| Overall Statistics |
|
Total Trades 1924 Average Win 0.05% Average Loss -0.04% Compounding Annual Return 382.415% Drawdown 1.500% Expectancy 0.279 Net Profit 14.299% Sharpe Ratio 12.41 Probabilistic Sharpe Ratio 99.897% Loss Rate 46% Win Rate 54% Profit-Loss Ratio 1.39 Alpha 0.881 Beta 2.05 Annual Standard Deviation 0.164 Annual Variance 0.027 Information Ratio 14.481 Tracking Error 0.102 Treynor Ratio 0.994 Total Fees $287858.10 Estimated Strategy Capacity $62000000.00 Lowest Capacity Asset P 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.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.LiquidWithFundamentalsFilter)
#self.SetWarmUp(timedelta(30))
self.spy = self.AddEquity("SPY",Resolution.Daily)
self.Schedule.On(self.DateRules.EveryDay("SPY"),
self.TimeRules.AfterMarketOpen("SPY", 10),
self.RefactorPortfolio)
self.lookback = 20
def LiquidWithFundamentalsFilter(self, coarse):
sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
filtered = [ x.Symbol for x in sortedByDollarVolume
if x.Price > 10 and x.DollarVolume > 10000000 and
x.HasFundamentalData]
return filtered[:25]
def RefactorPortfolio(self):
self.Liquidate()
tickers = [self.Symbol(key.ToString()) for key in self.ActiveSecurities.Keys if key.ToString() != "SPY"]
data = self.History(tickers,self.lookback,Resolution.Daily)
dates = data.index.get_level_values("time")
self.Debug(len(tickers))
alpha = {}
for ticker in tickers:
alpha[ticker] = 1/25
for ticker in alpha:
if np.isnan(alpha[ticker]):
alpha[ticker] = 0
self.SetHoldings(ticker,alpha[ticker])
def OnData(self, data):
pass