| Overall Statistics |
|
Total Trades 4386 Average Win 0.01% Average Loss -0.01% Compounding Annual Return -40.168% Drawdown 4.500% Expectancy -0.187 Net Profit -4.269% Sharpe Ratio -8.319 Probabilistic Sharpe Ratio 0.000% Loss Rate 55% Win Rate 45% Profit-Loss Ratio 0.80 Alpha -0.33 Beta 0.066 Annual Standard Deviation 0.035 Annual Variance 0.001 Information Ratio -11.174 Tracking Error 0.077 Treynor Ratio -4.4 Total Fees $86036.18 Estimated Strategy Capacity $220000000.00 Lowest Capacity Asset THS T9V5YFMQ412D |
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.SetWarmup(10)
self.UniverseSettings.Resolution = Resolution.Minute
self.UniverseSettings.MinimumTimeInUniverse = timedelta(hours=12)
self.AddUniverse(self.LiquidWithFundamentalsFilter)
self.spy = self.AddEquity("SPY",Resolution.Minute)
self.Schedule.On(self.DateRules.EveryDay("SPY"),
self.TimeRules.BeforeMarketClose("SPY", 10),
self.Liquidate)
self.Schedule.On(self.DateRules.EveryDay("SPY"),
self.TimeRules.AfterMarketOpen("SPY", 1),
self.RefactorPortfolio)
self.lookback = 20
self.activeStocks = []
self.maxWeight = .1
def LiquidWithFundamentalsFilter(self, coarse):
sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
filtered = [ x.Symbol for x in sortedByDollarVolume
if x.Price > 50 and x.DollarVolume > 10000000 and
x.HasFundamentalData]
return filtered[:100]
def OnSecuritiesChanged(self, changes):
for s in changes.RemovedSecurities:
if s.Symbol in self.activeStocks:
self.activeStocks.remove(s.Symbol)
for s in changes.AddedSecurities:
if str(s.Symbol) == "SPY": continue
self.activeStocks.append(s.Symbol)
def RefactorPortfolio(self):
tickers = [s for s in self.activeStocks if s.ToString() != "SPY"]
data = self.History(tickers,self.lookback,Resolution.Daily)
dates = data.index.get_level_values("time")
self.Debug("###"+str(dates[-1])+", "+str(len(tickers))+", "+str(len(self.ActiveSecurities)))
tickerIndustries = {}
for ticker in tickers:
tickerIndustries[ticker] = self.ActiveSecurities[ticker].Fundamentals.AssetClassification.MorningstarIndustryCode
alpha = {}
for ticker in tickers:
try:
delta1close = data.loc[(ticker,dates[-2]),"close"]-data.loc[(ticker,dates[-3]),"close"]
close = data.loc[(ticker,dates[-2]),"close"]
delay1close = data.loc[(ticker,dates[-3]),"close"]
volumeavailability = len([x for x in data.loc[(ticker,slice(None)),"volume"]])
sumvolume = sum([x for x in data.loc[(ticker,slice(None)),"volume"]])
adv20 = sumvolume/volumeavailability
volume = data.loc[(ticker,dates[-2]),"volume"]
vwap = sum([x[0]*x[1] for x in zip(data.loc[(ticker,slice(None)),"volume"], data.loc[(ticker,slice(None)), "close"])])/sumvolume
todaysopen = data.loc[(ticker,dates[-1]),"open"]
yesterdaysopen = data.loc[(ticker,dates[-2]),"open"]
value = -np.log(todaysopen/close)
except:
self.Debug("Alpha Calculation Threw Error")
value = np.NAN
alpha[ticker] = value
alpha = self.Neutralize(alpha, tickerIndustries)
for ticker in alpha:
if np.isnan(alpha[ticker]):
alpha[ticker] = 0
if abs(alpha[ticker]) > self.maxWeight:
alpha[ticker] = np.sign(alpha[ticker])*self.maxWeight
self.SetHoldings(ticker,math.floor(alpha[ticker]*100000)/100000)
def Neutralize(self, alpha, groupClassifications):
groupValues = {}
groupAvgs = {}
groupTotalMagnitude = {}
for group in set(groupClassifications.values()):
groupValues[group] = []
for ticker in alpha.keys():
groupValues[groupClassifications[ticker]].append(alpha[ticker])
for group in set(groupClassifications.values()):
groupAvgs[group] = np.nanmean(np.array(groupValues[group]))
groupTotalMagnitude[group] = np.nansum(np.array([abs(x) for x in groupValues[group]]))
for ticker in alpha.keys():
group = groupClassifications[ticker]
alpha[ticker] = (alpha[ticker]-groupAvgs[group])/(groupTotalMagnitude[group])
return alpha
def OnData(self, data):
pass
#self.Plot("Equity", 'Line', self.Portfolio.TotalPortfolioValue)
#self.Plot("Margin", 'Line', self.Portfolio.TotalMarginUsed)