| Overall Statistics |
|
Total Trades 336 Average Win 0.39% Average Loss -0.22% Compounding Annual Return 424.057% Drawdown 34.900% Expectancy 1.210 Net Profit 73.927% Sharpe Ratio 5.407 Probabilistic Sharpe Ratio 79.073% Loss Rate 20% Win Rate 80% Profit-Loss Ratio 1.78 Alpha 3.964 Beta -1.29 Annual Standard Deviation 0.775 Annual Variance 0.601 Information Ratio 3.714 Tracking Error 1.177 Treynor Ratio -3.251 Total Fees $437.25 Estimated Strategy Capacity $11000.00 |
# Volatility ETF SMA Portfolio
# ------------------------------------------------------------------------------------
ASSETS = ['IVOL', 'SVXY', 'UVXY', 'VIXM', 'VIXY', 'VXX', 'XVZ']; MA_F = 5; MA_S = 50;
# ------------------------------------------------------------------------------------
class SMA_Portfolio(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2020, 1, 1)
self.SetEndDate(2020, 5, 1)
self.SetCash(100000)
self.MKT = self.AddEquity('SPY', Resolution.Daily).Symbol
self.sma_slow = {}
self.sma_fast = {}
self.weight = {}
self.SetWarmUp(MA_S)
for sec in ASSETS:
self.AddEquity(sec, Resolution.Minute).Symbol
self.sma_fast[sec] = self.SMA(sec, MA_F, Resolution.Daily)
self.sma_slow[sec] = self.SMA(sec, MA_S, Resolution.Daily)
self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.AfterMarketOpen('SPY', 65),
self.rebalance)
def rebalance(self):
for sec in ASSETS:
if self.sma_fast[sec].Current.Value > self.sma_slow[sec].Current.Value:
self.weight[sec] = 1.0/len(ASSETS)
else:
self.weight[sec] = 0
for sec, weight in self.weight.items():
self.SetHoldings(sec, weight)