Step3_FactorCombination_AdaBoost_Quantopian.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Step 3: Factor Combination: AdaBoost\n",
    "\n",
    "In this part, we combine the factors to synthesis mega alpha using AdaBoost in the quantopian platform since we want the US stock analysis(cannot download locally freely). Fortunately, there are several machine learning libraries such as scikit learning available in Quantopian so that we can pull and analysis data online. Note the code is only valid in Quantopian $\\rightarrow$ Research $\\rightarrow$ Notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from quantopian.research import run_pipeline\n",
    "from quantopian import research\n",
    "from quantopian.pipeline import Pipeline\n",
    "from quantopian.pipeline.factors import Latest\n",
    "from quantopian.pipeline.data.builtin import USEquityPricing\n",
    "from quantopian.pipeline.data import morningstar\n",
    "from quantopian.pipeline.factors import CustomFactor, SimpleMovingAverage, AverageDollarVolume, Returns, RSI\n",
    "from quantopian.pipeline.classifiers.morningstar import Sector\n",
    "from quantopian.pipeline.filters import Q500US, Q1500US, QTradableStocksUS\n",
    "from quantopian.pipeline.data.quandl import fred_usdontd156n as libor\n",
    "from quantopian.pipeline.data.zacks import EarningsSurprises\n",
    "from quantopian.pipeline.factors import CustomFactor\n",
    "from quantopian.pipeline.data import USEquityPricing\n",
    "from quantopian.pipeline import data\n",
    "from quantopian.pipeline import filters\n",
    "from quantopian.pipeline import factors\n",
    "from quantopian.pipeline import classifiers\n",
    "\n",
    "\n",
    "import talib\n",
    "import pandas as pd\n",
    "idx = pd.IndexSlice\n",
    "import seaborn as sns; sns.set()\n",
    "\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "from time import time\n",
    "\n",
    "import alphalens as al\n",
    "import pyfolio as pf\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# from Ipython import display\n",
    "from sklearn import linear_model, decomposition, ensemble, preprocessing, isotonic, metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "bs = morningstar.balance_sheet\n",
    "cfs = morningstar.cash_flow_statement\n",
    "is_ = morningstar.income_statement\n",
    "or_ = morningstar.operation_ratios\n",
    "er = morningstar.earnings_report\n",
    "v = morningstar.valuation\n",
    "vr = morningstar.valuation_ratios\n",
    "\n",
    "\n",
    "def make_factors():\n",
    "    def Asset_Growth_3M():\n",
    "        return Returns(inputs=[bs.total_assets], window_length=63)\n",
    "\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 EBIT_To_Assets():\n",
    "        return (is_.ebit.latest * 4.) / \\\n",
    "            bs.total_assets.latest\n",
    "        \n",
    "    def Earnings_Quality():\n",
    "        return morningstar.cash_flow_statement.operating_cash_flow.latest / \\\n",
    "               EarningsSurprises.eps_act.latest\n",
    "        \n",
    "    def Return_On_Total_Invest_Capital():\n",
    "        return or_.roic.latest\n",
    "    \n",
    "    class Mean_Reversion_1M(CustomFactor):\n",
    "        inputs = [Returns(window_length=21)]\n",
    "        window_length = 252\n",
    "\n",
    "        def compute(self, today, assets, out, monthly_rets):\n",
    "            out[:] = (monthly_rets[-1] - np.nanmean(monthly_rets, axis=0)) / \\\n",
    "                np.nanstd(monthly_rets, axis=0)\n",
    "                \n",
    "    class MACD_Signal_10d(CustomFactor):\n",
    "        inputs = [USEquityPricing.close]\n",
    "        window_length = 60\n",
    "\n",
    "        def compute(self, today, assets, out, close):\n",
    "\n",
    "            sig_lines = []\n",
    "\n",
    "            for col in close.T:\n",
    "                # get signal line only\n",
    "                try:\n",
    "                    _, signal_line, _ = talib.MACD(col, fastperiod=12,\n",
    "                                                   slowperiod=26, signalperiod=10)\n",
    "                    sig_lines.append(signal_line[-1])\n",
    "                # if error calculating, return NaN\n",
    "                except:\n",
    "                    sig_lines.append(np.nan)\n",
    "            out[:] = sig_lines \n",
    "            \n",
    "    class Moneyflow_Volume_5d(CustomFactor):\n",
    "        inputs = [USEquityPricing.close, USEquityPricing.volume]\n",
    "        window_length = 5\n",
    "\n",
    "        def compute(self, today, assets, out, close, volume):\n",
    "\n",
    "            mfvs = []\n",
    "\n",
    "            for col_c, col_v in zip(close.T, volume.T):\n",
    "\n",
    "                # denominator\n",
    "                denominator = np.dot(col_c, col_v)\n",
    "\n",
    "                # numerator\n",
    "                numerator = 0.\n",
    "                for n, price in enumerate(col_c.tolist()):\n",
    "                    if price > col_c[n - 1]:\n",
    "                        numerator += price * col_v[n]\n",
    "                    else:\n",
    "                        numerator -= price * col_v[n]\n",
    "\n",
    "                mfvs.append(numerator / denominator)\n",
    "            out[:] = mfvs  \n",
    "            \n",
    "           \n",
    "    def Net_Income_Margin():\n",
    "        return or_.net_margin.latest           \n",
    "\n",
    "    def Operating_Cashflows_To_Assets():\n",
    "        return (cfs.operating_cash_flow.latest * 4.) / \\\n",
    "            bs.total_assets.latest\n",
    "\n",
    "    def Price_Momentum_3M():\n",
    "        return Returns(window_length=63)\n",
    "    \n",
    "    class Price_Oscillator(CustomFactor):\n",
    "        inputs = [USEquityPricing.close]\n",
    "        window_length = 252\n",
    "\n",
    "        def compute(self, today, assets, out, close):\n",
    "            four_week_period = close[-20:]\n",
    "            out[:] = (np.nanmean(four_week_period, axis=0) /\n",
    "                      np.nanmean(close, axis=0)) - 1.\n",
    "    \n",
    "    def Returns_39W():\n",
    "        return Returns(window_length=215)\n",
    "    \n",
    "    class Trendline(CustomFactor):\n",
    "        inputs = [USEquityPricing.close]\n",
    "        window_length = 252\n",
    "\n",
    "        # using MLE for speed\n",
    "        def compute(self, today, assets, out, close):\n",
    "\n",
    "            # prepare X matrix (x_is - x_bar)\n",
    "            X = range(self.window_length)\n",
    "            X_bar = np.nanmean(X)\n",
    "            X_vector = X - X_bar\n",
    "            X_matrix = np.tile(X_vector, (len(close.T), 1)).T\n",
    "\n",
    "            # prepare Y matrix (y_is - y_bar)\n",
    "            Y_bar = np.nanmean(close, axis=0)\n",
    "            Y_bars = np.tile(Y_bar, (self.window_length, 1))\n",
    "            Y_matrix = close - Y_bars\n",
    "\n",
    "            # prepare variance of X\n",
    "            X_var = np.nanvar(X)\n",
    "\n",
    "            # multiply X matrix an Y matrix and sum (dot product)\n",
    "            # then divide by variance of X\n",
    "            # this gives the MLE of Beta\n",
    "            out[:] = (np.sum((X_matrix * Y_matrix), axis=0) / X_var) / \\\n",
    "                (self.window_length)\n",
    "        \n",
    "    class Vol_3M(CustomFactor):\n",
    "        inputs = [Returns(window_length=2)]\n",
    "        window_length = 63\n",
    "\n",
    "        def compute(self, today, assets, out, rets):\n",
    "            out[:] = np.nanstd(rets, axis=0)\n",
    "            \n",
    "    def Working_Capital_To_Assets():\n",
    "        return bs.working_capital.latest / bs.total_assets.latest\n",
    "    def Sector_Code():\n",
    "        return data.Fundamentals.morningstar_sector_code.latest   \n",
    "    \n",
    "    all_factors = {\n",
    "        'Asset Growth 3M': Asset_Growth_3M,\n",
    "        'Asset to Equity Ratio': Asset_To_Equity_Ratio,\n",
    "        'Capex to Cashflows': Capex_To_Cashflows,\n",
    "        'EBIT to Assets': EBIT_To_Assets,\n",
    "        'EBITDA Yield': EBITDA_Yield,        \n",
    "        'Earnings Quality': Earnings_Quality,\n",
    "        'MACD Signal Line': MACD_Signal_10d,\n",
    "        'Mean Reversion 1M': Mean_Reversion_1M,\n",
    "        'Moneyflow Volume 5D': Moneyflow_Volume_5d,\n",
    "        'Net Income Margin': Net_Income_Margin,        \n",
    "        'Operating Cashflows to Assets': Operating_Cashflows_To_Assets,\n",
    "        'Price Momentum 3M': Price_Momentum_3M,\n",
    "        'Price Oscillator': Price_Oscillator,\n",
    "        'Return on Invest Capital': Return_On_Total_Invest_Capital,\n",
    "        '39 Week Returns': Returns_39W,\n",
    "        'Trendline': Trendline,\n",
    "        'Vol 3M': Vol_3M,\n",
    "        'Working Capital to Assets': Working_Capital_To_Assets,\n",
    "#         \"sector_code\": Sector_Code\n",
    "    }        \n",
    "    \n",
    "    return all_factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "HELPER FUNCTIONS\n",
    "\"\"\"\n",
    "\n",
    "def plus_dm_helper(high, low):\n",
    "    \"\"\"\n",
    "    Returns positive directional movement. Abstracted for use with more complex factors\n",
    "\n",
    "    https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/DMI\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    high : np.array\n",
    "        matrix of high prices\n",
    "    low : np.array\n",
    "        matrix of low prices\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    np.array : matrix of positive directional movement\n",
    "\n",
    "    \"\"\"\n",
    "    # get daily differences between high prices\n",
    "    high_diff = (high - np.roll(high, 1, axis=0))[1:]\n",
    "\n",
    "    # get daily differences between low prices\n",
    "    low_diff = (np.roll(low, 1, axis=0) - low)[1:]\n",
    "\n",
    "    # matrix of positive directional movement\n",
    "    return np.where(((high_diff > 0) | (low_diff > 0)) & (high_diff > low_diff), high_diff, 0.)\n",
    "\n",
    "def minus_dm_helper(high, low):\n",
    "    \"\"\"\n",
    "    Returns negative directional movement. Abstracted for use with more complex factors\n",
    "\n",
    "    https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/DMI\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    high : np.array\n",
    "        matrix of high prices\n",
    "    low : np.array\n",
    "        matrix of low prices\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    np.array : matrix of negative directional movement\n",
    "\n",
    "    \"\"\"\n",
    "    # get daily differences between high prices\n",
    "    high_diff = (high - np.roll(high, 1, axis=0))[1:]\n",
    "\n",
    "    # get daily differences between low prices\n",
    "    low_diff = (np.roll(low, 1, axis=0) - low)[1:]\n",
    "\n",
    "    # matrix of megative directional movement\n",
    "    return np.where(((high_diff > 0) | (low_diff > 0)) & (high_diff < low_diff), low_diff, 0.)\n",
    "\n",
    "\n",
    "def trange_helper(high, low, close):\n",
    "    \"\"\"\n",
    "    Returns true range\n",
    "\n",
    "    http://www.macroption.com/true-range/\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    high : np.array\n",
    "        matrix of high prices\n",
    "    low : np.array\n",
    "        matrix of low prices\n",
    "    close: np.array\n",
    "        matrix of close prices\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    np.array : matrix of true range\n",
    "\n",
    "    \"\"\"\n",
    "    # define matrices to be compared\n",
    "    close = close[:-1]\n",
    "    high = high[1:]\n",
    "    low = low[1:]\n",
    "\n",
    "    # matrices for comparison\n",
    "    high_less_close = high - close\n",
    "    close_less_low = close - low\n",
    "    high_less_low = high - low\n",
    "\n",
    "    # return maximum value for each cel\n",
    "    return np.maximum(high_less_close, close_less_low, high_less_low)\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Techincal Analysis Indicators\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "class AD(CustomFactor):\n",
    "    \"\"\"\n",
    "    Chaikin Accumulation Distribution Line\n",
    "\n",
    "    Volume indicator\n",
    "\n",
    "    **Default Inputs:** USEquityPricing.high, USEquityPricing.low, USEquitypricing.close, USEquityPricing.volume\n",
    "\n",
    "    **Default Window Length:** 14\n",
    "\n",
    "    http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:accumulation_distribution_line\n",
    "    \"\"\"\t    \n",
    "    inputs = [USEquityPricing.close, USEquityPricing.high, USEquityPricing.low, USEquityPricing.volume]\n",
    "    window_length = 14\n",
    "\n",
    "    def compute(self, today, assets, out, close, high, low, vol):\n",
    "\n",
    "        # close location value\n",
    "        clv = ((close - low) - (high - close)) / (high - low)\n",
    "        ad = clv * vol\n",
    "        out[:] =  np.sum(ad, axis=0)\n",
    "\n",
    "\n",
    "class ADX(CustomFactor):\n",
    "    \"\"\"\n",
    "    Average Directional Movement Index\n",
    "\n",
    "    Momentum indicator. Smoothed DX\n",
    "\n",
    "    **Default Inputs:** USEquityPricing.high, USEquityPricing.low, USEquitypricing.close\n",
    "\n",
    "    **Default Window Length:** 29\n",
    "\n",
    "    https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/DMI\n",
    "    \"\"\"\t    \n",
    "    inputs = [USEquityPricing.high, USEquityPricing.low, USEquityPricing.close]\n",
    "    window_length = 29\n",
    "\n",
    "    def compute(self, today, assets, out, high, low, close):\n",
    "\n",
    "        # positive directional index\n",
    "        plus_di = 100 * np.cumsum(plus_dm_helper(high, low) / trange_helper(high, low, close), axis=0)\n",
    "\n",
    "        # negative directional index\n",
    "        minus_di = 100 * np.cumsum(minus_dm_helper(high, low) / trange_helper(high, low, close), axis=0)\n",
    "\n",
    "        # full dx with 15 day burn-in period\n",
    "        dx_frame = (np.abs(plus_di - minus_di) / (plus_di + minus_di) * 100.)[14:]\n",
    "\n",
    "        # 14-day EMA\n",
    "        span = 14.\n",
    "        decay_rate = 2. / (span + 1.)\n",
    "        weights = weights_long = np.full(span, decay_rate, float) ** np.arange(span + 1, 1, -1)\n",
    "\n",
    "        # return EMA\n",
    "        out[:] = np.average(dx_frame, axis=0, weights=weights)\n",
    "\n",
    "\n",
    "def APO(shortperiod=12, longperiod=26):\n",
    "    \"\"\"\n",
    "    Absolute Price Oscillator\n",
    "\n",
    "    Momentum indeicator. Difference between EWMAs (exponential weighted moving averages) of \n",
    "    short and long periods.\n",
    "\n",
    "    **Default Inputs:** 12, 26\n",
    "\n",
    "    **Default Window Length:** 46 (26 + 20-day burn-in)\n",
    "\n",
    "    https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/apo\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    shortperiod : int > 0\n",
    "        window length for short EWMA\n",
    "    longperiod : int > 0 (>= shortperiod)\n",
    "        window length for longer EWMA\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    zipline.Factor\n",
    "    \"\"\"\n",
    "    buffer_window = longperiod + 20\n",
    "\n",
    "    fast = EWMA.from_span(inputs=[USEquityPricing.close], window_length=buffer_window, span=shortperiod)\n",
    "    slow = EWMA.from_span(inputs=[USEquityPricing.close], window_length=buffer_window, span=longperiod) \n",
    "    return fast - slow\n",
    "\n",
    "\n",
    "class ATR(CustomFactor):\n",
    "    \"\"\"\n",
    "    Average True Range\n",
    "\n",
    "    Momentum indicator\n",
    "\n",
    "    **Default Inputs:** USEquityPricing.high, USEquityPricing.low, USEquityPricing.close\n",
    "\n",
    "    **Default Window Length:** 15 (14+1)\n",
    "\n",
    "    https://en.wikipedia.org/wiki/Average_true_range\n",
    "    \"\"\"\n",
    "    inputs=[USEquityPricing.high, USEquityPricing.low, USEquityPricing.close]\n",
    "    window_length = 15\n",
    "\n",
    "    def compute(self, today, assets, out, high, low, close):\n",
    "\n",
    "        tr_frame = trange_helper(high, low, close)\n",
    "        decay_rate= 2./(len(tr_frame) + 1.)\n",
    "        weights = np.full(len(tr_frame), decay_rate, float) ** np.arange(len(tr_frame) + 1, 1, -1)\n",
    "        out[:] = np.average(tr_frame, axis=0, weights=weights)\n",
    "\n",
    "\n",
    "class BETA(CustomFactor):\n",
    "    \"\"\"\n",
    "    Market Beta (returns)\n",
    "\n",
    "    **Default Inputs:** USEquityPricing.close\n",
    "\n",
    "    **Default Window Length:** 6\n",
    "\n",
    "    https://en.wikipedia.org/wiki/Beta_(finance)\n",
    "    \"\"\"\n",
    "\n",
    "    inputs=[USEquityPricing.close]\n",
    "    window_length = 6\n",
    "\n",
    "    def compute(self, today, assets, out, close):\n",
    "\n",
    "        # get returns dataset\n",
    "        returns = ((close - np.roll(close, 1, axis=0)) / np.roll(close, 1, axis=0))[1:]\n",
    "\n",
    "        # get index of benchmark\n",
    "        benchmark_index = np.where((assets == 8554) == True)[0][0]\n",
    "\n",
    "        # get returns of benchmark\n",
    "        benchmark_returns = returns[:, benchmark_index]\n",
    "\n",
    "        # prepare X matrix (x_is - x_bar)\n",
    "        X = benchmark_returns\n",
    "        X_bar = np.nanmean(X)\n",
    "        X_vector = X - X_bar\n",
    "        X_matrix = np.tile(X_vector, (len(returns.T), 1)).T\n",
    "\n",
    "        # prepare Y matrix (y_is - y_bar)\n",
    "        Y_bar = np.nanmean(close, axis=0)\n",
    "        Y_bars = np.tile(Y_bar, (len(returns), 1))\n",
    "        Y_matrix = returns - Y_bars\n",
    "\n",
    "        # prepare variance of X\n",
    "        X_var = np.nanvar(X)\n",
    "\n",
    "        # multiply X matrix an Y matrix and sum (dot product)\n",
    "        # then divide by variance of X\n",
    "        # this gives the MLE of Beta\n",
    "        out[:] = (np.sum((X_matrix * Y_matrix), axis=0) / X_var) / (len(returns))\n",
    "\n",
    "\n",
    "class BOP(CustomFactor):\n",
    "    \"\"\"\n",
    "    Balance of Power\n",
    "\n",
    "    Momentum indicator\n",
    "\n",
    "    **Default Inputs:** USEquityPricing.close, USEquityPricing.open, USEquityPricing.high, USEquityPricing.low\n",
    "\n",
    "    **Default Window Length:** 1\n",
    "\n",
    "    https://www.interactivebrokers.com/en/software/tws/usersguidebook/technicalanalytics/balancepower.htm\n",
    "    \"\"\"\n",
    "    inputs = [USEquityPricing.close, USEquityPricing.open, USEquityPricing.high, USEquityPricing.low]\n",
    "    window_length = 1\n",
    "\n",
    "    def compute(self, today, assets, out, close, open, high, low):\n",
    "        out[:] = (close - open) / (high - low)\n",
    "\n",
    "\n",
    "class CCI(CustomFactor):\n",
    "    \"\"\"\n",
    "    Commodity Channel Index\n",
    "\n",
    "    Momentum indicator\n",
    "\n",
    "    **Default Inputs:** USEquityPricing.close, USEquityPricing.high, USEquityPricing.low\n",
    "\n",
    "    **Default Window Length:** 14\n",
    "\n",
    "    http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:commodity_channel_index_cci\n",
    "    \"\"\"\n",
    "\n",
    "    inputs = [USEquityPricing.high, USEquityPricing.low, USEquityPricing.close]\n",
    "    window_length = 14\n",
    "\n",
    "    def compute(self, today, assets, out, high, low, close):\n",
    "\n",
    "        # typical price matrix\n",
    "        typical_prices = (high + low + close) / 3.\n",
    "\n",
    "        # mean of each column\n",
    "        mean_typical = np.nanmean(typical_prices, axis=0)\n",
    "\n",
    "        # mean deviation\n",
    "        mean_deviation = np.sum(np.abs(typical_prices - np.tile(mean_typical, (len(typical_prices), 1))), axis=0) / self.window_length\n",
    "\n",
    "        # CCI\n",
    "        out[:] = (typical_prices[-1] - mean_typical) / (.015 * mean_deviation)\n",
    "\n",
    "\n",
    "class CMO(CustomFactor):\n",
    "    \"\"\"\n",
    "    Chande Momentum Oscillator\n",
    "\n",
    "    Momentum indicator. Descriptor of overought/oversold conditions\n",
    "\n",
    "    **Default Inputs:** USEquityPricing.close\n",
    "\n",
    "    **Default Window Length:** 15 (14 + 1-day for differences)\n",
    "\n",
    "    https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/cmo\n",
    "    \"\"\"\n",
    "    inputs=[USEquityPricing.close]\n",
    "    window_length = 15\n",
    "\n",
    "    def compute(self, today, assets, out, close):\n",
    "\n",
    "        # daily differences in close prices\n",
    "        close_diff = (close - np.roll(close, 1, axis=0))[1:]\n",
    "\n",
    "        # close differences on up days\n",
    "        su = np.sum(np.where(close_diff > 0, close_diff, 0), axis=0)\n",
    "\n",
    "        # absolute value of close differences on down days\n",
    "        sd = np.abs(np.sum(np.where(close_diff < 0, close_diff, 0), axis=0))\n",
    "\n",
    "        # CMO\n",
    "        out[:] = 100 * (su - sd) / (su + sd)\n",
    "\n",
    "\n",
    "class DX(CustomFactor):\n",
    "    \"\"\"\n",
    "    Directional Movement Index\n",
    "\n",
    "    Momentum indicator\n",
    "\n",
    "    **Default Inputs:** USEquityPricing.high, USEquityPricing.low, USEquitypricing.close\n",
    "\n",
    "    **Default Window Length:** 15\n",
    "\n",
    "    https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/DMI\n",
    "    \"\"\"\t    \n",
    "    inputs = [USEquityPricing.high, USEquityPricing.low, USEquityPricing.close]\n",
    "    window_length = 15\n",
    "\n",
    "    def compute(self, today, assets, out, high, low, close):\n",
    "\n",
    "        # positive directional index\n",
    "        plus_di = 100 * np.sum(plus_dm_helper(high, low) / (trange_helper(high, low, close)), axis=0)\n",
    "\n",
    "        # negative directional index\n",
    "        minus_di = 100 * np.sum(minus_dm_helper(high, low) / (trange_helper(high, low, close)), axis=0)\n",
    "\n",
    "        # DX\n",
    "        out[:] = np.abs(plus_di - minus_di) / (plus_di + minus_di) * 100.\n",
    "\n",
    "\n",
    "class LINEARREG_SLOPE(CustomFactor):\n",
    "    \"\"\"\n",
    "    Slope of Trendline\n",
    "\n",
    "    Momentum indicator.\n",
    "\n",
    "    **Default Inputs:**  USEquitypricing.close\n",
    "\n",
    "    **Default Window Length:** 14\n",
    "\n",
    "    http://www.stat.cmu.edu/~cshalizi/mreg/15/lectures/06/lecture-06.pdf\n",
    "    \"\"\"\t    \n",
    "    inputs=[USEquityPricing.close]\n",
    "    window_length=14\n",
    "\n",
    "    # using MLE for speed\n",
    "    def compute(self, today, assets, out, close):\n",
    "\n",
    "        # prepare X matrix (x_is - x_bar)\n",
    "        X = range(self.window_length)\n",
    "        X_bar = np.nanmean(X)\n",
    "        X_vector = X - X_bar\n",
    "        X_matrix = np.tile(X_vector, (len(close.T), 1)).T\n",
    "\n",
    "        # prepare Y matrix (y_is - y_bar)\n",
    "        Y_bar = np.nanmean(close, axis=0)\n",
    "        Y_bars = np.tile(Y_bar, (self.window_length, 1))\n",
    "        Y_matrix = close - Y_bars\n",
    "\n",
    "        # prepare variance of X\n",
    "        X_var = np.nanvar(X)\n",
    "\n",
    "        # multiply X matrix an Y matrix and sum (dot product)\n",
    "        # then divide by variance of X\n",
    "        # this gives the MLE of Beta\n",
    "        out[:] = (np.sum((X_matrix * Y_matrix), axis=0) / X_var) / (self.window_length)\n",
    "\n",
    "\n",
    "class LINEARREG_INTERCEPT(CustomFactor):\n",
    "    \"\"\"\n",
    "    Intercept of Trendline\n",
    "\n",
    "    **Default Inputs:**  USEquitypricing.close\n",
    "\n",
    "    **Default Window Length:** 14\n",
    "\n",
    "    http://www.stat.cmu.edu/~cshalizi/mreg/15/lectures/06/lecture-06.pdf\n",
    "    \"\"\"\n",
    "    inputs=[USEquityPricing.close]\n",
    "    window_length=14\n",
    "\n",
    "    # using MLE\n",
    "    def compute(self, today, assets, out, close):\n",
    "\n",
    "        # prepare X matrix (x_is - x_bar)\n",
    "        X = range(self.window_length)\n",
    "        X_bar = np.nanmean(X)\n",
    "        X_vector = X - X_bar\n",
    "        X_matrix = np.tile(X_vector, (len(close.T), 1)).T\n",
    "\n",
    "        # prepare Y vectors (y_is - y_bar)\n",
    "        Y_bar = np.nanmean(close, axis=0)\n",
    "        Y_bars = np.tile(Y_bar, (self.window_length, 1))\n",
    "        Y_matrix = close - Y_bars\n",
    "        \n",
    "        # prepare variance of X\n",
    "        X_var = np.nanvar(X)\n",
    "\n",
    "        # multiply X matrix an Y matrix and sum (dot product)\n",
    "        # then divide by variance of X\n",
    "        # this gives the MLE of Beta\n",
    "        betas = (np.sum((X_matrix * Y_matrix), axis=0) / X_var) / (self.window_length)\n",
    "\n",
    "        # now use to get to MLE of alpha\n",
    "        out[:] = Y_bar - (betas * X_bar)\n",
    "\n",
    "\n",
    "class MAX(CustomFactor):\n",
    "    \"\"\"\n",
    "    Maximum value for each column of a dataset\n",
    "\n",
    "    **Default Inputs:**  None\n",
    "\n",
    "    **Default Window Length:** None\n",
    "    \"\"\"\n",
    "    def compute(self, today, assets, out, data):\n",
    "        # get vector of maximums\n",
    "        out[:] = np.nanmax(data, axis=0)\n",
    "\n",
    "\n",
    "class MAXINDEX(CustomFactor):\n",
    "    \"\"\"\n",
    "    Index of maximum value for each column of a dataset\n",
    "\n",
    "    **Default Inputs:**  None\n",
    "\n",
    "    **Default Window Length:** None\n",
    "    \"\"\"\n",
    "    def compute(self, today, assets, out, data):\n",
    "        # get vector of indices\n",
    "        out[:] = np.argmax(data, axis=0)\n",
    "\n",
    "\n",
    "class MEDPRICE(CustomFactor):\n",
    "    \"\"\"\n",
    "    Mean of a day's high and low prices\n",
    "\n",
    "    **Default Inputs:**  USEquityPricing.high, USEquityPricing.low\n",
    "\n",
    "    **Default Window Length:** 1\n",
    "\n",
    "    http://www.fmlabs.com/reference/default.htm?url=MedianPrices.htm\n",
    "    \"\"\"\t    \n",
    "    inputs = [USEquityPricing.high, USEquityPricing.low]\n",
    "    window_length = 1\n",
    "\n",
    "    def compute(self, today, assets, out, high, low):\n",
    "        out[:] = (high + low) / 2.\n",
    "\n",
    "\n",
    "class MFI(CustomFactor):\n",
    "    \"\"\"\n",
    "    Money Flow Index\n",
    "\n",
    "    Volume Indicator\n",
    "\n",
    "    **Default Inputs:**  USEquityPricing.high, USEquityPricing.low, USEquityPricing.close, USEquityPricing.volume\n",
    "\n",
    "    **Default Window Length:** 15 (14 + 1-day for difference in prices)\n",
    "\n",
    "    http://www.fmlabs.com/reference/default.htm?url=MoneyFlowIndex.htm\n",
    "    \"\"\"\t    \n",
    "\n",
    "    inputs = [USEquityPricing.high, USEquityPricing.low, USEquityPricing.close, USEquityPricing.volume]\n",
    "    window_length = 15\n",
    "\n",
    "    def compute(self, today, assets, out, high, low, close, vol):\n",
    "\n",
    "        # calculate typical price\n",
    "        typical_price = (high + low + close) / 3.\n",
    "\n",
    "        # calculate money flow of typical price\n",
    "        money_flow = typical_price * vol\n",
    "\n",
    "        # get differences in daily typical prices\n",
    "        tprice_diff = (typical_price - np.roll(typical_price, 1, axis=0))[1:]\n",
    "\n",
    "        # create masked arrays for positive and negative money flow\n",
    "        pos_money_flow = np.ma.masked_array(money_flow[1:], tprice_diff < 0, fill_value = 0.)\n",
    "        neg_money_flow = np.ma.masked_array(money_flow[1:], tprice_diff > 0, fill_value = 0.)\n",
    "\n",
    "        # calculate money ratio\n",
    "        money_ratio = np.sum(pos_money_flow, axis=0) / np.sum(neg_money_flow, axis=0)\n",
    "\n",
    "        # MFI\n",
    "        out[:] = 100. - (100. / (1. + money_ratio))\n",
    "\n",
    "\n",
    "class MIDPOINT(CustomFactor):\n",
    "    \"\"\"\n",
    "    Average of maximum and minimum column values in a dataset\n",
    "\n",
    "    **Default Inputs:**  None\n",
    "\n",
    "    **Default Window Length:** None\n",
    "    \"\"\"\n",
    "    def compute(self, today, assets, out, data):\n",
    "        out[:] = (np.nanmax(data, axis=0) + np.nanmin(data, axis=0)) / 2.\n",
    "\n",
    "\n",
    "class MIN(CustomFactor):\n",
    "    \"\"\"\n",
    "    Minimum column values in a dataset\n",
    "\n",
    "    **Default Inputs:**  None\n",
    "\n",
    "    **Default Window Length:** None\n",
    "    \"\"\"\n",
    "    def compute(self, today, assets, out, data):\n",
    "        out[:] = np.nanmin(data, axis=0)\n",
    "\n",
    "\n",
    "class MININDEX(CustomFactor):\n",
    "    \"\"\"\n",
    "    Index of minimum column values in a dataset\n",
    "\n",
    "    **Default Inputs:**  None\n",
    "\n",
    "    **Default Window Length:** None\n",
    "    \"\"\"\n",
    "    def compute(self, today, assets, out, data):\n",
    "        out[:] = np.argmin(data, axis=0)\n",
    "\n",
    "\n",
    "class MINUS_DI(CustomFactor):\n",
    "    \"\"\"\n",
    "    Negative directional indicator\n",
    "\n",
    "    Momentum indicator\n",
    "\n",
    "    **Default Inputs:** USEquityPricing.high, USEquityPricing.low, USEquityPricing.close\n",
    "\n",
    "    **Default Window Length:** 15\n",
    "\n",
    "    https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/DMI\n",
    "    \"\"\"\t\n",
    "\n",
    "    inputs = [USEquityPricing.high, USEquityPricing.low, USEquityPricing.close]\n",
    "    window_length = 15\n",
    "\n",
    "    def compute(self, today, assets, out, high, low, close):\n",
    "            out[:] = 100 * np.sum(minus_dm_helper(high, low), axis=0) / np.sum(trange_helper(high, low, close), axis=0)\n",
    "\n",
    "\n",
    "class MINUS_DM(CustomFactor):\n",
    "    \"\"\"\n",
    "    Negative directional movement\n",
    "\n",
    "    Momentum indicator\n",
    "\n",
    "    **Default Inputs:** USEquityPricing.high, USEquityPricing.low\n",
    "\n",
    "    **Default Window Length:** 15\n",
    "\n",
    "    https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/DMI\n",
    "    \"\"\"\t\n",
    "\n",
    "    inputs = [USEquityPricing.high, USEquityPricing.low]\n",
    "    window_length = 15\n",
    "\n",
    "    def compute(self, today, assets, out, high, low):\n",
    "            out[:] = np.sum(minus_dm_helper(high, low), axis=0)\n",
    "\n",
    "\n",
    "class PLUS_DI(CustomFactor):\n",
    "    \"\"\"\n",
    "    Positive directional indicator\n",
    "\n",
    "    Momentum indicator\n",
    "\n",
    "    **Default Inputs:** USEquityPricing.high, USEquityPricing.low, USEquityPricing.close\n",
    "\n",
    "    **Default Window Length:** 15\n",
    "\n",
    "    https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/DMI\n",
    "    \"\"\"\t\n",
    "    inputs = [USEquityPricing.high, USEquityPricing.low, USEquityPricing.close]\n",
    "    window_length = 15\n",
    "\n",
    "    def compute(self, today, assets, out, high, low, close):\n",
    "            out[:] = 100 * np.sum(plus_dm_helper(high, low), axis=0) / np.sum(trange_helper(high, low, close), axis=0)\n",
    "\n",
    "\n",
    "class PLUS_DM(CustomFactor):\n",
    "    \"\"\"\n",
    "    Positive directional movement\n",
    "\n",
    "    Momentum indicator\n",
    "\n",
    "    **Default Inputs:** USEquityPricing.high, USEquityPricing.low\n",
    "\n",
    "    **Default Window Length:** 15\n",
    "\n",
    "    https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/DMI\n",
    "    \"\"\"\t\n",
    "    inputs = [USEquityPricing.high, USEquityPricing.low]\n",
    "    window_length = 15\n",
    "\n",
    "    def compute(self, today, assets, out, high, low):\n",
    "            out[:] = np.sum(plus_dm_helper(high, low), axis=0)\n",
    "\n",
    "\n",
    "def PPO(fast_period=12, slow_period=26):\n",
    "    \"\"\"\n",
    "    Function to produce CustomFactor of Percentage Price Oscillator\n",
    "    Called in same way as a normal class, but used in order to give variable\n",
    "    fast- and slow- periods\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    fast_period : int > 0\n",
    "        shorter period moving average \n",
    "    slow_period : int > 0 (> fast_period)\n",
    "        longer period moving average\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    zipline.CustomFactor : Percentage Price Oscillator factor\n",
    "\n",
    "    **Default Inputs:**  12, 26\n",
    "\n",
    "    http://www.investopedia.com/terms/p/ppo.asp\n",
    "    \"\"\"  \n",
    "    class PPO_(CustomFactor):\n",
    "\n",
    "        inputs=[USEquityPricing.close]\n",
    "        window_length = slow_period\n",
    "\n",
    "        def compute(self, today, assets, out, close):\n",
    "            slowMA = np.mean(close, axis=0)\n",
    "            fastMA = np.mean(close[-fast_period:], axis=0)\n",
    "            out[:] = ((fastMA - slowMA) / slowMA) * 100.\n",
    "\n",
    "    return PPO_()\n",
    "\n",
    "\n",
    "class STDDEV(CustomFactor):\n",
    "    \"\"\"\n",
    "    Standard Deviations of columns in a dataset\n",
    "\n",
    "    **Default Inputs:**  None\n",
    "\n",
    "    **Default Window Length:** None\n",
    "    \"\"\"\n",
    "    def compute(self, today, assets, out, data):\n",
    "        out[:] = np.nanstd(data, axis=0)\n",
    "\n",
    "\n",
    "class TRANGE(CustomFactor):\n",
    "    \"\"\"\n",
    "    True Range \n",
    "\n",
    "    **Default Inputs:**  USEquityPricing.high, USEquityPricing.low, USEquityPricing.close\n",
    "\n",
    "    **Default Window Length:** 2\n",
    "\n",
    "    https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/atr\n",
    "    \"\"\"    \n",
    "    inputs = [USEquityPricing.high, USEquityPricing.low, USEquityPricing.close]\n",
    "    window_length = 2\n",
    "\n",
    "    def compute(self, today, assets, out, high, low, close):\n",
    "        out[:] = np.nanmax([(high[-1] - close[0]), (close[0] - low[-1]), (high[-1] - low[-1])], axis=0)\n",
    "\n",
    "\n",
    "class TYPPRICE(CustomFactor):\n",
    "    \"\"\"\n",
    "    Typical Price \n",
    "\n",
    "    **Default Inputs:**  USEquityPricing.high, USEquityPricing.low, USEquityPricing.close\n",
    "\n",
    "    **Default Window Length:** 1\n",
    "\n",
    "    https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/typical-price\n",
    "    \"\"\"    \n",
    "    inputs = [USEquityPricing.high, USEquityPricing.low, USEquityPricing.close]\n",
    "    window_length = 1\n",
    "\n",
    "    def compute(self, today, assets, out, high, low, close):\n",
    "        out[:] = (high + low + close) / 3.\n",
    "\n",
    "\n",
    "class VAR(CustomFactor):\n",
    "    \"\"\"\n",
    "    Variances of columns in a dataset\n",
    "\n",
    "    **Default Inputs:**  None\n",
    "\n",
    "    **Default Window Length:** None\n",
    "    \"\"\"\n",
    "    def compute(self, today, assets, out, data):\n",
    "        out[:] = np.nanvar(data, axis=0)\n",
    "\n",
    "\n",
    "class WILLR(CustomFactor):\n",
    "    \"\"\"\n",
    "    Williams %R\n",
    "\n",
    "    **Default Inputs:**  USEquityPricing.high, USEquityPricing.low, USEquityPricing.close\n",
    "\n",
    "    **Default Window Length:** 14\n",
    "\n",
    "    http://www.fmlabs.com/reference/default.htm?url=WilliamsR.htm\n",
    "    \"\"\"    \t    \n",
    "    inputs = [USEquityPricing.high, USEquityPricing.low, USEquityPricing.close]\n",
    "    window_length = 14\n",
    "\n",
    "    def compute(self, today, assets, out, high, low, close):\n",
    "        out[:] = (np.nanmax(high, axis=0) - close[-1]) / (np.nanmax(high, axis=0) - np.nanmin(low, axis=0)) * -100.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def make_factors2():\n",
    "    class Value(CustomFactor):\n",
    "    \n",
    "        inputs = [data.Fundamentals.book_value_yield,\n",
    "              data.Fundamentals.sales_yield,\n",
    "              data.Fundamentals.fcf_yield] \n",
    "    \n",
    "        window_length = 1\n",
    "    \n",
    "        def compute(self, today, assets, out, book_value, sales, fcf):\n",
    "            value_table = pd.DataFrame(index=assets)\n",
    "            value_table[\"book_value\"] = book_value[-1]\n",
    "            value_table[\"sales\"] = sales[-1]\n",
    "            value_table[\"fcf\"] = fcf[-1]\n",
    "            out[:] = value_table.rank().mean(axis=1)\n",
    "    \n",
    "    \n",
    "    class Momentum(CustomFactor):\n",
    "    \n",
    "        inputs = [USEquityPricing.close]\n",
    "        window_length = 252\n",
    "    \n",
    "        def compute(self, today, assets, out, close):\n",
    "            out[:] = close[-20] / close[0]\n",
    "\n",
    "            \n",
    "    class Quality(CustomFactor):\n",
    "    \n",
    "        inputs = [data.Fundamentals.gross_profit, data.Fundamentals.total_assets]\n",
    "        window_length = 1\n",
    "    \n",
    "        def compute(self, today, assets, out, gross_profit, total_assets):       \n",
    "            out[:] = gross_profit[-1] / total_assets[-1]\n",
    "        \n",
    "        \n",
    "    class Volatility(CustomFactor):\n",
    "    \n",
    "        inputs = [USEquityPricing.close]\n",
    "        window_length = 252\n",
    "    \n",
    "        def compute(self, today, assets, out, close):  \n",
    "            close = pd.DataFrame(data=close, columns=assets) \n",
    "            # Since we are going to rank largest is best we need to invert the sdev.\n",
    "            out[:] = 1 / np.log(close).diff().std()\n",
    "        \n",
    "    class CashLiquidity(CustomFactor):\n",
    "        inputs = [data.Fundamentals.cash, data.Fundamentals.total_assets]\n",
    "        window_length = 1\n",
    "    \n",
    "        def compute(self, today, assets, out, cash, total_assets):\n",
    "            out[:] = cash[-1]/total_assets[-1]\n",
    "        \n",
    "    class ChangeInOutstanding(CustomFactor):\n",
    "        inputs = [data.Fundamentals.shares_outstanding]\n",
    "        window_length=22\n",
    "        def compute(self, today, assets, out, outstanding):\n",
    "            out[:] = outstanding[-1] - outstanding[-20]\n",
    "            \n",
    "    def Sector_Code():\n",
    "        return data.Fundamentals.morningstar_sector_code.latest\n",
    "    \n",
    "    def Net_Income_Margin():\n",
    "        return or_.net_margin.latest \n",
    "    def Market_Cap():\n",
    "        return data.Fundamentals.market_cap.latest\n",
    "    def Pb_Ratio():\n",
    "        return data.Fundamentals.pb_ratio.latest\n",
    "    def Ps_Ratio():\n",
    "        return data.Fundamentals.ps_ratio.latest\n",
    "    def Priceto_Ebitda():\n",
    "        return data.Fundamentals.priceto_ebitda.latest\n",
    "    def Roe():\n",
    "        return data.Fundamentals.roe.latest\n",
    "    def Roa():\n",
    "        return data.Fundamentals.roa.latest\n",
    "    def Sales_Yield():\n",
    "        return data.Fundamentals.sales_yield.latest\n",
    "    def Total_Yield():\n",
    "        return data.Fundamentals.total_yield.latest\n",
    "    \n",
    "    all_factors = {\n",
    "#         \"daily_return\": factors.Returns(window_length=2),\n",
    "        \"market_cap\": Market_Cap,\n",
    "        \n",
    "        \"pb_ratio\": Pb_Ratio,\n",
    "        'total_yield': Total_Yield,\n",
    "        'priceto_ebitda': Priceto_Ebitda,        \n",
    "        \"ps_ratio\": Ps_Ratio,\n",
    "#         \"sector_code\": Sector_Code,\n",
    "#         \"fcf_yield\": data.Fundamentals.fcf_yield.latest,\n",
    "        'gross_profit_assets_ratio': Quality,\n",
    "#         'gross_margin': data.Fundamentals.gross_margin.latest,        \n",
    "        \"roe\": Roe,\n",
    "        \"roa\": Roa,\n",
    "        \"sales_yield\": Sales_Yield,\n",
    "        \"momentum\": Momentum,\n",
    "#         \"accrued_investment_income\": data.Fundamentals.accrued_investment_income.latest,\n",
    "#         \"asset_turnover\": data.Fundamentals.assets_turnover.latest,\n",
    "#         \"cap_ex_sales_ratio\": data.Fundamentals.cap_ex_sales_ratio.latest,\n",
    "#         \"financial_health_grade\": data.Fundamentals.financial_health_grade.latest,\n",
    "        \"cash_liquidity\": CashLiquidity,\n",
    "        \"change_in_outstanding\": ChangeInOutstanding,\n",
    "        \"net_income_margin\":Net_Income_Margin,\n",
    "        \n",
    "        # Technical Analysis\n",
    "        \n",
    "        # volumn\n",
    "        \"AD\":AD, #55%\n",
    "        \"MFI\":MFI,\n",
    "        \n",
    "        # momentum\n",
    "        \"ADX\":ADX,\n",
    "        \"PPO\":PPO, #67%\n",
    "        \n",
    "#         \"WILLR\":WILLR,\n",
    "#         \"LINEARREG_SLOPE\":LINEARREG_SLOPE,\n",
    "        \"DX\":DX, #67%\n",
    "        \n",
    "        # volatility\n",
    "        \"TRANGE\":TRANGE,\n",
    "        \"ATR\":ATR,\n",
    "#         \"CMO\":CMO, #65%\n",
    "        # \n",
    "        \n",
    "        \n",
    "    }  \n",
    "    \n",
    "    return all_factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "sector = 207\n",
    "# universe = Q1500US()\n",
    "universe = QTradableStocksUS()\n",
    "# factors = make_factors()\n",
    "factors = make_factors2()\n",
    "n_fwd_days = 22 # number of days to compute returns over"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def make_history_pipeline(factors, universe, n_fwd_days=22):\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",
    "    factor_ranks[\"sector_code\"] = data.Fundamentals.morningstar_sector_code.latest\n",
    "    pipe = Pipeline(screen=universe & factor_ranks[\"sector_code\"].eq(sector), columns=factor_ranks)\n",
    "    \n",
    "    return pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "history_pipe = make_history_pipeline(factors, universe, n_fwd_days=n_fwd_days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "start_timer = time()\n",
    "start = pd.Timestamp(\"2016-03-06\")\n",
    "end = pd.Timestamp(\"2018-04-06\")\n",
    "results = run_pipeline(history_pipe, start_date=start, end_date=end)\n",
    "results.index.names = ['date', 'security']\n",
    "end_timer = time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to run pipeline 82.74 secs\n"
     ]
    }
   ],
   "source": [
    "print \"Time to run pipeline %.2f secs\" % (end_timer - start_timer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>AD</th>\n",
       "      <th>ADX</th>\n",
       "      <th>ATR</th>\n",
       "      <th>DX</th>\n",
       "      <th>MFI</th>\n",
       "      <th>PPO</th>\n",
       "      <th>Returns</th>\n",
       "      <th>TRANGE</th>\n",
       "      <th>cash_liquidity</th>\n",
       "      <th>change_in_outstanding</th>\n",
       "      <th>...</th>\n",
       "      <th>momentum</th>\n",
       "      <th>net_income_margin</th>\n",
       "      <th>pb_ratio</th>\n",
       "      <th>priceto_ebitda</th>\n",
       "      <th>ps_ratio</th>\n",
       "      <th>roa</th>\n",
       "      <th>roe</th>\n",
       "      <th>sales_yield</th>\n",
       "      <th>sector_code</th>\n",
       "      <th>total_yield</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>security</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016-03-07 00:00:00+00:00</th>\n",
       "      <th>Equity(161 [AEP])</th>\n",
       "      <td>1791.0</td>\n",
       "      <td>1529.0</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>553.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>-0.005891</td>\n",
       "      <td>1554.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>336.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1851.0</td>\n",
       "      <td>1524.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>970.0</td>\n",
       "      <td>1033.0</td>\n",
       "      <td>1102.0</td>\n",
       "      <td>1104.0</td>\n",
       "      <td>207</td>\n",
       "      <td>943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(166 [AES])</th>\n",
       "      <td>1960.0</td>\n",
       "      <td>1923.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>1794.0</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>0.108395</td>\n",
       "      <td>473.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>...</td>\n",
       "      <td>940.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>914.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>1871.0</td>\n",
       "      <td>207</td>\n",
       "      <td>1725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(612 [ATO])</th>\n",
       "      <td>249.0</td>\n",
       "      <td>1493.0</td>\n",
       "      <td>1657.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>502.0</td>\n",
       "      <td>0.008255</td>\n",
       "      <td>1668.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>359.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>892.0</td>\n",
       "      <td>682.0</td>\n",
       "      <td>988.0</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>1244.0</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>207</td>\n",
       "      <td>693.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(913 [BKH])</th>\n",
       "      <td>875.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>1067.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>522.0</td>\n",
       "      <td>1631.0</td>\n",
       "      <td>0.132771</td>\n",
       "      <td>1243.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>379.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1688.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>774.0</td>\n",
       "      <td>1385.0</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>207</td>\n",
       "      <td>826.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(1521 [CPK])</th>\n",
       "      <td>268.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>-0.116792</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1541.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1216.0</td>\n",
       "      <td>1049.0</td>\n",
       "      <td>678.0</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>1079.0</td>\n",
       "      <td>1042.0</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>207</td>\n",
       "      <td>603.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  AD     ADX     ATR      DX  \\\n",
       "date                      security                                             \n",
       "2016-03-07 00:00:00+00:00 Equity(161 [AEP])   1791.0  1529.0  1271.0   553.0   \n",
       "                          Equity(166 [AES])   1960.0  1923.0   368.0  1911.0   \n",
       "                          Equity(612 [ATO])    249.0  1493.0  1657.0    73.0   \n",
       "                          Equity(913 [BKH])    875.0  1790.0  1067.0   322.0   \n",
       "                          Equity(1521 [CPK])   268.0   484.0  1938.0   344.0   \n",
       "\n",
       "                                                 MFI     PPO   Returns  \\\n",
       "date                      security                                       \n",
       "2016-03-07 00:00:00+00:00 Equity(161 [AEP])    350.0   491.0 -0.005891   \n",
       "                          Equity(166 [AES])   1794.0  1197.0  0.108395   \n",
       "                          Equity(612 [ATO])     75.0   502.0  0.008255   \n",
       "                          Equity(913 [BKH])    522.0  1631.0  0.132771   \n",
       "                          Equity(1521 [CPK])    76.0   189.0 -0.116792   \n",
       "\n",
       "                                              TRANGE  cash_liquidity  \\\n",
       "date                      security                                     \n",
       "2016-03-07 00:00:00+00:00 Equity(161 [AEP])   1554.0             NaN   \n",
       "                          Equity(166 [AES])    473.0             NaN   \n",
       "                          Equity(612 [ATO])   1668.0             NaN   \n",
       "                          Equity(913 [BKH])   1243.0             NaN   \n",
       "                          Equity(1521 [CPK])  1943.0             NaN   \n",
       "\n",
       "                                              change_in_outstanding  \\\n",
       "date                      security                                    \n",
       "2016-03-07 00:00:00+00:00 Equity(161 [AEP])                   336.0   \n",
       "                          Equity(166 [AES])                    21.0   \n",
       "                          Equity(612 [ATO])                   359.0   \n",
       "                          Equity(913 [BKH])                   379.0   \n",
       "                          Equity(1521 [CPK])                 1541.0   \n",
       "\n",
       "                                                 ...       momentum  \\\n",
       "date                      security               ...                  \n",
       "2016-03-07 00:00:00+00:00 Equity(161 [AEP])      ...         1851.0   \n",
       "                          Equity(166 [AES])      ...          940.0   \n",
       "                          Equity(612 [ATO])      ...         2006.0   \n",
       "                          Equity(913 [BKH])      ...         1688.0   \n",
       "                          Equity(1521 [CPK])     ...         2018.0   \n",
       "\n",
       "                                              net_income_margin  pb_ratio  \\\n",
       "date                      security                                          \n",
       "2016-03-07 00:00:00+00:00 Equity(161 [AEP])              1524.0     640.0   \n",
       "                          Equity(166 [AES])               404.0     914.0   \n",
       "                          Equity(612 [ATO])              1442.0     892.0   \n",
       "                          Equity(913 [BKH])               368.0     774.0   \n",
       "                          Equity(1521 [CPK])             1216.0    1049.0   \n",
       "\n",
       "                                              priceto_ebitda  ps_ratio  \\\n",
       "date                      security                                       \n",
       "2016-03-07 00:00:00+00:00 Equity(161 [AEP])            334.0     970.0   \n",
       "                          Equity(166 [AES])             47.0     196.0   \n",
       "                          Equity(612 [ATO])            682.0     988.0   \n",
       "                          Equity(913 [BKH])           1385.0    1005.0   \n",
       "                          Equity(1521 [CPK])           678.0    1018.0   \n",
       "\n",
       "                                                 roa     roe  sales_yield  \\\n",
       "date                      security                                          \n",
       "2016-03-07 00:00:00+00:00 Equity(161 [AEP])   1033.0  1102.0       1104.0   \n",
       "                          Equity(166 [AES])    442.0   341.0       1871.0   \n",
       "                          Equity(612 [ATO])   1239.0  1244.0       1086.0   \n",
       "                          Equity(913 [BKH])    415.0   416.0       1070.0   \n",
       "                          Equity(1521 [CPK])  1079.0  1042.0       1057.0   \n",
       "\n",
       "                                              sector_code  total_yield  \n",
       "date                      security                                      \n",
       "2016-03-07 00:00:00+00:00 Equity(161 [AEP])           207        943.0  \n",
       "                          Equity(166 [AES])           207       1725.0  \n",
       "                          Equity(612 [ATO])           207        693.0  \n",
       "                          Equity(913 [BKH])           207        826.0  \n",
       "                          Equity(1521 [CPK])          207        603.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def calculate_valid_universe(df):\n",
    "        idx = pd.IndexSlice\n",
    "        valid_universe = set(df.index.get_level_values(level=1).unique())\n",
    "\n",
    "        for date,group in df.groupby(level=0):\n",
    "            size = group.shape[0]\n",
    "#             print(group.loc[idx[:,valid_universe],:])\n",
    "#             print(group.loc[idx[:,valid_universe]])\n",
    "#             print(group.loc[valid_universe])\n",
    "            new_set = set(group.loc[idx[:,valid_universe],:].index.get_level_values(1).values)\n",
    "            if(new_set<valid_universe):\n",
    "                valid_universe = new_set\n",
    "            \n",
    "        # self.valid_universe = valid_universe \n",
    "        valid_universe = np.sort(list(valid_universe))\n",
    "        return valid_universe     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def calculate_valid_dataframe(results,valid_universe):\n",
    "    n_valid_universe = len(valid_universe)\n",
    "    n_days = len(results.index.get_level_values(level=0).unique())\n",
    "    results_valid = results.loc[idx[:,valid_universe],:].copy()\n",
    "    returns_valid = results_valid.pop(\"Returns\")\n",
    "    results_valid = results_valid.ix[:-22*n_valid_universe]\n",
    "    returns_valid = returns_valid.shift(n_valid_universe*22).dropna(axis=0)\n",
    "    results_valid[\"fwd_returns\"] = returns_valid.values\n",
    "    # Preprocessing for missing values\n",
    "#     imputer = preprocessing.Imputer(strategy=\"mean\",axis=1)\n",
    "#     scaler = preprocessing.MinMaxScaler()\n",
    "#     results_valid = imputer.fit_transform(results_valid)\n",
    "#     results_valid = scaler.fit_transform(results_valid)\n",
    "    results_valid = results_valid.fillna(results_valid.mean())\n",
    "    return results_valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def results_to_tensors(results_valid):\n",
    "    n_valid_universe = len(valid_universe)\n",
    "    n_days = len(results_valid.index.get_level_values(level=0).unique())\n",
    "    X_tensor = results_valid.drop([\"sector_code\",\"fwd_returns\"],axis=1).values.reshape(n_days,n_valid_universe,-1)\n",
    "    Y_matrix = results_valid[\"fwd_returns\"].reshape(n_days,n_valid_universe)\n",
    "    return X_tensor, Y_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def train_test_split(X_tensor,Y_matrix,train_perc=0.8):\n",
    "    # Train-test split\n",
    "    n_days, n_stocks, n_factors = X_tensor.shape\n",
    "    train_size = np.int16(np.round(train_perc * n_days))\n",
    "    print(\"train size: {} days\".format(train_size))\n",
    "    X_tensor_train, Y_matrix_train = X_tensor[:train_size, ...], Y_matrix[:train_size]\n",
    "    # X_test, Y_test = X[(train_size+n_fwd_days):, ...], Y[(train_size+n_fwd_days):]\n",
    "    X_tensor_test, Y_matrix_test = X_tensor[train_size:, ...], Y_matrix[train_size:]\n",
    "    return X_tensor_train, Y_matrix_train, X_tensor_test, Y_matrix_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def mask_data(X_tensor,Y_matrix,upper_percentile=70,lower_percentile=30):\n",
    "    # X: (n_days, n_valid_universe, n_features)\n",
    "    # Y: (n_days, n_valid_universe)\n",
    "    # Look for biggest up and down movers\n",
    "    n_times,n_stocks, n_factors = np.shape(X_tensor)\n",
    "    upper = np.nanpercentile(Y_matrix, upper_percentile, axis=1)[:, np.newaxis]\n",
    "    lower = np.nanpercentile(Y_matrix, lower_percentile, axis=1)[:, np.newaxis]\n",
    "  \n",
    "    upper_mask = (Y_matrix > upper)\n",
    "    lower_mask = (Y_matrix <= lower)\n",
    "    \n",
    "    mask = upper_mask | lower_mask # This also drops nans\n",
    "    mask = mask.flatten() # mask: (n_times * n_stocks)\n",
    "    # Only try to predict whether a stock moved up/down relative to other stocks\n",
    "    Y_binary = np.zeros(n_times * n_stocks)\n",
    "    Y_binary[upper_mask.flatten()] = 1\n",
    "    Y_binary[lower_mask.flatten()] = -1\n",
    "    \n",
    "    # Flatten X\n",
    "    X = X_tensor.reshape((n_times * 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def preprocessing_transformation(X_train,X_test):\n",
    "    imputer = preprocessing.Imputer()\n",
    "    scaler = preprocessing.MinMaxScaler()\n",
    "    X_train_trans = imputer.fit_transform(X_train)\n",
    "    X_train_trans = scaler.fit_transform(X_train)\n",
    "    X_test_trans = imputer.fit_transform(X_test)\n",
    "    X_test_trans = scaler.fit_transform(X_test)\n",
    "    return X_train_trans, X_test_trans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def pca_transformation(X_train,X_test,explained_ratio_thresh=0.02):\n",
    "    pca = PCA()\n",
    "    pca.fit(X_train)\n",
    "    n_components = (pca.explained_variance_ratio_>explained_ratio_thresh).sum()\n",
    "    X_train_trans = pca.transform(X_train)[:,:n_components]\n",
    "    X_test_trans = pca.transform(X_test)[:,:n_components]\n",
    "    return X_train_trans,X_test_trans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def adaboost_classifier(X, Y_binary):\n",
    "    clf = ensemble.AdaBoostClassifier(n_estimators=400) # n_estimators controls how many weak classifiers are fitted\n",
    "    clf.fit(X, Y_binary)\n",
    "    return clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def decision_separate_plot(scores, Y_binary):\n",
    "    class_names=\"AB\"\n",
    "    plot_colors=\"br\"\n",
    "    plot_range = (scores.min(), scores.max())\n",
    "    for i, n, c in zip([-1,1], class_names, plot_colors):\n",
    "        plt.hist(scores[Y_binary == i],\n",
    "             bins=50,\n",
    "             range=plot_range,\n",
    "             facecolor=c,\n",
    "             label='Class %s' % n,\n",
    "             alpha=.5,\n",
    "             edgecolor='k')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.ylabel('Samples')\n",
    "    plt.xlabel('Score')\n",
    "    plt.title('Decision Scores')\n",
    "#         plt.ylim((0,500))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def prec_quantile_plot(scores,obs):\n",
    "    categ = pd.qcut(scores,5,labels=[20,40,60,80,100])\n",
    "#     categ = pd.qcut(scores,5,labels=\"abcde\")\n",
    "    categ = categ.get_values()\n",
    "    valid_df = pd.DataFrame()\n",
    "    valid_df[\"score\"]=scores\n",
    "    valid_df[\"obs\"] = obs\n",
    "    valid_df[\"category\"] = categ\n",
    "    binary = np.zeros(len(scores))\n",
    "    binary[scores>0]=1\n",
    "    binary[scores<=0]=-1\n",
    "    valid_df[\"score_binary\"] = binary\n",
    "    valid_df[\"is_correct\"] = valid_df[\"obs\"] * valid_df[\"score_binary\"]\n",
    "    valid_df[\"is_correct\"][valid_df[\"is_correct\"]==-1]=0\n",
    "    valid_df\n",
    "#     print(valid_df)\n",
    "    prec_dict = dict()\n",
    "    for cat,group in valid_df.groupby(by=\"category\"):\n",
    "        prec_dict[cat]=group[\"is_correct\"].sum()/len(group)\n",
    "        \n",
    "    prec_df = pd.DataFrame()\n",
    "    prec_df[\"quantile\"] = prec_dict.keys()\n",
    "    prec_df[\"precision\"] = prec_dict.values()\n",
    "    plt.ylim(0,1)\n",
    "    sns.barplot(x=\"quantile\",y=\"precision\",data=prec_df.copy())\n",
    "\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train size: 403 days\n"
     ]
    }
   ],
   "source": [
    "valid_universe = calculate_valid_universe(results)\n",
    "results_valid = calculate_valid_dataframe(results,valid_universe)\n",
    "X_tensor, Y_matrix = results_to_tensors(results_valid)\n",
    "X_tensor_train, Y_matrix_train, X_tensor_test, Y_matrix_test = \\\n",
    "    train_test_split(X_tensor,Y_matrix,train_perc=0.8)\n",
    "X_train,Y_binary_train = \\\n",
    "    mask_data(X_tensor_train,Y_matrix_train,upper_percentile=70,lower_percentile=30)\n",
    "X_test,Y_binary_test = mask_data(X_tensor_test,Y_matrix_test,upper_percentile=50,lower_percentile=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X_train_pre_trans, X_test_pre_trans = preprocessing_transformation(X_train,X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X_train_pca_trans, X_test_pca_trans = pca_transformation(X_train_pre_trans,X_test_pre_trans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABWcAAAJ8CAYAAACSmBPKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAA4AM0ZwEAAAAAAADAB/4f3UICtvOZXZoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f49fe264dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,9))\n",
    "# print(\"corr of the raw data\")\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"corr of the raw data\")\n",
    "\n",
    "X_columns = results_valid.drop([\"fwd_returns\",\"sector_code\"],axis=1).columns.values\n",
    "sns.heatmap(pd.DataFrame(X_train,columns=X_columns).corr(),annot=True)\n",
    "\n",
    "# print(\"sanity check: corr after pca transformation\")\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"sanity check: corr after pca transformation\")\n",
    "X_after_pca_df = pd.DataFrame(X_train_pca_trans)\n",
    "sns.heatmap(X_after_pca_df.corr(),annot=True)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "clf = adaboost_classifier(X_train_pca_trans,Y_binary_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on train set = 78.45%\n"
     ]
    }
   ],
   "source": [
    "Y_pred_train = clf.predict(X_train_pca_trans)\n",
    "print('Accuracy on train set = {:.2f}%'.format(metrics.accuracy_score(Y_binary_train, Y_pred_train) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on train set = 66.59%\n"
     ]
    }
   ],
   "source": [
    "Y_pred_test = clf.predict(X_test_pca_trans)\n",
    "print('Accuracy on train set = {:.2f}%'.format(metrics.accuracy_score(Y_binary_test, Y_pred_test) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "Y_train_scores = clf.decision_function(X_train_pca_trans)\n",
    "Y_test_scores = clf.decision_function(X_test_pca_trans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+QAAAI0CAYAAACUFOOKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGnIAAAAAACygIQcAAAAAwIL/DwoBY7nprYnoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f49fd0ded10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,2,1)\n",
    "decision_separate_plot(Y_train_scores,Y_binary_train)\n",
    "plt.title(\"train set\")\n",
    "plt.subplot(2,2,2)\n",
    "decision_separate_plot(Y_test_scores,Y_binary_test)\n",
    "plt.title(\"test set\")\n",
    "plt.subplot(2,2,3)\n",
    "prec_quantile_plot(Y_train_scores,Y_binary_train)\n",
    "plt.subplot(2,2,4)\n",
    "prec_quantile_plot(Y_test_scores,Y_binary_test)\n",
    "# axes = plt.axes()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "In these pictures, I draw the score histogram and the mean precision vs. quantile plot. The left side is the train set, and the right side is the test set.\n",
    "\n",
    "Surprisingly, even the average accuracy in test set is about 67%. What if we only trade the extreme quantile? That is around 80% accuracy! It literally shows that technical factors are really important in US stock market and can be used to find arbitrage opportunity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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
}