Overall Statistics |
Total Orders 2 Average Win 0% Average Loss 0% Compounding Annual Return 1.402% Drawdown 0.000% Expectancy 0 Start Equity 500000 End Equity 500318 Net Profit 0.064% Sharpe Ratio -0.407 Sortino Ratio -2.555 Probabilistic Sharpe Ratio 99.579% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha -0.002 Beta 0.002 Annual Standard Deviation 0.002 Annual Variance 0 Information Ratio -14.929 Tracking Error 0.044 Treynor Ratio -0.254 Total Fees $2.00 Estimated Strategy Capacity $240000.00 Lowest Capacity Asset GOOCV WIXFALAHKNC6|GOOCV VP83T1ZUHROL Portfolio Turnover 0.04% |
from AlgorithmImports import * class LongCallCalendarSpreadStrategy(QCAlgorithm): def initialize(self): self.set_start_date(2017, 2, 1) self.set_end_date(2017, 2, 19) 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.strikes(-1, 1).expiration(timedelta(0), timedelta(62)) 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 # get at-the-money strike atm_strike = sorted(chain, key=lambda x: abs(x.strike - chain.underlying.price))[0].strike # filter the call options from the contracts which is ATM in the option chain. calls = [i for i in chain if i.strike == atm_strike and i.right == OptionRight.CALL] if len(calls) == 0: return # sorted the optionchain by expiration date expiries = sorted([x.expiry for x in calls], key = lambda x: x) # select the farest expiry as far-leg expiry, and the nearest expiry as near-leg expiry near_expiry = expiries[0] far_expiry = expiries[-1] option_strategy = OptionStrategies.short_call_calendar_spread(self.symbol, atm_strike, near_expiry, far_expiry) # We open a position with 1 unit of the option strategy self.buy(option_strategy, 1)