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 -1.271 Tracking Error 0.154 Treynor Ratio 0 Total Fees $0.00 Estimated Strategy Capacity $0 Lowest Capacity Asset |

# region imports from AlgorithmImports import * # endregion class AlertRedOrangeGalago(QCAlgorithm): def Initialize(self): self.SetStartDate(2020, 4, 1) self.SetCash(100_000) self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverse(self.CoarseSelection) self.OHL = {} def CoarseSelection(self, coarse): # Refining a bit to assets with fundamental data and price > 5$ selected = [x for x in coarse if x.HasFundamentalData and (x.Price > 5.0)] # Priming self.OHL if len(self.OHL) == 0: return [x.Symbol for x in selected] # self.OHL is primed - using it # Using previous day's High to filter assets by selecting only those with High > 100$ filtered = [x for x in selected if x.Symbol in self.OHL and (self.OHL[x.Symbol][1] > 100.0)] # Outputting length of returned Universe day after day - for minimal debugging self.Debug(len(filtered)) # Use or save (for use in another part of the algo) "filtered" however or whereever (self.whatever) you want depending on what you want to do... # Return the selected Universe, not the filtered return [x.Symbol for x in selected] def OnData(self, data: Slice): for d in data: self.OHL[d.Key] = (data.Bars[d.Key].Open, data.Bars[d.Key].High, data.Bars[d.Key].Low)