Overall Statistics |
Total Trades
844
Average Win
1.23%
Average Loss
-1.33%
Compounding Annual Return
12.839%
Drawdown
29.100%
Expectancy
0.227
Net Profit
313.822%
Sharpe Ratio
0.921
Probabilistic Sharpe Ratio
32.308%
Loss Rate
36%
Win Rate
64%
Profit-Loss Ratio
0.92
Alpha
0.03
Beta
0.696
Annual Standard Deviation
0.155
Annual Variance
0.024
Information Ratio
-0.174
Tracking Error
0.112
Treynor Ratio
0.205
Total Fees
$1399.42
Estimated Strategy Capacity
$630000000.00
Lowest Capacity Asset
SPY 31RSQCIAC8NS6|SPY R735QTJ8XC9X
|
# https://quantpedia.com/strategies/volatility-risk-premium-effect/ # # Each month, at-the-money straddle, with one month until maturity, is sold at the bid price with a 5% option premium, and an offsetting 15% # out-of-the-money puts are bought (at the ask price) as insurance against a market crash. The remaining cash and received option premium are # invested in the index. The strategy is rebalanced monthly. class VolatilityRiskPremiumEffect(QCAlgorithm): def Initialize(self): self.SetStartDate(2010, 1, 1) self.SetCash(100000) data = self.AddEquity("SPY", Resolution.Minute) data.SetLeverage(5) self.symbol = data.Symbol option = self.AddOption("SPY", Resolution.Minute) option.SetFilter(-20, 20, 25, 35) self.last_day = -1 def OnData(self,slice): # Check once a day. if self.Time.day == self.last_day: return self.last_day = self.Time.day for i in slice.OptionChains: chains = i.Value if not self.Portfolio.Invested: # divide option chains into call and put options calls = list(filter(lambda x: x.Right == OptionRight.Call, chains)) puts = list(filter(lambda x: x.Right == OptionRight.Put, chains)) # if lists are empty return if not calls or not puts: return underlying_price = self.Securities[self.symbol].Price expiries = [i.Expiry for i in puts] # determine expiration date nearly one month expiry = min(expiries, key=lambda x: abs((x.date()-self.Time.date()).days-30)) strikes = [i.Strike for i in puts] # determine at-the-money strike strike = min(strikes, key=lambda x: abs(x-underlying_price)) # determine 15% out-of-the-money strike otm_strike = min(strikes, key = lambda x:abs(x - float(0.85) * underlying_price)) atm_call = [i for i in calls if i.Expiry == expiry and i.Strike == strike] atm_put = [i for i in puts if i.Expiry == expiry and i.Strike == strike] otm_put = [i for i in puts if i.Expiry == expiry and i.Strike == otm_strike] if atm_call and atm_put and otm_put: options_q = int(self.Portfolio.MarginRemaining / (underlying_price * 100)) # Set max leverage. self.Securities[atm_call[0].Symbol].MarginModel = BuyingPowerModel(5) self.Securities[atm_put[0].Symbol].MarginModel = BuyingPowerModel(5) self.Securities[otm_put[0].Symbol].MarginModel = BuyingPowerModel(5) # sell at-the-money straddle self.Sell(atm_call[0].Symbol, options_q) self.Sell(atm_put[0].Symbol, options_q) # buy 15% out-of-the-money put self.Buy(otm_put[0].Symbol, options_q) # buy index. self.SetHoldings(self.symbol, 1) invested = [x.Key for x in self.Portfolio if x.Value.Invested] if len(invested) == 1: self.Liquidate(self.symbol)