| Overall Statistics |
|
Total Trades 3524 Average Win 1.15% Average Loss -0.95% Compounding Annual Return 17.374% Drawdown 35.500% Expectancy 0.222 Net Profit 3356.721% Sharpe Ratio 1.074 Probabilistic Sharpe Ratio 49.997% Loss Rate 45% Win Rate 55% Profit-Loss Ratio 1.21 Alpha 0.114 Beta 0.513 Annual Standard Deviation 0.142 Annual Variance 0.02 Information Ratio 0.549 Tracking Error 0.139 Treynor Ratio 0.297 Total Fees $80745.74 |
## T. Smith - Reductionist ROC comparison XLI-XLU - Faster trading - Long SPY only - Sharpe >1 since 1999 - Inspired by Vladimir & Peter Guenther
import numpy as np
class DualMomentumInOut(QCAlgorithm):
def Initialize(self):
self.SetStartDate(1999, 1, 1)
self.cap = 100000
self.RETURN = 10
self.STK = self.AddEquity('SPY', Resolution.Hour).Symbol
self.XLI = self.AddEquity('XLI', Resolution.Hour).Symbol
self.XLU = self.AddEquity('XLU', Resolution.Hour).Symbol
self.MKT = self.AddEquity('SPY', Resolution.Daily).Symbol
self.pairs = [self.XLI, self.XLU]
self.wt = {}
self.real_wt = {}
self.mkt = []
self.SetWarmUp(timedelta(350))
self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.Every(TimeSpan.FromMinutes(240)),
self.daily_check)
symbols = self.pairs
for symbol in symbols:
self.consolidator = TradeBarConsolidator(timedelta(hours=1))
self.consolidator.DataConsolidated += self.consolidation_handler
self.SubscriptionManager.AddConsolidator(symbol, self.consolidator)
self.history = self.History(symbols, self.RETURN + 1, Resolution.Hour)
if self.history.empty or 'close' not in self.history.columns:
return
self.history = self.history['close'].unstack(level=0).dropna()
def consolidation_handler(self, sender, consolidated):
self.history.loc[consolidated.EndTime, consolidated.Symbol] = consolidated.Close
self.history = self.history.iloc[-(self.RETURN + 1):]
def daily_check(self):
r = self.history.pct_change(self.RETURN).iloc[-1]
if (r[self.XLI] < r[self.XLU]):
self.wt[self.STK] = 0
self.trade()
else:
self.wt[self.STK] = 1
self.trade()
def trade(self):
for sec, weight in self.wt.items():
if weight == 0 and self.Portfolio[sec].IsLong:
self.Liquidate(sec)
cond1 = weight == 0 and self.Portfolio[sec].IsLong
cond2 = weight > 0 and not self.Portfolio[sec].Invested
if cond1 or cond2:
self.SetHoldings(sec, weight)
def OnEndOfDay(self):
mkt_price = self.Securities[self.MKT].Close
self.mkt.append(mkt_price)
mkt_perf = self.mkt[-1] / self.mkt[0] * self.cap
self.Plot('Strategy Equity', 'SPY', mkt_perf)
for sec, weight in self.wt.items():
self.real_wt[sec] = round(self.ActiveSecurities[sec].Holdings.Quantity * self.Securities[sec].Price / self.Portfolio.TotalPortfolioValue,4)
self.Plot('Holdings', self.Securities[sec].Symbol, round(self.real_wt[sec], 3))