from sklearn.ensemble import RandomForestRegressor from sklearn.model_selection import train_test_split import numpy as np class AlphaFiveUSTreasuries(QCAlgorithm):     def Initialize(self):         #1. Required: Five years of backtest history         self.SetStartDate(2014, 1, 1)              #2. Required: Alpha Streams Models:         self.SetBrokerageModel(BrokerageName.AlphaStreams)              #3. Required: Significant AUM Capacity         self.SetCash(100000)         self.AddUniverse(self.CoarseSelectionFunction)         self.UniverseSettings.Resolution = Resolution.Hour              self.spy = self.AddEquity("SPY", Resolution.Hour).Symbol                  #4. Required: Benchmark to SPY         self.SetBenchmark("SPY")                  self.SetPortfolioConstruction(InsightWeightingPortfolioConstructionModel())                  self.SetExecution(ImmediateExecutionModel())              # self.assets = ["IEF", "SHY", "TLT", "IEI", "SHV", "TLH", "EDV", "BIL",         #               "SPTL", "TBT", "TMF", "TMV", "TBF", "VGSH", "VGIT",         #               "VGLT", "SCHO", "SCHR", "SPTS", "GOVT"]         self.assets = []         self.symbols = {}                  self.portfolioValue = RollingWindow[Decimal](500)                  self.SetWarmup(500)                  # Add Equity ------------------------------------------------          # add equities from coarse universe to self.symbols         # for i in range(len(self.assets)):         #     self.symbols[self.assets[i]] = self.AddEquity(self.assets[i],Resolution.Hour).Symbol                   # test different timings after market opens affects the returns         self.Schedule.On(self.DateRules.Every(DayOfWeek.Monday), self.TimeRules.AfterMarketOpen("SPY", 0), self.EveryDayAfterMarketOpen)          def CoarseSelectionFunction(self, coarse):         # add the top 20 assets         # Sort symbols by Dollar Volume and if fundamental data exists, descending         sortedByDollarVolume = sorted([x for x in coarse if x.HasFundamentalData and x.Price > 10],                 key=lambda x: x.DollarVolume, reverse=True)         # for y in range(20):         #     self.Debug(sortedByDollarVolume[y])         #     self.Debug(f"Test going into range")         # if self.lastMonth == algorithm.Time.month:         #     return Universe.Unchanged         # self.lastMonth = algorithm.Time.month         self.Debug(f"before dictionary completion")                  self.assets = sortedByDollarVolume[:20]                  for x in range(20):             if self.assets[x].Value is None:                 self.assets.RemoveSecurity(self.assets[x])                  self.Debug(f"test went through assets list")                  # for asset in self.assets:         for x in self.assets:             self.Debug(x)             # if not self.Securities.ContainsKey(asset.Symbol):             # self.symbols[self.assets[x]] = self.AddEquity(self.assets[x],Resolution.Hour).Symbol                           # add assets list          # for asset in self.assets:         #     self.Debug(f"test going in")         #     self.Debug(asset)             # self.Debug("{}".format(type(self.assets[x])))             # create symbols and tickers dictionary             #self.symbols[self.assets[x]] = self.AddEquity(self.assets[x],Resolution.Hour).Symbol                   # add tickers and symbols to dictionary         # for i in range(len(self.assets)):         #     self.symbols[self.assets[x]] = self.AddEquity(self.assets[x],Resolution.Hour).Symbol                   self.Debug(f"test dictionary completion")         # for ticker, symbol in self.symbols:         #     self.Debug(symbol)         #     self.Debug(ticker)                  # return self.symbols         # return [x.Symbol for x in sortedByDollarVolume[:10]]              def EveryDayAfterMarketOpen(self):         for symbol, ticker in self.symbols.items():             self.Debug(symbol, ticker)                      # for ticker, symbol in self.symbols:         #     self.Debug(f"test going in")         #     self.Debug(ticker, symbol)                  # training for model          # if not self.Portfolio.Invested:         #     insights = []         #     for ticker, symbol in self.symbols.items():         #         insights.append( Insight.Price(symbol, timedelta(days=5), InsightDirection.Up, 0.01, None, None, 1/len(self.symbols)) )         #     self.EmitInsights(insights)         # else:         #     qb = self          #     #==============================         #     # Initialize instance of Random Forest Regressor         #     regressor = RandomForestRegressor(n_estimators=100, min_samples_split=5, random_state = 1990)              #     # Fetch history on our universe         #     df = qb.History(qb.Securities.Keys, 500, Resolution.Hour)                      #     # Get train/test data         #     returns = df.unstack(level=1).close.transpose().pct_change().dropna()         #     X = returns         #     y = [x for x in qb.portfolioValue][-X.shape[0]:]         #     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 1990)                      #     # Fit regressor         #     regressor.fit(X_train, y_train)                      #     # Get long-only predictions         #     weights = regressor.feature_importances_         #     symbols = returns.columns[np.where(weights)]         #     selected = zip(symbols, weights)         #     # ==============================                      #     insights = []         #     for symbol, weight in selected:         #         insights.append( Insight.Price(symbol, timedelta(days=5), InsightDirection.Up, 0.01, None, None, weight) )         #     self.EmitInsights(insights)              def OnData(self, data):         self.portfolioValue.Add(self.Portfolio.TotalPortfolioValue)

Hi guys, I'm trying to create a universe sorted by dollar volume. Subsequently, the ticker and corresponding symbols will be the key value pairs of a dictionary "self.symbols". However, I am not able to append all 20 symbols I want to from the sorted dollar volume list to self.assets. I am being thrown the error "System.ArgumentNullException: Value cannot be null.
Parameter name: source". 

I have tried removing values that are null from my self.assets list but I'm still getting the error. Any help is appreciated (new to QuantConnect), thank you!