I am getting this error when I backtest:

Runtime Error: Trying to retrieve an element from a collection using a key that does not exist in that collection throws a KeyError exception. To prevent the exception, ensure that the ANGI V1OBWK1IM6HX key exist in the collection and/or that collection is not empty.
at <lambda> in FundamentalUniverseSelectionModel.py:line 3
KeyError : 'the label [ANGI V1OBWK1IM6HX] is not in the [index]'

Would anyone be willing to look at my code and tell me what I am doing wrong? I believe it is in the #Top 1500 Volume section (because it worked before I added that).

from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel class VentralModulatedThrustAssembly(QCAlgorithm): def Initialize(self): self.SetStartDate(2019, 7, 9) # Set Start Date self.SetCash(10000) # Set Strategy Cash self.SetUniverseSelection(SelectionModel()) self.SetAlpha(ConstantAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(1), 0.025, None)) self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel()) class SelectionModel(FundamentalUniverseSelectionModel): def __init__(self, filterFineData = True, universeSettings = None, securityInitializer = None): super().__init__(filterFineData, universeSettings, securityInitializer) self.periodCheck = -1 self.coarse = dict() self.num_screener = 50 self.smaDictionary = {} def SelectCoarse(self, algorithm, coarse): if algorithm.Time.year == self.periodCheck: return Universe.Unchanged self.coarse = {x.Symbol: x for x in coarse if x.HasFundamentalData and x.Volume > 0 and x.Price > 0} return [symbol for symbol,_ in self.coarse.items()] def SelectFine(self, algorithm, fine): # Filter by Market Capitalization marketCap = {} for i in fine: marketCap[i] = (i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths * i.ValuationRatios.PERatio)) filter_market_cap = list(filter(lambda x: marketCap[x] > 500000000, fine)) symbols = [x.Symbol for x in filter_market_cap] history = algorithm.History(symbols, 200, Resolution.Daily) #Top 1500 Volume volumeTop = {} for x in filter_market_cap: ## Find hsitory for specific symbol #self.debug(f'Updated this {str(x.Symbol)} ...') symbolVolumeHistory = history.loc[str(x.Symbol)] ## Create SMA for symbol and register it with algorithm symbolSMA = SimpleMovingAverage(200) ## Iterate through historical data for tuple in symbolVolumeHistory.itertuples(): ## Update SMA with data time and volume symbolSMA.Update(tuple.Index, tuple.volume) #self.Debug(f'Updating {symbol.Value} SMA...') ## Add SMA to dictionary so you can access it later volumeTop[x] = symbolSMA #volume_all = list(filter(lambda x: volumeTop[x] > 0, filter_market_cap)) filter_volume = sorted(filter_market_cap, key=lambda x: volumeTop[x], reverse=True)[:1500] self.periodCheck = algorithm.Time.year sorted_top_quality = sorted(filter_volume, key=lambda x: x.OperationRatios.ROIC.ThreeMonths + x.OperationRatios.LongTermDebtEquityRatio.ThreeMonths + (x.ValuationRatios.CashReturn + x.ValuationRatios.FCFYield), reverse=True)[:self.num_screener] symbols = [x.Symbol for x in sorted_top_quality] averages = dict() history = algorithm.History(symbols, 200, Resolution.Daily).close.unstack(0) #Filter top momentum for symbol in symbols: # Remove NaN: symbol does not have 200 daily data points df = history[symbol].dropna() if df.empty: continue mom = Momentum(126) for time, close in df.iteritems(): mom.Update(time, close) # Adds Momentum to dict only if it is ready if mom.IsReady: averages[symbol] = mom # Update with current data for symbol, mom in averages.items(): c = self.coarse.pop(symbol, None) mom.Update(c.EndTime, c.AdjustedPrice) sortedbyMomentum = sorted(averages.items(), key=lambda x: x[1], reverse=True) return [x[0] for x in sortedbyMomentum[:5]]

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