Overall Statistics |
Total Orders 4 Average Win 0% Average Loss 0% Compounding Annual Return -14.590% Drawdown 1.000% Expectancy 0 Start Equity 100000 End Equity 99111 Net Profit -0.889% Sharpe Ratio -3.738 Sortino Ratio -2.174 Probabilistic Sharpe Ratio 1.017% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha -0.117 Beta 0.057 Annual Standard Deviation 0.032 Annual Variance 0.001 Information Ratio -0.755 Tracking Error 0.063 Treynor Ratio -2.136 Total Fees $4.00 Estimated Strategy Capacity $2500000.00 Lowest Capacity Asset GOOCV 30JDODOEFOQTI|GOOCV VP83T1ZUHROL Portfolio Turnover 0.37% |
# region imports from AlgorithmImports import * # endregion class ShortButteflyOptionStrategy(QCAlgorithm): def initialize(self): self.set_start_date(2017, 4, 1) self.set_end_date(2017, 4, 23) self.set_cash(100000) option = self.add_option("GOOG", Resolution.MINUTE) self._symbol = option.symbol # set our strike/expiry filter for this option chain option.set_filter(lambda x: x.include_weeklys().iron_butterfly(30, 5)) def on_data(self, slice): if self.portfolio.invested: return # Get the OptionChain chain = slice.option_chains.get(self._symbol, None) if not chain: return # Select expiry expiry = max([x.expiry for x in chain]) # Separate the call and put contracts calls = [i for i in chain if i.right == OptionRight.CALL and i.expiry == expiry] puts = [i for i in chain if i.right == OptionRight.PUT and i.expiry == expiry] if not calls or not puts: return # Get the ATM and OTM strike prices atm_strike = sorted(calls, key = lambda x: abs(x.strike - chain.underlying.price))[0].strike otm_put_strike = min([x.strike for x in puts]) otm_call_strike = 2 * atm_strike - otm_put_strike short_iron_butterfly = OptionStrategies.short_iron_butterfly(self._symbol, otm_put_strike, atm_strike, otm_call_strike, expiry) self.buy(short_iron_butterfly, 1)