| Overall Statistics |
|
Total Trades 391 Average Win 3.83% Average Loss -1.02% Compounding Annual Return 30.164% Drawdown 19.300% Expectancy 1.872 Net Profit 2943.546% Sharpe Ratio 1.667 Probabilistic Sharpe Ratio 97.357% Loss Rate 39% Win Rate 61% Profit-Loss Ratio 3.75 Alpha 0.252 Beta 0.057 Annual Standard Deviation 0.154 Annual Variance 0.024 Information Ratio 0.685 Tracking Error 0.234 Treynor Ratio 4.493 Total Fees $17526.80 |
"""
DUAL MOMENTUM-IN OUT v2 by Vladimir
https://www.quantconnect.com/forum/discussion/9597/the-in-amp-out-strategy-continued-from-quantopian/p3/comment-28146
https://www.quantconnect.com/forum/discussion/10039/dual-momentum-in-out/p1
inspired by Peter Guenther, Tentor Testivis, Dan Whitnable, Thomas Chang and T Smith.
"""
import numpy as np
class DualMomentumInOut(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2008, 1, 1)
# self.SetEndDate(2020, 11, 27)
self.cap = 100000
self.STK1 = self.AddEquity('QQQ', Resolution.Minute).Symbol
self.STK2 = self.AddEquity('FDN', Resolution.Minute).Symbol
self.BND1 = self.AddEquity('TLT', Resolution.Minute).Symbol
self.BND2 = self.AddEquity('TLH', Resolution.Minute).Symbol
self.ASSETS = [self.STK1, self.STK2, self.BND1, self.BND2]
self.MKT = self.AddEquity('SPY', Resolution.Daily).Symbol
self.XLI = self.AddEquity('XLI', Resolution.Daily).Symbol
self.XLU = self.AddEquity('XLU', Resolution.Daily).Symbol
self.SLV = self.AddEquity('SLV', Resolution.Daily).Symbol
self.GLD = self.AddEquity('GLD', Resolution.Daily).Symbol
self.FXA = self.AddEquity('FXA', Resolution.Daily).Symbol
self.FXF = self.AddEquity('FXF', Resolution.Daily).Symbol
self.DBB = self.AddEquity('DBB', Resolution.Daily).Symbol
self.UUP = self.AddEquity('UUP', Resolution.Daily).Symbol
self.IGE = self.AddEquity('IGE', Resolution.Daily).Symbol
self.SHY = self.AddEquity('SHY', Resolution.Daily).Symbol
self.FORPAIRS = [self.XLI, self.XLU, self.SLV, self.GLD, self.FXA, self.FXF]
self.SIGNALS = [self.XLI, self.DBB, self.IGE, self.SHY, self.UUP]
self.PAIR_LIST = ['S_G', 'I_U', 'A_F']
self.INI_WAIT_DAYS = 15
self.SHIFT = 55
self.MEAN = 11
self.RET = 126
self.EXCL = 5
self.leveragePercentage = 101
self.selected_bond = self.BND1
self.selected_stock = self.STK1
self.init = 0
self.bull = 1
self.count = 0
self.outday = 0
self.in_stock = 0
self.spy = []
self.wait_days = self.INI_WAIT_DAYS
self.wt = {}
self.real_wt = {}
self.SetWarmUp(timedelta(126))
self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 100),
self.calculate_signal)
self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 120),
self.trade_out)
self.Schedule.On(self.DateRules.WeekEnd(), self.TimeRules.AfterMarketOpen('SPY', 120),
self.trade_in)
self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.BeforeMarketClose('SPY', 0),
self.record_vars)
def returns(self, symbol, period, excl):
prices = self.History(symbol, TimeSpan.FromDays(period + excl), Resolution.Daily).close
return prices[-excl] / prices[0]
def calculate_signal(self):
symbols = self.SIGNALS + [self.MKT] + self.FORPAIRS
hist = self.History(symbols, 252, Resolution.Daily)['close'].unstack(level=0).dropna()
history_shift_mean = hist.shift(self.SHIFT).rolling(self.MEAN).mean()
mom = (hist / history_shift_mean - 1)
mom[self.UUP] = mom[self.UUP] * (-1)
mom['S_G'] = mom[self.SLV] - mom[self.GLD]
mom['I_U'] = mom[self.XLI] - mom[self.XLU]
mom['A_F'] = mom[self.FXA] - mom[self.FXF]
pctl = np.nanpercentile(mom, 1, axis=0)
extreme = mom.iloc[-1] < pctl
self.wait_days = int(
max(0.50 * self.wait_days,
self.INI_WAIT_DAYS * max(1,
np.where((mom[self.GLD].iloc[-1]>0) & (mom[self.SLV].iloc[-1]<0) & (mom[self.SLV].iloc[-2]>0), self.INI_WAIT_DAYS, 1),
np.where((mom[self.XLU].iloc[-1]>0) & (mom[self.XLI].iloc[-1]<0) & (mom[self.XLI].iloc[-2]>0), self.INI_WAIT_DAYS, 1),
np.where((mom[self.FXF].iloc[-1]>0) & (mom[self.FXA].iloc[-1]<0) & (mom[self.FXA].iloc[-2]>0), self.INI_WAIT_DAYS, 1)
)))
adjwaitdays = min(60, self.wait_days)
# self.Debug('{}'.format(self.wait_days))
if (extreme[self.SIGNALS + self.PAIR_LIST]).any():
self.bull = False
self.outday = self.count
if self.count >= self.outday + adjwaitdays:
self.bull = True
self.count += 1
self.Plot("In Out", "in_market", int(self.bull))
self.Plot("In Out", "num_out_signals", extreme[self.SIGNALS + self.PAIR_LIST].sum())
self.Plot("Wait Days", "waitdays", adjwaitdays)
if self.returns(self.BND1, self.RET, self.EXCL) < self.returns(self.BND2, self.RET, self.EXCL):
self.selected_bond = self.BND2
elif self.returns(self.BND1, self.RET, self.EXCL) > self.returns(self.BND2, self.RET, self.EXCL):
self.selected_bond = self.BND1
if self.returns(self.STK1, self.RET, self.EXCL) < self.returns(self.STK2, self.RET, self.EXCL):
self.selected_stock = self.STK2
elif self.returns(self.STK1, self.RET, self.EXCL) > self.returns(self.STK2, self.RET, self.EXCL):
self.selected_stock = self.STK1
def trade_out(self):
if not self.bull:
for sec in self.ASSETS:
self.wt[sec] = 0.99 if sec is self.selected_bond else 0 if sec is self.selected_bond else 0
self.trade()
def trade_in(self):
if self.bull:
for sec in self.ASSETS:
self.wt[sec] = 0.99 if sec is self.selected_stock 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 record_vars(self):
hist = self.History([self.MKT], 2, Resolution.Daily)['close'].unstack(level= 0).dropna()
self.spy.append(hist[self.MKT].iloc[-1])
spy_perf = self.spy[-1] / self.spy[0] * self.cap
self.Plot("Strategy Equity", "SPY", spy_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))