I'm interested in adding volume to the EMA universe selection, but I am having trouble. Any help is appreciated. I basically tried to mesh the volume example on the Universe page with the EMA code. The error I'm getting is;

Runtime Error: UnboundLocalError : local variable 'x' referenced before assignment
at CoarseSelectionFunction in main.py:line 55
UnboundLocalError : local variable 'x' referenced before assignment (Open Stacktrace)

from clr import AddReference AddReference("System") AddReference("QuantConnect.Algorithm") AddReference("QuantConnect.Indicators") AddReference("QuantConnect.Common") from System import * from QuantConnect import * from QuantConnect.Data import * from QuantConnect.Algorithm import * from QuantConnect.Indicators import * from System.Collections.Generic import List ### <summary> ### In this algorithm we demonstrate how to perform some technical analysis as ### part of your coarse fundamental universe selection ### </summary> ### <meta name="tag" content="using data" /> ### <meta name="tag" content="indicators" /> ### <meta name="tag" content="universes" /> ### <meta name="tag" content="coarse universes" /> class EmaCrossUniverseSelectionAlgorithm(QCAlgorithm): def Initialize(self): '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.''' self.SetStartDate(2020,1,1) #Set Start Date self.SetEndDate(2020,7,22) #Set End Date self.SetCash(100000) #Set Strategy Cash self.UniverseSettings.Resolution = Resolution.Daily #self.UniverseSettings.Leverage = 2 self.coarse_count = 10 self.averages = { }; # this add universe method accepts two parameters: # - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol> self.AddUniverse(self.CoarseSelectionFunction) # sort the data by daily dollar volume and take the top 'NumberOfSymbols' def CoarseSelectionFunction(self, coarse): # We are going to use a dictionary to refer the object that will keep the moving averages for cf in coarse: if cf.Symbol not in self.averages: self.averages[cf.Symbol] = SymbolData(cf.Symbol) # Updates the SymbolData object with current EOD price avg = self.averages[cf.Symbol] avg.update(cf.EndTime, cf.AdjustedPrice, cf.DollarVolume) # Filter the values of the dict: we only want up-trending securities values = list(filter(lambda x: x.is_uptrend, x.volume>1000000, self.averages.values())) # Sorts the values of the dict: we want those with greater difference between the moving averages values.sort(key=lambda x: x.scale, reverse=True) for x in values[:self.coarse_count]: self.Log('symbol: ' + str(x.symbol.Value) + ' scale: ' + str(x.scale)) # we need to return only the symbol objects return [ x.symbol for x in values[:self.coarse_count] ] # this event fires whenever we have changes to our universe def OnSecuritiesChanged(self, changes): # liquidate removed securities for security in changes.RemovedSecurities: if security.Invested: self.Liquidate(security.Symbol) # we want 20% allocation in each security in our universe for security in changes.AddedSecurities: self.SetHoldings(security.Symbol, 0.1) class SymbolData(object): def __init__(self, symbol): self.symbol = symbol self.tolerance = 1.01 self.fast = ExponentialMovingAverage(100) self.slow = ExponentialMovingAverage(300) self.is_uptrend = False self.scale = 0 self.volume=0 def update(self, time, value, volume): if self.fast.Update(time, value) and self.slow.Update(time, value): fast = self.fast.Current.Value slow = self.slow.Current.Value self.is_uptrend = fast > slow * self.tolerance if self.is_uptrend: self.scale = (fast - slow) / ((fast + slow) / 2.0) self.volume= volume