Overall Statistics
Total Orders
2
Average Win
0%
Average Loss
0%
Compounding Annual Return
-1.798%
Drawdown
0.200%
Expectancy
0
Start Equity
500000
End Equity
499238.5
Net Profit
-0.152%
Sharpe Ratio
-26.375
Sortino Ratio
-29.107
Probabilistic Sharpe Ratio
0.000%
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
-0.021
Beta
-0.002
Annual Standard Deviation
0.001
Annual Variance
0
Information Ratio
-8.354
Tracking Error
0.058
Treynor Ratio
9.773
Total Fees
$2.00
Estimated Strategy Capacity
$35000000.00
Lowest Capacity Asset
GOOCV 30HNN6TRH910M|GOOCV VP83T1ZUHROL
Portfolio Turnover
0.02%
 
from AlgorithmImports import *

class BearPutSpreadStrategy(QCAlgorithm): 
    def initialize(self):
        self.set_start_date(2017, 2, 1)
        self.set_end_date(2017, 3, 5)
        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().put_spread(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

        # Get the furthest expiry date of the contracts
        expiry = sorted(chain, key = lambda x: x.expiry, reverse=True)[0].expiry
        
        # Select the put Option contracts with the furthest expiry
        puts = [i for i in chain if i.expiry == expiry and i.right == OptionRight.PUT]
        if len(puts) == 0: return

        # Select the ITM and OTM contract strike prices from the remaining contracts
        put_strikes = sorted([x.strike for x in puts])
        otm_strike = put_strikes[0]
        itm_strike = put_strikes[-1]
        
        option_strategy = OptionStrategies.bear_put_spread(self.symbol, itm_strike, otm_strike, expiry)
        self.buy(option_strategy, 1)