Hello,

I am new to QuantConnect, I am trying to migrate a Quantopian Project. However, I am having difficulty accessing data object from other classes.

I am using the Algorithm Framework. I have a complicated indicator(s) which is in a class that does not extend any other class, let’s called it Class X. The reason for this, is that I want to save a lot of class variables per security and keep them updated. 

So, I create an instance of this Class X per security so the AlphaModel could check the logic by accessing the class variables for each security, and then decide to return insights or not.

The Problem: I can not pass the data Object appropriately, so I can access it in Class X

Please see attached

Any hint is highly appreciated

  1. # region imports
  2. from AlgorithmImports import *
  3. # endregion
  5. class SwimmingBlueTermite(QCAlgorithm):
  7.     def Initialize(self):
  8.         self.SetStartDate(2020, 1, 1)  # Set Start Date
  9.         self.SetEndDate(2022,1,1)
  10.         self.SetCash(100000)  # Set Strategy Cash
  12.         self.num_coarse = 500
  13.         self.__numberOfSymbolsFine = 4
  15.         self.UniverseSettings.Resolution = Resolution.Daily
  16.         self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
  18.         self.AddAlpha(TestAlphaModel())
  21.         # Portfolio construction model
  22.         self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
  23.          # Risk model
  24.         self.SetRiskManagement(NullRiskManagementModel())
  25.          # Execution model
  26.         self.SetExecution(ImmediateExecutionModel())
  28.         # set a warm-up period to initialize the indicator
  29.         self.SetWarmUp(timedelta(60))
  32.         # Take 500 stocks worth more than $10, with more than $10M daily
  33.     def CoarseSelectionFunction(self, coarse):
  34.          sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
  35.          filtered = [ x.Symbol for x in sortedByDollarVolume 
  36.                       if x.Price > 10 and x.DollarVolume > 10000000 ]
  37.          return filtered[:500]
  40.        # sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
  41.     def FineSelectionFunction(self, fine):
  42.         # sort descending by P/E ratio
  43.         sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio, reverse=True)
  45.         # take the top entries from our sorted collection
  46.         return  [ x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ]
  47.   
  48.   
  49.     def OnSecuritiesChanges(self, changes):
  50.         # Remove security 
  51.         for security in changes.RemovedSecurities:
  52.             self.ActiveSecurities.remove(security)
  53.   
  54.         # Add security 
  55.         for security in changes.AddedSecurities:
  56.             if security not in self.ActiveSecurities:
  57.                 self.ActiveSecurities.append(security)
  60. class TestAlphaModel(AlphaModel):
  62.     def __init__(self):
  64.         self.InstancePerSecurity = []
  65.         self.ListComprehensionCount = 0
  67.     def Update(self, algorithm, data):
  68.             
  69.         if (self.ListComprehensionCount == 0):
  70.             self.InstancePerSecurity = [ClassX(security, data) for security in algorithm.ActiveSecurities.Keys]
  71.             self.ListComprehensionCount += 1
  72.         
  73.         insights = []
  75.         for instance in self.InstancePerSecurity: 
  76.             instance.Run()
  77.            
  79. class ClassX():
  80.         def __init__(self, security, data):
  81.             self.security = security
  82.             self.data = data
  84.         def Run(self):
  85.             
  86.             # Returns Error "Runtime Error: type(s) expected"
  87.             if self.data.Bars.ContainsKey[self.security]:
  88.                 pass
+ Expand

 

 

Author

Adham Al-Harazi

June 2022