| Overall Statistics |
|
Total Trades
246
Average Win
1.26%
Average Loss
-1.27%
Compounding Annual Return
2.347%
Drawdown
15.100%
Expectancy
0.198
Net Profit
35.743%
Sharpe Ratio
0.34
Probabilistic Sharpe Ratio
0.202%
Loss Rate
40%
Win Rate
60%
Profit-Loss Ratio
0.99
Alpha
0.006
Beta
0.126
Annual Standard Deviation
0.051
Annual Variance
0.003
Information Ratio
-0.552
Tracking Error
0.137
Treynor Ratio
0.139
Total Fees
$246.00
Estimated Strategy Capacity
$490000.00
Lowest Capacity Asset
OEF RZ8CR0XXNOF9
|
# 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)