZ8TKHwYtq3XJjT2yJS8Y3uy/g5IVc\naDVqaNQquLtpsOSWAU2uQSFydimZtRWvLrZ7DXQJ7AStRoXcYvsPXnqDESkZxTAKAXc3DXqE+li8\n7s0a9hzLwLe/XUT3UB8899BY+HjVVfCFECgsrULG1TL4+7gjPLj59c3+Ph4YO6gLxg7qAkBOX7yc\nVcyKF1ErMXiR8/HyAgYOBI4cAfR6QNvMjzmba1Atd50GS2dH4e0vT+DClULoDcL0vS5BnbBszmAb\njo7IdlIyi6FWAd3DbPcBlUajRniwN7JySyGE6NAgY05JeTUKS6oQHtQJSWmFeOO/xxpUlYb1DcbD\ndw5G99COef7iT2UCAJ5aMrJB6AJkQxN/Hw/4+7SuYqXTqtG7m1+bx0jkqhi8yDmNHAmcOAGcOQMM\nGdL07VjxonqmRHfHlOjuAGo/GS6pwn0v/oSrBRU2HhmRbQghkJJRhPBg7wbt4W2he6gPUrNKkFdU\niSA7qLgYjALf772Iz344i4oqA9y0atQYjBACmDy8GwI7e+BiRhGOnb+Kx17diZtG9cC8qX1Me5vV\nZzQKZOWXAUJuc9HadaZCCJy8kIsAX3dE2DAoE1HjGLzIOY0aBXz0kZxu2FzwYnMNaoJKpYKfjzvc\ndBrkFJTbejhENpFbWImySj2G97Pd+i5F9xD5ezo1u6TdgldFlR75xZXoamYK3qWMIry16RjOpxbC\n21OHKdFdkJZdAp1GjaW3D0RUr0AAMggdOJ2Fj747jZ/iL+OXA5cxYWhX3H1TH9P05fLKGjz3/n6c\nu1wAQDbBeHf1TQgN8Grx+NOvlqKgpAqThne1q6ogEUkMXuSc6jfY+N3vmr4dK17UDJVKhWA/T+Tk\ns+JFriklU26cXL+joa30qJ2qdyW7BCP6Wb+tvBACL34QjzOX8vDMA6MxZlAX5BdXYvfRdEwe3hX+\nvh6oqjFg48/nsCUuGQajwOTh3fDQnEHw83Fv9D5VKhXGDuqCUVFh2Hc8A5t2nMfuY+nYfSwdIweE\n4s7Jkfj853M4d7kAQ3oHQaNW4ej5q9h/MhN3To5s8TmcvCC7FA6ODGrTc0FE7YPBi5zT4MGAm5v5\nBhsMXmRGiL8n0q+WoqJKD093/sok51ZUWoXUrBJczipGYUkVzqfKKkwvGzbWUHQLlVWo1Hrrp6wp\nITHH1F79ldgEPHTHIGz4KREFJVXY+Ms5zJkUiZ2H05CZV4YQf0/8ft5QjBwQatF9a9QqTBzeFROG\nhSMhMQebtp/H4bPZOHw2GwAwfkg4nlwcjaKyatz/4k84dCarVcHrVHIuAGBwbwYvInvEdxHknNzc\n5BTD48eBmhpAp2v8diUlgEolG3IQNSKkdrpPTkE5IsJs/6k/UVtdySnBZz8mwsNNg/AgbxSWVuFy\nZjFSs0tQWFJ13e01ahUiu9k+eHUN9oZKhQaNK6xFCIHPfjwLlQpYPGsAPvvxLP61+TjUahWmj+6B\nPcczsOGnRKhVwJ2TI3HvzP7waMUHMSqVCiMHhGLkgFCcvpiHLXHJ6OSpw6Mxw6DRqBHg64G+Pfxw\n+mIeSitq4O3ZxP9dTZyDsr4rvJF1ZERkewxe5LwGDZIVr4sXm95IuaREdjTkXHhqQoi/DF5XCyoY\nvMjhZeWV4S/v7kNe0fUbEYcGeGFUVCgiwnwREeaDID9P2QXP1x2BnW3fzMJNp4F/Jy3Sskus3tkw\n/lQmLlwpwqRhXTH/5r7w6eSGbXsvYdmdgzCkdzDundUfP+xPwdiBXdC7u3W6+g28IRADbwi87vrR\nUWE4n1qII4nZmDS8W6PHVlTpsf9kJjp7uyG6v6y6cX0Xkf1j8CLnNWCAvDxzpungVVrKaYbUrBB/\n+YaTDTbIUWXnl2PH4TQYjQJxR9KQV1SJpbOjMHpgGDJyy+Dn7Y7uoT4OMZU2uLMW59IrUVRa3eS6\nKksVlVYh/lQW9p3IwPGkq1CrgIUz5f8Vt4zriVvG9TTdNrCzJxbPGtCmx7PU6IFh+OzHRBw83TB4\nCSFwOacKezYexZ7j6aisNgAAHpozCHdMvAEHTmUB4PouIntm/79liVorKkpenj0L3HVX47cpKQH8\n/TtuTORwgmsrXjn5DF7NMRiM2HM8A2WVNdAbjHDXaeHtpUOwnyfCg71bNGWKrGvzjiT8uD/F9PcF\n0/th7tQ+AIBuIY71wVNQZx3OpVciLaekVcErv7gS+09mYt+JDJy6kAtj7XZ9vbt1xpzJve3i+ejZ\nxRdBfp44nJiN0vJqlFfqsevoFfx6MBUZuWUA5AdCt0/shu2HUvHBN6fww75LSL9aBq1GhWF9g218\nBkTUFAYvcl71K15NKSkBevTomPGQQ6o/1dARlJZX46u4ZNwyrheC/TtuetgP+1Pw3paTTX7fz9sd\nXUO8ER7UCd1CvBHk5wm1WgVvTx2G9A6GWs2pUe0lt1D+7L6wbCwCO3uiZxfHnTIb7Cvftnz2w1mE\nBHjBaBQQQu6DZRTCdNm5kztmjotA/4gAAHLPra92JiH2x0QYatNW/wh/3DgkHOMGd0FYoP2siVKp\nVBgzMAzf772Ehc/+YLreTavG4J5euGfWMAyODIJarcKMMRF47v39yMgtw5iBYZg3tY9dnQsRNcTg\nRc6rZ0/Aw6Pp4KXXAxUVnGpIzQro7AGNWmWaavj9notIzy1DRJgPIsJ80T3UB53spJojhMDrXxxF\n/KkslFfqsXxuM3vYWZHeYMSWuGS46TRYETMUOp0GVdUGlJZXI7ugHOk5pci4Woazl/JMXePqmzSs\nKx5fMBxuNt6g11kVllbBTavGiH4hDr/2p3uQG9RqFc5cyseZS/nN3vbXQ6no2cUXPUJ9cLWwAmdT\n8hHY2QNzp/bGjYPD7WIT5qbcMfEGFJZWoabGCI1GheH9QjBpWFcknjmBoX3qKlphgZ3wxqopKKus\nsYt1eETUPAYvcl4aDdC/P5CYCBiNgFrd8PvcPJksoFGrEOjniZyCcuQVVeDdRqo6QX6e6BHmg6he\nAZg7pQ90WvV1tzEYBZ56czfSskvg5+0BPx93+Pm4w9/HHf6+HvDzll+ra/fxOXspD2P76BBtZnwX\nrhTiclYxhvcNwf5TmYivXeex/2QmHr5zcKsqSUIIJCTmmColXQI7oV9Pf3i4Nf5fxp5j6cgpqMDs\n8b0wJbp7k/dbozcgK68cV3JKkV8k73vXUbmnUU5BOUYOCIVarcLEYV3NfmovhEBltQFCCGg1aoa2\nZhSWVMHPx93hQxcABPrq8J/nZ6KiSg+1SgW1uvZPg6+B5CuF+Hb3RRw6m42UzGIAwNhBYVgxfzh8\nO7nZ+CzMCw/2xur7Rll0Ww93bas6LBJRx+MrlZzbgAHAsWPA5ctAr14Nv6fs4eXt3fHjIocS6u+F\nkxdysftoOgBg7pTe6BbijdTsElMb7iOJOTiSmIPKKgPuvy3quvs4fv4qzqcWws/HHVU1epxLLYdR\nWWDShIyrasyYXAl/H48G11fXGLDneAa27buEc5flPktqlZyi5OOlQ58e/jiSmIOktAL0q51q1RJb\n4pLx8daGlWKtRo1+Ef4YHBmEwb0D0T8iAG46DYQQ+HJnMtRqFeaY2XdIp9Wge6gPuofWfdgxfUwE\nXtt4FL8dS0di7bls2p6ER2OGwsMokJFbiqy8cmTnlSEzrxxZeWXIzitHZl4ZKqr0tWNTYe3y8Y12\niHN1QggUllThhq6OO73wWp293dHZu/n1XUN6B2NI72AYDEYUlFShslpf247e8cMnETkuBi9ybvUb\nbFwbvFjxIgspa6W27rkIALhj0g3XTespLKnCk2/uxlc7kzAqKhRRvRqGgO2HUgEAzywdjf4RATAY\nBUrKqlFYWoWC4koUlFShsKQSFVUGDLwhABfTi/Hx1tN444tjeO7BMVCpVMjKK8OP+1Pw84FUlJRX\nQ6UCRg4IRVSvABw4lYUL6UV4YuEIGI0CRxJzsP9kZpPB63JmMTbvTMKx81chhIC7mxYzx0QgLNAL\nn3x/BoGdPXD/bVEQAkjJLMbJ5KumqYIbfwF0WjUiwnwgIL8/abj5KlVj3HQa/PneaMyZdAMqqvTI\nyC3DJ1tP45XPEqBSAUKkX3eMu5sGYQFeCPTzRFFpFS5cKUJqVjGDVyPKKmSzk2vDu6vQaNR2PaWQ\niFwLgxc5t/oNNm69teH3lIoXgxeZoTTYyCmowICeAY2upfDzcccTC0Zgzdt78M/Pj+D1VVPg5SHX\nfpWWV2P/qUx0C/FGvx6yi6ZGrTJNN2ys2cGQ3sGIO5SMw2ez8dircVCrVLiUWQQhAN9Obpg3tTdm\njetpCjsxN/WFwWCERqNGVY0B7m4axJ/KxP23RTX4lD/xcj42b0/CgdNySmJQZw94euiQV1SJT384\nC0CGob/8bgx6d2u4X1FpRQ3OXMzDieRcnLyQi8tZJabxzL+5b6ufX7VaZQqIw/oCQ/sE499fn8TV\nvEL0jghFWGAnhAV6ISygE8KCvODnXTdtbt+JDKxbfwg1emOrH9+ZFdRuiNzW1utERNR2DF7k3OpX\nvK5VIKc1sZ08mRNSrzvgjUPCm7zdwBsCMXdKb3y5MxkvfXgAzz80Fp7uWuw+lo4avRE3j+ph8VQn\ntVqFO8f5Y8uBclML6X49/HHr+F4YPyS80TVNGo1cW+au0yC6fwj2nchEWnYJuof64Oj5q9i8PQkn\nL+QCkB3dYm7qa1pXVV5Zg29/u4i4hCu4/7ao60IXAHh76jB6YBhGDwyz6Bxaq2uwN15YNg4JCQmI\njh7R7G2V9XQMXo0rLK0NXmam5hERuaoDBw5g5cqVWLduHSZPntyuj8XgRc6td29Aq228s2F+bUes\ngJavgSHXolS8AODGIV2ave3iWwYgK78ce49n4Pn39+P2CTfgh30pUKuAKdHdmj32Wp29tHjzz1Nb\nNeZxg7pg34lMrHl7LzRqlanyMaJfCO6+qQ8G3RDYIAR6eeiwYHo/LJjexGbjdsoUvAwMXo0pZMWL\niKhJqamp+PTTTzFy5MgOeTwGL3JuOh3Qp4+seAkB1K82MHiRhUICZPDq28OvQQhrjFajxpP3RkOj\nUmH3sXScTZE/ZyMHhHZou+fRA8MwoGcA8mq7B04YGo550/o0WslyZDqtrPyx4tU4Bi8ioqaFhYXh\nrbfewpo1azrk8Ri8yPkNGCCDV2YmEF5vmhiDF1koLNAL998WhUGRljVv0GjUWLVoBMYN6YLismqo\nVCqMjgpt51E25OWhw99XTOzQx7QFpeJVXWOw8UjsU0FJJQBONSQiaoybW8duL8HgRc4vKgr46isZ\nvhi8qBVUKhXuntanRcdoNGpMGNq1nUZECiV46VnxahQrXkRE0qZNm7B582aoVCoIIaBSqbBixQqM\nHz++w8agEkI0v5GMHUhISLD1EIiIyA7lFtfgra3ZGBHZCXeMYaOca22Iy8X5jEqsvjscHm7Xb+xN\nRORsoqOjW3zMmjVrMGvWLDbXULTmSXRUspOX65yvot3O+9gxYPhwYPly4J136q6fPRv4/nuguNiu\nWsq76r+/wtXPvz5Xfy4sOf/s/HJg6y/w8w8w2wHRUbXl5yD2t13Qaatx49iRTrF5MF8Trn3+Cld/\nHlz9/Ou79rloS7GmI2pR/PiLnF+/frKpxrUt5fPzZcdDb2/bjIuI2ozt5JtXWFoFPx93pwhdRETW\n9ssvv+D222/Hjh078NJLL2HevHnt+ngOU/EiajVPT6BXr+tbyufny/VdfENC5LDqgheba1xLCIHC\nkqpGN+gmIiJg+vTpmD59eoc9Hite5BqiooCrV4Hc3LrrlOBFRA5Lp2HFqylllXrU6I1srEFEZCcY\nvMg1DBggL5XphkIweBE5AU41bFohW8kTEdkVBi9yDVFR8lIJXiUlgMHA4EXk4DQaNdQqBq/GsJU8\nEZF9YfAi16AEL2WdF/fwInIaOp2Ga7waUVgqg5e/j4eNR0JERACDF7mK/v3lpVLxYvAicho6jZoV\nr0aw4kVEZF8YvMg1+PoC3bqx4kXkhHRaBq/GFDB4ERHZlQ5vJ5+fn4+nn34aVVVV0Ov1WL16NYYM\nGdLRwyBXNGAA8MsvcsNkBi8ip6HTqlFjYPC6lqnixeYaRER2ocMrXt9++y3uvPNO/Oc//8HKlSvx\n+uuvd/QQyFUp67wSExm8iJwIK17Xq6zS42JGEQDAnxUvIiK70OEVrwceeMD0dUZGBsLCwjp6COSq\n6reUZ/Aicho6rQY1+mpbD8NuJKUV4NXYBKRfLcOgyEB08tTZekhERAQbBC8AyM3NxfLly1FeXo71\n69fbYgjkiup3NjTUdkBj8CJyeFqtGjU17GpoMAp8tTMJsT8mwmAUuHNyJO67dQBUKpWth0ZERABU\nQgjRXne+adMmbN68GSqVCkIIqFQqrFixAuPHjwcA7N69G+vXr8eHH37Y7P0kJCS01xDJhWgKCzHs\n5ptROHEi9H5+CPruO5z8+mtUd+tm66ERURt89EsOUq9W4/mFXV02ZBSW6bFlfz4u51TDx1ONO8cF\nIDKMbeSJyPVER0fbeghNatfg1ZiDBw+iX79+6Ny5MwBg7NixiI+Pb/aYhIQEu34Src3VzlfRIecd\nEiI7HA4aBHzzjZxy6O/fvo/ZQq76769w9fOvz9WfC0vP/y/v7sXxpFxs+fvt0Gqcr1mvuefht6Pp\n+NfmYyir1GPc4C54NGYYfDu5deAIOw5fE659/gpXfx5c/fzru/a5sPfnpsP/h/rll1/w9ddfAwDO\nnTuH8PDwjh4CubKoKODiRSA9HVCrgdoPAIjIcem0GgBwyQYb2fnl+H+xh2EwCjw2fxjW3D/KaUMX\nEZGj6/A1Xn/4wx+wevVq/Prrr6iursYLL7zQ0UMgVzZgALBrF3DsmKx0qZ3v03EiV6PTytdxjd4I\nTxdr4HcxvQhGAdwzvR+mj4mw9XCIiKgZHR68/P398d5773X0wxJJSoMNvZ6NNYichE6jBC/Xa7Bx\nJacEANAj1MfGIyEiInP4cT+5FqWlPMDgReQktPUqXq7mSk4pAKBbiLeNR0JEROYweJFrUSpeAIMX\nkZPQuXTwKoFWo0ZogJeth0JERGYweJFr6dJFdjUEGLyInISbzjWbawghkJZdivDgTtA4YTdHIiJn\nw9/U5FpUqrqqF4MXkVNw1TVe+cWVqKjSc5ohEZGDYPAi16Os82LwInIKrjrVUFnf1T2EjTWIiBwB\ngxe5Hla8iJyKywavbNnRkBUvIiLHwOBFrueuu4Bx44CZM209EiKyAlPwMrhY8DJ1NGTFi4jIEXT4\nPl5ENhcZCezbZ+tREJGVuGo7eSV4dWXFi4jIIbDiRUREDk2ndc2uhmk5JQjy84SnOz9DJSJyBAxe\nRETk0NxqK156F+pqWF5Zg7yiSq7vIiJyIPyYjIiIHJqyxqvaiSteu45cwTtfnYDRaIRarYaPlw4A\n0D2U67uIiBwFgxcRETk0V+hqeOz8VZRV1CAiTAatvKJKqNUqDO0dZOORERGRpRi8iIjIobnCGq/S\nimoAwLo/ToCPlxsAwGAwQqPhigEiIkfB39hEROTQdBrnr3iVVtQAALw8dKbrGLqIiBwLf2sTEZFD\nq2sn77zNNcoqauDloYVGrbL1UIiIqJUYvIiIyKG5whqv0ooaeHvqzN+QiIjsFoMXERE5NJ2pnbwT\nB6/yGnRi8CIicmgMXkRE5NDcdLK5hrO2kzcYBSqq9PD2dLP1UIiIqA0YvIiIyKHVNddwzjVeVTUy\nUHp7seJFROTIGLyIiMihOfsar4pqAQDo5MHgRUTkyBi8iIjIoTl78KqsZsWLiMgZMHgREZFDM7WT\nNzh58GJzDSIih8bgRUREDk2nlc01nLWrYUVt8GJXQyIix8bgRUREDk2jVkGtVjnvVMMaVryIiJwB\ngxcRETk8N63aabsa1q3xYjt5IiJHxuBFREQOT6dVO+0+XuxqSETkHBi8iIjI4em0auedasiuhkRE\nToHBi4iIHJ5Wq3H+4MU1XkREDo3Bi4iIHJ5Oo2ZXQyIismsMXkRE5PB0Tt5cw02rhptOY+uhEBFR\nGzB4ERGRw3PuNV6C67uIiJwAgxcRETk8N50GNQYjhBC2HorVVdQYOc2QiMgJMHgREZHD02nUEAIw\nGJ0reAkhUFlthLcn9/AiInJ0DF5EROTwtFr531l1jXOt86qo0kMINtYgInIGDF5EROTwdLXBy9nW\neZVW1ABgK3kiImfA4EVERA5PCV56g3MFrzIGLyIip8HgRUREDs/ZK16cakhE5PgYvIiIyOHptHKP\nK6cLXuW1FS+2kycicngMXkRE5PDcnLTiVVZRDYBTDYmInAGDFxEROby6qYbO1dWwtEIPgFMNiYic\nAYMXERE5PK2TVrxKTRUv7uNFROToGLyIiMjhKRWvaicLXmVc40VE5DQYvIiIyOHpNLK5ht7Jgldp\nZW1XQw8GLyIiR8fgRUREDs8Z28lXVuuRklEMgBUvIiJnoLX1AIiIiNrK2ZprVFbp8dePDiAlsxgD\ne3jCixUvIiKHx+BFREQOz03nPBWviio9XvowHqcu5GHc4C64KYqTU4iInAF/mxMRkcNT1njVGBw7\neFVU6fHiBzJ03TikC55aMhJajcrWwyIiIitgxYuIiByeM7STL6+swQv/jsfZlHyMHxqOP98bDa2G\nn48SETkLBi8iInJ4pnbyNY65xqt+6Jo0rCtWLRoBDUMXEZFTYfAiIiKHpwQvR2wnX15Zg+ff34/E\nywWYPLwbVi4cztBFROSE+JudiIgcnoebXON1tbCixccaDEbsPZGB0vJqaw/LrIoqPV74dzwSLxdg\nSnQ3rGSli4jIafG3OxERObzIbn4ICfBC3JEryC+utPg4IQTe23IS/7f+EF78IL5D14hV1Rjw4ge1\n0wuHd8UTC0ZAo2YjDSIiZ8XgRUREDk+rUSNmWh/U6I3YEpds8XFf7UzGD/tToNWokXi5AJ9sPd1+\ng7zGlzuScPpiHsYPCceqhQxdRETOjsGLiIicwk2juiOoswd+2J+CotKqZm9bVFqF978+iU++P4Og\nzh5468mp6B7qg29/u4i4hLR2H2tOQTm+3JGEAF93PL6Aa7qIiFzfoLLnAAAcBElEQVQBm2sQEZFT\n0Gk1mDu1D97/+iQefWUnPNw1UKlUUKsgL9UqqFUqqFRAdn45yiv1CAv0wjNLx6BrsDfW3D8Kf3p9\nN17beBRenjqMjgprt7F+svUMqvVG3H/bQHi6879iIiJXwI/YiIjIacwYG4HBkUHQ6dTQ642oqjag\nvFKP0vIaFBRXIbewAjn55fDy0GHZnEF4+6mb0LOLLwCge6gPnn9oLDQaNf5v/SEcO59j9vFKK2rw\nxhdHcSmjyOIxnr2Uj9+OpaNfD39MGdGt1edKRESOhR+zERGR03DXafC/fxjf6uMH3hCIvywdjZc+\nPIC1Hx/Ei8vGYeANgU3e/rvdF/DLwVScTy3Aa6umWLTh8Xd7LgIAlt4+EGqu6yIichmseBEREdUz\nvF8I1tw/Cnq9ES9+EI/zqQWN3q6qxoDv910CAFzOKsH3ey+Zve/ismrsP5mJ7qE+iOoVYNVxExGR\nfWPwIiIiusbogWH48+JoVFXr8fz7+3EpowhCCHz6w1ks/evPOHkhF3EJV1BUWo0ZYyLg7alD7I+J\nyCtqfh+xuIQ06A1GTB/dAyoVq11ERK6EUw2JiIgaMWFoV1RVG/DaxqN49r19GNYnBLuOXgEAvPDv\nePh66aBRq7BoZj/07u6Htzcfx/L/246hfYIxckAoRg4IRZCfp+n+hBD45WAqNGoVpkZ3t9VpERGR\njTB4ERERNeGmUT1QXWPA21+ewK6jV3BDeGfMmRyJtzYdQ25RJaZEd0NgZ0/MGBOBwuJK7Dp6BQdO\nZ+HA6SwAQM8uvhgVFYp+PfxRUFKFlMxijBvcBX4+7jY+MyIi6mgMXkRERM245cZe0GjUOHMpDw/N\nGQxvTx0CfT3w9e4LWDSjPwBAo1Zh4cz+WDizPzJyS3H4bDYOn8nGyQt5SMksbnB/M8ZE2OI0iIjI\nxhi8iIiIzJgxJqJBYBraNxhD+wY3etvwIG/cMdEbd0yMRGWVHseTruJKTikAoLO3O6L7h3TImImI\nyL4weBEREbUTD3ctxgzqgjG2HggREdkcuxoSERERERG1MwYvIiIiIiKidmaz4JWbm4vRo0fj0KFD\nthoCERERERG5KIPBgNWrV2PRokVYsGABjhw50q6PZ7M1Xq+88gq6d+c+JkRERERE1PG++eYbeHh4\nYMOGDUhOTsaaNWuwadOmdns8mwSv+Ph4+Pj4oG/fvrZ4eCIiIiIicnF33HEHbrvtNgBAQEAAioqK\n2vXxOnyqYU1NDd555x088cQTHf3QREREREREAACtVgt3d7mh/fr16zF79ux2fTyVEEK0151v2rQJ\nmzdvhkqlghACKpUKEyZMQO/evTFr1iysWbMGd911F0aPHt3s/SQkJLTXEImIiIiIyElER0c3en1j\nuWTFihUYP348YmNjERcXh3fffRcajabdxtauwasxCxcuhBACQgikpqYiMDAQr7/+OiIjIztyGERE\nRERE5OI2bdqEn3/+GW+//TZ0Ol27PlaHB6/61qxZg7lz52LUqFG2GgIREREREbmgtLQ0rFy5ErGx\nsaYph+3JZl0NiYiIiIiIbGXz5s0oKirCsmXLTNMPP/roI2i17RORbFrxIiIiIiIicgU220CZiIiI\niIjIVTB4ERERERERtTMGLyIiIiIionbG4NUG6enpGDFiBO677z4sWbIE9913H9atW9fk7desWYNd\nu3Y1e59///vfsWDBAsTExOCXX34BAGRlZWHJkiVYvHgxVq5ciZqaGgBAUVERHnzwQTz++OMN7uPD\nDz/EnXfeiZiYGJw6daqNZ3m99PR09O/fHydPnmxw/d133401a9a06j4d4bzN2bp1KwYNGoTCwsJW\n38f69esRExODmJgYbNiwAQBQWlqKRx55BIsWLcKyZctQXFwMAKiursbTTz+Nu+++u8F9fPvtt5gz\nZw7mzZtn9uetLdrj5wCQ5/uHP/zB9G9/8eJFAMC+ffsQExODBQsW4O233zbdPjExEdOnT0dsbKzp\nOr1ejz/96U+IiYnB0qVLUVJS0urxtNSyZcswYcKENj33jv4cAJY9D9OmTUNFRUWD6xITE3Hvvfdi\nyZIlePTRR1FVVQUA+OCDDxATE4N77rmnwX1u27YNw4cPR3Jysum6rKwsLFq0CPPnz8cLL7xg3ROz\nkDV+Hyji4+Nxzz33YNGiRXjmmWdM169btw4LFizAwoULG7wO169fj0GDBjV4bhMTEzFv3jzcfffd\nDX522lNsbCzuueceLFmyBPPnz8f+/fvbdH+O+LORlpaG5cuXIyYmBnPnzsXatWtN425MZmYmTpw4\ncd31jvozAMj/K6KionD+/HnTdVu2bMHXX3/d6vt0pJ+Fa98rLl26tM2vhaysLCxduhRLlizB7373\nO+Tl5QGQ///ffffduOeee7B582bT7Q8cOIAbb7yxwXNSWlqKZcuWYf78+XjsscdM77E6gr38P/n4\n44+b/l3uuOMOPPfcc60/KUsJarUrV66IefPmWXz71atXi7i4uCa/Hx8fL5YtWyaEEKKgoEBMmTLF\ndNxPP/0khBDiH//4h/j888+FEEKsXLlSvP/+++Kxxx4z3UdSUpKYN2+eMBqN4syZM+LNN99s8XmZ\nc+XKFTF9+nTx8ssvm65LT08X06dPF6tXr27x/TnKeZvzyCOPiFWrVomNGze26vjU1FQxZ84cYTQa\nRXV1tZg6daooKSkRb775pvjwww+FEEJ88cUX4pVXXhFCCPHXv/5VfPbZZw1+BgsKCsSMGTNEeXm5\nuHr1qnj22WfbfmJNsPbPgeKNN94Q77//vhBCiLi4OPHEE08IIYS49dZbRVZWljAajWLRokUiOTlZ\nlJeXiwceeEA8//zz4rPPPjPdR2xsrPjb3/4mhBDiv//9r9ixY0erx9Ma5l7r5jjDcyCE+edh2rRp\nory8vMF1ixcvFsePHxdCCPHyyy+LDRs2iLS0NDF37lyh1+tFXl6emDVrljAajSI+Pl48++yzYuHC\nhSIpKcl0H48//rj49ddfhRBCvPTSSyIzM7Mdzq55bf19UN+MGTNEVlaWEEKIxx57TOzatUscPHhQ\nPPLII0IIIZKTk8U999wjhBBiy5Yt4o033hBTp05t8NzGxMSIs2fPCiGEWLVqlaisrGzzuJpz5coV\nMWfOHGEwGIQQQly6dEksXry4TffpaD8bRqNRzJkzR8THx5uu++ijj8STTz7Z5DFfffVVg9exwhF/\nBhRXrlwRs2fPFg8//LDpuq+++kps2bKl1ffpSD8L175XTE1NFbfeeqs4d+5cq+/z6aefFtu2bRNC\nCPHZZ5+JV155RZSXl4uZM2eK0tJSUVlZKWbPni2KiorE5cuXxR//+EexYsWKBr+P//73v4v169cL\nIYT417/+JU6cONHq8bSGPfw/Wd+aNWs65Dlgxaud/POf/8SSJUuwaNEibNu2zXT99u3b8cADD+Cu\nu+7C2bNnGxwzatQovP766wAAX19fVFRUwGg04uDBg5g6dSoAYOrUqdi3bx8A4G9/+xuGDh3a4D52\n7tyJW265BSqVCgMGDMCjjz7aLuc3ZMgQxMfHm/7+008/YcKECaa/f/fdd5g/fz7uvfde0ycIW7Zs\nwapVq7B48WJkZ2c75Hk3paioCCkpKXj44YexdetW0/VLlizBK6+8gvvuuw8LFixAZmYmDh48iOXL\nl+O+++5rUJnr3r07YmNjoVKpoNPp4OXlhbKyMsTHx2P69OkAGj4Pf/rTnzBlypQG49i3bx/Gjx8P\nT09PBAUF4aWXXmrX827Nz8H8+fORlpYGQH5qN3fu3Ab3+cgjj+CBBx4AAPj7+6OwsBBpaWnw8/ND\naGgoVCoVJk+ejPj4eLi7u+O9995DUFBQg/vYuXMnbr/9dgBATEyM6eeoo23ZsgUvv/wyAKC8vBzT\npk0DAMyYMQMffvghFi9ejHvuuQfl5eUNjnOm5wBo+nkQjTTVfeeddzBkyBAAQEBAAAoLC3HgwAFM\nmjQJGo0GAQEB6Nq1K5KTkzFkyBC89NJL0Gg0puOFEEhISDA9xrPPPouwsLD2PsUGmvt9oHziHhsb\ni7feegt6vR5PPPEEFixYgJdffvm61zQAfPnllwgNDQVQ95zs378fN998MwAgMjISxcXFKCsrw8yZ\nM7FixYoGx+fl5aGiogL9+/cHALz66qvtvl9NSUkJqqurTZWInj174tNPPwUAXLhwAffffz+WLl2K\nRx99FP+/vXOPiqr6HvhnBsUERBBDBbEFku+3JiIoLDW11LRUJF6KmimKmYqKC0Ez0yTNBS41BUME\nH/lYRIj4WJGvWJRp4DtBjMWgPERU8DHizO8P1twvwwxI6dhv6nz+Yt1z53D3vvueffY++9xbUVGB\nQqFgwoQJhISEMGHCBFasWKHTp7HZxunTp3F0dMTFxUU6FhgYSHZ2NmVlZRQWFkrZ+kWLFnHnzh2i\no6OJj48nPT1dqy9jtIGadOvWDTMzMy1/oWHHjh14e3vj7e1NTEwM5eXljBgxQmpPSkqSxg8NxmYL\nNXFwcGDWrFnSyktiYiIffvghfn5+xMXFAdXPz8cff4yvry8zZ87UqQyIiIiQdKSRPysrix49emBu\nbk6TJk3o06cP586do3Xr1mzcuBFzc3OtPtLT0xk9ejQAQUFBdO/e3cCS66eiooIZM2YQEBDApEmT\npJXbV+EnNeTl5VFRUfFKdCACrxdE38Th7NmzFBYWsnPnTuLi4ti0aRNKpRIAuVxOXFwcn3zyCZs3\nb9b6nVwup2nTpkD1V7Q9PT2Ry+U8evRI+pK2jY0NJSUlANK5NVEoFBQWFjJ9+nQCAwO5evXqS5VX\nQ+PGjenUqZNUEpGeno6Hh4fU/uTJE2JiYkhMTCQvL4/r168DUFhYSEJCguRAjE3uukhLS8PT05OO\nHTtSXFxMcXGx1GZlZUV8fDyjR4+WBtU//viD7du3061bN61+NAPj6dOnsba2plWrVpSUlGBtbQ00\nTA+PHj1i1qxZ+Pn5vXA5w/P4O3YwduxYkpOTATh+/LgUHGgwNTWV7rtGb6WlpbRo0UI6p0WLFhQX\nFyOXyzE1NdW5LoVCwYkTJ/D392fBggVSeeY/gUwm0/m7qqoKZ2dnEhISsLe317lP/zYdgH496MPC\nwgKoDtC+//57RowYoVf2kpISvc9AWVkZZmZmrFq1Ch8fH9avX/8SpWgY9Y0HtTl16hRPnz5lz549\nuLi46D1Xo5Pi4mJ+/vlnPDw8dHRibW1NaWlpneOCpaUloaGh+Pj4sGPHjpcgZf106tSJ7t27M3To\nUEJDQzl8+DDPnj0DYOXKlaxcuZJvv/2WgQMHShPQa9eusXDhQvbv38+FCxe4du2aVp/GZhs3btyg\nc+fOOsc7dOjAzZs3+frrr5k2bRoJCQnY2tqiUCj44IMPCAgI0EmUGKMN1ObTTz9lw4YNWscKCgpI\nSkpi9+7dJCYmkpqayoMHD7CzsyM3NxeoTljXDMTA+GyhNl27diU3N5eCggKOHDnC7t27SUhIIC0t\njdu3bxMbG8ugQYNITEzE1dVVSrhqaNq0KXK5HJVKxa5du+r0ESUlJXr9A0BpaSl79uzB19eXiIiI\nV1pqWJM7d+7g5eVFfHw88+fPZ9u2bcCr8ZMa4uPj8fPzM4B0uojA6wXJy8vT2uP1zTffcP78ebKz\nswkICGDatGkAkjPVZL569OhBXl6e3j6PHz/OwYMHWbZsGaA9SdEX6NVErVajUqmIiYlhzpw5hIWF\nvbCMdTFy5EhSU1O5ffs2VlZWWoNbs2bNmD17Nv7+/uTm5kr7HOrLJhiL3PpISUmRMo9DhgzRWuUc\nOHAgAL169eLmzZtA9aSkro/z/f7770RGRvLVV18Bunqob9KqVqspLy9n06ZNrF69mqVLl76QXA3h\nr9rBqFGjOHr0KFDtUEeNGqW338jISJo0acL48eN12hpiD+3bt2fnzp04OzuzZcuWF5DQMPTt2xeA\nVq1a1bn/6t+ug7p4+PAhQUFBTJs2DScnJ532+mRXq9UUFxczZcoUEhISuHz5skH3OuqjvvGgNrm5\nufTp0wcADw8Prax8Te7cucOsWbNYvnw5zZs312l/nk4UCgWhoaFs376dgwcPSpNaQ/Lll1+SkJBA\n586diYmJYerUqQBkZ2cTFhaGv78/ycnJlJaWAtWrYpqkXM+ePfX6SGOyDZlMhkql0jmuUqkwMTHh\n8uXL9O7dG4CFCxdKKzh1YYw2UJN27drRtWtXrefhypUr9OrVC5lMhomJCX369OHatWu8/fbb/Pjj\njyiVSnJycujVq5dOf8ZkC7WprKxELpeTnZ3Nn3/+Kc0jHz16REFBAZcvX5bGhcmTJzN06FCdPlQq\nFSEhIbi6ujJgwACd9uf5iCdPnuDu7k5iYiIqlYp9+/a9HOH+IjY2Nhw9ehQfHx8iIyO19sUa2k8C\nPH36lHPnztG/f/+/KcFfwzCfZf4P4eTkRHx8vNaxuLg4xo8fz4wZM3TOf17W99SpU2zdupXY2Fhp\n9cPMzAylUompqSlFRUXY2trWeT0tW7akffv2QLXBFhYW/i25GoKrqyvr1q3Dzs5OKoWDaiP+7LPP\n+OGHH2jRogUzZ86U2jTZidoYk9y1KSoqIisri88//xyAx48fY2lpKS2BaxxvzaCpLj1cvXqVZcuW\nsXXrVmkCYmtrS2lpKRYWFg3SQ+/evZHJZDg4OGBubk5ZWZlWBuhl81ftwMrKCgcHBzIyMpDL5Xrl\niYqK4u7du3zxxRdAtQ40K31Ag/Tw1ltvAeDu7s7GjRtfiqz18eDBA5o2bUqjRo2kiVXNZ7yqqkrr\n/Lom2BqMUQfw1/VQm2fPnjF79mzee+89xo0bB1TLXnMSXp/s1tbW2Nvb07ZtW6DaPnNycrRWYg1J\nfeNBTT3UzC7L5f/LgerzC5pN8AsWLMDV1RX437igobi4mNdff11vPzY2Njg7O2NpaQlUj5HXr1+X\nxkxDoVQqcXJywsnJCT8/P9555x0KCwsxMzPT8ZsKhUIrSNGXZDI223BycmL37t06x3NycnB0dJRW\nLBqCsdpAbTSBkq+vL40bN9YJTpVKJTKZjGHDhjFv3jzefPNNrfJ1DcZmC7W5ePEiXbp0wdTUFE9P\nT53S2piYmOfaRmhoKI6OjgQFBQH6fYQmsNdHmzZtpGDfzc2NX3755e+K02D0+Ye4uDhat27N2rVr\nuXjxImvXrpXON7SfBPj111+fm/R4mYgVrxdEXzTds2dP0tPTUavVPHnyRHLAUF2GCHD+/HmdAa+i\nooLIyEi2bNlCs2bNpOOurq4cOXIEqN5DM2jQIK3/X/MaBg8ezKlTp4DqTKoha5YbN25Mly5dOHDg\ngFZZRGVlJY0aNaJFixbcunWLixcvSqWW+jA2uWuTkpKCr68vSUlJJCUlkZaWxr1796R9TL/99htQ\nvZJVn5NTqVQsXbqU6Oho2rRpIx13d3cnLS0NgKNHj9arBzc3NzIzM1Gr1dy9e5eHDx8aNOiChtvB\nhQsXpMnm2LFjWb58Oe+++65Of2fPniU7O1saSAHs7e2prKyksLCQqqoqfvrpJ73OWMPgwYM5efIk\nAJcuXcLR0fFliVsnK1as4NixY6jVam7cuIGjoyMWFhbSarfm2W8IxqoDeHE9bN26FRcXF629fwMG\nDODEiRNUVVVRVFREcXExzs7OWr/TPAcmJia0bduW/Px84NXKDvWPB82aNZMmBefOnQOqVwE0expO\nnz4tlePVZM2aNQQGBuLm5iYdc3Nzk8bHS5cu0apVK8zMzKT2mmND27Ztqays5P79+6hUKq5cuWJw\nnezbt4/Q0FDpGu7fv49araZly5Z07NhRss3U1FRp309+fj6lpaWoVCqysrJ07rGx2YabmxsKhUKS\nFaoTs/369cPS0lJrj2xUVBQZGRnIZDK9yQljtAF92NjYMGzYMPbs2QNA586dycrKQqVSUVVVRXZ2\nNl26dMHW1haZTEZKSopOmSEYny3U9NP5+fnExcURGBhI165dyczM5PHjx6jValatWoVSqaR79+6S\nbezdu1fnDZDJycmYmppq7Wfv2bMnFy9epKKigsrKSs6fPy+tGOm7jgEDBpCZmQn8s36yvLwcBwcH\nAI4dO9bgkseX4ScBLly4IO19fBWIFa8XRF92snfv3ri4uDBp0iQAfHx8tNpnzpxJUVGRVlQP1Q6o\nvLycefPmSdm+tWvXEhwczOLFi9m7dy92dna8//77qFQqxo4dy6NHj7h37x5jxoxh8eLFuLu7c/Lk\nSby9vYHqDZiGZOTIkdy9e1eqt4bqFY2BAwcyceJEnJ2dmT59OmvWrCEgIEBvH8Yod00OHTqkcy/H\njRvHoUOHkMlk0t6ziooKoqKipHLD2mRkZKBQKAgPD5f0EBISgp+fHyEhIfj6+mJpaUlkZCRQvUn7\n9u3b3Lp1izFjxjBlyhTGjx/P8OHD8fLyQiaTvZpXo9IwO/joo49YvXo1SUlJeHp6EhYWpteh7t69\nm9u3bxMQEIBarcba2pqoqCgiIiKYP38+AKNHj+aNN94gKyuLsLAwysrKMDExYc+ePSQkJODv78/i\nxYvZv38/5ubmOhuzDYHGXuPj4/Hw8MDe3p7mzZuzefNmAgICtMrInrfybaw6eBE9aNi1axdt27bl\nzJkzyGQyBgwYQFBQkPSSFplMJmWHExIS2Lt3LwUFBcyZM4f27duzadMmli5dypIlS1Cr1XTo0EHa\nQP8qqGs8SE1NlV5b7ejoKE00PD092b9/P76+vvTv3x8rKyut3z5+/Jjk5GTy8/P57rvvkMlkjBkz\nhokTJ9KlSxe8vb0xMTGRxrz169eTnp5OSUkJXl5e9OvXj+XLlxMaGsr06dORy+W4u7vTsWNHg+ph\n/Pjx5OXl4eXlhZmZGc+ePSMsLAxTU1OWLl1KeHg427Zt47XXXmPdunU8ePAAR0dH1q9fT05ODn37\n9tVJVBmbbchkMmJjYwkPDycqKgqVSkW3bt2kUvjg4GBCQ0PZtWsXdnZ2BAcHo1arWbJkCTY2NtKL\nD4zVBupi6tSpUuBlb28v3T+1Wo2Xl5eUeBwyZAg7d+6Uyu5rYmy2cPPmTQICAlAqlahUKiIiIqSq\nlsmTJ+Pr60ujRo0YOnQopqamTJ48mUWLFuHv74+FhQXr1q3TkV+pVOLv749MJsPZ2Znw8HAWLFjA\n1KlTkcvlBAcHY2FhwbFjx4iKiqK4uJjMzEyio6M5cOAAc+fOJSQkhOjoaGxsbJg9e7bB5NdQ0z94\nenpib2/P2LFjWbx4Mampqfj5+ZGamsrBgwdfiZ9s3rw5JSUltGvXzuCyS7KoG1IAKRAI/hb+/v5E\nREToZN3+65w5c4aUlJR6v3snEPwXuHfvHpmZmQwfPpyioiICAwPr3RP2b0WhUDB37lwOHDjwT1+K\nQCAQGAyx4iUQGJD6XoTxX2XDhg1kZGQQHR39T1+KQPCPY25uzuHDh4mNjUWtVr+SF+L8f0WMlwKB\n4N+OWPESCAQCgUAgEAgEAgMjXq4hEAgEAoFAIBAIBAZGBF4CgUAgEAgEAoFAYGBE4CUQCAQCgUAg\nEAgEBkYEXgKBQCAQCAQCgUBgYETgJRAIBAKBQCAQCAQG5v8A0rHYU9c2Kd8AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting Normalized factors\n", + "fig, ax1 = plt.subplots()\n", + "\n", + "ax1.plot(normalized_momentum, color = 'red')\n", + "ax1.set_ylabel('Normalized Momentum Factor Returns', color = 'red')\n", + "\n", + "ax2 = ax1.twinx()\n", + "ax2.plot(normalized_value)\n", + "ax2.set_ylabel('Normalized Value Factor Returns')\n", + "\n", + "plt.title('Normalized factors returns');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the two normalized factors shows us that they are on much more even footing than the non-normalized ones. From here we can proceed to implement them in a strategy where they work in synergy to obtain a greater value of alpha." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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LpE25xuUaz0qu8aITEhKinPMqlYo2bdrQpEkT5XV7e3vlRvall15i1KhR9OrV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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "normalized_v_and_m = normalized_momentum + normalized_value\n", + "\n", + "\n", + "plt.plot(normalized_v_and_m, \n", + " label = 'Normalized Combined Factor daily mean')\n", + "plt.plot(momentum_factor_daily_mean + value_factor_daily_mean, \n", + " label = 'Non Normalized Combined Factor daily mean',\n", + " linestyle = 'dashed')\n", + "\n", + "plt.plot(momentum_factor_daily_mean[0:120], \n", + " color = 'red',\n", + " linestyle = 'dashed',\n", + " label = 'First 120 values of Momentum Factor Daily Means')\n", + "\n", + "plt.title(\"Normalized Value and Momentum Together\")\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Returns\")\n", + "plt.legend(ncol = 3)\n", + "plt.grid(axis='x')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the plot above we can clearly see the value of having the two factors be normalized. The red dashed graph (momentum factor's daily means) and the green dashed graph (Non-Normalized combined factor's daily mean) are nearly identical. This is because the Momentum Factor's values are much greater than those of the Value factor. The blue normalized graph shows the two factors on equal scale. \n", + "\n", + "We can start to think of aggregated factors made up of many smaller factors as combinations of long-short equity portfolios/assets themselves. By combining many of these assets into one factor (portfolio), we get the same effect that you get from portfolio diversification ~ *higher return for less volatility.*\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Static Weights\n", + "The above combined factor is an example of a static aggregation method. We simply add the two normalized factors together so that the aggregate factor is 50% momentum, 50% value. We have selected a weighting scheme and will carry it forward without any modifications. *An alternative approach is to adjust the weighting between the underlying factors.*\n", + "\n", + "As before, we begin by creating our factor. Here we use the SPY-Volatility factor, which simply the standard deviation of spy. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ___ Building Custom Factor ___ #\n", + "class SPYVol(CustomFactor):\n", + " inputs = [Returns(window_length=2)] \n", + " def compute(self, today, assets, out, returns):\n", + " idx = np.where(assets == 8554) # Check the dataframe for the SID of SPY\n", + " spy_returns = returns[:,idx]\n", + " out[:] = np.nanstd(spy_returns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Defining short and long term volatility as the 21 and 63 day volatility respectively, and normalizing our values allows us to run the pipeline on our selected universe. In this case, pipeline will output the tradeoff_factor, the returns, along with a Boolean expression of the high tradeoff and low tradeoff." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Calculating short and long term volatility\n", + "short_term_spy_vol = SPYVol(window_length=21)\n", + "long_term_spy_vol = SPYVol(window_length=63)\n", + "\n", + "# Zscore() to normalize\n", + "tradeoff_factor = Momentum().zscore() * (short_term_spy_vol/long_term_spy_vol) + \\\n", + " Volatility().zscore() * (1-(short_term_spy_vol/long_term_spy_vol))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Ranking and running pipeline\n", + "tradeoff_rank = tradeoff_factor.rank()\n", + "\n", + "pipe = Pipeline(\n", + " columns = {\n", + " \"MomentumVolSwitch\" : tradeoff_factor,\n", + " 'returns': Returns(inputs=[USEquityPricing.close], window_length=2),\n", + " \"hightradeoff\" : tradeoff_rank.top(NUM_LONG_POSITIONS),\n", + " \"lowtradeoff\" : tradeoff_rank.bottom(NUM_SHORT_POSITIONS)\n", + " },\n", + " screen=universe\n", + ")\n", + "\n", + "tradeoff_results = run_pipeline(pipe, '2016-01-01', '2017-01-01')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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MomentumVolSwitchhightradeofflowtradeoffreturns
2016-01-04 00:00:00+00:00Equity(2 [ARNC])-2.089693FalseFalse-0.010040
Equity(24 [AAPL])0.537339FalseFalse-0.019474
Equity(62 [ABT])0.152191FalseFalse-0.007733
Equity(67 [ADSK])0.484903FalseFalse-0.021365
Equity(76 [TAP])1.132976FalseFalse-0.006558
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" + ], + "text/plain": [ + " MomentumVolSwitch hightradeoff \\\n", + "2016-01-04 00:00:00+00:00 Equity(2 [ARNC]) -2.089693 False \n", + " Equity(24 [AAPL]) 0.537339 False \n", + " Equity(62 [ABT]) 0.152191 False \n", + " Equity(67 [ADSK]) 0.484903 False \n", + " Equity(76 [TAP]) 1.132976 False \n", + "\n", + " lowtradeoff returns \n", + "2016-01-04 00:00:00+00:00 Equity(2 [ARNC]) False -0.010040 \n", + " Equity(24 [AAPL]) False -0.019474 \n", + " Equity(62 [ABT]) False -0.007733 \n", + " Equity(67 [ADSK]) False -0.021365 \n", + " Equity(76 [TAP]) False -0.006558 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tradeoff_results.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "tradeoff_returns = tradeoff_results[tradeoff_results.hightradeoff]['returns'].groupby(level=0).mean()- \\\n", + " tradeoff_results[tradeoff_results.lowtradeoff]['returns'].groupby(level=0).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tradeoff mean returns : -0.0126726445716\n", + "Tradeoff standard deviation : 0.0431842268144\n" + ] + } + ], + "source": [ + "print 'Tradeoff mean returns :', tradeoff_returns.mean()\n", + "print 'Tradeoff standard deviation :', tradeoff_returns.std()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def scale(value):\n", + " return (value - value.mean())/value.std()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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E4QimHMGVvqW6JYQQQgghRKO1StA0ZswYhg8fDoC/vz8GgwFFUVCpVK3RHeFG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\n3Z43KQvqbpzrFCkNk0maUM7tGfRRXGDO2o7lTDGIEoXWgnhPE6gl8Nu3N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MwHpFQ9TVms\nNFlMUlwkaeSDaBIEgYZqF0db+vEHIxkJBApHZIzjPs+JoklDH247ObpomgXmcdn+8RlNed7TNBUG\nSeTWq+r57xYTiDKBaBCbmKP0PLNmz5t6uO3g6OKz553qb+Y/255gNOKhrqCKv9zyUZpK6ic9viRW\nPdrXfhBREGkqbAA6ccm1fPmmW9l1/i0eO/IMfb5B6lzVSY8VZIngiSu49yPpz08yGhMrTRfP5cPR\n2MCyv/okzuXLMZdO3WBtq61h5Rf+mtDwMAO79yIHgwiiSMlVV6Y9IFf7DBhNhriAmClFGy/l8n//\nIcGYzc5cUoyptHTSHWnRaMS5vAnn8qaUf5+K6YRbNtAa8pMa+tPEYVPFr92ancAMs8GEUTImVZq6\ntUrTPMWNu0+rw7A7zaUUjBNNmg0ncZG6eU0lOzYv4cW3YjOD8qzSpLO4MKRIzxOlfLHn5WZOk0ZD\nTQFHmvs53zXKqjnMo9MIR6IThJA2q+lCjzvhOD1yfDIunlVPBplQaRryUegwL/hF5M1X1vPb8zZC\nwGjAjc2YW9GUbqVpsYgmRVH4l93/iTvi5T1rbuG9a27DmKK6lEhxrHoUjIZYXtKA3az+m0WiMqIg\ncm39FVxZdxld7h6WFiZHacsKKF4Xxdb0L8bGhEVyvsxNyxaVN904o+NNhYVU3ToxeCUdtM+AyTC3\nxYO5rDRtobZQmYtoumpdFZ//0GVcsbYq06c1KQ6TDXdo7Fw745WmiskeMifcp04jORwMGZ1x+5NG\n3J43bnH08bvXcvB0L95AWK806cwrWuhD8nDb7C3WzSZDUrAXqKJJELK3mTIZWhhES+dIRkRTKCJP\nEE0uhwmH1ZhUaRqfsqczhv6KzILEniZZVugb8i/oKpNGodPMLZvV3pbR4MwXIJnCYpIQhHSCIPyI\nAhQ6crsblCm63D30+QZZ4ajng+vunlYwwVilCeCS8hXxnaHE4bYmyThBMAGzarrVLqKiKOi7UPNI\npkTTxYDBofYJzcaeZzEZuP7yuqwKA6fJkWTP63b3YpSMFNsyb2UNDY8Q7OnFvrwJBAFZmcSeN+4a\n4LAa+fanruHv/3Lrgu2V0VkYCIKAJArJPU3ZDIJIMatp1BvCYTVl9TxS0RBP0MtMGEQ4ImMaJ4QE\nQaC23EH3gDduy9PteZOjvyKzwJIgmobcASJReUGHQCRSYFZLtaNB9zRHzh+CIGA1G3D7QihTpGUN\njAQodFqy2jQ6nxzuUZvw6601aT+mxDpeNMWsDhF5sofEkaMzj3fVUsasJklfTM0jtliCZLZ6bRYy\notmMYDDMqtKUC5xmO/5wgIis7m53eXqpdJTFZ6hlEk/MmudcoW6GRcfNb4tM0XhfXmy76PttdLKD\nJImxSlNyj102SFzPabi9oZz2M2nUVTgxSALnOjITBhGOyCmTGmvLnURlha5+b/w40O15qdBfkVkw\nZs+L0Duo9jMt5BCIRArMqtUll5UmgPqqAtp7Pfzrfx+cMKgVVCvb4GhgUc1oOtx9AoAGW/qiSdud\nlgSRlSWNGAyph9umIqrMfFcvPhQvA02pOpOjV5rSRxAEDHb7rCpNucARix33hryMBN34wwGq5qmf\nafTkKYD4zKvx6XlyDiKedXTGY5AEolE5N/Y8Y7JokmWFUV9+iCajQaS23Mm5rtEJn93ZEA5P7GmC\niWEQYb3SNCn6ymcWJNrz4jOaFoE9D8YqTSOB3FWaAP7mY5v55k/28MK+Ntp7Pdy4ZQmb11RS6FSt\neG5fmHBEXjRx4xE5yrHe01Q6ynAZnWk/rtBcgMNkp76wFovRgiKrlsZIdPoLrDxJP8NUaLtUk8WN\n62QGXTTNDIPDvnAqTQmzmrRAiEzFjSvRKO7TZxh8622G3t6vRsKLIgUrmoC2CQuveBCEvqOsk0Mk\nUSQSTexpymalKRbsFbPn+QJhZFnJC9EE0FjjorVrlK5+Tzy0YTYoikI4OrGnCdSKFkB7rxuoijtV\nDLpomoAummZBYhCElrKyeOx5+VFpKnJa+L//axvfe2Q/e491c6J1EEGA1fXFXLm2isoSdeGxWEIg\nzg604o8EuKZiy4weJ4oi/3Djl7Ea1NdBW/ykY8/T/OPiDGx2eqUpOzTVFbKs1kVDRX7cuPMdg8NB\noLtnQQz4TJzV1O3pA6DSMX0M/VQMHXiHvp2vMXTgABG3eu0WjEaKNm2kbPt2jE4noijEN0o0xoIg\n8vs101ncxCtNk/TYzSfaJrgWPJUPceOJNCQMuZ2LaIrKCoqSuno0aaVJ30yZgL7ymQWJlabFMKMp\nEZcl+z1NkWiEH7/9CLXhUhInsljNBv73n19BZ7+HvUe72XO0ixOtgxw/Nxg/pmSRzGg63KNa89ZX\nroae6DRHJ1ORsODSeprC6djz5hAEsdCTIvOdEpeVBz9/Hfv378/1qSwIDA4HSjSKHAggWfP7mpA4\nq2ksbnz2osl9+gzHv/F/AHUWWMXNN1F8+WW41q9DsoxtKkmpRFMOFqk6OuORJJFIYnpeFhfrTpsq\njjw+dcRJ/ommWBhE1wjXbEzfuj+eUKySlqqnqaLYhkES6NBEk56eNyn6ymcWaNPjg+HoopjRlMhY\nEET2Kk0tQ2282rqHpdZq7ub2CX9fXerg3dc18e7rmhh2B3nreDd7jnbT3DHM+uWLI075SM9JBEHg\nkvIVnIwJqNkgCAIGSUirpymmmWa0y2zS7Xk6eUhi7Hjei6ZYpckd9NLt6QfmJpp6XnwJgBV//TlK\nr902aaVNEoX4RolGJN54ry+OdHKHJI7vacqeiHfGZrWN+kJJ/8830dQyxzCIqYSQJIlUlTq40OtG\nURTdnjcFumiaBVqlKRCrNDltJmyW3Ob5ZwqzwYRZMjGaxZ4mrap1wd+NL+yfcj5UodPMjVcs5cYr\nlmbr9OYdWZY5O9hKQ2EddtPcK5YGSUwvCCK2YJpJep5eadLJRxJjx81lc7O6zTdaEIQn5KHH04dR\nNMw6bjwaDNL/+huYSoop3Xb1lNZEVTSNC4KI9zTplSad3GGQBIJhJf5+zKZd1BkTR+5YhWnUk1+i\nqcBuotRlmXPs+HQx4rXlDi70uBlyBxPsefrm6Hh0GTkLxkRThL4hH+XF+b2zOVMKzI6sVpq055KR\nOdR9PGvPmy+MBN1E5SjljsxUzQySmF5PkzyXnib9YqqTP0h2TTTlfxhEcqWpj3J76azjxgf37CPq\n81G+/TqEaRY4YgrRFMlBWpmOzngkSYxVmrJvFx2z58VEU9yelz/zHxtqXAyOBhh2B2f9M0Jh9bU1\npbDnQWJfk3ssclwPIpqAfqWcBVoQRO+gj1BEXjT9TBoFFiejQfeUM5IySWJS3/6OI1l5znxi0D8M\nJM9cmgsGQ3qVptmk5xU6zQgClBYuro0CnYXNmD0v/2PHtUpTt6cPb8hHxRw2S3pffgWA8uu3T3us\nJIqT9zTplSadHGKIpefF54ZlUcRroiluz/OqwiRfKk2QOOR29ha9cETraZqs0qQl6Hnim66p+p8u\ndnSPzSzQKk0XetTFfsUiSc7TKDA7CcsR/JHAlFa5TDESs+cJCLzTdRRZlhEvop3PAd8QACWztOiM\nxyCJhNOIHJ+Nf7zEZeXfv7Jj0QRw6CwO4vY87wKoNMVEU/PgeSD9fqahdw7S9sivEUQBwWBAMBgY\nOXwE56qVWGuqp318qkqT9rXhIrre6uQf0vg5Tdm059k0e15+BkEANMZF0ygbV85uPMF04Q6JCXqa\nPU+37U5EF02zQKs0adOTF0sIhEZi7Hg2RJPWP9Vgq6HF187pgRZWlTXN+/NmG384wK7zb9FQVEdj\n8ZK4JUerNBVbMyOajJIYnzkxFdqu80x6moB43LuOTr6wkCpNWt9iv09NAa1IUzT1vvgynjNnEAwG\nlIgaj4woUn3nxPCcVEhSCnueXmnSyQMkUSAiKzkZtuy0q/3obv94e17+iKZ47PicKk1piqYed/w6\noVeaJqKLplmgVZq0+0/FIrPnxWPHA+45zw9JB63StL5gJS2+dt7uPLLoRJOiKPzb3ofZ13EQAJfZ\nyeeu/gSXlK9IEE2ZsucJeAPzEzmuo5OPjAVB5H+lSRIl7EYr3rCavJquaPJ3dCBaLFz56C9AEFCi\nUVAURGN6IUSiIMTteBrRaPbTynR0xiNJqnU0bs/LYpqj2ShhNIhjQRDeEKJAXoV7VZbYsZikDImm\n1ELIZjFS4rLQ3ueh1GVFFAX9upACvSY/C7RKk8ZiGWyrke0Bt6NBD1ajhUZbHQBnB85l5XmzyR9O\nv8y+joOsKGnk6iWXMxJ0s+fCAWDMnjfbBK3xpJuep6UPzyQIQkcnHzE41I2ehSCaAByxayxApXN6\n0aQoCv6OTqw11QiiiCAIiAZD2oIJYnOalEnseXrkuE4Oic8XjPXdZHOxLggCTpsJd6ynye0L4bSb\nZuzAmE9EUaC+qoALvZ74vKWZEpqmpwnUalPfkB+PP6RfEyZBf1VmgSAI8WoTQNkiqzSNzWrKTuz4\naMBNgdmJUTTgMjvjlZfFwun+Fn556AlclgIe2Ho/f3H5RwDocquDLeOVJosrI8+XfnqejCjM3J6n\no5NvLKRKE4z1NQmCQLmtZNrjlZER5FAIa83sh1tOZc/TrwE6uUSrLAVD2RdNoFrx3AnDbfPJmqfR\nUONClhXaema3LtMqTSbjVKJJXft19Hn1wbaToL8qs0SrNtmtRhzW/CnjZoJsVppkRWY06MYVE2rF\n1kIG/cNZS+6bbzxBLw/u/imyovCZK++jyOrCarRQZHXR6e4BYNA3jMvsxCBlxi2b/pwmRV8s6SwK\nFlJPE4zFjpdai9L63Cv9AwDYaucgmkQxbsfTiEb1SpNO7tGCSLRe3GxH4DttJrz+MOGIjMcXy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dPc9XaKSJOU0IA1TPQ3VA0ATH63baecr15+q7lM0Br6cezguamtU9tSp2q0JFeYOP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\n5zfKzaV6HgAAQFnRewI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GG2+chKMMrl27djrnnHN0+eWXKykpSe+//758Pp8k6YknntAT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\nYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(scale(tradeoff_returns.fillna(method = 'ffill')), label = 'Tradeoff_returns ')\n", + "plt.plot(scale(momentum_factor_daily_mean))\n", + "plt.plot(scale(value_factor_daily_mean))\n", + "plt.title('Scaled graph of all three factors.')\n", + "plt.ylabel('Returns')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have computed the mean returns and standard deviation of this factor, we can find efficient ways of combining it to the Momentum and Value factors. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Means: -0.0126726445716 || -0.0270783673986 || 0.00662625960785 || -0.000791575314159\n", + "Standard deviations: 0.0431842268144 || 0.759855672779 || 0.00599811444754 || -0.000791575314159\n" + ] + } + ], + "source": [ + "# Mean and Standard Deviation of all four factors. \n", + "print 'Means:', tradeoff_returns.mean(), '||', momentum_factor_daily_mean.mean(), \\\n", + " '||', value_factor_daily_mean.mean(), '||', volatility_returns.mean()\n", + "\n", + "print 'Standard deviations:', tradeoff_returns.std(), '||', momentum_factor_daily_mean.std(), \\\n", + " '||', value_factor_daily_mean.std(), '||', volatility_returns.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yet again, we would like to assign greater weights to factors with Sharpe ratio's closest to one (returns to volatility)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tradeoff ratio : -0.293455400419\n", + "momentum ratio: -0.335756873581\n", + "value ratio: 0.0642354496431\n", + "volatility ratio: -0.142082889053\n" + ] + } + ], + "source": [ + "# Sharpe Calculations\n", + "print 'tradeoff ratio :', tradeoff_returns.mean() / tradeoff_returns.std()\n", + "print 'momentum ratio:', momentum_ratio\n", + "print 'value ratio:', value_ratio\n", + "print 'volatility ratio:', volatility_ratio" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have all the four Sharpe Ratios, we can set about assigning weights and checking the returns of our combined portfolio versus the individual factor ones. Again, we will pseudo-arbitrarily assign the weights. We leave it as an exercise to the user to find the best weights associated with this timeframe. " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Calculating Portfolio Returns\n", + "portfolio_returns = 0.05 * tradeoff_returns.fillna(method = 'ffill') + \\\n", + " 0.05 * momentum_returns + \\\n", + " 0.8 * value_returns + \\\n", + " 0.1 * volatility_returns" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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ql3aDWv11NnYWdO7ujlZbMV3T3ELH1LlhHNp7ib4DO9Cxc9MMzwvo4kJsdAax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4242WOCVVl9Bd7e9QEAYG/RYbeGKgBQBwcj/NrpCL92eps1Cj8/qIOD4B0dBZlKDUEugyCTQTc4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\nOzs7AIClpSXu37+P+Ph4DBkyBAYGBrC0tIS1tTWSk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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting cummulative Returns of portfolio as well as four factors. \n", + "plt.plot(np.cumsum(portfolio_returns.fillna(method = 'ffill')), label = 'Portfolio Returns')\n", + "plt.plot(np.cumsum(tradeoff_returns), linestyle = 'dashed', label = 'SPYVol Factor')\n", + "plt.plot(np.cumsum(momentum_returns), linestyle = 'dashed', label = 'Momentum Factor')\n", + "plt.plot(np.cumsum(value_returns), linestyle = 'dashed', label = 'Value Factor')\n", + "plt.plot(np.cumsum(volatility_returns), linestyle= 'dashed', label = 'Volatility Factor')\n", + "plt.legend(ncol = 5, bbox_to_anchor = (1, 1))\n", + "plt.ylabel('Returns')\n", + "plt.title('Factor Returns');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that our portfolio of combined factors does better than it would had we constructed a single portfolio. *To state with confidence that the combination of factors is more beneficial, we would have to check it’s out of sample performances.*\n", + "\n", + "Note however that the factor's performance varies with time, whereas our weights do not. This is a cause for concern especially when markets change regime, as our factors are no longer weighed optimally. We tackle that problem in the next section on Dynamic Weighting." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----\n", + "\n", + "# Dynamic Weighting.\n", + "While stationary weights are helpful at flavoring weaker signals, it does not insure against an ever-changing landscape. To ensure that each asset's assigned weight truly is the optimal one, we need to dynamically allocate them, which simply means allocating the weights as a function of time.\n", + "There are multiple ways of going about it; one crude but feasible option is with if and for statements. In this lecture however, we will work with the scikit library to use Machine Learning to find the optimal weights associated with each factor.\n", + "\n", + "### Defining and building the pipeline\n", + "We start our study by picking 5 factors which will be implemented in our algorithm. Here we chose `EBITDA to Yield`, as well as `EBIT to Assets`, `Net income margin`, `3 month volume`, and `Working Capital to Assets`. You can find all datasets and how to implement them in research and algorithm environments in Quantopian's [data page](https://www.quantopian.com/data)." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Defining factors. \n", + "from quantopian.pipeline.data.zacks import EarningsSurprises\n", + "\n", + "def make_factors(): \n", + " def Asset_To_Equity_Ratio():\n", + " return bs.total_assets.latest / bs.common_stock_equity.latest\n", + "\n", + " def Capex_To_Cashflows():\n", + " return (cfs.capital_expenditure.latest * 4.) / \\\n", + " (cfs.free_cash_flow.latest * 4.)\n", + " \n", + " def EBITDA_Yield():\n", + " return (is_.ebitda.latest * 4.) / \\\n", + " USEquityPricing.close.latest \n", + " \n", + " def EPS_Surprise():\n", + " return EarningsSurprises.eps_amt_diff_surp.latest\n", + "\n", + " all_factors = {\n", + " 'Asset_To_Equity_Ratio' : Asset_To_Equity_Ratio,\n", + " 'Capex_To_Cashflows' : Capex_To_Cashflows,\n", + " 'EBITDA_Yield' : EBITDA_Yield, \n", + " 'EPS_Surprise' : EPS_Surprise,\n", + " } \n", + " \n", + " return all_factors" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "In the interest of saving time, we have defined a `make_factors` function, which returns a dictionary of all our factors. Now that we have the factors set up, we need to find a universe to work on. We chose the predefined Q500US equities universe. Because of the common practices associated with machine learning, we set about building a Train-Test universe, along with historic data, and 'future' data which our model will attempt to predict. Here we will predict 3-days returns and train our model on daily data. *You can read more about the machine learning classification processes [here](https://en.wikipedia.org/wiki/Machine_learning#Model_assessments).* \n", + "\n", + "------\n", + "\n", + "### Running pipeline\n", + "Bellow, we run pipeline and get our historic data by defining a `make_history_pipeline` function which takes `factors`, `universe`, and the `number_of_forward_days` as parameters and outputs a dataframe of assets that meet our qualifications. You need not bother with the logistics of this function, besides knowing that plugging it into the pipeline will output a Multi-Index DataFrame with days and assets as indices, and ranked factors with respect to each other as values. Recall, `n_fwd_days = 3` since we are trying to predict 3-day returns." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Defining universe and factors.\n", + "universe = Q500US()\n", + "factors = make_factors()\n", + "\n", + "# number of days to compute returns over\n", + "n_fwd_days = 3\n", + "\n", + "# Defining historic pipeline.\n", + "def make_history_pipeline(factors, universe, n_fwd_days=3):\n", + " # Call .rank() on all factors and mask out the universe\n", + " factor_ranks = {name: f().rank(mask=universe) for name, f in factors.iteritems()}\n", + " # Get cumulative returns over last n_fwd_days days. We will later shift these.\n", + " factor_ranks['Returns'] = Returns(inputs=[USEquityPricing.open],\n", + " mask=universe, window_length=n_fwd_days)\n", + " \n", + " pipe = Pipeline(screen=universe, columns=factor_ranks)\n", + " \n", + " return pipe\n", + "\n", + "history_pipe = make_history_pipeline(factors, universe, n_fwd_days=n_fwd_days)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Running Historic Pipeline.\n", + "start = pd.Timestamp(\"2013-01-01\")\n", + "end = pd.Timestamp(\"2013-05-01\")\n", + "results = run_pipeline(history_pipe, start_date=start, end_date=end)\n", + "results.index.names = ['Date', 'Security']" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Asset_To_Equity_RatioCapex_To_CashflowsEBITDA_YieldEPS_SurpriseReturns
DateSecurity
2013-03-11 00:00:00+00:00Equity(42277 [ZNGA])52.0254.0373.0285.0-0.008357
2013-01-03 00:00:00+00:00Equity(5822 [PCP])34.0133.0121.053.00.017599
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" + ], + "text/plain": [ + " Asset_To_Equity_Ratio \\\n", + "Date Security \n", + "2013-03-11 00:00:00+00:00 Equity(42277 [ZNGA]) 52.0 \n", + "2013-01-03 00:00:00+00:00 Equity(5822 [PCP]) 34.0 \n", + "\n", + " Capex_To_Cashflows \\\n", + "Date Security \n", + "2013-03-11 00:00:00+00:00 Equity(42277 [ZNGA]) 254.0 \n", + "2013-01-03 00:00:00+00:00 Equity(5822 [PCP]) 133.0 \n", + "\n", + " EBITDA_Yield EPS_Surprise \\\n", + "Date Security \n", + "2013-03-11 00:00:00+00:00 Equity(42277 [ZNGA]) 373.0 285.0 \n", + "2013-01-03 00:00:00+00:00 Equity(5822 [PCP]) 121.0 53.0 \n", + "\n", + " Returns \n", + "Date Security \n", + "2013-03-11 00:00:00+00:00 Equity(42277 [ZNGA]) -0.008357 \n", + "2013-01-03 00:00:00+00:00 Equity(5822 [PCP]) 0.017599 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.sample(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have our five factors associated with each asset. We would like to find a way to combine theses weakly predictive factors such that we maximize alpha. *Note the Returns columns: this the column we are trying to predict.*\n", + "\n", + "However, before successfully digging into the `scikit` classification, we need to do some additional transformation to our data to match the required inputs : \n", + "- Our factor ranks need to be shifted in order to align with the future return `n_fwd_days` in the future. \n", + "- We need to find the top and bottom 30% stocks based on their return *(we do this to ignore stocks that don't move that much)*. \n", + "- And finally, we need to bin the returns by their percentile to turn the Machine Learning problem into a classification one. \n", + "\n", + "We define the `shift_mask_data` utility function to do all this for us. As well as the `get_last_value` function that will give us the last factor values. *(You can ignore this function for now as it will be use later in the lecture.)*" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def shift_mask_data(X, Y, upper_percentile=70, lower_percentile=30, n_fwd_days=1):\n", + " # Shift X to match factors at t to returns at t+n_fwd_days (we want to predict future returns after all)\n", + " shifted_X = np.roll(X, n_fwd_days+1, axis=0)\n", + " \n", + " # Slice off rolled elements\n", + " X = shifted_X[n_fwd_days+1:]\n", + " Y = Y[n_fwd_days+1:]\n", + " \n", + " n_time, n_stocks, n_factors = X.shape\n", + " \n", + " # Look for biggest up and down movers\n", + " upper = np.nanpercentile(Y, upper_percentile, axis=1)[:, np.newaxis]\n", + " lower = np.nanpercentile(Y, lower_percentile, axis=1)[:, np.newaxis]\n", + " \n", + " upper_mask = (Y >= upper)\n", + " lower_mask = (Y <= lower)\n", + " \n", + " mask = upper_mask | lower_mask # This also drops nans\n", + " mask = mask.flatten()\n", + " \n", + " # Only try to predict whether a stock moved up/down relative to other stocks\n", + " Y_binary = np.zeros(n_time * n_stocks)\n", + " Y_binary[upper_mask.flatten()] = 1\n", + " Y_binary[lower_mask.flatten()] = -1\n", + " \n", + " # Flatten X\n", + " X = X.reshape((n_time * n_stocks, n_factors))\n", + "\n", + " # Drop stocks that did not move much (i.e. are in the 30th to 70th percentile)\n", + " X = X[mask]\n", + " Y_binary = Y_binary[mask]\n", + " \n", + " return X, Y_binary\n", + "\n", + "def get_last_values(input_data):\n", + " last_values = []\n", + " for dataset in input_data:\n", + " last_values.append(dataset[-1])\n", + " return np.vstack(last_values).T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before successfully running the `shift_mask_data`, we need to make sure our dataset matches the required input of our function. Namely, we need our data to be of the `numpy.array` type and not `pandas DataFrame` which we currently have. *The challenge here is not compromising the data as we shift from the time sensitive DataFrame to the array.* " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Manipulate the data to be in the form expected by shift_mask_data()\n", + "\n", + "results_wo_returns = results.copy()\n", + "returns = results_wo_returns.pop('Returns')\n", + "\n", + "Y = returns.unstack().values\n", + "X = results_wo_returns.to_panel() \n", + "X = X.swapaxes(2, 0).swapaxes(0, 1).values # (factors, time, stocks) -> (time, stocks, factors)...\n", + "\n", + "results_wo_returns.index = results_wo_returns.index.set_levels(\n", + " results_wo_returns.index.get_level_values(1).map(lambda x: x.symbol), \n", + " 1)\n", + "results_wo_returns.index = results_wo_returns.index.set_levels(\n", + " results_wo_returns.index.get_level_values(0).map(lambda x: x.date), \n", + " 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we used the `unstack` function to change the returns from a Multi-Index series to a DataFrame. \n", + "We also use the `to_panel` function to transform the stacked dataframe format into a 3D panel. \n", + "*You can read more about the unstack and to_panel functions and what they do [here](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.unstack.html) and [here](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_panel.html)* We can print the results out to make sure we have not compromised the structure of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RETURNS:\n", + "Date Security \n", + "2013-01-02 00:00:00+00:00 Equity(2 [ARNC]) -0.026286\n", + " Equity(24 [AAPL]) -0.005859\n", + "Name: Returns, dtype: float64\n", + "----\n", + "Y VALUES:\n", + "[-0.02628571 -0.00585934]\n" + ] + } + ], + "source": [ + "print 'RETURNS:\\n', returns.head(2)\n", + "print '----'\n", + "print 'Y VALUES:\\n', Y[0][0:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RESULTS WITHOUT RETURNS:\n", + " Asset_To_Equity_Ratio Capex_To_Cashflows EBITDA_Yield \\\n", + "Date Security \n", + "2013-01-02 ARNC 318.0 439.0 16.0 \n", + " AAPL 69.0 123.0 383.0 \n", + "\n", + " EPS_Surprise \n", + "Date Security \n", + "2013-01-02 ARNC NaN \n", + " AAPL 91.0 \n", + "----\n", + "X VALUES:\n", + "[ 318. 439. 16. nan]\n" + ] + } + ], + "source": [ + "print 'RESULTS WITHOUT RETURNS:\\n', results_wo_returns.head(2)\n", + "print '----'\n", + "print 'X VALUES:\\n', X[0][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have all the data set up and verified, we need to split it into training (80%) and testing(20%). Our model will attempt to match the training data, and run it on the 'testing' data as an accuracy test. \n", + "*Note : Since we are dealing with a timeseries, we need to split along the time dimension to make sure we only test on future data.*" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Train-test split\n", + "train_size_perc = 0.5\n", + "n_time, n_stocks, n_factors = X.shape\n", + "train_size = np.int16(np.round(train_size_perc * n_time))\n", + "X_train, Y_train = X[:train_size, ...], Y[:train_size]\n", + "X_test, Y_test = X[(train_size+n_fwd_days):, ...], Y[(train_size+n_fwd_days):]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X_train_shift, Y_train_shift = shift_mask_data(X_train, Y_train, n_fwd_days=n_fwd_days)\n", + "X_test_shift, Y_test_shift = shift_mask_data(X_test, Y_test, n_fwd_days=n_fwd_days, \n", + " lower_percentile=50, \n", + " upper_percentile=50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our Train and Test arrays ready, we can set about testing our data using Machine Learning Classifiers.\n", + "\n", + "----\n", + "\n", + "### Testing our ML Pipeline.\n", + "Now that we have our train and test datasets, we can run our model at test it for its accuracy. We will use the `scikit` library's `Gradient Boosting Classifier` to 'rank' our factors from most to least valuable. We begin by defining imputer and scaler, which transform the data for completing missing values and standardizes the data by scaling it to a given range. \n", + "*You can read about scikit's Gradient Boosting Classifier and other classifiers [here](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier)*" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Train classifier\n", + "imputer = preprocessing.Imputer() # Imputation transformer for completing missing values.\n", + "scaler = preprocessing.MinMaxScaler() # Standardizes features by scaling each feature to a given range.\n", + "\n", + "# n_estimators controls how many weak classifiers are fit\n", + "clf = GradientBoostingClassifier(n_estimators=200, learning_rate=0.1) \n", + "\n", + "X_train_trans = imputer.fit_transform(X_train_shift)\n", + "X_train_trans = scaler.fit_transform(X_train_trans)\n", + "\n", + "clf.fit(X_train_trans, Y_train_shift);" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy on train set = 63.69%\n" + ] + } + ], + "source": [ + "Y_pred = clf.predict(X_train_trans)\n", + "print('Accuracy on train set = {:.2f}%'.format(metrics.accuracy_score(Y_train_shift, Y_pred) * 100))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Transform test data\n", + "X_test_trans = imputer.transform(X_test_shift)\n", + "X_test_trans = scaler.transform(X_test_trans)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To test the accuracy of our model, we use the `metrics` parameter to find the accuracy score, and the log loss of our predicted data, and our predicted probability. You can get more information about how to implement these by doing `metrics.accuracy_score??`, or `metrics.log_loss??`. *You can also read more about log_loss [here](https://en.wikipedia.org/wiki/Loss_functions_for_classification)*" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Predictions\n", + "Y_pred = clf.predict(X_test_trans)\n", + "\n", + "Y_pred_prob = clf.predict_proba(X_test_trans)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy on test set = 51.17%\n", + "Log-loss = 0.70141\n" + ] + } + ], + "source": [ + "print('Accuracy on test set = {:.2f}%'.format(metrics.accuracy_score(Y_test_shift, Y_pred) * 100))\n", + "print('Log-loss = {:.5f}'.format(metrics.log_loss(Y_test_shift, Y_pred_prob)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can visualize our factors using bar graphs, ranking them from most to least valuable based on their Gini Coefficient. You can read more about Gini Coefficients [here](https://en.wikipedia.org/wiki/Gini_coefficient). We have reached an important part of our study. By knowing which factors are more valuable that the others *(in each time period)*, we can start assigning greater weights to the best rank factors. Furthermore, since the process is automated, our model can account for changes over time, and adapt itself to perpetually tell us what the most valuable factor is given the data. In the next step, we implement our study into pipeline to do exactly that." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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KiiQpJiZGhw4dUn19vSSpvLxcoaGhioqKksfj0cCBA/X+++9LkizL\nsjMWAAAAAPjYWooqKysVHh7u2w4LC1NlZeVZj4WHh6uiokKSVFpaqilTpmjkyJHavHmznREBAAAA\nGM72e4q+6lwrQKePdenSRRkZGUpNTVV5ebnGjBmj9evXKzDw3FG3bt16SbM6qayszO0IkLRz507V\n1dW5HcNozIWWg/ngLuZCy8FccB/zoWW43OeCraUoMjLStzIkSRUVFerYsaPv2BdffOE7duDAAUVG\nRioyMlKpqamSpE6dOikiIkIHDhxQdHT0Oc+VmJhow3fgjJCQEGntfrdjGC8uLk7du3d3O4bRmAst\nB/PBXcyFloO54D7mQ8twOcyFcy2i2Hr5XHJysgoKCiRJJSUlioqKUnBwsCQpOjpa9fX12rt3r06d\nOqWNGzeqf//+WrNmjRYtWiRJqqqqUnV1taKiouyMCQAAAMBgtq4UJSQkqFevXkpLS5PX61V2drby\n8/MVEhKilJQUzZ49W9OmTZMkDRs2TJ07d1ZERIQyMzOVnp4uy7I0Z84cv5fOAQAAAMDFsr1tnC49\np8XGxvpe9+3bt8kjuiWpXbt2ev755+2OBQAAAACSbL58DgAAAABaOkoRAAAAAKNRigAAAAAYjVIE\nAAAAwGiUIgAAAABGoxQBAAAAMBqlCAAAAIDRKEUAAAAAjEYpAgAAAGA0ShEAAAAAo1GKAAAAABiN\nUgQAAADAaJQiAAAAAEajFAEAAAAwGqUIAAAAgNEoRQAAAACMRikCAAAAYDRKEQAAAACjUYoAAAAA\nGI1SBAAAAMBolCIAAAAARqMUAQAAADAapQgAAACA0ShFAAAAAIxGKQIAAABgNEoRAAAAAKNRigAA\nAAAYjVIEAAAAwGiUIgAAAABGoxQBAAAAMBqlCAAAAIDRKEUAAAAAjEYpAgAAAGA0ShEAAAAAo1GK\nAAAAABiNUgQAAADAaJQiAAAAAEajFAEAAAAwGqUIAAAAgNEoRQAAAACMRikCAAAAYDRKEQAAAACj\nUYoAAAAAGI1SBAAAAMBolCIAAAAARqMUAQAAADAapQgAAACA0ShFAAAAAIxGKQIAAABgNEoRAAAA\nAKNRigAAAAAYjVIEAAAAwGiUIgAAAABGoxQBAAAAMBqlCAAAAIDRAu0+QU5OjrZv3y6Px6OsrCzF\nx8f7jm3evFkLFiyQ1+vVgAEDNGXKFL9jAAAAAOBSsrUUFRUVqaysTHl5eSotLdXMmTOVl5fnOz5v\n3jy99NJLioyM1KhRozRkyBBVV1efcwwAAAAAXEq2lqLCwkKlpKRIkmJiYnTo0CHV19erXbt2Ki8v\nV2hoqKKioiRJAwcOVGFhoaqrq5sdAwAAAACXmq2lqLKyUnFxcb7tsLAwVVZWql27dqqsrFR4eLjv\nWHh4uMrLy3Xw4MFmx1zOjtRWuB3BaPz8Ww5+F+7jd9Ay8HtwH7+DloPfhbtM+Pnbfk/RV1mWdcHH\nzjXmq7Zu3XpRmVqKxVnD3Y5gvLq6uq/9v0eXA+ZCy8B8cB9zoWVgLrQMzAf3Xe5zwdZSFBkZqcrK\nSt92RUWFOnbs6Dv2xRdf+I4dOHBAkZGRatWqVbNjmpOYmHiJkwMAAAAwha2P5E5OTlZBQYEkqaSk\nRFFRUQoODpYkRUdHq76+Xnv37tWpU6e0ceNG9e/f/5xjAAAAAOBS81jne33aRXrqqae0ZcsWeb1e\nZWdn65NPPlFISIhSUlL04Ycf6oknnpAk3XLLLRo3btxZx8TGxtoZEQAAAIDBbC9FAAAAANCS2Xr5\nHAAAAAC0dJQiAAAAAEajFAEAAAAwGqUIAAAAgNEc/fBWXH5OnDihiooKXX311W5HAVy1detW7dq1\nSx6PR/Hx8erdu7fbkQAAwHni6XO4aG+99ZZ+/etfS5LWrl2rRx55RHFxcbr11ltdTgY4a/78+Sot\nLVVSUpJOnjypDz/8UNdee61++tOfuh0NcNyBAwdUUVGh+Ph4vfXWW9q5c6fS0tLUuXNnt6MBjli0\naNE5j2dkZDiUBBeCy+dw0XJzc7V69WqFhYVJkv73f/9XK1ascDkV4LyPP/5Yv/nNbzRx4kRNmTJF\nS5cu1QcffOB2LMAVP//5zxUQEKAdO3YoLy9P3//+9zV37ly3YwGOCQsLU1hYmMrLy7Vjxw61bt1a\nQUFBKi4u1r59+9yOh2Zw+RwumtfrVVBQkDwejyQpKCjI5USAO06dOqXjx4+rdevWkqTjx4+roaHB\n5VSAOwICAtSrVy89/vjjGjt2rJKSkvTcc8+5HQtwzMiRIyVJf/7zn7V06VLf/okTJ+q+++5zKxb8\noBThovXp00f/+7//qwMHDmjJkiX685//rBtuuMHtWIDjRo8ereHDhysmJkaWZelf//qXfvazn7kd\nC3DFqVOntGTJEr3zzjuaOnWqSkpKdOTIEbdjAY6rqKjQZ599pu7du0uSysrKtGfPHpdToTncU4T/\nyocffqji4mIFBQXpmmuu0bXXXut2JMAVhw8f1j//+U95PB59+9vfVrt27dyOBLhiz549WrdunQYM\nGKDu3btr7dq16ty5s+Lj492OBjiqsLBQTz31lPbs2aOAgABFRUXpgQce0I033uh2NJwFpQgXbdeu\nXaqqqlL//v313HPPqaSkRBMmTFBiYqLb0QBHLF68WFOmTNG0adN8l5F+1ZNPPulCKsBdP/jBD3Tt\ntdfquuuu03XXXaeIiAi3IwGAX5QiXLS0tDQ98cQTKisrU15enmbNmqXp06frt7/9rdvRAEeUlJSo\nV69eKiwsPOvxfv36OZwIcN/Jkyf1ySef6KOPPlJxcbFqa2vVtWtXzZkzx+1ogCOuv/76s/6hzLIs\neTyeZv8/A+7iniJctKCgIF199dV68cUXlZ6erqioKDU2NrodC3BMr169JEmvvfaann766SbH0tPT\nKUUwUqtWrRQSEqIrrrhCHTp00IkTJ1RXV+d2LMAx77//vtsRcBEoRbhorVq10qxZs7Rt2zb98pe/\n1KZNm3Tq1Cm3YwGOKSgo0JIlS/Tpp5+qf//+vv2nTp1St27dXEwGuOe6665Tjx49lJaWpgceeEBX\nXnml25EAV+zfv1/PPfecamtr9cwzz+itt97Stddeq+joaLej4Sy4fA4X7fDhwyosLNS1116rjh07\nqrCwUN/61reY7DDOkiVLNGHCBN92QECASktL9Z3vfMfFVIA7ioqKVFxcrJ07d+rEiRPq2rWr+vTp\no5tuusntaICjJkyYoDFjxug3v/mNXnnlFRUWFmrx4sVavny529FwFpQiXLANGzYoJSVFubm5Zz1+\n+vn8gCnq6ur01ltvqaamRtKX91SsWrVKGzdudDcY4KLy8nIVFxcrPz9f//jHP/TXv/7V7UiAo8aP\nH69ly5Zp9OjRviI0atQovfLKKy4nw9lw+Rwu2Olrww8ePOhyEqBlmDp1quLj4/XHP/5Rd9xxhz74\n4ANlZWW5HQtwxeTJk7V3717FxMQoKSlJM2fOZNUURgoMDFRhYaEaGxtVWVmp9evX+z7kGy0PK0W4\naHPnzlV2drbbMQDXjR07Vr/73e98fw08fvy4pk6dqueff97taIDj/vnPf6p169b69NNP5fV61aNH\nD0VFRbkdC3BcRUWFFi5cqOLiYrVq1UrXXHONMjIyFBkZ6XY0nAUrRbhoAQEBeu2119S7d2+1atXK\nt5+/CMI0x48f19///ne1bt1a77//vjp16sSnlsNYGzdu1JtvvqmEhASdOHFCTz31lNLT05WWluZ2\nNMARJ06cUFBQkEJCQjRr1iy34+A8sVKEizZ69Ogz9nk8Hr388ssupAHcs2vXLlVXV+vKK6/Uww8/\nrJqaGo0cOVL33HOP29EAx6WlpSk3N1der1fSl/fYjRkzRq+++qrLyQBnZGZm6sknn9SgQYOafF7R\n6c8peuedd1xMh+ZQivBfqamp0e7duxUQEKAuXbqoffv2bkcCWoSamhqFhoa6HQNwXFpaml599VXf\nfww2NjZq1KhRWrFihcvJAGdUVVXxKPqvIS6fw0V74YUXtHLlSnXr1k2WZam0tFTp6elNHk0MXM6K\ni4s1c+ZM1dbW6qqrrtITTzyhzp0767XXXtOSJUv4ayCMdPPNN2vEiBFKSEiQZVkqLi7W7bff7nYs\nwDHp6em68847NW7cuCa3F6BlY6UIF+32229XXl6egoKCJH15X0V6erpWr17tcjLAGenp6crJyVGX\nLl1UWFiohQsXSvryvrqf/exn/KUQxtq9e7dKSkoUEBCgnj17qlOnTm5HAhxTX1+vF198URs2bNCU\nKVOUmprqdiScB1aKcNGio6PV2NjYZF/Xrl1dSgM4LzAwUF26dJEk9evXT48++qgee+wx9ezZ091g\ngAuefPLJJvdPnFZSUiJJmjZtmtORAFe0a9dOU6dOVXp6uubOnasXX3xRV199te/46T+goWWhFOGi\nHTt2TIMGDVLv3r1lWZZKSkr0ne98R1OnTpXEpMfl7//+B2BYWBiFCMb61re+5XYEoMU4duyYVq5c\nqX/961+69957WS39GqAU4aLde++9vqcLASaqqanRX//6V992bW1tk+3+/fu7EQtwxR//+EctXbpU\nkydP5jO6YLSVK1dq6dKluu2227R69Wo+sPVrglKEi7Zo0SLl5ua6HQNwTWxsrN544w3fdvfu3X3b\nHo+HUgSjeL1e9evXT3V1dWf9d/+rfzAALmc7duxQbm6uIiIi3I6CC8CDFnDRHnzwQZ08eVLx8fFN\nnq4ycuRIF1MBLcvrr7/+/9u7/6iq6zuO46+LCHlkoR0Xqauzg1bSUtkRGZtuHrQdy6lpaiM9ioqm\nqOE8Dg01ECFxesoStM1SETlmZosJ0hV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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "feature_importances = pd.Series(clf.feature_importances_, index=results_wo_returns.columns)\n", + "feature_importances = feature_importances.sort_values(ascending = False)\n", + "ax = feature_importances.plot(kind = 'bar')\n", + "ax.set(ylabel = 'Importance (Gini Coefficient)', title = 'Feature importances')\n", + "ax.grid(axis = 'x');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Out of Sample Testing.\n", + "Now that we have successfully found the most valuable factors in the period from `2013-01-01` to `2013-05-01`, we can now find the most important factors during the period `2013-05-01 - 2013-10-01`. Running the same functions as before, we again use the GradientBoostinClassifier to classify our factors, using a training size of 0.8." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Running Historic Pipeline.\n", + "start = pd.Timestamp(\"2013-05-01\")\n", + "end = pd.Timestamp(\"2013-10-01\")\n", + "results = run_pipeline(history_pipe, start_date=start, end_date=end)\n", + "results.index.names = ['Date', 'Security']" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Manipulate the data to be in the form expected by shift_mask_data()\n", + "\n", + "results_wo_returns = results.copy()\n", + "returns = results_wo_returns.pop('Returns')\n", + "\n", + "Y = returns.unstack().values\n", + "X = results_wo_returns.to_panel() \n", + "X = X.swapaxes(2, 0).swapaxes(0, 1).values # (factors, time, stocks) -> (time, stocks, factors)...\n", + "\n", + "results_wo_returns.index = results_wo_returns.index.set_levels(\n", + " results_wo_returns.index.get_level_values(1).map(lambda x: x.symbol), \n", + " 1)\n", + "results_wo_returns.index = results_wo_returns.index.set_levels(\n", + " results_wo_returns.index.get_level_values(0).map(lambda x: x.date), \n", + " 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Train-test split\n", + "train_size_perc = 0.5\n", + "n_time, n_stocks, n_factors = X.shape\n", + "train_size = np.int16(np.round(train_size_perc * n_time))\n", + "X_train, Y_train = X[:train_size, ...], Y[:train_size]\n", + "X_test, Y_test = X[(train_size+n_fwd_days):, ...], Y[(train_size+n_fwd_days):]" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X_train_shift, Y_train_shift = shift_mask_data(X_train, Y_train, n_fwd_days=n_fwd_days)\n", + "X_test_shift, Y_test_shift = shift_mask_data(X_test, Y_test, n_fwd_days=n_fwd_days, \n", + " lower_percentile=50, \n", + " upper_percentile=50)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Train classifier\n", + "imputer = preprocessing.Imputer() # Imputation transformer for completing missing values.\n", + "scaler = preprocessing.MinMaxScaler() # Standardizes features by scaling each feature to a given range.\n", + "\n", + "# n_estimators controls how many weak classifiers are fit\n", + "clf = GradientBoostingClassifier(n_estimators=200, learning_rate=0.1) \n", + "\n", + "X_train_trans = imputer.fit_transform(X_train_shift)\n", + "X_train_trans = scaler.fit_transform(X_train_trans)\n", + "\n", + "clf.fit(X_train_trans, Y_train_shift);" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy on train set = 61.37%\n" + ] + } + ], + "source": [ + "Y_pred = clf.predict(X_train_trans)\n", + "print('Accuracy on train set = {:.2f}%'.format(metrics.accuracy_score(Y_train_shift, Y_pred) * 100))" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Transform test data\n", + "X_test_trans = imputer.transform(X_test_shift)\n", + "X_test_trans = scaler.transform(X_test_trans)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Predictions\n", + "Y_pred = clf.predict(X_test_trans)\n", + "\n", + "Y_pred_prob = clf.predict_proba(X_test_trans)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy on test set = 50.40%\n", + "Log-loss = 0.70547\n" + ] + } + ], + "source": [ + "print('Accuracy on test set = {:.2f}%'.format(metrics.accuracy_score(Y_test_shift, Y_pred) * 100))\n", + "print('Log-loss = {:.5f}'.format(metrics.log_loss(Y_test_shift, Y_pred_prob)))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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t0ksvvaTw8HCNGzdOI0aMkCQlJCRo6dKldkYDAAAAAEk2Xz5XUFCg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\nSIeoc2IFihDChXv3R6ebP7gmA+/ntvt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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "feature_importances = pd.Series(clf.feature_importances_, index=results_wo_returns.columns)\n", + "feature_importances = feature_importances.sort_values(ascending = False)\n", + "ax = feature_importances.plot(kind = 'bar')\n", + "ax.set(ylabel = 'Importance (Gini Coefficient)', title = 'Feature importances')\n", + "ax.grid(axis = 'x');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the bar graph above, we can clearly see the advantage of dynamically allocated factor aggregation. Had we assigned static weights given the Gini score during the five months starting January 2013, we would have given greater importance to the EPS-Surprise factor, which greatly underperforms in the period from May to December of 2013.\n", + " \n", + " \n", + "*If you'd like to read more about alpha-factor aggregation and how to incorporate them into a trading algorithm, check out the 'Machine Learning on Quantopian : Building an Algorithm' posts [here](https://www.quantopian.com/posts/machine-learning-on-quantopian-part-3-building-an-algorithm).*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. (\"Quantopian\"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company. In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file