Hello,

I am not understanding how the below algorithm is deciding when to sell.  Which line is telling the algorithm when to sell?  Thank you and apologies if this is a stupid question!

Sean

```from Execution.ImmediateExecutionModel import ImmediateExecutionModel from Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel class QuantumHorizontalRegulators(QCAlgorithm): def Initialize(self): self.SetStartDate(2020, 5, 18) # Set Start Date self.SetEndDate(2020, 5, 20) self.SetCash(100000) # Set Strategy Cash self.AddEquity("W5000", Resolution.Second) self.scaning = False self.lastToggle = None self.__numberOfSymbols =10 self.SetUniverseSelection(FineFundamentalUniverseSelectionModel(self.CoarseSelectionFunction, self.FineSelectionFunction, None, None)) self.UniverseSettings.Resolution = Resolution.Second self.AddAlpha(FadeTheGapModel(self)) self.SetExecution(ImmediateExecutionModel()) self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel()) self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen("W5000", 0), self.toggleScan) self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen("W5000", 45), self.toggleScan) def toggleScan(self): self.scaning = not self.scaning self.lastToggle = self.Time if not self.scaning: self.needs_reset = True def CoarseSelectionFunction(self, coarse): # Stocks with the most dollar volume traded yesterday sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True) return [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ] def FineSelectionFunction(self, fine): return [ x.Symbol for x in fine ] class FadeTheGapModel(AlphaModel): symbolData = {} def __init__(self, algo): self.algo = algo def Update(self, algorithm, slice): if algorithm.IsWarmingUp: return [] # If it's the end of the day, update the yesterday close of each indicator if not algorithm.Securities['W5000'].Exchange.ExchangeOpen: for symbol in self.symbolData: if symbol in slice.Bars: self.symbolData[symbol].yest_close = slice.Bars[symbol].Close if not self.algo.scaning: # Reset max indicator if self.algo.needs_reset: for symbol in self.symbolData: self.symbolData[symbol].max.Reset() self.algo.needs_reset = False return [] insights = [] duration = algorithm.Time - self.algo.lastToggle insight_seconds = 45 * 60 - duration.seconds # Create insights for symbols up at least 10% on the day for symbol in self.symbolData: # If already invested, continue to next symbol if algorithm.Securities[symbol].Invested or symbol not in slice.Bars or self.symbolData[symbol].max.Samples == 0: continue # Calculate return sign yesterday's close yest_close = self.symbolData[symbol].yest_close close = slice[symbol].Close ret = (close - yest_close) / yest_close high_of_day_break = close > self.symbolData[symbol].max.Current.Value if ret >= 0.1 and high_of_day_break: # Up 10% on the day & breaks high of day insights.append(Insight(symbol, timedelta(seconds=insight_seconds), InsightType.Price, InsightDirection.Up)) # Update max indicator for all symbols for symbol in self.symbolData: if symbol in slice.Bars: self.symbolData[symbol].max.Update(slice.Time, slice.Bars[symbol].High) return Insight.Group(insights) def OnSecuritiesChanged(self, algorithm, changes): if len(changes.AddedSecurities) > 0: # Get history of symbols over lookback window added_symbols = [x.Symbol for x in changes.AddedSecurities] history = algorithm.History(added_symbols, 1, Resolution.Daily)['close'] for added in changes.AddedSecurities: # Save yesterday's close closes = history.loc[[str(added.Symbol.ID)]].values if len(closes) < 1: continue self.symbolData[added.Symbol] = SymbolData(closes[0]) for removed in changes.RemovedSecurities: # Delete yesterday's close tracker self.symbolData.pop(removed.Symbol, None) class SymbolData: def __init__(self, yest_close): self.yest_close = yest_close self.max = Maximum(45*60) # 45 minutes```