| Overall Statistics |
|
Total Trades 47 Average Win 5.32% Average Loss -1.74% Compounding Annual Return 9.666% Drawdown 19.000% Expectancy 1.298 Net Profit 74.002% Sharpe Ratio 0.713 Probabilistic Sharpe Ratio 19.304% Loss Rate 43% Win Rate 57% Profit-Loss Ratio 3.07 Alpha 0.063 Beta 0.404 Annual Standard Deviation 0.122 Annual Variance 0.015 Information Ratio 0.195 Tracking Error 0.138 Treynor Ratio 0.215 Total Fees $497.34 |
"""
Intersection of ROC comparison using OUT_DAY approach by Vladimir
inspired by Peter Guenther, Tentor Testivis, Dan Whitnable, Thomas Chang.
T Smith setup (Reduced signal to XLI-XLU)
"""
import numpy as np
# ------------------------------------
LEV = 1.0
# ------------------------------------
class DualMomentumInOut(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2002, 1, 1)
self.SetEndDate(2008, 1, 1)
self.cap = 100000
self.VOLA = 126 # int(self.GetParameter('VOLA'))
self.BASE_RET = 85 # int(self.GetParameter('BASE_RET'))
self.STK = self.AddEquity('SPY', Resolution.Hour).Symbol
self.BND = self.AddEquity('TLT', Resolution.Hour).Symbol
self.ASSETS = [self.STK, self.BND]
self.XLI = self.AddEquity('XLI', Resolution.Daily).Symbol
self.XLU = self.AddEquity('XLU', Resolution.Daily).Symbol
self.MKT = self.AddEquity('SPY', Resolution.Daily).Symbol
self.pairs = [self.XLI, self.XLU]
self.bull = 1
self.count = 0
self.outday = 0
self.wt = {}
self.real_wt = {}
self.mkt = []
self.SetWarmUp(timedelta(350))
self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 65),
self.daily_check)
symbols = [self.MKT] + self.pairs
for symbol in symbols:
self.consolidator = TradeBarConsolidator(timedelta(days=1))
self.consolidator.DataConsolidated += self.consolidation_handler
self.SubscriptionManager.AddConsolidator(symbol, self.consolidator)
self.history = self.History(symbols, self.VOLA + 1, Resolution.Daily)
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.VOLA + 1):]
def daily_check(self):
vola = self.history[[self.MKT]].pct_change().std() * np.sqrt(252)
wait_days = int(vola * self.BASE_RET)
period = int((1.0 - vola) * self.BASE_RET)
r = self.history.pct_change(period).iloc[-1]
exit = (r[self.XLI] < r[self.XLU])
if exit:
self.bull = False
self.outday = self.count
if self.count >= self.outday + wait_days:
self.bull = True
self.count += 1
if not self.bull:
for sec in self.ASSETS:
self.wt[sec] = LEV if sec is self.BND else 0
self.trade()
elif self.bull:
for sec in self.ASSETS:
self.wt[sec] = LEV if sec is self.STK else 0
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)
account_leverage = self.Portfolio.TotalHoldingsValue / self.Portfolio.TotalPortfolioValue
self.Plot('Holdings', 'leverage', round(account_leverage, 1))
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))