| Overall Statistics |
|
Total Trades 115 Average Win 0.07% Average Loss 0.00% Compounding Annual Return 88.771% Drawdown 6.500% Expectancy 226.331 Net Profit 33.960% Sharpe Ratio 4.183 Probabilistic Sharpe Ratio 93.883% Loss Rate 7% Win Rate 93% Profit-Loss Ratio 243.38 Alpha 0.543 Beta 0.691 Annual Standard Deviation 0.173 Annual Variance 0.03 Information Ratio 2.976 Tracking Error 0.155 Treynor Ratio 1.049 Total Fees $133.21 Estimated Strategy Capacity $5600000.00 Lowest Capacity Asset WGP VC8EGN8O5EG5 |
class SquareBrownScorpion(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2021, 1, 1)
self.SetCash(100000)
self.SetBenchmark("SPY")
self.AddUniverseSelection(FineFundamentalUniverseSelectionModel(self.SelectCoarse, self.SelectFine))
# self.AddAlpha( MyAlphaModel() )
self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel(lambda time: Expiry.EndOfMonth(time)))
self.AddRiskManagement( NullRiskManagementModel() )
self.SetExecution( ImmediateExecutionModel() )
self.next = self.Time
def SelectCoarse(self, coarse):
if self.next >= self.Time:
return Universe.Unchanged
sorted_by_dollarvolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
return [x.Symbol for x in coarse if x.DollarVolume > 20000000 and x.HasFundamentalData]
def SelectFine(self, fine):
if self.next >= self.Time:
return Universe.Unchanged
sorted_by_dividend = sorted(fine, key=lambda c: c.ValuationRatios.DivYield5Year, reverse=True)
return [c.Symbol for c in sorted_by_dividend if c.MarketCap > 5000000000][:20]
def OnData(self, data):
insights = []
if self.next >= self.Time:
return
for security in self.ActiveSecurities.Values:
insights.append(Insight.Price(security.Symbol, Expiry.EndOfMonth, InsightDirection.Up))
self.EmitInsights(insights)
self.next = Expiry.EndOfMonth(self.Time)