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Victor Goujon INVESTOR

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Hello to all !

I am trying to use a custom indicator class in Alpha framework and load history data to warmup the indicators.

However I am getting the runtime error :

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 key exist in the collection and/or that collection is not empty. KeyError : (<QuantConnect.Symbol object at 0x7f7c9c8d6390>,) (Open Stacktrace)

Any ideas where that might come from?

Is the implementation ok apart from that?

Thank you!

Update Backtest

Victor Goujon

Accepted Answer

Update Backtest

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Victor Goujon

1.4k ,

from Execution.ImmediateExecutionModel import ImmediateExecutionModel

from Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel

import numpy as np

from collections import deque

from datetime import datetime, timedelta

from array import *

from MovingReturn import MovRet

from MovingVolatility import MovVol

from clr import AddReference

AddReference("QuantConnect.Common")

AddReference("QuantConnect.Algorithm")

AddReference("QuantConnect.Algorithm.Framework")

AddReference("QuantConnect.Indicators")



from QuantConnect import *

from QuantConnect.Indicators import *

from QuantConnect.Algorithm import *

from QuantConnect.Algorithm.Framework import *

from QuantConnect.Algorithm.Framework.Alphas import *



class NadionResistanceAutosequencers(QCAlgorithmFramework):



    def Initialize(self):

        self.SetStartDate(2020, 6, 4)  # Set Start Date

        self.SetCash(100000)  # Set Strategy Cash

        # self.AddEquity("SPY", Resolution.Minute)

        self.SetExecution(ImmediateExecutionModel())

        

        self.SetPortfolioConstruction(NullPortfolioConstructionModel()) 

        self.SetRiskManagement(NullRiskManagementModel())

        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())

        self.tickers = ["SPY","IWM","QQQ","EFA","EEM","VNQ","LQD","GLD","SHY","IEF","TLT","AGG"] # Define symbols for Trading

        self.UniverseSettings.Resolution = Resolution.Daily

        # self.AddUniverseSelection(ScheduledUniverseSelectionModel(self.DateRules.Every(DayOfWeek.Friday),self.TimeRules.Every(timedelta(hours = 24)),self.SelectSymbols))

        self.sym=[]

        self.SetAlpha(RisingAssetsAlphaModel())

        for x in self.tickers:

            

            a=self.AddEquity(x, Resolution.Daily)

            self.sym.append(self.AddEquity(x, Resolution.Daily).Symbol)

            

        self.SetUniverseSelection( ManualUniverseSelectionModel(self.sym) )    



    # def SelectSymbols(self,datetime):

    #     sym=[]

    #     for x in self.tickers:

            

    #         a=self.AddEquity(x, Resolution.Daily)

    #         sym.append(self.AddEquity(x, Resolution.Daily).Symbol)

    #     return sym

        

class RisingAssetsAlphaModel(AlphaModel):

    

    def __init__(self):

        

        self.M1={}

        self.M3={}

        self.M6={}

        self.M12={}

        self.MomentumScore={}

        self.V61={}

        self.active_sym=[]

        self.ready={}

        self.N=5

    def OnSecuritiesChanged(self,algorithm, changes):

        

        

            for security in changes.AddedSecurities:

                symbol = security.Symbol

            



                self.M1[symbol] = MovRet(symbol,21)

                self.M3[symbol] = MovRet(symbol,21*3)

                self.M6[symbol] = MovRet(symbol,21*6)

                self.M12[symbol] = MovRet(symbol,21*12)

            # self.V61[symbol]=MovVol(symbol,21*3) 

                algorithm.Debug(self.M12[symbol].Value)

            

            

                history = algorithm.History(symbol, 21*12+1)

    

                self.M1[symbol].Warmup(history)

                self.M3[symbol].Warmup(history)

                self.M6[symbol].Warmup(history)

                self.M12[symbol].Warmup(history)

                # self.V61[symbol].Warmup(history)        

                

                

                # if self.M1[symbol].IsReady and self.M3[symbol].IsReady and self.M6[symbol].IsReady and self.M12[symbol].IsReady and self.V61[symbol].IsReady:

                #     self.ready[symbol]= True

                

                algorithm.RegisterIndicator(symbol, self.M1[symbol], Resolution.Daily)

                algorithm.RegisterIndicator(symbol, self.M3[symbol], Resolution.Daily)

                algorithm.RegisterIndicator(symbol, self.M6[symbol], Resolution.Daily)

                algorithm.RegisterIndicator(symbol, self.M12[symbol], Resolution.Daily)

                algorithm.RegisterIndicator(symbol, self.V61[symbol], Resolution.Daily)

            

                self.active_sym.append(symbol)

            

        

            

            

    def Update(self, algorithm, data):

        return  

        for symbol in self.active_sym:

            algorithm.Debug(self.M12[symbol].IsReady)

            if  self.M12[symbol].IsReady : 

                algorithm.Debug(2)

                self.MomentumScore[symbol]=np.mean([self.M1[symbol].Value,self.M3[symbol].Value,self.M6[symbol].Value,self.M12[symbol].Value],dtype=np.float64)

        algorithm.Debug(self.MomentumScore)

        

        

        # algorithm.Debug(self.MomentumScore)

        # #3. Return a group of insights, emitting InsightDirection.Up for the first item of ordered, and InsightDirection.Flat for the second

        #     return Insight.Group([

        #     # Create a grouped insight

        #     Insight.Price(ordered[0]["symbol"], timedelta(1), InsightDirection.Up), 

        #     Insight.Price(ordered[1]["symbol"], timedelta(1),InsightDirection.Flat)

        #  ])

        





class MovRet():

    def __init__(self,symbol, periods):

        self.symbol=symbol

        self.N=periods

        self.window = RollingWindow[float](periods)

        self.window_Close=RollingWindow[float](2)

        self.IsReady=False

        self.IsReadyClose=False

        # Volatility is a mandatory attribute

        self.Value = 0

        self.Close=0

        self.Time=None

    # Updates this model using the new price information in the specified security instance

    # Update is a mandatory method

    def Update(self,input):

        self.Close=input.Close

        if self.window_Close.IsReady:

            self.IsReadyClose=True

            self.window.Add(float(self.window_Close[0] / self.window_Close[1]) - 1.0)

  

            if self.window.IsReady:

                self.Value=np.mean([ x for x in self.window ])

                self.IsReady=self.window.IsReady

            

            if self.window.Count<self.N:

                self.Value=0

                

        if self.window.Count<2:

            self.Value=0

            self.IsReadyClose=False

    

    def Warmup(self,history): 

        for index, row in history.loc[str(self.symbol)].iterrows():

            self.Close=row["close"]

            self.window_Close.Add(row["close"])

            self.Time=index

Shile Wen

63.5k ,

Hi Victor,
If we are using an Algo Framework Universe, in this case a Manual Universe, we do not need to call AddEquity, and we can instead create Symbol objects using Symbol.Create. On the other hand, if we call AddEquity, we don't need to use the Manual Universe, as it would be redundant. Furthermore, our Update method of our Alpha Model needs to return a collection of Insights, so I've made it return an empty list.
The reason we have a KeyError is because is commented out self.V61[symbol].Warmup(history), so when we try to call algorithm.RegisterIndicator(symbol, self.V61[symbol], Resolution.Daily), we try to access a dict that doesn't doesn't contain our key, and so we just need to comment this line out.I've shown these changes in the attached backtest. For future reference please use our Debugger to find bugs in the logic.
Best,
Shile Wen

Victor Goujon

1.4k ,

Thank you !

Best,

Victor

Victor Goujon INVESTOR

Update Backtest

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