Simple ML Strategies to generate Trading Signal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Application of Machine Learning Techniques to Trading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This post is inspired by our observations of some common caveats and pitfalls during the competition when trying to apply ML techniques to trading problems.\n",
    "Let’s look into how we can use ML to create a trade signal by data mining."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "import sklearn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime, timedelta\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To setup an ML framework, We create features which could have some predictive power (X), a target variable that we’d like to predict(Y) and use historical data to train a ML model that can predict Y as close as possible to the actual value. Finally, we use this model to make predictions on new data where Y is unknown."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Setup your problem\n",
    "\n",
    "We're going to use a problem from [QuantQuest](http:https://quant-quest.auquan.com/problem/qq2p1) for demo.\n",
    "We are going to create a prediction model that predicts future expected value of basis, where:\n",
    "\n",
    "$basis$ = Price of Stock - Price of Future\n",
    "\n",
    "$basis_t=S_t−F_t$\n",
    "\n",
    "$Y$ = future expected value of basis = $Average(basis_{(t+1)}, basis_{(t+2)}, basis_{(t+3)},basis_{(t+4)},basis_{(t+5)})$\n",
    "\n",
    "*Note Y(t) will only be known during a backtest, but when using our model live, we won’t know Price(t+1) at time t. We make a prediction Y(Predicted,t) using our model and compare it with actual value only at time t+1. **This means you cannot use Y as a feature in your predictive model)** *\n",
    "\n",
    "Since this is a regression problem, we will evaluate the model on RMSE. We’ll also use Total Pnl as an evaluation criterion\n",
    "\n",
    "$RMSE = \\sqrt{\\sum{(Y_{actual} - Y_{predicted})^2} / n} $\n",
    "\n",
    "Our Objective: Create a model so that predicted value is as close as possible to Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Collect Reliable Data\n",
    "\n",
    "We are using the folliwing data for our stock at minute interval data for trading days over one months(~8000 data points):\n",
    "Stock Bid Price, Ask Price, Bid Volume, Ask Volume \n",
    "Future Bid Price, Ask Price, Bid Volume, Ask Volume\n",
    "$Stock VWAP = {Bid Price*Ask Volume +  Ask Price*Bid Volume}/(Ask Volume +  Bid Volume)$\n",
    "Future VWAP\n",
    "\n",
    "Data is already cleaned for Dividends, Splits, Rolls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from backtester.dataSource.csv_data_source import CsvDataSource"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing data for stock: MQK\n"
     ]
    }
   ],
   "source": [
    "# Load the data\n",
    "cachedFolderName = '/data/'\n",
    "dataSetId = 'trainingData1'\n",
    "startDate = '2017/01/06'\n",
    "endDate = '2017/02/09'\n",
    "instrumentIds = ['MQK']\n",
    "downloadUrl = 'https://github.com/Auquan/auquan-historical-data/raw/master/qq2Data'\n",
    "\n",
    "ds = CsvDataSource(cachedFolderName='historicalData/',\n",
    "                             dataSetId=dataSetId,\n",
    "                             instrumentIds=instrumentIds,\n",
    "                             downloadUrl = downloadUrl,\n",
    "                             timeKey = 'datetime',\n",
    "                             timeStringFormat = '%Y-%m-%d %H:%M:%S',\n",
    "                             startDateStr=startDate,\n",
    "                             endDateStr=endDate,\n",
    "                             liveUpdates=False,\n",
    "                             pad=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Auquan's Toolbox has downloaded and loaded the data into a dictionary of dataframes for you. We now need to prepare the data in a format we like.\n",
    "Let's first check if the data has loaded properly and the columns in data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MQK']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.getInstrumentIds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['stockVWAP',\n",
       " 'futureVWAP',\n",
       " 'basis',\n",
       " 'stockTopBidVol',\n",
       " 'stockTopAskVol',\n",
       " 'stockTopBidPrice',\n",
       " 'stockTopAskPrice',\n",
       " 'futureTopBidVol',\n",
       " 'futureTopAskVol',\n",
       " 'futureTopBidPrice',\n",
       " 'futureTopAskPrice',\n",
       " 'stockNextBidVol',\n",
       " 'stockNextAskVol',\n",
       " 'stockNextBidPrice',\n",
       " 'stockNextAskPrice',\n",
       " 'futureNextBidVol',\n",
       " 'futureNextAskVol',\n",
       " 'futureNextBidPrice',\n",
       " 'futureNextAskPrice',\n",
       " 'stockTotalBidVol',\n",
       " 'stockTotalAskVol',\n",
       " 'futureTotalBidVol',\n",
       " 'futureTotalAskVol',\n",
       " 'stockAverageBidPrice',\n",
       " 'stockAverageAskPrice',\n",
       " 'futureAverageBidPrice',\n",
       " 'futureAverageAskPrice',\n",
       " 'FairValue',\n",
       " 'benchmark_score']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.getBookDataFeatures()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we load data in a format we like\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading our data\n",
    "def loadData(ds, id):\n",
    "    data = ds._bookDataByInstrument[id].getBookData()\n",
    "\n",
    "    data['Stock Price'] =  data['stockTopBidPrice'] +\\\n",
    "                           data['stockTopAskPrice'] / 2.0\n",
    "    data['Future Price'] = data['futureTopBidPrice'] +\\\n",
    "                           data['futureTopAskPrice'] / 2.0\n",
    "    data['Y(Target)'] = data['basis'].shift(-5)\n",
    "    del data['benchmark_score']\n",
    "    del data['FairValue']\n",
    "    return data\n",
    "\n",
    "data = loadData(ds,instrumentIds[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     stockVWAP  futureVWAP  basis  stockTopBidVol  \\\n",
      "2017-01-06 09:17:00    517.773     512.492  5.425            79.0   \n",
      "2017-01-06 09:18:00    515.300     510.682  4.425            40.0   \n",
      "2017-01-06 09:19:00    516.225     511.342  4.950            81.0   \n",
      "2017-01-06 09:20:00    519.792     513.959  5.650           179.0   \n",
      "2017-01-06 09:21:00    520.270     513.819  6.400            68.0   \n",
      "2017-01-06 09:22:00    524.792     519.118  5.625          2299.0   \n",
      "\n",
      "                     stockTopAskVol  stockTopBidPrice  stockTopAskPrice  \\\n",
      "2017-01-06 09:17:00            79.0            518.00            518.30   \n",
      "2017-01-06 09:18:00            10.0            515.05            515.30   \n",
      "2017-01-06 09:19:00          1496.0            516.20            516.45   \n",
      "2017-01-06 09:20:00          1940.0            519.65            519.95   \n",
      "2017-01-06 09:21:00            93.0            520.45            520.60   \n",
      "2017-01-06 09:22:00          4924.0            524.60            525.00   \n",
      "\n",
      "                     futureTopBidVol  futureTopAskVol  futureTopBidPrice  ...  \\\n",
      "2017-01-06 09:17:00           4000.0           2000.0             512.60  ...   \n",
      "2017-01-06 09:18:00           2000.0           4000.0             510.65  ...   \n",
      "2017-01-06 09:19:00           2000.0           2000.0             511.25  ...   \n",
      "2017-01-06 09:20:00           2000.0           2000.0             514.05  ...   \n",
      "2017-01-06 09:21:00           2000.0           6000.0             513.85  ...   \n",
      "2017-01-06 09:22:00           2000.0           2000.0             519.00  ...   \n",
      "\n",
      "                     stockTotalAskVol  futureTotalBidVol  futureTotalAskVol  \\\n",
      "2017-01-06 09:17:00            2780.0            12000.0            12000.0   \n",
      "2017-01-06 09:18:00            1756.0            10000.0            24000.0   \n",
      "2017-01-06 09:19:00            3010.0            10000.0            14000.0   \n",
      "2017-01-06 09:20:00            3530.0            10000.0            12000.0   \n",
      "2017-01-06 09:21:00            2128.0            12000.0            24000.0   \n",
      "2017-01-06 09:22:00            5890.0            10000.0            12000.0   \n",
      "\n",
      "                     stockAverageBidPrice  stockAverageAskPrice  \\\n",
      "2017-01-06 09:17:00               517.493               518.654   \n",
      "2017-01-06 09:18:00               514.919               515.708   \n",
      "2017-01-06 09:19:00               515.986               516.488   \n",
      "2017-01-06 09:20:00               519.549               520.079   \n",
      "2017-01-06 09:21:00               520.022               520.756   \n",
      "2017-01-06 09:22:00               524.558               525.054   \n",
      "\n",
      "                     futureAverageBidPrice  futureAverageAskPrice  \\\n",
      "2017-01-06 09:17:00                512.000                513.142   \n",
      "2017-01-06 09:18:00                510.340                511.283   \n",
      "2017-01-06 09:19:00                511.050                511.650   \n",
      "2017-01-06 09:20:00                513.470                514.650   \n",
      "2017-01-06 09:21:00                513.392                514.775   \n",
      "2017-01-06 09:22:00                518.740                519.592   \n",
      "\n",
      "                     Stock Price  Future Price  Y(Target)  \n",
      "2017-01-06 09:17:00      777.150       769.025      5.625  \n",
      "2017-01-06 09:18:00      772.700       766.075      5.400  \n",
      "2017-01-06 09:19:00      774.425       767.000      5.450  \n",
      "2017-01-06 09:20:00      779.625       771.175      5.175  \n",
      "2017-01-06 09:21:00      780.750       771.050      5.050  \n",
      "2017-01-06 09:22:00      787.100       778.675      5.225  \n",
      "\n",
      "[6 rows x 30 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data.head(6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Split Data\n",
    "\n",
    "For this problem we have three datasets available, we will use one as training set, second as validation set and the third as our test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing data for stock: MQK\n",
      "Processing data for stock: MQK\n",
      "Processing data for stock: MQK\n"
     ]
    }
   ],
   "source": [
    "# Training Data\n",
    "dataSetId =  'trainingData1'\n",
    "ds_training = CsvDataSource(cachedFolderName='historicalData/',\n",
    "                             dataSetId=dataSetId,\n",
    "                             instrumentIds=instrumentIds,\n",
    "                             downloadUrl = downloadUrl,\n",
    "                             timeKey = 'datetime',\n",
    "                             timeStringFormat = '%Y-%m-%d %H:%M:%S',\n",
    "                             liveUpdates=False,\n",
    "                             pad=True)\n",
    "\n",
    "training_data = loadData(ds_training, instrumentIds[0])\n",
    "\n",
    "# Validation Data\n",
    "dataSetId =  'trainingData2'\n",
    "ds_validation = CsvDataSource(cachedFolderName='historicalData/',\n",
    "                             dataSetId=dataSetId,\n",
    "                             instrumentIds=instrumentIds,\n",
    "                             downloadUrl = downloadUrl,\n",
    "                             timeKey = 'datetime',\n",
    "                             timeStringFormat = '%Y-%m-%d %H:%M:%S',\n",
    "                             liveUpdates=False,\n",
    "                             pad=True)\n",
    "validation_data = loadData(ds_validation, instrumentIds[0])\n",
    "\n",
    "# Test Data\n",
    "dataSetId =  'trainingData3'\n",
    "ds_test = CsvDataSource(cachedFolderName='historicalData/',\n",
    "                             dataSetId=dataSetId,\n",
    "                             instrumentIds=instrumentIds,\n",
    "                             downloadUrl = downloadUrl,\n",
    "                             timeKey = 'datetime',\n",
    "                             timeStringFormat = '%Y-%m-%d %H:%M:%S',\n",
    "                             liveUpdates=False,\n",
    "                             pad=True)\n",
    "out_of_sample_test_data = loadData(ds_test, instrumentIds[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing Data\n",
    "\n",
    "We add the target variable $Y$, defined as average of next five values of basis  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepareData(data, period):\n",
    "    data['Y(Target)'] = data['basis'].rolling(period).mean().shift(-period)\n",
    "    if 'FairValue' in data.columns:\n",
    "        del data['FairValue']\n",
    "    data.dropna(inplace=True)\n",
    "\n",
    "period = 5\n",
    "prepareData(training_data, period)\n",
    "prepareData(validation_data, period)\n",
    "prepareData(out_of_sample_test_data, period)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Feature Engineering\n",
    "\n",
    "Let's try to create some features that may have some predictive power on our data.\n",
    "\n",
    "*** The golden rule of feature selection is that the predictive power should come from primarily from the features and not from the model. You will find that the choice of features has a far greater impact on performance than the choice of model. *\n",
    "\n",
    "We shouldn't randomly choose a very large set of features without exploring relationship with target variable, because little or no relationship with target variable will likely lead to overfitting.\n",
    "The features might be highly correlated with each other, in that case a fewer number of features will explain the target just as well.\n",
    "\n",
    "For this first iteration, we create a large number of features, using a mix of parameters. Later we will try to see if can reduce the number of features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def difference(dataDf, period):\n",
    "    return dataDf.sub(dataDf.shift(period), fill_value=0)\n",
    "\n",
    "def ewm(dataDf, halflife):\n",
    "    return dataDf.ewm(halflife=halflife,ignore_na=False,min_periods=0,adjust=True).mean()\n",
    "\n",
    "def rsi(data, period):\n",
    "    data_upside = data.sub(data.shift(1), fill_value=0)\n",
    "    data_downside = data_upside.copy()\n",
    "    data_downside[data_upside > 0] = 0\n",
    "    data_upside[data_upside < 0] = 0\n",
    "    avg_upside = data_upside.rolling(period).mean()\n",
    "    avg_downside = - data_downside.rolling(period).mean()\n",
    "    rsi = 100 - (100 * avg_downside / (avg_downside + avg_upside))\n",
    "    rsi[avg_downside == 0] = 100\n",
    "    rsi[(avg_downside == 0) & (avg_upside == 0)] = 0\n",
    "\n",
    "    return rsi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_features(data):\n",
    "    basis_X = pd.DataFrame(index = data.index, columns =  [ 'emabasis3','emabasis5','emabasis2','emabasis7','emabasis10',\\\n",
    "                                                             'emabasis4','rsi15','rsi10','rsi5','mom1','mom10',\\\n",
    "                                                             'mom3','mom5'])\n",
    "    \n",
    "    basis_X['mom1'] = difference(data['basis'],2)\n",
    "    basis_X['mom3'] = difference(data['basis'],4)\n",
    "    basis_X['mom5'] = difference(data['basis'],6)\n",
    "    basis_X['mom10'] = difference(data['basis'],11)\n",
    "\n",
    "    basis_X['rsi15'] = rsi(data['basis'],15)\n",
    "    basis_X['rsi10'] = rsi(data['basis'],10)\n",
    "    basis_X['rsi5'] = rsi(data['basis'],5)\n",
    "    \n",
    "    basis_X['emabasis2'] = ewm(data['basis'],2)\n",
    "    basis_X['emabasis3'] = ewm(data['basis'],3)\n",
    "    basis_X['emabasis4'] = ewm(data['basis'],4)\n",
    "    basis_X['emabasis5'] = ewm(data['basis'],5)\n",
    "    basis_X['emabasis7'] = ewm(data['basis'],7)\n",
    "    basis_X['emabasis10'] = ewm(data['basis'],10)\n",
    "\n",
    "    basis_X['basis'] = data['basis']\n",
    "    basis_X['vwapbasis'] = data['stockVWAP']-data['futureVWAP']\n",
    "    \n",
    "    basis_X['swidth'] = data['stockTopAskPrice']-data['stockTopBidPrice']\n",
    "    basis_X['fwidth'] = data['futureTopAskPrice']-data['futureTopBidPrice']\n",
    "    \n",
    "    basis_X['btopask'] = data['stockTopAskPrice']-data['futureTopAskPrice']\n",
    "    basis_X['btopbid'] =data['stockTopBidPrice']-data['futureTopBidPrice']\n",
    "    basis_X['bavgask'] = data['stockAverageAskPrice']-data['futureAverageAskPrice']\n",
    "    basis_X['bavgbid'] = data['stockAverageBidPrice']-data['futureAverageBidPrice']\n",
    "    basis_X['bnextask'] = data['stockNextAskPrice']-data['futureNextAskPrice']\n",
    "    basis_X['bnextbid'] = data['stockNextBidPrice']-data['futureNextBidPrice']\n",
    "    basis_X['topaskvolratio'] = data['stockTopAskVol']/data['futureTopAskVol']\n",
    "    basis_X['topbidvolratio'] = data['stockTopBidVol']/data['futureTopBidVol']\n",
    "    basis_X['totalaskvolratio'] = data['stockTotalAskVol']/data['futureTotalAskVol']\n",
    "    basis_X['totalbidvolratio'] = data['stockTotalBidVol']/data['futureTotalBidVol']\n",
    "    basis_X['nextbidvolratio'] = data['stockNextBidVol']/data['futureNextBidVol']\n",
    "    basis_X['nextaskvolratio'] = data['stockNextAskVol']-data['futureNextAskVol']\n",
    "    \n",
    "    basis_X['emabasisdi4'] = basis_X['emabasis7'] - basis_X['emabasis5'] + basis_X['emabasis2']\n",
    "    basis_X['emabasisdi7'] = basis_X['emabasis7'] - basis_X['emabasis5']+ basis_X['emabasis3']\n",
    "    basis_X['emabasisdi1'] = basis_X['emabasis10'] - basis_X['emabasis5'] + basis_X['emabasis3']\n",
    "    basis_X['emabasisdi3'] = basis_X['emabasis10'] - basis_X['emabasis3']+ basis_X['emabasis5']\n",
    "    basis_X['emabasisdi5'] = basis_X['emabasis7']- basis_X['emabasis5'] + data['basis']\n",
    "    basis_X['emabasisdi'] = basis_X['emabasis5'] - basis_X['emabasis3'] + data['basis']\n",
    "    basis_X['emabasisdi6'] = basis_X['emabasis7'] - basis_X['emabasis3']+ data['basis']\n",
    "    basis_X['emabasisdi2'] = basis_X['emabasis10'] - basis_X['emabasis5']+ data['basis']\n",
    "    basis_X['emabasisdi3'] = basis_X['emabasis10'] - basis_X['emabasis3']+ basis_X['emabasis5']\n",
    "    \n",
    "    basis_X = basis_X.fillna(0)\n",
    "    \n",
    "    basis_y = data['Y(Target)']\n",
    "    basis_y.dropna(inplace=True)\n",
    "    \n",
    "    print(\"Any null data in y: %s, X: %s\"%(basis_y.isnull().values.any(), basis_X.isnull().values.any()))\n",
    "    print(\"Length y: %s, X: %s\"%(len(basis_y.index), len(basis_X.index)))\n",
    "    \n",
    "    return basis_X, basis_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Any null data in y: False, X: False\n",
      "Length y: 8737, X: 8737\n",
      "Any null data in y: False, X: False\n",
      "Length y: 6499, X: 6499\n"
     ]
    }
   ],
   "source": [
    "basis_X_train, basis_y_train = create_features(training_data)\n",
    "basis_X_test, basis_y_test = create_features(validation_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Model Selection\n",
    "\n",
    "Let's start with a Linear Regression model with non-normalized features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "def linear_regression(basis_X_train, basis_y_train, basis_X_test,basis_y_test):\n",
    "    regr = linear_model.LinearRegression()\n",
    "    # Train the model using the training sets\n",
    "    regr.fit(basis_X_train, basis_y_train)\n",
    "    # Make predictions using the testing set\n",
    "    basis_y_pred = regr.predict(basis_X_test)\n",
    "\n",
    "    # The coefficients\n",
    "    print('Coefficients: \\n', regr.coef_)\n",
    "    # The mean squared error\n",
    "    print(\"Mean squared error: %.2f\"\n",
    "          % mean_squared_error(basis_y_test, basis_y_pred))\n",
    "    # Explained variance score: 1 is perfect prediction\n",
    "    print('Variance score: %.2f' % r2_score(basis_y_test, basis_y_pred))\n",
    "\n",
    "    # Plot outputs\n",
    "    plt.scatter(basis_y_pred, basis_y_test,  color='black')\n",
    "    plt.plot(basis_y_test, basis_y_test, color='blue', linewidth=3)\n",
    "\n",
    "    plt.xlabel('Y(actual)')\n",
    "    plt.ylabel('Y(Predicted)')\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "    return regr, basis_y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6: Train, Validate and Optimize (repeat steps 4-6)\n",
    "\n",
    "Now we will try to itereate over various models, model parameters and feature space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 1.38355593e+08 -5.26884630e+07 -1.70045909e+07 -1.86792848e+07\n",
      " -7.21406734e+06 -5.64840998e+01 -1.04174206e-03 -6.17704226e-04\n",
      " -8.06353650e-05  4.34496754e-02  1.16549870e-02  5.76276277e-02\n",
      " -3.06277287e-02 -2.38321924e+07  8.39982870e-02  4.34917628e-03\n",
      " -6.74806559e-03 -2.38321924e+07 -2.38321924e+07  6.47961994e-02\n",
      "  5.72135463e-02  2.83844718e-03  1.58542282e-02 -7.61685644e-03\n",
      " -7.64534854e-04 -3.88286727e-02  3.12711966e-02  3.41591420e-03\n",
      " -1.32251298e-06  1.70045873e+07 -4.53455823e+07 -3.38803649e+07\n",
      "  1.94522302e+07  1.01769858e+07  2.83410512e+06  3.68432833e+07\n",
      "  2.16422033e+07]\n",
      "Mean squared error: 0.02\n",
      "Variance score: 0.96\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, basis_y_pred = linear_regression(basis_X_train, basis_y_train, basis_X_test,basis_y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the model coeffecients. We can't really compare them or tell which ones are important since they all belong to different scale. Let's try normalization to conform them to same scale and also enforce some stationarity.\n",
    "\n",
    "Normalization is generally tricky because future range of data is unknown. We overcome this by using rolling histrical mean and standard deviation over lookback period. However, this means the same normalized value of feature will mean different actual value at different times. This may be a cause of errors in your model; hence normalization is tricky and you have to figure what actually improves performance of your model(if at all)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(basis_X, basis_y, period):\n",
    "    basis_X_norm = (basis_X - basis_X.rolling(period).mean())/basis_X.rolling(period).std()\n",
    "    basis_X_norm.dropna(inplace=True)\n",
    "    basis_y_norm = (basis_y - basis_X['basis'].rolling(period).mean())/basis_X['basis'].rolling(period).std()\n",
    "    basis_y_norm = basis_y_norm[basis_X_norm.index]\n",
    "    \n",
    "    return basis_X_norm, basis_y_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [-3.62562336e+00 -9.15274240e+00  1.44465722e+00  8.87792905e+00\n",
      " -5.55687316e+00  1.98323173e+01 -3.52677595e-03 -1.46185137e-02\n",
      " -2.28603171e-02  8.89158364e-03  1.96775578e-02 -1.09431839e-03\n",
      " -3.32224637e-03 -7.91656252e+00  4.98693313e-02  6.53834026e-03\n",
      "  1.12615956e-02  2.08691443e+00  2.03221946e+00  9.75083631e-02\n",
      "  6.10888994e-02 -3.13066921e-02  4.78554884e-02  5.84281791e-04\n",
      "  1.12986615e-02 -2.19490620e-02  2.31187541e-02  5.69928897e-03\n",
      " -5.78663706e-03  3.02309761e+00 -2.45138755e+01  1.00726718e+01\n",
      "  2.00852825e-02  1.53648992e+01 -9.16940626e+00  4.29745408e+00\n",
      " -6.36285008e+00]\n",
      "Mean squared error: 0.28\n",
      "Variance score: 0.77\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm_period = 375\n",
    "basis_X_norm_test, basis_y_norm_test = normalize(basis_X_test,basis_y_test, norm_period)\n",
    "basis_X_norm_train, basis_y_norm_train = normalize(basis_X_train, basis_y_train, norm_period)\n",
    "\n",
    "regr_norm, basis_y_pred = linear_regression(basis_X_norm_train, basis_y_norm_train, basis_X_norm_test, basis_y_norm_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model doesn't seem to predict normalized $Y$ that well. We first convert prediction $Y$ back to unnormalized value to evaluate model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.05\n",
      "Variance score: 0.90\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "basis_y_pred = basis_y_pred * basis_X_test['basis'].rolling(period).std()[basis_y_norm_test.index] + basis_X_test['basis'].rolling(period).mean()[basis_y_norm_test.index]\n",
    "\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(basis_y_test[basis_y_norm_test.index], basis_y_pred))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % r2_score(basis_y_test[basis_y_norm_test.index], basis_y_pred))\n",
    "\n",
    "# Plot outputs\n",
    "plt.scatter(basis_y_pred, basis_y_test[basis_y_norm_test.index],  color='black')\n",
    "plt.plot(basis_y_test, basis_y_test, color='blue', linewidth=3)\n",
    "\n",
    "plt.xlabel('Y(actual)')\n",
    "plt.ylabel('Y(Predicted)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model doesn't improve on the previous model, but it's not much worse either.And now we can actually compare coeffecients to see which features are actually important.\n",
    "\n",
    "Let's look at the coeffecients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3.6256, emabasis3\n",
      "-9.1527, emabasis5\n",
      "1.4447, emabasis2\n",
      "8.8779, emabasis7\n",
      "-5.5569, emabasis10\n",
      "19.8323, emabasis4\n",
      "-0.0035, rsi15\n",
      "-0.0146, rsi10\n",
      "-0.0229, rsi5\n",
      "0.0089, mom1\n",
      "0.0197, mom10\n",
      "-0.0011, mom3\n",
      "-0.0033, mom5\n",
      "-7.9166, basis\n",
      "0.0499, vwapbasis\n",
      "0.0065, swidth\n",
      "0.0113, fwidth\n",
      "2.0869, btopask\n",
      "2.0322, btopbid\n",
      "0.0975, bavgask\n",
      "0.0611, bavgbid\n",
      "-0.0313, bnextask\n",
      "0.0479, bnextbid\n",
      "0.0006, topaskvolratio\n",
      "0.0113, topbidvolratio\n",
      "-0.0219, totalaskvolratio\n",
      "0.0231, totalbidvolratio\n",
      "0.0057, nextbidvolratio\n",
      "-0.0058, nextaskvolratio\n",
      "3.0231, emabasisdi4\n",
      "-24.5139, emabasisdi7\n",
      "10.0727, emabasisdi1\n",
      "0.0201, emabasisdi3\n",
      "15.3649, emabasisdi5\n",
      "-9.1694, emabasisdi\n",
      "4.2975, emabasisdi6\n",
      "-6.3629, emabasisdi2\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(basis_X_train.columns)):\n",
    "    print('%.4f, %s'%(regr_norm.coef_[i], basis_X_train.columns[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly see that some features have a much higher coeffecient compared to others, and probably have more predictive power.\n",
    "\n",
    "Let's also look at correlation between different features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn\n",
    "\n",
    "c = basis_X_train.corr()\n",
    "plt.figure(figsize=(10,10))\n",
    "seaborn.heatmap(c, cmap='RdYlGn_r', mask = (np.abs(c) <= 0.8))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The areas of dark red indicate highly correlated variables. Let's create/modify some features again and try to improve our model. \n",
    "\n",
    "For example, I can easily discard features line *emabasisdi7* that are just a linear combination of other features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_features_again(data):\n",
    "    basis_X = pd.DataFrame(index = data.index, columns =  [])\n",
    "    \n",
    "    basis_X['mom10'] = difference(data['basis'],11)\n",
    "    \n",
    "    basis_X['emabasis2'] = ewm(data['basis'],2)\n",
    "    basis_X['emabasis5'] = ewm(data['basis'],5)\n",
    "    basis_X['emabasis10'] = ewm(data['basis'],10)\n",
    "\n",
    "    basis_X['basis'] = data['basis']\n",
    "\n",
    "    basis_X['totalaskvolratio'] = (data['stockTotalAskVol']-data['futureTotalAskVol'])/100000\n",
    "    basis_X['totalbidvolratio'] = (data['stockTotalBidVol']-data['futureTotalBidVol'])/100000\n",
    "    \n",
    "    basis_X = basis_X.fillna(0)\n",
    "    \n",
    "    basis_y = data['Y(Target)']\n",
    "    basis_y.dropna(inplace=True)\n",
    "    \n",
    "    print(\"Any null data in y: %s, X: %s\"%(basis_y.isnull().values.any(), basis_X.isnull().values.any()))\n",
    "    print(\"Length y: %s, X: %s\"%(len(basis_y.index), len(basis_X.index)))\n",
    "    \n",
    "    return basis_X, basis_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Any null data in y: False, X: False\n",
      "Length y: 6499, X: 6499\n",
      "Any null data in y: False, X: False\n",
      "Length y: 8737, X: 8737\n",
      "Coefficients: \n",
      " [-0.0067528   0.52879292 -0.37816556  0.2323171   0.53062944 -0.01874965\n",
      "  0.028642  ]\n",
      "Mean squared error: 0.28\n",
      "Variance score: 0.77\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "basis_X_test, basis_y_test = create_features_again(validation_data)\n",
    "basis_X_train, basis_y_train = create_features_again(training_data)\n",
    "norm_period = 375\n",
    "basis_X_norm_test, basis_y_norm_test = normalize(basis_X_test,basis_y_test, norm_period)\n",
    "basis_X_norm_train, basis_y_norm_train = normalize(basis_X_train, basis_y_train, norm_period)\n",
    "\n",
    "regr_norm, basis_y_pred = linear_regression(basis_X_norm_train, basis_y_norm_train, basis_X_norm_test, basis_y_norm_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.05\n",
      "Variance score: 0.90\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.006752804064526624 mom10\n",
      "0.5287929217358883 emabasis2\n",
      "-0.37816555527187795 emabasis5\n",
      "0.2323170952384799 emabasis10\n",
      "0.5306294374732965 basis\n",
      "-0.01874964878360222 totalaskvolratio\n",
      "0.028641995632552722 totalbidvolratio\n"
     ]
    }
   ],
   "source": [
    "basis_y_pred = basis_y_pred * basis_X_test['basis'].rolling(period).std()[basis_y_norm_test.index] + basis_X_test['basis'].rolling(period).mean()[basis_y_norm_test.index]\n",
    "\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(basis_y_test[basis_y_norm_test.index], basis_y_pred))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % r2_score(basis_y_test[basis_y_norm_test.index], basis_y_pred))\n",
    "\n",
    "# Plot outputs\n",
    "plt.scatter(basis_y_pred, basis_y_test[basis_y_norm_test.index],  color='black')\n",
    "plt.plot(basis_y_test, basis_y_test, color='blue', linewidth=3)\n",
    "\n",
    "plt.xlabel('Y(actual)')\n",
    "plt.ylabel('Y(Predicted)')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "for i in range(len(basis_X_train.columns)):\n",
    "    print(regr_norm.coef_[i], basis_X_train.columns[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See, our model performance does not change, and we only need a few features to explain our target variable. I recommend playing with more features above, trying new combinations etc to see what can improve our model.\n",
    "\n",
    "Let's go back to the non-normalized model, since normalization doesn't really help our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 0.03249183  0.49675487 -0.22289464  0.2025182   0.5080227  -0.21557005\n",
      "  0.17128488]\n",
      "Mean squared error: 0.02\n",
      "Variance score: 0.96\n"
     ]
    },
    {
     "data": {
      "image/png": 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3d+vg4OCMUYaITMvu6qU65iPZUcaLFH7hGWU8rXB65iMMb7ERhz/YVJUJh9F61Ns0KSgCujJv710i6ic0yZS/UdjuEY1bFIKFMI3S2dk5I9AxKb+D+TjaoJhwGK3KyMhIQxfJYrHoOxpJ9uK8WOG/PYKxU+GfFDpSF4pisTgjtiLNeItWj+2IIhziHN9eLF26VNevX5+1GYbREH19fb5xFoVCYWqnvuw5ESc54XOr6u4HTgd+nbo13d3ddX08Rm1E5HZVXRrmWHOOG0bOqGznWk13dzcDAwOISEZWTVkCXAJ8n+mi8SXgKLIQjY6OjsREY1bFZkQh7NCklYpNVbUvrT4dEJagzxkmaV91iXe6aqnCA56pqd8rHJ/6tFSlzJkzJ7HfQDv4LaKA+ThMONqR2faPHERYP4iI6JIlS2IQj4LCkMIuj2h8RyHb1Oy1VpQ1SzuslIqCCYcJR1vSqv/IjY6Sar0vbOR386JxqMLPPYKxVeGMTAUjje++XffdCCKKcFiSQ6NlaMUgq3K5zMDAwNSOcWNjYwwMDADUDdYLeh/A1q1bQ/XvXA8a5Qwc38U+VXU/B5YDjzbRbnwk+d0HJYPs7e1NrM+WIazCtFKxEUd70oojjkZtDnpfoVDQnp6ehO/ki+pMQ1WPMnYpnKfOtFV6I4pKgGMW3/1smxrFpqpMONqRVvxHbnS6I7s9M45Xx+FdLRoPKByVui2FQmHqu83qu58tizFUExAOnGW7RwJ/BbwZOCBsB1kUE472pdX+kWulEallf/q79M1T+JJHMFThYoU0os/ri2uU777Vfid5IDbhAF4IrAYeAa4DRoDvAHcDtwLvBzrCdpZWMeEw8kK9NCJdXV1aLBZnXODqvS/ecqTCfR7B+KPCiZkIRqU0Og3ViiPTPBCncHwTeCM4Eeae1w4A/g5YEbaztIoJh5EnqhMN1ivVF7jBwcGEL84d6qQH2ekRjf9WJ51IdqLRzIW+FX1heSA24WjVYsJh5JGwfovKyMOboC/eUlInEWG1YGxXJ2FhdiOMOKaWZtsy2riIIhw1l+OKyLtqva6q3631umEYewm71/fGjRs5++yz2bVrV0KWnA5cDOxbVXcrzjLbDQn1WZtSqcTo6Ggsbdky2uSpl6vqHW45C2e3+X63fBU4M1nTDKO98MtB5YeqsmPHjgQs2B+4EljLXtHYg7P50hvISjS6u7sZHh4G4skNFZTrq9KHEQNhhiXAj4ADq54fCFwXdliTdrGpKiOvVK/2KRaL2tGRVvrxZQqPeaamHlI4OpNpqUKhEGpBQKO+DltVFR0SWI77G8/zDm9dlAKcCtwHTAJLaxx3AvAgzq3QuWHbN+EwWoWwqUMaL3MVvugRDFX4skLSgYT+JUgMzKmdLVGEI2xa9RtF5DoROUNEzsDJqXxDyPf6cS/wLuAnQQeISAFnIvZtwBLgvSKypIk+DSMXVE/HbN68OcGejgDWA39fVfcn4CTgbCCJ6bCZFIvFUHujt2JKmdlKKOFQ1Q8BlwGvcMtqVf3bRjtV1d+o6oN1Djsa2KCqj6jqLuBbwMmN9mm0Dq2yB0JYO6uPW7RoEe9///sZGxurjKoTQICPALcBL6uq/z7wcuB7CfU7nWKxiKqyadMmNm3axOTkJKOjo4E5uoKc1+bUziFhhyZACTjOfdwN7BP2vTXavJmAqSrg3cBXq54vBy6q0dYAzu3V+t7e3rhGb0bKtErwVlg70w3kQ+EQhZs801I7FM5uuu1ly5YF7nvuLY18Z63y3bcrJODj+ADwK+Bh9/lhwI113nMDzpSUt5xcdUxswlFdzMfRurTKPHdYO9NNHXKawlMe0bhN4cVNt125eIeJRSkWiw1f7M2pnR1RhCPUnuMicifO1NEvVfVIt+4eVX153TfXbvdm4KOqOmODcBF5HfDPqvpW9/nHAFT1s/XatT3HW5eOjg7fKRwRYXJyMgOL/AlrZ9Bx8bIfznauf11VNwF8BvgUzpLb5qh8hkWLFtX0yxSLRTZt2tR0f0b6JLHn+E51/AyVDubg3F0kya+Aw0TkBSLSBZwGXJtwn0bG5Gmeu5YPI6ydCxcuTNRGOBYndVy1aDwC/C/gE8QhGoVCIfSxW7Zsabo/owUIMywBPgecBzwAvAW4GhgOO6zxae8U4DFgJ/AEbkwIcBCwruq4E4GHgIeBobDt21RV65KXee56dvi9XpnGqUyxhN3itbHSpfA5hQnP1NTXFBbE2lf19qz1pqryNqVohIeE0qp/ALgKJzvuB8J2kEUx4Wht8jDPHcaHUZ280HtB7e7uTnDDpZcq3OkRjE0Kp8TeV09PT6jzkpXAG/GRhHCcE6YuL8WEw2iWeonyqsUt7Eqj5osonKPwjEc0fqBwYCJ9hl0l1oxD3MgHSQjHHT51vw7bSdrFhGN2kcQIpdbWrYODgykvsUXhYIXrPYIxrvDBxPosFoupnW8je2ITDuC9ONFCT+E4pivlx9RZjptlMeGYPSTlE6kVf5H+tq6nKmz2iMYdCocn1qfXX2O0P1GEo+ZyXBEpAS8APgucW/XSNuBuVW1+yUYC2HLc2UNfX59vCu040nSXy2VWrFjBxMREU+00zr7AfwDvq6qbBM7HyWi7O5FeRYTq60J3d3dgmhCjfYiyHDdsHMehwO9V9Vn3+Xzguao62oyhSWHCMXtIOu4jnTgMP96Ak/68r6puFEdEfppIj93d3cyfP983TiPO/TKMfJJEHMe3cW51KkzgrLAyjNiJkqsq6biP9ONHOnEC925humhcgZMmLhnR6OnpCRQNsESDxnTCCsccrQoAdB93JWOSMZspl8sMDAxMJQEcGxtjYGAgUDyS3rQn7OZL8XA4zk58H2Pvv+YW4D3ACmBr0z10dPj/y4+Pj9eMCLdEg8Y0wjhCgOuBk6qen4w5x40EaCRXVdKrfAYHB1NwgH9QnVVS1Q7wHykc1HTbHR0dkXJNeUtnZ6c5yGcBxLXneBUrgbKIXISTs/l3TPfYGUYsNLInQ39/f6KO2yuuuCKxtuF5wOU4285UeBZnLcqXcK7dzTE5OcnQ0BDgpECJugeIiDRtg9FehN2P42FVfS3OhkqHq+oxqprNBsVGWxOHz6KZ/Ty87z3uuOMS2v8bnMw79zBdNO4ClgIXEodoVBgbG+PMM8/kqaeeivzeXbt2TQmPYQB14zhOd//+g18JO6xJu9hUVesSJS7Db4qqmbiOdKakUCeX1Nc801IT6uSe6krJhr2lp6enbkBjJWLeaF+IMQDwbPfvJ/1K2E7SLiYczZNldHCYvoMEImgP73rJ95JNSFhdXqewwSMaYwrHpi4Y1aIwMjJSc/9zS17Y/sQmHK1aTDiaIy8ZamsRdYOkWnfMIyMjKeSbmqPwKYU9HtEoK+yXmWhURKFWpHzevnsjGaIIR73I8S8Fvgio6odrvZ4VFgDYHElGY8dF1MC86g2GyuUyQ0NDbNy4kYULF7Jt2zZ27dpVp4VmeDEwAry6qu7PwCrgmwn2O52uri5Uld2790acV6LCh4aGfL/zQqHAmjVrLGp8FhBnAODtbpkHvAr4rVteicVxtC2NrGxKmyBnebFYpKtr5k9z69atlMvlGXEimzdvTlg0VgK/Zrpo/Bg4gjREo1AoICKUSiUuv/xyvv71r1MsFqdenz9/PhD83U5MTLB8+fLIiwyMNifMsAQnKmlO1fNO4Naww5q0i01VNUetzLBx+Twa9aFU74ERVDo6OgKnZNLbA/wAhe95pqV2KnxEnfTo6U1FVabhSqWSb2bfWr4h73E2ZdW+kEBa9QeBhVXP9wceDNtJ2sWEozlqzXfHcRFp1IcSxq5aRURSymz7DoUnPKJxj8IRifRX+UzNfLZisaidnZ11jzMnefuShHC8HxgDvgGsAR4FVoTtJO1iwtE8YTYqavQi0kh0eK33hS3Jjzh6FL7sEQxV+KLC3Nj6mTNnjhaLxRnLkJv5bCISatRhy3LblyjCESo7LoCIPA94jfv0l6r6x1BvzABzjsdL3BloG22v2Uy1xWKR97znPVx66aUNtxHMa3Cy2R5WVfc4To6pG2Ptqaenh3nz5k1FgC9YsICdO3dOc3pHpVQqsXHjxrrnN08LJIx4iT07rjg5B44DXqGq1wBdInJ0EzYaLUTcGWgbba/ZRHubN29OQDQKOGFNP2O6aHwbeDlxiwbAjh07pqUN2b59e1OiUUkKWe/8xpk80mhxwgxLgEuBi4HfuM/3B34VdliTdmnnqaosAvPijuvIyscRf3mhwi8801JPK5yeA9tqF7+FDn7n13YCnD2Q1J7jVO0zDtwVtpO0S7sKR5aBeXELVjOrqsLMxSdfzlLY5hGNWxRKObCtdqn1m7H9xGcvSQjHL3HG5BUBWVwtInkr7SocjTqV80zYC1Wzzt/4yiKF//YIxk6Ff1LwXwachxLnUmqjPUlCOPqBa4HHgGGc5bmnhu0k7dKuwhG03LJVV7qEHUHlZ4rqbQp/8IjGfQpHZm5bZaVV5fdQ75xWzquNLowKsQuH0yYvAT4IfAgntXrDF3bgVOA+nO1ol9Y4bhQn7/SdUT5UuwpHq484vBeqsAkJsx9pzFe42CMYqnChwrzMRQOYdtH3TucVi8VQYmwBfrObWIUDZ4rqgbANhurU2SPzL4CbQwjHoqjtt6twtNI/u1ck/CKWa5XqNrK9KB+l8IBHMH6v8NbMxcJ7vqrPfb3fSavfhBjxE6twOO1xDdAbttHQnZtwRKYVphdqrc4JU0QkstDEXwoK5yns8ojGfykk45xfsGBBQ5+5WCxOO/9hRKHdpj2N5klCOH4CbMNZlH5tpYTtpEa79YTjUeAOnESLA3XaGgDWA+t7e3vjP6tGaOIYJSSf5rxWeYHCzzyCsVXhjET7rUwpeUdqtVKB+O0HHkYUbMRheElCOP7Sr9R5zw3AvT7l5Kpj6gnHwe7fA3D21HxjGHvbecTRCqSTDyqpcoYrEtWi8TNXTJLtO+huv9by48HBwRnHhxGFVpr2NNIhVuEA3gl8FHhr2EZDd15HODzH/jPw0TDHmnBkS/Z+iUZKUeE7HsHYpTCkzrRV8jZ47/bD+HjCrkILOi7v055GesQmHMAlwC3AZ4HbgP8btuFQndcQDqAH2Kfq8f8DTgjTrglHtuRn+WzYcrw6Du9q0XhAYWlqNninnKKcQ7/pJRMFIypxCse9QMF93A3cHrbhOu2eghMTshN4ArjOrT8IWOc+PhRneuounKW7Q2HbN+HInlrbsRaLxbp30un4OOYpfMkjGKpwiUK6whfWwe1XzKFtxEEU4ZhDbXap6gSAqo67yQ6bRlWvBq72qf89cKL7+BHgFXH0Z6RPZavRgYEBxsfHp+o7OzsBZ8e5QqHAxMSE7/uD6uPjSJztXJdU1T0BnAmsS7jvmWzZsmXa8yi7LTab/NEwolIvO+5LRORut9xT9fweEbk7DQON1qW/v58VK1ZQKBQAJy365OQkmzdvRlVTEAc/OoB/wtnUslo0rsHJZpu+aMDMi3+QGHjv3SxjrZEF9YTjcOAdbnl71fO3u38NI5ByucyaNWumBGJycjIjsahQwtnv+1+Byr7kO4AP4KwB+VMmVvld/IeHh+nu7p5x3MqVKymVSlP7iK9evXpqdGcYqVFrHgucjZ6aPSbtMlt8HFEdoGk7TPO1uup0hT97fBm/UHhRpnYVCgXLVGvkAmJ0jt8M/C2eqHGc27U342wje0bYztIqs0E4oq7Dz2Ldfj7iOfZX+JZHMHYrfELTWmYbVLq6ukwMjNwQp3DMA1YBPwd+D9wPPIKz//hXgCPDdpRmmQ3CETXyN+h472qeKNS6I661qiq9skzhdx7ReEjh6MwFzS/xoGFkSZzCsQ54gfu4EzgQeE7YxrMqs0E4ouYaqnX338gFrNYIJvs4jrkKX/AIhip8WaEnc9Gw5bNGHolTOE4FHgLOAzrDNpp1mQ3CEdeIo9Z7Gul/wYIFGY80jlC42yMYTyqclLlgVEoljsV8F0aeiCIcNVdVqepVwKuA/YD1IvJREfmHSqn1XiMc5XKZvr4+Ojo66Ovro1wuh3pf0KqboKWZtZZsRokZqPee7du3Z7RySoCP4CQ4eHlV/ffd59cm0mupVIp0fFdXF1vx+LXCAAAV10lEQVS3bmVsbAxVZWxsjIGBgdDfu2HkgnrKguMI/wTwAPAvwCcrJaw6pV1aZcTRrMM66qqbsBsnhSEf+35XyiEKN3pGGTsUVibab2VFlN+0XE9Pj4qIFotFLRaLkTevMoy0IcapqhNwHOL/CnSHbTTr0irCkXZq6zhXVuVHOE5TeMojGr9SeHFqNvT09EwTh1rn0/bBMPJKnMLxU+ClYRvLS2kV4cjiIhJllFLr2OyX2u6nUPYIxh6FTyvMSd0ev30x/LB9MIy8EptwtGppFeHI80Wk3ugk2+C+YxXGPKLxsMIxmYpZmO/N9sEw8ooJR4sIR54vIvVEbXBwMIOLc5fC5xQmPKLxNYV9MhUNCD9StIhwI4+YcLSIcKjm9yJSbxot/RHHSxXu9AjGJoVTMhcMr6gaRisSRTjqpVU3Eqa/vz+XSeoWLlzI5s2bfevL5TJjY2MpWSLAh3HWZ8yrqv8hTgr0P6RkR206OzstS60xa6iXHdeYZVTiSvxEA+DZZ5/ljDPOSMmag4DrgH9nr2g8A3wIeBtZiUZHRwc9PT1Tz0WE3bt3MzQ0ZPEYxqzAhMOYYtWqVSxfvrzmaGLHjh3s2bMnBWveDdwDvKWq7tfAUcDFKfTvz4IFC7jiiivYvn07IyMjdHd3O3O+YMF8xqzBhMMAnJHGZZddNnURzI59gW8AVwEL3bpJnG3vXwP8JhOrSqUSIyMjbNu2bWpqcWhoaNruhgDj4+MMDQ1lYaJhpIb5OGYx5XKZoaGhFP0V9XgDsBboq6obBd6HE1KUDSLC6OjojPqgtCuNpHAxjFbCRhyzjIoPQ0TqTkulRyfwGeAWpovGFTjbzmcnGjBzG9fKOQwandke4Ea7Y8KREo0mM4zbhoGBgSmxyH5aCuAlOPt/f4y9P8engP8NrAC2ZmTXXrZv3z71fXnPoR8nnniib30efgOGEQth1+22UslbHEdeAv3ytZUrCh9UGPfEZlyvcHCmds2bN29GXeX7CnMO/eI58vIbMIwgiBDHIZqLu854Wbp0qa5fvz5rM6bo6+vzvUMtlUq+c+dJ0dHRkZNRxvOAy3GW1FZ4FmfUcSHOdTUbRITe3t7A72vjxo11z6GIMDk5Oa0uL78BwwhCRG5X1aVhjrWpqhTI0olaPT3S0ZGHr/sUnGW21aJxN/BqnHiNbIVt4cKFNb+vMP4LVZ0xFWWOdKOdyORKIiIXiMgDInK3iFwtIs8JOO4EEXlQRDaIyLlp2xkXQRebpJ2o1fPxqprRBksVFgBfA74LLHLrJoELcETj3ozsmkmt78tvAy0/vDEdWf0GDCMRws5pxVmA44E57uPzgfN9jikADwOH4mwmdRewJEz75uNwCJqPT39r19cpbPD4Mjaqk+U2az/L9CIidb+v6vxilY2agtqr+DvMx2HkHVopySHO3EXZp/51wHVVzz8GfCxMm3kTDtVskhlmv2fGHIVPqbNPRrVolBWek7lI1LvQR/m+wuytkteEloah2nrC8T3gdJ/6dwNfrXq+HLioRjsDwHpgfW9vb5zns2XJdhXVYQq3eQTjKYX3ZioMlQt8sVjUrq6uaa81MwLI894qhhGGKMKRmI9DRG4QkXt9yslVxwwBe4CmF7Sr6mpVXaqqSxcvXtxsc23B8PAwXV1dGfR8Nk5eqVdX1f0YOAL4Zgb2OBQKBdauXYuqsmnTJi6//HJKpRIiQqlUYvXq1Q1nKvbzfXR3d1vGXKMtSSzliKoeV+t1ETkDeDuwzFU7L48Dz696fohbZ0Rg9+7dKfZ2AI4D/O1VdbuA84Av4tyEZ8fk5OQ0YYgzpX11/qrK6qvh4eFcpsw3jKYJOzSJswAnAPcDi2scMwd4BHgBe53jofY/z6OPIwvSnap6h8ITnqmpexSOyHRqqroUi0Xf82S+B8PIyVRVHS4C9gGuF5E7ReQyABE5SETWAajqHpyNF67DSYn6bVW9L0mj2i0lRDoxAj3Al4FrcUYcFf4NWIoTo5EPtm3bNuM79S5ZbjY1erv9hgzDl7AK00qlkRFHOy6XTH7E8SHPCEMVHlNYltmoojJiCFoi63VWx+nUbsffkDF7oJVWVSVRGhGOVl4VU51DqRKjUSqVdMmSJQldoOfqzCW2qvBthf0zEQzvBTrM8tgox4WhlX9DhhFFOPKQgyIXJJ0SIqkpDG+21kp0+NjYGPfff38sfUzneJy8UgVP/fuA9+Bktk0f74qosJHaUSO6a32PllbEmDWEVZhWKnkbcSQ5hZGuA/wXPqMMVfjLTEYZlVIsFmc4t8Oe8yjfTb1jbcRhtDLYVFW+fBxJXlDSiQ4/PEAwnlLozFQ0Ojo6tLNzug2V7y3saqmwx9X7Hs3HYbQyJhwNLsdNallmnPPoXluTvzj/R4BonJWpYNQrSdzlW1oRo52JIhy2H0cKxL0XQ8WvMT4+HoN1QRSBTTVe25Jg383jtydGs9ieGkY7Y/tx5Iy401EMDQ0lLBpn4y8a/w4IeRcNSCZduaUVMQyXsEOTVip5jByPcwojOb9Gp8KOgKmpv8h8+ilsSdKvYFNRRruCTVXla6oqboKmTJrjTcBNPvU/Bd4Yc1/xsmzZMjZs2GA5ogyjCaJMVSWW5NBIjuHh4Zh9HD8GjvWpfwtwQ0x9xI+IsHLlSi655JKsTTGMWYX5OFqQ/v5+VqxYgYg02dJhOLM7x3rqnwXmkmfRGBwcZHJy0kTDMDLAhKNFWbduHc1NM34eeMinfhUwHycdenYUCgUGBwcD9/des2aNJRA0jIww4WgxKikvGvdx7I8zyviIz2uLgUsbti0uuru7WbNmDZdccgmrV6+mUPCmN4Hx8XGGhoYysM4wDBOOFmLVqlUsX768CdE4A/+ltJfiLLMNittIjmKxyODgYOBOfP39/YHxGJYDyjCywZzjLUK5XOayyy5rcHpqDvAEsNDntZcBiW5zEkjYwLne3l5fsUwiVsMwjPrYiCNH1Mq8OjQ01KBovBHYzUzRuA1nlJGNaIgIw8PDobIGW+CdYeSMsAEfrVTyGABYj3oJ8mgoGO6HAcF8b8s8SA+InJnWAu8MIzmwJIfpCkccF7VamVdHRkYiRosfGiAYqjAvc8GofC5LQ24Y+SGKcNhUVZPEtWd1rU2Aok1TDQMP+9T/Hc7U1LOR7IoD76qoyjSTbXxkGK2JCUeT+CUcbGSpaK2d6MJdSPfFuWE/z+e15wEXRrInDkqlEiMjI6xZs2bGqimAjg7/n585vQ0j35hwNElcd821HMD1L6R/DTztU385zijjiUi2NMvg4CCqyujoKP39/fT39zM6Osrk5OTUKqqBgYGpbW6rMae3YeQfE44mibpndRD9/f2sXr3aN57BT1QcCsDjgN+02CuAsyLZEAednZ28/vWvr3lMUFr4QqEwY+9wwzDyhwlHk8S5VNR7Z159AZ0/f77n6NcCe4CDPPV343ytd0fuPw52795dd5ouaDQ2OTlpomEYLYAJR5PUGinEQcX5vnnz5qraq4Ff+Bx9Ms5Io5F4j/ioN00X1yjNMIxsyEQ4ROQCEXlARO4WkatF5DkBx42KyD0icqeItO8GGzWYPq1TwhGFd/oc2Q1cG6rNRrPqdnV1USwW6x5XTwAsoM8wWpyw63bjLMDxwBz38fnA+QHHjQKLorafZhxHlCC2Rtgbv/GJgLiMf2wojmJwcHCG3bVKoVBQQIvFonZ2dgYeF/azW0CfYeQLWikAEDgFKAe8lnvhSDqI7fnPX1IjmO+gpgLwKhfvWsctWLBghsB0dXVpsVhUEdFisTj12ATAMFqXKMKR+daxIvI94EpVHfF57VHgKZwL1pdVdXWYNtPcOrajo8M3OE9EArO6huXKK+G00/xe+U+gOR+K176gzxFE2ASFhmG0BrnYOlZEbsCJPPMypKrXuMcM4SwNCgqzfoOqPi4iBwDXi8gDqvqTgP4GgAFI18kalLl14UK/TLThmJiAF70I/K7L++13HOPjP2H37r11IoKqUiwW2bZtG7t21d+EyXuOgj5HEBbdbRizl8Sc46p6nKq+zKdUROMM4O1Avwbc6qrq4+7fJ3GWEh1do7/VqrpUVZcuXrw49s8TxPDwMF1dXTPqt27d2tAOdbfdBnPmzBSNww6DPXvgz3++ga9//evTVnGtXbsWVWXTpk1cfvnl017z20XPzxEd5LAOcoaramA2W8Mw2pywc1pxFuAE4H5gcY1jeoB9qh7/P+CEMO2nneSwWCzG4ud4z3v8fRlXXdWcfWEc0SMjI9M+R7FY1JGREV/nf3WJcyGAYRjZQd6d48AG4HfAnW65zK0/CFjnPj4UuMst9+FMceXOOa6qgZlrRSTU+zduDHJ+q27blrDxWn9lWD0numWzNYzWJ4pwZO4cT4I0neNA4B7gYRzIn/0snOeTl/DTn4aPfzwmA+sQ1v4kFwIYhpEtuXCOzyaGh4cZGBiYln+pXkDbjh2wYIH/a2NjkGYQddhEjbaFq2EYYClHYiFq2pGrr/YXjXe9y5mgSvs6HDYFiEV8G4YBZB8AmERJ0sfRTMTzxITq4Yf7+zJuvTUxk2fg/Qx+UeS2hathzC7Iu3M86ZKUcDSTXuSOO/wFo7dXdc+eRMz1JegzDA4OmiAYxiwminCYczwCjTrBV6yAK66YWf+f/wnvfW+MBoagGUe+YRjtiznHEyLqbn9/+AMc5N0uw+Xpp2HffeOyLDy2z7dhGM1izvEIRNlH4gtf8BeNj3/cmaTKQjTA9sIwDKN5TDgiEGZV0TPPgAh89KMz3//ww058RpbYyijDMJrFhCMC9Zbd/s//gN/W4H/1V84o49BDk7OtXC7T19dHR0dHzRxSSe9YaBjGLCCsF72VStopRyYmVF/5Sv9VUz/9aXNth80zleRmUoZhtD/Yqqr0Uo48/jgccsjM+gMOgMceg87Oxtuu7DfujUj3jhBspZRhGM0SZVWVTVU1ySc+MbPuG9+AJ55oTjTAu9+4w/j4OENDQ9PqbKWUYRhpYstxm8Q7YNuyBfbfP562LYeUYRh5xEYcTXLRRfCVr8CNNzoiEpdogOWQMgwjn5hwNEl3N/zN38Cb3xx/22EFwVZKGYaRJuYczznlcpmhoSE2btxIb28vw8PDJgiGYcROFOe4CYdhGIZhq6oMwzCM5DDhMAzDMCJhwmEYhmFEwoTDMAzDiIQJh2EYhhEJEw7DMAwjEm25HFdE/gTMzMHhsAjYlKI5cWK2Z4PZng1me7qUVHVxmAPbUjhqISLrw65VzhtmezaY7dlgtucXm6oyDMMwImHCYRiGYURiNgrH6qwNaAKzPRvM9mww23PKrPNxGIZhGM0xG0cchmEYRhO0vXCIyKdF5G4RuVNEfiQiBwUct0JEfuuWFWnb6YeIXCAiD7j2Xy0izwk4blRE7nE/Yy7SAkew/QQReVBENojIuWnb6YeInCoi94nIpIgErozJ6XkPa3sez/tCEbne/R+8XkR8t0UTkQn3nN8pItembafHlprnUUTmisiV7uu/FJG+9K1MAFVt6wLsW/X4w8BlPscsBB5x/+7vPt4/B7YfD8xxH58PnB9w3CiwKGt7o9oOFICHgUOBLuAuYEkObD8c+AvgZmBpjePyeN7r2p7j8/454Fz38bk1fu/bs7Y17HkEVlWuOcBpwJVZ2x1HafsRh6purXraA/g5dd4KXK+qW1T1KeB64IQ07KuFqv5IVfe4T28FDsnSniiEtP1oYIOqPqKqu4BvASenZWMQqvobVX0wazsaIaTtuTzvODascR+vAd6ZoS1hCHMeqz/Td4BlIiIp2pgIbS8cACIyLCK/A/qBT/gccjDwu6rnj7l1eeJM4AcBrynwIxG5XUQGUrQpLEG2t8J5r0Xez3sQeT3vz1XVP7iP/wg8N+C4eSKyXkRuFZEsxSXMeZw6xr2RehoopmJdgszJ2oA4EJEbgOf5vDSkqteo6hAwJCIfAz4EfDJVA2tQz3b3mCFgD1AOaOYNqvq4iBwAXC8iD6jqT5KxeC8x2Z4JYWwPQW7Pe16pZXv1E1VVEQla8llyz/uhwE0ico+qPhy3rUYwbSEcqnpcyEPLwDpmCsfjwLFVzw/BmSNOnHq2i8gZwNuBZepOlPq08bj790kRuRpnCJ34BSwG2x8Hnl/1/BC3LnEi/GZqtZHL8x6CXJ53EXlCRA5U1T+IyIHAkwFtVM77IyJyM3Akjq8hbcKcx8oxj4nIHGA/YHM65iVH209VichhVU9PBh7wOew64HgR2d9dyXG8W5cpInIC8H+Ak1R1POCYHhHZp/IYx/Z707PSnzC2A78CDhORF4hIF47zMNNVMmHJ63kPSV7P+7VAZUXjCmDG6Mn9H53rPl4EvB64PzULpxPmPFZ/pncDNwXdALYUWXvnky7Af+H8Q98NfA842K1fCny16rgzgQ1ueX/Wdrs2bcCZH73TLZXVGQcB69zHh+Ks5rgLuA9nuqIlbHefnwg8hHPHmBfbT8GZr94JPAFc10Lnva7tOT7vReBG4LfADcBCt37qfxU4BrjHPe/3AGdlbPOM8wh8CueGCWAecJX7/3AbcGjW5zmOYpHjhmEYRiTafqrKMAzDiBcTDsMwDCMSJhyGYRhGJEw4DMMwjEiYcBiGYRiRMOEwDMMwImHCYRg+iMPPRORtVXWnisgPRWS+iNwiIoUG2n2niCxpwq4+EbnXffxyEflGo20ZRqOYcBiGD+oEOK0Evigi80RkAfAZ4IM4waLfVdWJBpp+J9CwcHhsvAc4RER642jPMMJiwmEYAajqvTjZBv4JJ6vyFeok0+vHTYchIgtE5EYRucPd1GkqrbaIvM/dyOouEVkrIscAJwEXuJsQvVBEbq5suCQii0Rk1H3cJyI/ddu9w32vH9/DSXVhGKnRFkkODSNB/gW4A9gFLHVzEh2qqqPu688Cp6jqVjd30q3urnRLgI8Dx6jqJhFZqKpb3Nf+R1W/A1Bja4Yngbeo6rNuvrVv4qTe8LIeZ9Ojz8XxYQ0jDCYchlEDVd0hIlfi7Dq3U5yth/9cdYgAnxGRNwKTOPsvPBd4M3CVqm5y29kSsetO4CIReSUwAbw44LgncfJQGUZqmHAYRn0m3QLwDE7iugr9wGLgKFXd7U41zSM8e9g7ZVz9vr/HSVL4Cvf1ZwPeP8+1yTBSw3wchhEBdbYWLohI5SK/H/CkKxpvAkpu/U3AqSJSBBCRhW79NmCfqiZHgaPcx++uqt8P+IOqTgLLcfa39uPFtE46d6NNMOEwjOj8CHiD+7iM4/u4B3gf7n4vqnofMAzcIiJ3AV90j/8W8I8i8msReSHweWBQRH4NLKrq4xJghfvelwA7Amx5E/D92D6ZYYTA0qobRkRE5FXA36vq8oztmAvcgrOF7Z4sbTFmFzbiMIyIqOodwI8bCQCMmV7gXBMNI21sxGEYhmFEwkYchmEYRiRMOAzDMIxImHAYhmEYkTDhMAzDMCJhwmEYhmFE4v8Dby8NlorPQJIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, basis_y_pred = linear_regression(basis_X_train, basis_y_train, basis_X_test,basis_y_test)\n",
    "\n",
    "basis_y_regr = basis_y_pred.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You work more on the code above, try different combinations of features etc  to see what can improve our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also try more sophisticated models to see if change of model may improve performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K Nearest Neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.02\n",
      "Variance score: 0.95\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import neighbors\n",
    "n_neighbors = 5\n",
    "\n",
    "model = neighbors.KNeighborsRegressor(n_neighbors, weights='distance')\n",
    "model.fit(basis_X_train, basis_y_train)\n",
    "basis_y_pred = model.predict(basis_X_test)\n",
    "\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(basis_y_test, basis_y_pred))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % r2_score(basis_y_test, basis_y_pred))\n",
    "\n",
    "# Plot outputs\n",
    "plt.scatter(basis_y_pred, basis_y_test,  color='black')\n",
    "plt.plot(basis_y_test, basis_y_test, color='blue', linewidth=3)\n",
    "\n",
    "plt.xlabel('Y(actual)')\n",
    "plt.ylabel('Y(Predicted)')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "basis_y_knn = basis_y_pred.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.02\n",
      "Variance score: 0.95\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "model = SVR(kernel='rbf', C=1e3, gamma=0.1)\n",
    "\n",
    "model.fit(basis_X_train, basis_y_train)\n",
    "basis_y_pred = model.predict(basis_X_test)\n",
    "\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(basis_y_test, basis_y_pred))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % r2_score(basis_y_test, basis_y_pred))\n",
    "\n",
    "# Plot outputs\n",
    "plt.scatter(basis_y_pred, basis_y_test,  color='black')\n",
    "plt.plot(basis_y_test, basis_y_test, color='blue', linewidth=3)\n",
    "\n",
    "\n",
    "plt.xlabel('Y(actual)')\n",
    "plt.ylabel('Y(Predicted)')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "basis_y_svr = basis_y_pred.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.02\n",
      "Variance score: 0.95\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import ensemble\n",
    "model=ensemble.ExtraTreesRegressor()\n",
    "model.fit(basis_X_train, basis_y_train)\n",
    "basis_y_pred = model.predict(basis_X_test)\n",
    "\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(basis_y_test, basis_y_pred))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % r2_score(basis_y_test, basis_y_pred))\n",
    "\n",
    "# Plot outputs\n",
    "plt.scatter(basis_y_pred, basis_y_test,  color='black')\n",
    "plt.plot(basis_y_test, basis_y_test, color='blue', linewidth=3)\n",
    "\n",
    "\n",
    "plt.xlabel('Y(actual)')\n",
    "plt.ylabel('Y(Predicted)')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "basis_y_trees = basis_y_pred.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ensemble Method\n",
    "\n",
    "We could try averaging predictions from all of the above methods to make our final prediction. Some models may work well in prediction certain scenarios and other in prediction other scenarios. Or a model may be extremely overfitting in a certain scenario. Aggregating predictions from different models can reduce error and overfitting, with errors from different models likely getting cancelled out or reduced. \n",
    "\n",
    "One simple way is to simply average the predictions. Other common ensemble methods are Bagging and Boosting.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "basis_y_pred_ensemble = (basis_y_trees + basis_y_svr + +basis_y_knn + basis_y_regr)/4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.02\n",
      "Variance score: 0.95\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(basis_y_test, basis_y_pred))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % r2_score(basis_y_test, basis_y_pred))\n",
    "\n",
    "# Plot outputs\n",
    "plt.scatter(basis_y_pred, basis_y_test,  color='black')\n",
    "plt.plot(basis_y_test, basis_y_test, color='blue', linewidth=3)\n",
    "\n",
    "\n",
    "plt.xlabel('Y(actual)')\n",
    "plt.ylabel('Y(Predicted)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note thatI have only mentioned a few common models here. You should try feature egineering with each of them and also play with model parameters to further optimize.\n",
    "\n",
    "You loop over this stage multiple times till you finally have a model that you’re happy with. Only when you have a model who’s performance you like, proceed to the next step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7: Backtest on Test Data\n",
    "\n",
    "Finally I backtest performance of our final, optimized model from last step on that Test Data that we had kept aside at the start and did not touch yet.\n",
    "This provides you with realistic expectation of how your model is expected to perform on new and unseen data when you start trading live. Hence, it is necessary to ensure you have a clean dataset that you haven’t used to train or validate your model.\n",
    "\n",
    "We're going to choose our linear regression model as our final model of choice. For this purpose, I'm going to use Auquan’s [python based free and open source toolbox](https://bitbucket.org/auquan/auquantoolbox). You can install it via pip: \n",
    "\n",
    "`pip install -U auquan_toolbox`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The toolbox allows you to simulate and control every part of a trading system, however for now, we will only focus on testing the quality of our predictions and use out of the box implementations of everything else\n",
    "\n",
    "The predictions are implemented in `getPrediction()` function below. We are using the same features and coeffeceints as the linear regression model we trained above.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from backtester.trading_system import TradingSystem\n",
    "from backtester.trading_system_parameters import TradingSystemParameters\n",
    "from backtester.features.feature import Feature\n",
    "from backtester.executionSystem.simple_execution_system_fairvalue import SimpleExecutionSystemWithFairValue\n",
    "from backtester.orderPlacer.backtesting_order_placer import BacktestingOrderPlacer\n",
    "from backtester.version import updateCheck\n",
    "from backtester.constants import *\n",
    "from backtester.timeRule.us_time_rule import USTimeRule\n",
    "from backtester.logger import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyTradingParams(TradingSystemParameters):\n",
    "    def __init__(self):\n",
    "        super(MyTradingParams, self).__init__()\n",
    "        self.count = 0 \n",
    "        self.params = {}\n",
    "        self.start = '2017/01/06'\n",
    "        self.end = '2017/01/10'\n",
    "        self.instrumentIds = ['MQK']\n",
    "\n",
    "    '''\n",
    "    Returns an instance of class DataParser. Source of data for instruments\n",
    "    '''\n",
    "\n",
    "    def getDataParser(self):\n",
    "        dataSetId = 'trainingData3'\n",
    "        downloadUrl = 'https://github.com/Auquan/auquan-historical-data/raw/master/qq2Data'\n",
    "        \n",
    "        z= CsvDataSource(cachedFolderName='historicalData/',\n",
    "                             dataSetId=dataSetId,\n",
    "                             instrumentIds=self.instrumentIds,\n",
    "                             downloadUrl = downloadUrl,\n",
    "                             timeKey = '',\n",
    "                             timeStringFormat = '%Y-%m-%d %H:%M:%S',\n",
    "                             startDateStr=self.start,\n",
    "                             endDateStr=self.end,\n",
    "                             liveUpdates=True,\n",
    "                             pad=True)\n",
    "        return z\n",
    "\n",
    "\n",
    "    def getTimeRuleForUpdates(self):\n",
    "        return USTimeRule(startDate = self.start,\n",
    "                        endDate = self.end,\n",
    "                        startTime='9:30',\n",
    "                        endTime='15:30',\n",
    "                        frequency='M', sample='1')\n",
    "\n",
    "    \n",
    "    '''\n",
    "    Return starting capital\n",
    "    '''\n",
    "    def getStartingCapital(self):\n",
    "        return 10000\n",
    "    \n",
    "    '''\n",
    "    This is a way to use any custom features you might have made.\n",
    "    '''\n",
    "\n",
    "    def getCustomFeatures(self):\n",
    "        return {'prediction': TrainingPredictionFeature}\n",
    "\n",
    "    def getInstrumentFeatureConfigDicts(self):\n",
    "\n",
    "        predictionDict = {'featureKey': 'prediction',\n",
    "                                'featureId': 'prediction',\n",
    "                                'params': {}}\n",
    "\n",
    "        # ADD RELEVANT FEATURES HERE\n",
    "        expma5dic = {'featureKey': 'emabasis5',\n",
    "                 'featureId': 'exponential_moving_average',\n",
    "                 'params': {'period': 5,\n",
    "                              'featureName': 'basis'}}\n",
    "        expma10dic = {'featureKey': 'emabasis10',\n",
    "                 'featureId': 'exponential_moving_average',\n",
    "                 'params': {'period': 10,\n",
    "                              'featureName': 'basis'}}                     \n",
    "        expma2dic = {'featureKey': 'emabasis2',\n",
    "                 'featureId': 'exponential_moving_average',\n",
    "                 'params': {'period': 2,\n",
    "                              'featureName': 'basis'}}\n",
    "        mom10dic = {'featureKey': 'mom10',\n",
    "                 'featureId': 'difference',\n",
    "                 'params': {'period': 10,\n",
    "                              'featureName': 'basis'}}\n",
    "        scoreDict = {'featureKey': 'score',\n",
    "                     'featureId': 'score_fv',\n",
    "                     'params': {'predictionKey': 'prediction',\n",
    "                                'price': 'basis'}}\n",
    "        return {INSTRUMENT_TYPE_STOCK: [expma5dic,expma2dic,expma10dic,mom10dic,\n",
    "                                        predictionDict, scoreDict]}\n",
    "\n",
    "    '''\n",
    "    Returns an array of market feature config dictionaries\n",
    "    '''\n",
    "\n",
    "    def getMarketFeatureConfigDicts(self):\n",
    "    # ADD RELEVANT FEATURES HERE\n",
    "        scoreDict = {'featureKey': 'score',\n",
    "                     'featureId': 'score_fv',\n",
    "                     'params': {'featureName': self.getPriceFeatureKey(),\n",
    "                                'instrument_score_feature': 'score'}}\n",
    "        return [scoreDict]\n",
    "\n",
    "\n",
    "    '''\n",
    "    A function that returns your predicted value based on your heuristics.\n",
    "    '''\n",
    "\n",
    "    def getPrediction(self, time, updateNum, instrumentManager):\n",
    "\n",
    "        predictions = pd.Series(0.0, index = self.instrumentIds)\n",
    "\n",
    "        # holder for all the instrument features\n",
    "        lbInstF = instrumentManager.getLookbackInstrumentFeatures()\n",
    "\n",
    "        ### TODO : FILL THIS FUNCTION TO RETURN A BUY (1) or SELL (0) prediction for each stock\n",
    "        ### USE TEMPLATE BELOW AS EXAMPLE\n",
    "\n",
    "        # dataframe for a historical instrument feature (mom10 in this case). The index is the timestamps\n",
    "        # of upto lookback data points. The columns of this dataframe are the stock symbols/instrumentIds.\n",
    "\n",
    "        # Get the last row of the dataframe, the most recent datapoint\n",
    "        mom10 = lbInstF.getFeatureDf('mom10').iloc[-1]\n",
    "        emabasis2 = lbInstF.getFeatureDf('emabasis2').iloc[-1]\n",
    "        emabasis5 = lbInstF.getFeatureDf('emabasis5').iloc[-1]\n",
    "        emabasis10 = lbInstF.getFeatureDf('emabasis10').iloc[-1] \n",
    "        basis = lbInstF.getFeatureDf('basis').iloc[-1]\n",
    "        totalaskvol = (lbInstF.getFeatureDf('stockTotalAskVol').iloc[-1] - lbInstF.getFeatureDf('futureTotalAskVol').iloc[-1])/100000.0\n",
    "        totalbidvol = (lbInstF.getFeatureDf('stockTotalBidVol').iloc[-1] - lbInstF.getFeatureDf('futureTotalBidVol').iloc[-1])/100000.0\n",
    "        \n",
    "        coeff = [ 0.03249183, 0.49675487, -0.22289464, 0.2025182, 0.5080227, -0.21557005, 0.17128488]\n",
    "\n",
    "        predictions['MQK'] = coeff[0] * mom10['MQK'] + coeff[1] * emabasis2['MQK'] +\\\n",
    "                      coeff[2] * emabasis5['MQK'] + coeff[3] * emabasis10['MQK'] +\\\n",
    "                      coeff[4] * basis['MQK'] + coeff[5] * totalaskvol['MQK']+\\\n",
    "                      coeff[6] * totalbidvol['MQK']\n",
    "                    \n",
    "        predictions.fillna(emabasis5,inplace=True)\n",
    "        \n",
    "        print('Current basis: %.3f, predicted basis: %.3f'%(basis['MQK'], predictions['MQK']))\n",
    "        if updateNum>1:\n",
    "            print('Current position: %.0f'%lbInstF.getFeatureDf('position').iloc[-1]['MQK'])\n",
    "\n",
    "        return predictions\n",
    "\n",
    "    '''\n",
    "    Here we convert prediction to intended positions for different instruments.\n",
    "    '''\n",
    "\n",
    "    def getExecutionSystem(self):\n",
    "        return SimpleExecutionSystemWithFairValue(enter_threshold_deviation=0.5, exit_threshold_deviation=0.2, \n",
    "                                                longLimit=250, shortLimit=250, capitalUsageLimit=0.05, \n",
    "                                                enterlotSize=10, exitlotSize=10, \n",
    "                                                limitType='L', price=self.getPriceFeatureKey())\n",
    "\n",
    "    '''\n",
    "    For Backtesting, we use the BacktestingOrderPlacer, which places the order which we want, \n",
    "    and automatically confirms it too.\n",
    "    '''\n",
    "\n",
    "    def getOrderPlacer(self):\n",
    "        return BacktestingOrderPlacer()\n",
    "\n",
    "    '''\n",
    "    Returns the amount of lookback data you want for your calculations.\n",
    "    '''\n",
    "\n",
    "    def getLookbackSize(self):\n",
    "        return 90\n",
    "\n",
    "\n",
    "    def getPriceFeatureKey(self):\n",
    "        return 'basis'\n",
    "\n",
    "\n",
    "    def getMetricsToLogRealtime(self):\n",
    "        # Everything will be logged if left as is\n",
    "        return {\n",
    "            'market': None,\n",
    "            'instruments': None\n",
    "        }\n",
    "\n",
    "\n",
    "class TrainingPredictionFeature(Feature):\n",
    "\n",
    "    @classmethod\n",
    "    def computeForInstrument(cls, updateNum, time, featureParams, featureKey, instrumentManager):\n",
    "        t = MyTradingParams()\n",
    "        return t.getPrediction(time, updateNum, instrumentManager)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing data for stock: MQK\n",
      "Logging all the available market metrics in tensorboard\n",
      "Logging all the available instrument metrics in tensorboard\n",
      "2017-01-06 09:30:00\n",
      "Current basis: 3.685, predicted basis: 3.651\n",
      "2017-01-06 09:31:00\n",
      "Current basis: 3.815, predicted basis: 3.724\n",
      "Current position: 0\n",
      "2017-01-06 09:32:00\n",
      "Current basis: 3.812, predicted basis: 3.723\n",
      "Current position: -10\n",
      "2017-01-06 09:33:00\n",
      "Current basis: 3.661, predicted basis: 3.641\n",
      "Current position: -20\n",
      "2017-01-06 09:34:00\n",
      "Current basis: 3.811, predicted basis: 3.728\n",
      "Current position: -30\n",
      "2017-01-06 09:35:00\n",
      "Current basis: 3.987, predicted basis: 3.836\n",
      "Current position: -40\n",
      "2017-01-06 09:36:00\n",
      "Current basis: 4.011, predicted basis: 3.891\n",
      "Current position: -50\n",
      "2017-01-06 09:37:00\n",
      "Current basis: 4.041, predicted basis: 3.928\n",
      "Current position: -60\n",
      "2017-01-06 09:38:00\n",
      "Current basis: 4.397, predicted basis: 4.162\n",
      "Current position: -70\n",
      "2017-01-06 09:39:00\n",
      "Current basis: 4.343, predicted basis: 4.187\n",
      "Current position: -80\n",
      "2017-01-06 09:40:00\n",
      "Current basis: 4.269, predicted basis: 4.160\n",
      "Current position: -90\n",
      "2017-01-06 09:41:00\n",
      "Current basis: 4.445, predicted basis: 4.292\n",
      "Current position: -100\n",
      "2017-01-06 09:42:00\n",
      "Current basis: 4.418, predicted basis: 4.295\n",
      "Current position: -110\n",
      "2017-01-06 09:43:00\n",
      "Current basis: 4.216, predicted basis: 4.161\n",
      "Current position: -120\n",
      "2017-01-06 09:44:00\n",
      "Current basis: 4.438, predicted basis: 4.309\n",
      "Current position: -130\n",
      "2017-01-06 09:45:00\n",
      "Current basis: 4.361, predicted basis: 4.267\n",
      "Current position: -140\n",
      "2017-01-06 09:46:00\n",
      "Current basis: 4.391, predicted basis: 4.291\n",
      "Current position: -150\n",
      "2017-01-06 09:47:00\n",
      "Current basis: 4.291, predicted basis: 4.220\n",
      "Current position: -160\n",
      "2017-01-06 09:48:00\n",
      "Current basis: 4.516, predicted basis: 4.369\n",
      "Current position: -170\n",
      "2017-01-06 09:49:00\n",
      "Current basis: 4.393, predicted basis: 4.301\n",
      "Current position: -180\n",
      "2017-01-06 09:50:00\n",
      "Current basis: 4.270, predicted basis: 4.209\n",
      "Current position: -190\n",
      "2017-01-06 09:51:00\n",
      "Current basis: 4.021, predicted basis: 4.047\n",
      "Current position: -200\n",
      "2017-01-06 09:52:00\n",
      "Current basis: 4.096, predicted basis: 4.062\n",
      "Current position: -210\n",
      "2017-01-06 09:53:00\n",
      "Current basis: 3.846, predicted basis: 3.883\n",
      "Current position: -220\n",
      "2017-01-06 09:54:00\n",
      "Current basis: 3.845, predicted basis: 3.856\n",
      "Current position: -230\n",
      "2017-01-06 09:55:00\n",
      "Current basis: 3.820, predicted basis: 3.818\n",
      "Current position: -240\n",
      "2017-01-06 09:56:00\n",
      "Current basis: 3.770, predicted basis: 3.774\n",
      "Current position: -250\n",
      "2017-01-06 09:57:00\n",
      "Current basis: 3.916, predicted basis: 3.836\n",
      "Current position: -250\n",
      "2017-01-06 09:58:00\n",
      "Current basis: 4.111, predicted basis: 3.966\n",
      "Current position: -250\n",
      "2017-01-06 09:59:00\n",
      "Current basis: 3.761, predicted basis: 3.772\n",
      "Current position: -250\n",
      "2017-01-06 10:00:00\n",
      "Current basis: 4.008, predicted basis: 3.921\n",
      "Current position: -250\n",
      "2017-01-06 10:01:00\n",
      "Current basis: 4.157, predicted basis: 4.031\n",
      "Current position: -250\n",
      "2017-01-06 10:02:00\n",
      "Current basis: 4.006, predicted basis: 3.959\n",
      "Current position: -250\n",
      "2017-01-06 10:03:00\n",
      "Current basis: 4.131, predicted basis: 4.045\n",
      "Current position: -250\n",
      "2017-01-06 10:04:00\n",
      "Current basis: 4.081, predicted basis: 4.029\n",
      "Current position: -250\n",
      "2017-01-06 10:05:00\n",
      "Current basis: 4.157, predicted basis: 4.080\n",
      "Current position: -250\n",
      "2017-01-06 10:06:00\n",
      "Current basis: 3.934, predicted basis: 3.926\n",
      "Current position: -250\n",
      "2017-01-06 10:07:00\n",
      "Current basis: 4.061, predicted basis: 3.989\n",
      "Current position: -250\n",
      "2017-01-06 10:08:00\n",
      "Current basis: 4.060, predicted basis: 4.012\n",
      "Current position: -250\n",
      "2017-01-06 10:09:00\n",
      "Current basis: 4.114, predicted basis: 4.033\n",
      "Current position: -250\n",
      "2017-01-06 10:10:00\n",
      "Current basis: 4.160, predicted basis: 4.068\n",
      "Current position: -250\n",
      "2017-01-06 10:11:00\n",
      "Current basis: 4.238, predicted basis: 4.129\n",
      "Current position: -250\n",
      "2017-01-06 10:12:00\n",
      "Current basis: 4.336, predicted basis: 4.212\n",
      "Current position: -250\n",
      "2017-01-06 10:13:00\n",
      "Current basis: 4.039, predicted basis: 4.035\n",
      "Current position: -250\n",
      "2017-01-06 10:14:00\n",
      "Current basis: 4.137, predicted basis: 4.079\n",
      "Current position: -250\n",
      "2017-01-06 10:15:00\n",
      "Current basis: 4.114, predicted basis: 4.065\n",
      "Current position: -250\n",
      "2017-01-06 10:16:00\n",
      "Current basis: 4.040, predicted basis: 4.016\n",
      "Current position: -250\n",
      "2017-01-06 10:17:00\n",
      "Current basis: 4.115, predicted basis: 4.055\n",
      "Current position: -250\n",
      "2017-01-06 10:18:00\n",
      "Current basis: 4.165, predicted basis: 4.086\n",
      "Current position: -250\n",
      "2017-01-06 10:19:00\n",
      "Current basis: 4.239, predicted basis: 4.116\n",
      "Current position: -250\n",
      "2017-01-06 10:20:00\n",
      "Current basis: 3.964, predicted basis: 3.960\n",
      "Current position: -250\n",
      "2017-01-06 10:21:00\n",
      "Current basis: 3.813, predicted basis: 3.854\n",
      "Current position: -250\n",
      "2017-01-06 10:22:00\n",
      "Current basis: 3.888, predicted basis: 3.882\n",
      "Current position: -250\n",
      "2017-01-06 10:23:00\n",
      "Current basis: 3.763, predicted basis: 3.781\n",
      "Current position: -250\n",
      "2017-01-06 10:24:00\n",
      "Current basis: 3.712, predicted basis: 3.723\n",
      "Current position: -250\n",
      "2017-01-06 10:25:00\n",
      "Current basis: 3.886, predicted basis: 3.829\n",
      "Current position: -250\n",
      "2017-01-06 10:26:00\n",
      "Current basis: 3.861, predicted basis: 3.821\n",
      "Current position: -250\n",
      "2017-01-06 10:27:00\n",
      "Current basis: 3.785, predicted basis: 3.766\n",
      "Current position: -250\n",
      "2017-01-06 10:28:00\n",
      "Current basis: 3.811, predicted basis: 3.772\n",
      "Current position: -250\n",
      "2017-01-06 10:29:00\n",
      "Current basis: 3.734, predicted basis: 3.720\n",
      "Current position: -250\n",
      "2017-01-06 10:30:00\n",
      "Current basis: 3.832, predicted basis: 3.805\n",
      "Current position: -250\n",
      "2017-01-06 10:31:00\n",
      "Current basis: 3.755, predicted basis: 3.667\n",
      "Current position: -250\n",
      "2017-01-06 10:32:00\n",
      "Current basis: 4.051, predicted basis: 3.946\n",
      "Current position: -250\n",
      "2017-01-06 10:33:00\n",
      "Current basis: 3.828, predicted basis: 3.810\n",
      "Current position: -250\n",
      "2017-01-06 10:34:00\n",
      "Current basis: 4.053, predicted basis: 3.945\n",
      "Current position: -250\n",
      "2017-01-06 10:35:00\n",
      "Current basis: 4.178, predicted basis: 4.034\n",
      "Current position: -250\n",
      "2017-01-06 10:36:00\n",
      "Current basis: 4.916, predicted basis: 4.574\n",
      "Current position: -250\n",
      "2017-01-06 10:37:00\n",
      "Current basis: 4.545, predicted basis: 4.406\n",
      "Current position: -250\n",
      "2017-01-06 10:38:00\n",
      "Current basis: 4.595, predicted basis: 4.461\n",
      "Current position: -250\n",
      "2017-01-06 10:39:00\n",
      "Current basis: 4.720, predicted basis: 4.561\n",
      "Current position: -250\n",
      "2017-01-06 10:40:00\n",
      "Current basis: 4.645, predicted basis: 4.558\n",
      "Current position: -250\n",
      "2017-01-06 10:41:00\n",
      "Current basis: 5.267, predicted basis: 4.942\n",
      "Current position: -250\n",
      "2017-01-06 10:42:00\n",
      "Current basis: 5.117, predicted basis: 4.935\n",
      "Current position: -250\n",
      "2017-01-06 10:43:00\n",
      "Current basis: 5.219, predicted basis: 5.008\n",
      "Current position: -250\n",
      "2017-01-06 10:44:00\n",
      "Current basis: 5.194, predicted basis: 5.020\n",
      "Current position: -250\n",
      "2017-01-06 10:45:00\n",
      "Current basis: 4.874, predicted basis: 4.808\n",
      "Current position: -250\n",
      "2017-01-06 10:46:00\n",
      "Current basis: 4.928, predicted basis: 4.842\n",
      "Current position: -250\n",
      "2017-01-06 10:47:00\n",
      "Current basis: 5.180, predicted basis: 5.005\n",
      "Current position: -250\n",
      "2017-01-06 10:48:00\n",
      "Current basis: 5.231, predicted basis: 5.059\n",
      "Current position: -250\n",
      "2017-01-06 10:49:00\n",
      "Current basis: 5.256, predicted basis: 5.092\n",
      "Current position: -250\n",
      "2017-01-06 10:50:00\n",
      "Current basis: 5.330, predicted basis: 5.135\n",
      "Current position: -250\n",
      "2017-01-06 10:51:00\n",
      "Current basis: 5.230, predicted basis: 5.088\n",
      "Current position: -250\n",
      "2017-01-06 10:52:00\n",
      "Current basis: 5.281, predicted basis: 5.117\n",
      "Current position: -250\n",
      "2017-01-06 10:53:00\n",
      "Current basis: 5.231, predicted basis: 5.107\n",
      "Current position: -250\n",
      "2017-01-06 10:54:00\n",
      "Current basis: 5.206, predicted basis: 5.084\n",
      "Current position: -250\n",
      "2017-01-06 10:55:00\n",
      "Current basis: 5.280, predicted basis: 5.155\n",
      "Current position: -250\n",
      "2017-01-06 10:56:00\n",
      "Current basis: 5.104, predicted basis: 5.027\n",
      "Current position: -250\n",
      "2017-01-06 10:57:00\n",
      "Current basis: 5.028, predicted basis: 4.960\n",
      "Current position: -250\n",
      "2017-01-06 10:58:00\n",
      "Current basis: 5.102, predicted basis: 4.993\n",
      "Current position: -250\n",
      "2017-01-06 10:59:00\n",
      "Current basis: 4.829, predicted basis: 4.814\n",
      "Current position: -250\n",
      "2017-01-06 11:00:00\n",
      "Current basis: 5.005, predicted basis: 4.902\n",
      "Current position: -250\n",
      "2017-01-06 11:01:00\n",
      "Current basis: 4.929, predicted basis: 4.852\n",
      "Current position: -250\n",
      "2017-01-06 11:02:00\n",
      "Current basis: 5.029, predicted basis: 4.912\n",
      "Current position: -250\n",
      "2017-01-06 11:03:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current basis: 4.804, predicted basis: 4.781\n",
      "Current position: -250\n",
      "2017-01-06 11:04:00\n",
      "Current basis: 4.956, predicted basis: 4.836\n",
      "Current position: -250\n",
      "2017-01-06 11:05:00\n",
      "Current basis: 4.560, predicted basis: 4.589\n",
      "Current position: -250\n",
      "2017-01-06 11:06:00\n",
      "Current basis: 4.833, predicted basis: 4.735\n",
      "Current position: -250\n",
      "2017-01-06 11:07:00\n",
      "Current basis: 4.879, predicted basis: 4.777\n",
      "Current position: -250\n",
      "2017-01-06 11:08:00\n",
      "Current basis: 4.755, predicted basis: 4.713\n",
      "Current position: -250\n",
      "2017-01-06 11:09:00\n",
      "Current basis: 4.710, predicted basis: 4.654\n",
      "Current position: -250\n",
      "2017-01-06 11:10:00\n",
      "Current basis: 4.685, predicted basis: 4.621\n",
      "Current position: -250\n",
      "2017-01-06 11:11:00\n",
      "Current basis: 4.611, predicted basis: 4.570\n",
      "Current position: -250\n",
      "2017-01-06 11:12:00\n",
      "Current basis: 4.657, predicted basis: 4.605\n",
      "Current position: -250\n",
      "2017-01-06 11:13:00\n",
      "Current basis: 4.683, predicted basis: 4.612\n",
      "Current position: -250\n",
      "2017-01-06 11:14:00\n",
      "Current basis: 4.582, predicted basis: 4.564\n",
      "Current position: -250\n",
      "2017-01-06 11:15:00\n",
      "Current basis: 4.507, predicted basis: 4.494\n",
      "Current position: -250\n",
      "2017-01-06 11:16:00\n",
      "Current basis: 4.507, predicted basis: 4.474\n",
      "Current position: -250\n",
      "2017-01-06 11:17:00\n",
      "Current basis: 4.607, predicted basis: 4.534\n",
      "Current position: -250\n",
      "2017-01-06 11:18:00\n",
      "Current basis: 4.582, predicted basis: 4.521\n",
      "Current position: -250\n",
      "2017-01-06 11:19:00\n",
      "Current basis: 4.506, predicted basis: 4.473\n",
      "Current position: -250\n",
      "2017-01-06 11:20:00\n",
      "Current basis: 4.481, predicted basis: 4.460\n",
      "Current position: -250\n",
      "2017-01-06 11:21:00\n",
      "Current basis: 4.481, predicted basis: 4.456\n",
      "Current position: -250\n",
      "2017-01-06 11:22:00\n",
      "Current basis: 4.382, predicted basis: 4.381\n",
      "Current position: -250\n",
      "2017-01-06 11:23:00\n",
      "Current basis: 4.336, predicted basis: 4.326\n",
      "Current position: -250\n",
      "2017-01-06 11:24:00\n",
      "Current basis: 4.310, predicted basis: 4.309\n",
      "Current position: -250\n",
      "2017-01-06 11:25:00\n",
      "Current basis: 4.334, predicted basis: 4.307\n",
      "Current position: -250\n",
      "2017-01-06 11:26:00\n",
      "Current basis: 4.333, predicted basis: 4.296\n",
      "Current position: -250\n",
      "2017-01-06 11:27:00\n",
      "Current basis: 4.359, predicted basis: 4.308\n",
      "Current position: -250\n",
      "2017-01-06 11:28:00\n",
      "Current basis: 4.307, predicted basis: 4.286\n",
      "Current position: -250\n",
      "2017-01-06 11:29:00\n",
      "Current basis: 4.433, predicted basis: 4.350\n",
      "Current position: -250\n",
      "2017-01-06 11:30:00\n",
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      "Current position: -250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-01-06 12:38:00\n",
      "Current basis: 3.152, predicted basis: 2.998\n",
      "Current position: -250\n",
      "2017-01-06 12:39:00\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "2017-01-06 14:14:00\n",
      "Current basis: 1.778, predicted basis: 1.832\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "2017-01-09 09:47:00\n",
      "Current basis: 1.311, predicted basis: 1.306\n",
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     ]
    },
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     "text": [
      "2017-01-09 11:21:00\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-01-09 14:30:00\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current basis: 0.665, predicted basis: 0.691\n",
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      "2017-01-10 10:26:00\n",
      "Current basis: 1.295, predicted basis: 1.169\n",
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      "2017-01-10 10:27:00\n",
      "Current basis: 1.246, predicted basis: 1.149\n",
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      "Current basis: 1.144, predicted basis: 1.044\n",
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      "Current basis: 0.871, predicted basis: 0.819\n",
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      "Current basis: 1.257, predicted basis: 1.152\n",
      "Current position: -250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-01-10 11:38:00\n",
      "Current basis: 1.182, predicted basis: 1.124\n",
      "Current position: -250\n",
      "2017-01-10 11:39:00\n",
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      "Current position: -250\n",
      "2017-01-10 13:08:00\n",
      "Current basis: -0.094, predicted basis: -0.082\n",
      "Current position: -250\n",
      "2017-01-10 13:09:00\n",
      "Current basis: -0.020, predicted basis: -0.031\n",
      "Current position: -250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-01-10 13:10:00\n",
      "Current basis: 0.077, predicted basis: 0.029\n",
      "Current position: -250\n",
      "2017-01-10 13:11:00\n",
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      "Current position: -250\n",
      "2017-01-10 14:35:00\n",
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      "Current position: -250\n",
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      "Current position: -250\n",
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      "Current basis: 1.075, predicted basis: 0.945\n",
      "Current position: -250\n",
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      "Current position: -250\n",
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      "Current basis: 1.421, predicted basis: 1.237\n",
      "Current position: -250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-01-10 14:46:00\n",
      "Current basis: 1.369, predicted basis: 1.249\n",
      "Current position: -250\n",
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      "Current position: -250\n",
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      "Current position: -250\n",
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      "Current position: -250\n",
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      "Current position: -250\n",
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      "Current position: -250\n",
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      "Current position: -250\n",
      "2017-01-10 15:11:00\n",
      "Current basis: 0.131, predicted basis: 0.427\n",
      "Current position: -250\n",
      "2017-01-10 15:12:00\n",
      "Current basis: -0.016, predicted basis: 0.239\n",
      "Current position: -250\n",
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      "Current basis: 0.504, predicted basis: 0.523\n",
      "Current position: -250\n",
      "2017-01-10 15:14:00\n",
      "Current basis: 1.023, predicted basis: 0.899\n",
      "Current position: -250\n",
      "2017-01-10 15:15:00\n",
      "Current basis: 1.222, predicted basis: 1.016\n",
      "Current position: -250\n",
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      "Current basis: 1.223, predicted basis: 1.086\n",
      "Current position: -250\n",
      "2017-01-10 15:17:00\n",
      "Current basis: 1.300, predicted basis: 1.143\n",
      "Current position: -250\n",
      "2017-01-10 15:18:00\n",
      "Current basis: 1.080, predicted basis: 1.069\n",
      "Current position: -250\n",
      "2017-01-10 15:19:00\n",
      "Current basis: 0.829, predicted basis: 0.904\n",
      "Current position: -250\n",
      "2017-01-10 15:20:00\n",
      "Current basis: 0.882, predicted basis: 0.904\n",
      "Current position: -250\n",
      "2017-01-10 15:21:00\n",
      "Current basis: 0.683, predicted basis: 0.794\n",
      "Current position: -250\n",
      "2017-01-10 15:22:00\n",
      "Current basis: 0.928, predicted basis: 0.917\n",
      "Current position: -250\n",
      "2017-01-10 15:23:00\n",
      "Current basis: 1.500, predicted basis: 1.272\n",
      "Current position: -250\n",
      "2017-01-10 15:24:00\n",
      "Current basis: 1.251, predicted basis: 1.224\n",
      "Current position: -250\n",
      "2017-01-10 15:25:00\n",
      "Current basis: 1.276, predicted basis: 1.186\n",
      "Current position: -250\n",
      "2017-01-10 15:26:00\n",
      "Current basis: 0.481, predicted basis: 0.681\n",
      "Current position: -250\n",
      "2017-01-10 15:27:00\n",
      "Current basis: -0.387, predicted basis: 0.035\n",
      "Current position: -250\n",
      "2017-01-10 15:28:00\n",
      "Current basis: -0.709, predicted basis: -0.339\n",
      "Current position: -250\n",
      "2017-01-10 15:29:00\n",
      "Current basis: -0.464, predicted basis: -0.195\n",
      "Current position: -240\n",
      "2017-01-10 15:30:00\n",
      "Current basis: -0.464, predicted basis: -0.244\n",
      "Current position: -240\n",
      "[Timestamp('2017-01-06 09:30:00'), datetime.datetime(2017, 1, 11, 9, 17)]\n"
     ]
    }
   ],
   "source": [
    "if updateCheck():\n",
    "        print('Your version of the auquan toolbox package is old. Please update by running the following command:')\n",
    "        print('pip install -U auquan_toolbox')\n",
    "else:\n",
    "    tsParams = MyTradingParams()\n",
    "#     import pdb;pdb.set_trace()\n",
    "    tradingSystem = TradingSystem(tsParams)\n",
    "    \n",
    "    results = tradingSystem.startTrading(onlyAnalyze=False, shouldPlot=False, makeInstrumentCsvs=False)\n",
    "    # Set onlyAnalyze to True to quickly generate csv files with all the features\n",
    "    # Set onlyAnalyze to False to run a full backtest\n",
    "    # Set makeInstrumentCsvs to False to not make instrument specific csvs in runLogs. \n",
    "    # This improves the performance BY A LOT\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can view the results of this backtest below. The toolbox shows you the Mean squared error (`score`), the pnl from trading the strategy as well as other metrics below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'instrument_names': ['MQK'],\n",
       " 'instrument_stats': [{'pnl': {'MQK': 0.10994934999999996},\n",
       "   'score': {'MQK': 0.07316344660879642}}],\n",
       " 'pnl': 0.10994934999999996,\n",
       " 'trading_days': 3,\n",
       " 'score': 0.07316344660879642,\n",
       " 'total_profit': 12422.779500000002,\n",
       " 'capitalUsage': 1384.2569999999996,\n",
       " 'count_profit': 346,\n",
       " 'capital': 10060.683500000001,\n",
       " 'maxDrawdown': 569.5740000000005,\n",
       " 'maxPortfolioValue': 11158.2935,\n",
       " 'variance': 1582.480531330842,\n",
       " 'count_loss': 355,\n",
       " 'portfolio_value': 11099.4935,\n",
       " 'total_loss': 11323.286000000002}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, there is a loss in performance on test data, the new Mean squared error is 0.07."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also view live plots of how the features/prediction/pnl evolves in realtime in tensorboard. Simply type this in your terminal (from the same folder where you have this notebook)\n",
    "\n",
    "`tensorboard --logdir=tb_logs/`"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "It is important to remember that if you don’t like the results of your backtest on test data, discard the model and start again. DO NOT go back and re-optimize your model, this will lead to over fitting! (Also recommend to create a new test data set, since this one is now tainted; in discarding a model, we implicitly know something about the dataset)."
   ]
  }
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