| Overall Statistics |
|
Total Trades 679 Average Win 0.03% Average Loss -0.03% Compounding Annual Return 134.552% Drawdown 1.000% Expectancy 0.315 Net Profit 7.509% Sharpe Ratio 9.043 Probabilistic Sharpe Ratio 99.492% Loss Rate 29% Win Rate 71% Profit-Loss Ratio 0.84 Alpha 0.196 Beta 1.07 Annual Standard Deviation 0.088 Annual Variance 0.008 Information Ratio 5.688 Tracking Error 0.041 Treynor Ratio 0.747 Total Fees $42852.98 Estimated Strategy Capacity $85000000.00 Lowest Capacity Asset KO R735QTJ8XC9X |
import numpy as np
import pandas as pd
import math
import time
class StatArb1(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2012, 1, 4)
self.SetEndDate(2012, 2, 5)
self.SetCash(20000000)
self.filtered = []
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.LiquidWithFundamentalsFilter)
self.spy = self.AddEquity("SPY",Resolution.Daily)
self.Schedule.On(self.DateRules.EveryDay("SPY"),
self.TimeRules.AfterMarketOpen("SPY", 10),
self.RefactorPortfolio)
def LiquidWithFundamentalsFilter(self, coarse):
sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
self.filtered = [ x.Symbol for x in sortedByDollarVolume
if x.Price > 10 and x.DollarVolume > 10000000 and
x.HasFundamentalData][:25]
return self.filtered
def RefactorPortfolio(self):
targets = []
for x in self.Portfolio.Values:
if x.Invested and x.Symbol not in self.filtered:
targets.append(PortfolioTarget(x.Symbol, 0.))
for symbol in self.filtered:
targets.append(PortfolioTarget(symbol, 1/25))
self.SetHoldings(targets)