Overall Statistics |
Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Probabilistic Sharpe Ratio 0% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio 2.832 Tracking Error 0.177 Treynor Ratio 0 Total Fees $0.00 Estimated Strategy Capacity $0 Lowest Capacity Asset |
#region imports from AlgorithmImports import * #endregion class ParticleTransdimensionalAutosequencers(QCAlgorithm): def Initialize(self): self.SetStartDate(2022, 12, 5) self.SetEndDate(2022, 12, 8) self.SetCash(100000) self.AddEquity("SPY", Resolution.Minute) self.AddUniverseSelection(CoarseFundamentalUniverseSelectionModel(self.CoarseSelectionFunction)) self.UniverseSettings.Resolution = Resolution.Minute self.Schedule.On( self.DateRules.EveryDay("SPY"), self.TimeRules.At(10, 0), self.SelectUniverse ) self.universe = [] self.volume_by_symbol = {} self.logged = False def OnData(self, data): if len(self.volume_by_symbol) == 0: if not self.logged: self.logged = True self.Debug(f"Universe size after volume filter: {len(self.universe)}") return for symbol in self.volume_by_symbol.keys(): if symbol in data.Bars: self.volume_by_symbol[symbol] += data[symbol].Volume def CoarseSelectionFunction(self, coarse): self.volume_by_symbol = {c.Symbol: 0 for c in coarse if c.Price > 400} self.Debug(f"Universe size before volume filter: {len(self.volume_by_symbol)}") return list(self.volume_by_symbol.keys()) def SelectUniverse(self): self.universe = [] for symbol, volume in self.volume_by_symbol.items(): if volume > 50000: self.universe.append(symbol) self.volume_by_symbol.clear() self.logged = False