Overall Statistics 
Total Trades
338
Average Win
1.02%
Average Loss
0.71%
Compounding Annual Return
1.486%
Drawdown
8.800%
Expectancy
0.273
Net Profit
36.818%
Sharpe Ratio
0.431
Probabilistic Sharpe Ratio
0.353%
Loss Rate
48%
Win Rate
52%
ProfitLoss Ratio
1.45
Alpha
0.013
Beta
0.035
Annual Standard Deviation
0.036
Annual Variance
0.001
Information Ratio
0.378
Tracking Error
0.192
Treynor Ratio
0.449
Total Fees
$1757.60
Estimated Strategy Capacity
$270000000.00

# https://quantpedia.com/strategies/momentumfactoreffectincountryequityindexes/ # # The investor is invested in stocks during FOMC meetings (going long S&P 500 ETF, fund, future, or CFD on a close one day before the meeting and closing position on close after the meeting). # Otherwise, he is invested in cash during the remaining days. The strategy has very low exposure to the stock market (8 days during the average year); therefore, it can be very easily leveraged # to gain very significant returns. # # QC implementation: #  FED dates are imported from text file. from pandas.tseries.offsets import BDay class MomentumFactorEffectinCountryEquityIndexes(QCAlgorithm): def Initialize(self): self.SetStartDate(2000, 1, 1) self.SetCash(100000) self.SetBrokerageModel(BrokerageName.AlphaStreams) self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel()) self.SetExecution(ImmediateExecutionModel()) self.symbol = self.AddEquity("SPY", Resolution.Minute).Symbol # fed days csv_string_file = self.Download('data.quantpedia.com/backtesting_data/economic/fed_days.csv') dates = csv_string_file.split('\r\n') dates = [datetime.strptime(x, "%Y%m%d")  BDay(1) for x in dates] self.Schedule.On(self.DateRules.On(dates), self.TimeRules.BeforeMarketClose(self.symbol, 1), self.DayBeforeFED) def DayBeforeFED(self): self.EmitInsights(Insight.Price(self.symbol, timedelta(minutes = 6.5*60  1), InsightDirection.Up, None, None, None, 1))