Backtest

Overall Statistics
Total Orders 229 Average Win 2.19% Average Loss -1.51% Compounding Annual Return 4.933% Drawdown 15.200% Expectancy 0.483 Start Equity 10000 End Equity 20523.35 Net Profit 105.234% Sharpe Ratio 0.291 Sortino Ratio 0.191 Probabilistic Sharpe Ratio 1.969% Loss Rate 39% Win Rate 61% Profit-Loss Ratio 1.45 Alpha -0.001 Beta 0.245 Annual Standard Deviation 0.064 Annual Variance 0.004 Information Ratio -0.555 Tracking Error 0.112 Treynor Ratio 0.076 Total Fees $229.00 Estimated Strategy Capacity $710000.00 Lowest Capacity Asset OEF RZ8CR0XXNOF9 Portfolio Turnover 3.98%

# https://quantpedia.com/strategies/option-expiration-week-effect/
#
# Investors choose stocks from the S&P 100 index as his/her investment universe (stocks could be easily tracked via ETF or index fund).
# He/she then goes long S&P 100 stocks during the option-expiration week and stays in cash during other days.

from AlgorithmImports import *

class OptionExpirationWeekEffect(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2010, 1, 1)
        self.SetCash(10000)

        self.symbol = self.AddEquity("OEF", Resolution.Minute).Symbol
        
        option = self.AddOption("OEF")
        option.SetFilter(-3, 3, timedelta(0), timedelta(days = 60))       

        self.SetBenchmark("OEF")
        self.near_expiry = datetime.min
        
        self.Schedule.On(self.DateRules.Every(DayOfWeek.Monday, DayOfWeek.Monday), self.TimeRules.AfterMarketOpen(self.symbol, 1), self.Rebalance)

    def OnData(self, slice):
        if self.Time.date() == self.near_expiry.date():
            self.Liquidate()

    def Rebalance(self):
        calendar = self.TradingCalendar.GetDaysByType(TradingDayType.OptionExpiration, self.Time, self.EndDate)
        expiries = [i.Date for i in calendar]
        if len(expiries) == 0: return

        self.near_expiry = expiries[0]

        if (self.near_expiry - self.Time).days <= 5:
            self.SetHoldings(self.symbol, 1)