| Overall Statistics |
|
Total Trades 199 Average Win 1.17% Average Loss -0.55% Compounding Annual Return -5.003% Drawdown 23.600% Expectancy -0.204 Net Profit -14.246% Sharpe Ratio -0.248 Probabilistic Sharpe Ratio 0.541% Loss Rate 75% Win Rate 25% Profit-Loss Ratio 2.13 Alpha -0.023 Beta -0.062 Annual Standard Deviation 0.115 Annual Variance 0.013 Information Ratio -0.704 Tracking Error 0.158 Treynor Ratio 0.462 Total Fees $707.42 Estimated Strategy Capacity $71000000.00 Lowest Capacity Asset MSFT R735QTJ8XC9X |
from Alphas.EmaCrossAlphaModel import EmaCrossAlphaModel
from Execution.ImmediateExecutionModel import ImmediateExecutionModel
from Portfolio.MeanVarianceOptimizationPortfolioConstructionModel import MeanVarianceOptimizationPortfolioConstructionModel
from Risk.MaximumDrawdownPercentPerSecurity import MaximumDrawdownPercentPerSecurity
from Selection.QC500UniverseSelectionModel import QC500UniverseSelectionModel
class RetrospectiveBlackBuffalo(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2015, 1, 2) # Set Start Date
self.SetEndDate(2017, 12, 29) # Set Start Date
self.SetCash(100000) # Set Strategy Cash
self.AddEquity("MSFT", Resolution.Daily)
self.AddAlpha(RsiAlphaModel(period = 14,
resolution = Resolution.Daily))
self.AddAlpha(MacdAlphaModel( fastPeriod = 12,
slowPeriod = 26,
signalPeriod = 9,
movingAverageType = MovingAverageType.Exponential,
resolution = Resolution.Daily))
self.SetExecution(ImmediateExecutionModel())
self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel(rebalance = Resolution.Daily
, portfolioBias = PortfolioBias.Long))
self.Settings.MinAbsolutePortfolioTargetPercentage=0.0
#self.SetRiskManagement(MaximumDrawdownPercentPerSecurity(0.10))
#self.SetUniverseSelection(SP500SectorsETFUniverse())
def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
Arguments:
data: Slice object keyed by symbol containing the stock data
'''
# if not self.Portfolio.Invested:
# self.SetHoldings("SPY", 1)