Overall Statistics |
Total Trades 36 Average Win 0.15% Average Loss -0.09% Compounding Annual Return 38.220% Drawdown 1.400% Expectancy 0.182 Net Profit 1.070% Sharpe Ratio 3.348 Probabilistic Sharpe Ratio 64.383% Loss Rate 57% Win Rate 43% Profit-Loss Ratio 1.76 Alpha -0.027 Beta 0.465 Annual Standard Deviation 0.084 Annual Variance 0.007 Information Ratio -4.279 Tracking Error 0.089 Treynor Ratio 0.602 Total Fees $39.50 |
from universe_selection_model import MyUniverseModel class TestAlgo(QCAlgorithm): def Initialize(self): self.SetStartDate(2018, 5, 28) self.SetEndDate(2018, 6, 9) self.SetCash(100000) # Universe selection settings self.UniverseSettings.Resolution = Resolution.Minute self.SetUniverseSelection(MyUniverseModel()) self.day = 0 def OnSecuritiesChanged(self, changes): self.changes = changes for security in changes.RemovedSecurities: if security.Invested: self.Liquidate(security.Symbol, 'Removed from Universe') def OnData(self, data): if data.Time.day == self.day: return self.day = data.Time.day if self.changes is not None: for security in self.changes.AddedSecurities: if self.CurrentSlice.ContainsKey(security.Symbol): self.SetHoldings(security.Symbol, 0.1) self.changes = None
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel class MyUniverseModel(FundamentalUniverseSelectionModel): def __init__(self): super().__init__(False) def SelectCoarse(self, algorithm, coarse): sortedByDollarVolume = sorted(coarse, key=lambda c: c.DollarVolume, reverse=True) symbols_by_price = [c.Symbol for c in sortedByDollarVolume if c.Price > 10] algorithm.filteredByPrice = symbols_by_price[:8] return algorithm.filteredByPrice def SelectFine(self, algorithm, fine): return [f.Symbol for f in fine]