Overall Statistics
Total Trades
91
Average Win
5.03%
Average Loss
-1.69%
Compounding Annual Return
3.171%
Drawdown
23.700%
Expectancy
0.500
Net Profit
26.385%
Sharpe Ratio
0.298
Loss Rate
62%
Win Rate
38%
Profit-Loss Ratio
2.97
Alpha
0.034
Beta
0.329
Annual Standard Deviation
0.136
Annual Variance
0.018
Information Ratio
0.151
Tracking Error
0.136
Treynor Ratio
0.123
Total Fees
$91.00
from sklearn import linear_model
import numpy as np
import pandas as pd
from scipy import stats
from math import floor
from datetime import timedelta


class PairsTradingAlgorithm(QCAlgorithm):
    
    def Initialize(self):
        
        self.SetStartDate(2010,1,1)
        self.SetEndDate(2017,6,30)
        self.SetCash(10000)
        self.numdays = 250  # set the length of training period
        tickers = ["XLK","QQQ"]
        self.symbols = []
        
        self.threshold = 1.
        for i in tickers:
            self.symbols.append(self.AddSecurity(SecurityType.Equity, i, Resolution.Daily).Symbol)
        for i in self.symbols:
            i.hist_window = RollingWindow[TradeBar](self.numdays) 


    def OnData(self, data):
        
        if not (data.ContainsKey("XLK") and data.ContainsKey("QQQ")): return
        
        for symbol in self.symbols:
            symbol.hist_window.Add(data[symbol])
        
    
        price_x = pd.Series([float(i.Close) for i in self.symbols[0].hist_window], 
                             index = [i.Time for i in self.symbols[0].hist_window])
                             
        price_y = pd.Series([float(i.Close) for i in self.symbols[1].hist_window], 
                             index = [i.Time for i in self.symbols[1].hist_window])
        if len(price_x) < 250: return
        spread = self.regr(np.log(price_x), np.log(price_y))
        mean = np.mean(spread)
        std = np.std(spread)
        ratio = floor(self.Portfolio[self.symbols[1]].Price / self.Portfolio[self.symbols[0]].Price)
        # quantity = float(self.CalculateOrderQuantity(self.symbols[0],0.4)) 
        
        if spread[-1] > mean + self.threshold * std:
            if not self.Portfolio[self.symbols[0]].Quantity > 0 and not self.Portfolio[self.symbols[0]].Quantity < 0:
                self.Sell(self.symbols[1], 100) 
                self.Buy(self.symbols[0],  ratio * 100)
        
        elif spread[-1] < mean - self.threshold * std:
            if not self.Portfolio[self.symbols[0]].Quantity < 0 and not self.Portfolio[self.symbols[0]].Quantity > 0:
                self.Sell(self.symbols[0], 100)
                self.Buy(self.symbols[1], ratio * 100) 

        else:
            self.Liquidate()

    
    def regr(self,x,y):
        regr = linear_model.LinearRegression()
        x_constant = np.column_stack([np.ones(len(x)), x])
        regr.fit(x_constant, y)
        beta = regr.coef_[0]
        alpha = regr.intercept_
        spread = y - x*beta - alpha
        return spread