Backtest

Overall Statistics
Total Orders 352 Average Win 0.02% Average Loss -1.15% Compounding Annual Return 2.443% Drawdown 1.300% Expectancy 0.014 Start Equity 1000000 End Equity 1029320 Net Profit 2.932% Sharpe Ratio -7.008 Sortino Ratio -3.34 Probabilistic Sharpe Ratio 98.429% Loss Rate 1% Win Rate 99% Profit-Loss Ratio 0.02 Alpha -0.038 Beta 0 Annual Standard Deviation 0.005 Annual Variance 0 Information Ratio -0.882 Tracking Error 0.109 Treynor Ratio -261.257 Total Fees $0.00 Estimated Strategy Capacity $4000.00 Lowest Capacity Asset SPXW YPZ4T6Y6SOKU\|SPX 31 Portfolio Turnover 0.01%

# region imports
from AlgorithmImports import *
# endregion

class OneDTEIndexOptionUniverseAlgorithm(QCAlgorithm):

    def initialize(self):
        self.set_start_date(2024, 1, 1)
        self.set_cash(1_000_000)
        self.settings.automatic_indicator_warm_up = True
        # Add 1DTE SPY contracts.
        self._index = self.add_index('SPX')
        self._index.std = IndicatorExtensions.Of(StandardDeviation(21), self.roc(self._index.symbol, 1, Resolution.DAILY))
        self._option = self.add_index_option(self._index.symbol, 'SPXW')
        self._option.set_filter(lambda u: u.include_weeklys().expiration(1, 1).strikes(-40, 40))
        # Create a member to track when the algorithm should trade.
        self._can_trade = False
        date_rule = self.date_rules.every_day(self._option.symbol)
        self.schedule.on(date_rule, self.time_rules.at(15, 45), lambda: setattr(self, '_can_trade', True))
        self.schedule.on(date_rule, self.time_rules.at(16, 15), lambda: setattr(self, '_can_trade', False))
        self.set_warm_up(timedelta(31))

    def on_data(self, data):
        if self.is_warming_up or self.portfolio.invested:
            return
        # Only process data between 3:45 PM and 4:15 PM on Thursdays.
        if not self._can_trade:
            return
        # Get the Option chain.
        chain = data.option_chains.get(self._option.symbol)
        if not chain:
            return

        upper_threshold = self._index.price * (1 + 4*self._index.std.current.value)
        lower_threshold = self._index.price * (1 - 4*self._index.std.current.value)
        self.plot('STD', "Price", self._index.price)
        self.plot('STD', "upper_threshold", upper_threshold)
        self.plot('STD', "lower_threshold", lower_threshold)

        # Order the OTM calls by strike to find the nearest to ATM
        call_contracts = sorted(
            [contract for contract in chain if contract.right == OptionRight.CALL and contract.strike > upper_threshold],
            key=lambda x: x.strike
        )
        if not call_contracts:
            return
        # Order the OTM puts by strike to find the nearest to ATM
        put_contracts = sorted(
            [contract for contract in chain if contract.right == OptionRight.PUT and contract.strike < lower_threshold],
            key=lambda x: x.strike, reverse=True
        )
        if not put_contracts:
            return
        # Select the call and put contracts.
        call = call_contracts[0]
        put = put_contracts[0]
        # Place the trade.
        self.buy(OptionStrategies.short_strangle(self._option.symbol, call.strike, put.strike, call.expiry), 10)