| Overall Statistics |
|
Total Trades 619 Average Win 0.22% Average Loss -0.31% Compounding Annual Return -52.328% Drawdown 26.700% Expectancy -0.139 Net Profit -12.003% Sharpe Ratio -1.195 Probabilistic Sharpe Ratio 11.519% Loss Rate 50% Win Rate 50% Profit-Loss Ratio 0.72 Alpha -0.707 Beta 1.077 Annual Standard Deviation 0.334 Annual Variance 0.111 Information Ratio -2.148 Tracking Error 0.319 Treynor Ratio -0.37 Total Fees $1049.98 Estimated Strategy Capacity $5800000.00 Lowest Capacity Asset MDIAV XAUQQ1QHPEUD |
class WellDressedBrownShark(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2021, 5, 25)
self.SetCash(100000)
self.AddEquity("SPY", Resolution.Minute)
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.UniverseSettings.Resolution = Resolution.Minute
self.AddAlpha(CustomAlpha())
self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
self.SetExecution(ImmediateExecutionModel())
self.AddRiskManagement(MaximumDrawdownPercentPerSecurity(0.05))
self.SetSecurityInitializer(lambda security: security.SetMarketPrice(self.GetLastKnownPrice(security)))
def CoarseSelectionFunction(self, coarse):
sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
filtered = [ x.Symbol for x in sortedByDollarVolume if x.HasFundamentalData ]
return filtered[:50]
def FineSelectionFunction(self, fine):
sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio, reverse=False)
return [ x.Symbol for x in sortedByPeRatio[:10] ]
class CustomAlpha(AlphaModel):
def __init__(self):
self.day = -1
self.selected = []
def Update(self, algorithm, data):
if algorithm.Time.day == self.day:
return []
self.day = algorithm.Time.day
return [Insight.Price(symbol, Expiry.EndOfDay, InsightDirection.Up) for symbol in self.selected]
def OnSecuritiesChanged(self, algorithm, changes):
[self.selected.append(change.Symbol) for change in changes.AddedSecurities]
[self.selected.remove(change.Symbol) for change in changes.RemovedSecurities if change.Symbol in self.selected]