Backtest

Overall Statistics
Total Orders 6 Average Win 0.63% Average Loss -0.46% Compounding Annual Return -2.405% Drawdown 0.300% Expectancy 0.185 Start Equity 500000 End Equity 498878.7 Net Profit -0.224% Sharpe Ratio -3.655 Sortino Ratio -3.099 Probabilistic Sharpe Ratio 15.470% Loss Rate 50% Win Rate 50% Profit-Loss Ratio 1.37 Alpha 0.002 Beta -0.063 Annual Standard Deviation 0.006 Annual Variance 0 Information Ratio -6.641 Tracking Error 0.061 Treynor Ratio 0.358 Total Fees $3.30 Estimated Strategy Capacity $600000.00 Lowest Capacity Asset GOOCV WIJN2CDLLEBQ\|GOOCV VP83T1ZUHROL Portfolio Turnover 1.44%

from AlgorithmImports import *

class LongCallButterflyStrategy(QCAlgorithm): 
    def initialize(self):
        self.set_start_date(2017, 2, 1)
        self.set_end_date(2017, 3, 6)
        self.set_cash(500000)

        option = self.add_option("GOOG", Resolution.MINUTE)
        self.symbol = option.symbol
        option.set_filter(self.universe_func)

    def universe_func(self, universe):
        return universe.include_weeklys().strikes(-15, 15).expiration(timedelta(0), timedelta(31))

    def on_data(self, data):
        # avoid extra orders
        if self.portfolio.invested: return

        # Get the OptionChain of the self.symbol
        chain = data.option_chains.get(self.symbol, None)
        if not chain: return

        # sorted the optionchain by expiration date and choose the furthest date
        expiry = sorted(chain, key = lambda x: x.expiry, reverse=True)[0].expiry
        
        # filter the call options from the contracts which expire on the furthest expiration date in the option chain.
        calls = [i for i in chain if i.expiry == expiry and i.right == OptionRight.CALL]
        if len(calls) == 0: return

        # sort the call options with the same expiration date according to their strike price.
        call_strikes = sorted([x.strike for x in calls])

        # get at-the-money strike
        atm_strike = sorted(calls, key=lambda x: abs(x.strike - chain.underlying.price))[0].strike

        # Get the distance between lowest strike price and ATM strike, and highest strike price and ATM strike. 
        # Get the lower value as the spread distance as equidistance is needed for both side.
        spread = min(abs(call_strikes[0] - atm_strike), abs(call_strikes[-1] - atm_strike))

        # select the strike prices for forming the option legs
        itm_strike = atm_strike - spread
        otm_strike = atm_strike + spread

        option_strategy = OptionStrategies.call_butterfly(self.symbol, otm_strike, atm_strike, itm_strike, expiry)
        # We open a position with 1 unit of the option strategy
        self.buy(option_strategy, 1)
        # self.sell(option_strategy, 1) if short call butterfly