Overall Statistics |
Total Trades 270 Average Win 1.79% Average Loss -1.69% Compounding Annual Return 3.767% Drawdown 12.900% Expectancy 0.153 Net Profit 34.436% Sharpe Ratio 0.555 Loss Rate 44% Win Rate 56% Profit-Loss Ratio 1.06 Alpha 0.067 Beta -2.163 Annual Standard Deviation 0.058 Annual Variance 0.003 Information Ratio 0.27 Tracking Error 0.058 Treynor Ratio -0.015 Total Fees $270.00 |
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(2009,1,1) self.SetEndDate(2017,1,1) self.SetCash(10000) self.numdays = 250 # set the length of training period tickers = ["XOM", "CVX"] 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("CVX") and data.ContainsKey("XOM")): 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