Overall Statistics
Total Trades
6
Average Win
0.00%
Average Loss
-0.01%
Compounding Annual Return
-2.485%
Drawdown
0.300%
Expectancy
-0.656
Net Profit
-0.211%
Sharpe Ratio
-6.562
Probabilistic Sharpe Ratio
0.038%
Loss Rate
67%
Win Rate
33%
Profit-Loss Ratio
0.03
Alpha
0.005
Beta
-0.047
Annual Standard Deviation
0.003
Annual Variance
0
Information Ratio
-8.131
Tracking Error
0.061
Treynor Ratio
0.383
Total Fees
$3.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
SPY R735QTJ8XC9X
#region imports
from AlgorithmImports import *
#endregion
# https://quantpedia.com/Screener/Details/20

class VolatilityRiskPremiumStrategy(QCAlgorithm): 
    def Initialize(self):
        self.SetStartDate(2017, 2, 1)
        self.SetEndDate(2017, 3, 5)
        self.SetCash(500000)

        option = self.AddOption("SPY", Resolution.Minute)
        self.symbol = option.Symbol
        option.SetFilter(self.UniverseFunc)

    def UniverseFunc(self, universe):
        return universe.IncludeWeeklys().Strikes(-20, 20).Expiration(timedelta(0), timedelta(31))

    def OnData(self, slice: Slice) -> None:
        if self.Portfolio.Invested: return

        # Get the OptionChain
        chain = slice.OptionChains.get(self.symbol, None)
        if not chain: return

        # Get the nearest expiration date of the contracts
        expiry = sorted(chain, key = lambda x: x.Expiry)[0].Expiry
        
        # Select the put Option contracts
        puts = [i for i in chain if i.Expiry == expiry and i.Right == OptionRight.Put]
        if len(puts) == 0: return

        # Select the ATM strike price from the remaining contracts
        underlying_price = chain.Underlying.Price
        atm_strikes = sorted([x.Strike for x in puts], key=lambda x: abs(x - underlying_price))[0]

        option_strategy = OptionStrategies.Straddle(self.symbol, atm_strikes, expiry)
        # Select 15% OTM put
        otm_put = sorted(puts, key=lambda x: abs(x.Strike - 0.85*underlying_price))[0]
        self.Buy(option_strategy, 1)
        self.Sell(otm_put.Symbol, 1)