Momentum strategy by John Fogel

John Fogel INVESTOR

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Hi all, I am new to QuantConnect and I am currently coding up a momentum strategy (Andreas Clenow).

1) Base screen of the top 500 companies based on trading volume

2) Secondary screen to calculate the screened securities' their regression coefficient multiplied by R squared values. I then store these values in a dictionary for sorting to obtain the top 10 securities with the highest scores

3) Equal weighting portfolio with monthly rebalancement - Liquidate positions if stock is no longer in the long list.

Based on all of this, I still am unable to get my backtest to run as I run into the following error

Runtime Error: Value cannot be null.
Parameter name: source

I am not sure where I went wrong so any help is appreciated! Thank you.

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John Fogel

Accepted Answer

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John Fogel

891 ,

import pandas as pd

import numpy as np

from datetime import datetime, timedelta

from QuantConnect.Data.UniverseSelection import *

from scipy.stats import linregress



class SystematicMomentum(QCAlgorithm):



    def Initialize(self):

        self.num_coarse = 500

        self.num_fine = 10

        self.SetStartDate(2018, 1, 1)  # Set Start Date

        self.SetCash(10000)  # Set Strategy Cash

        # set the flag for rebalance

        self.reb = 1

        self.AddUniverse(self.CoarseSelectionFunction,self.FineSelectionFunction)

        self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol

        self.Schedule.On(self.DateRules.MonthStart(self.spy), 

        self.TimeRules.AfterMarketOpen(self.spy,5), Action(self.rebalance))

        

    def CoarseSelectionFunction(self, coarse):

    # if the rebalance flag is not 1, return null list to save time.

        if self.reb != 1:

            return self.long 

        

        # make universe selection once a month

        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)  

        filtered = [x.Symbol for x in sortedByDollarVolume if x.HasFundamentalData]

        

        # filtered down to the 500 most liquid stocks

        return filtered[:self.num_coarse]

        

    def FineSelectionFunction(self, fine):

    # return null list if it's not time to rebalance

        if self.reb != 1:

            return self.long 

            

        # drop counter (will update back to 1 after rebalancement has occurred)

        self.reb = 0

        

        # create dictionaries to store the indicator values

        

        stock_filter1 = {}

        

        # sort the fine list by their momentum 

        for security in fine: 

    

            hist = self.History(security.Symbol, timedelta(days=365), Resolution.Daily)

            hist["log"] = np.log(hist["close"])

            x1 = np.arange(hist["close"].count())

            slope, _, rvalue, _, _ = linregress(x1, hist["log"])

            coeff = slope*252*(rvalue**2)

            

            # we now have a dictionary storing the values 

            stock_filter1[security.Symbol] = coeff

            

        # we only want the highest values for the coeff

        self.sorted1 = sorted(stock_filter1.items(), key=lambda d:d[1],reverse=True)

        sorted1_symbol = [x[0] for x in self.sorted1]

        

        

        # long the top 10

        self.long = sorted1_symbol[:self.num_fine]



    def OnData(self, data):

        pass

    

    def rebalance(self):

        # at the start of rebalancement, if the stock is no longer in the long list, liquidate

        long_list = self.long

        for i in self.Portfolio.Values:

            if (i.Invested) and (i not in long_list):

                self.Liquidate(i.Symbol)

            

    # Assign each stock equally. Alternatively you can design your own portfolio construction method

        for i in self.long:

            self.SetHoldings(i, 0.9/self.num_fine)

        

        self.reb = 1

John Fogel

891 ,

I have fixed the above error and managed to get the backtest to run. However, 2 months into the backtest, I ran into an error - Can someone please explain to me the meaning of this error and a way to rectify it. Thank you!

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 close key exist in the collection and/or that collection is not empty.
at FineSelectionFunction in main.py:line 56
:: hist["log"] = np.log(hist["close"])
KeyError : 'close'

Shile Wen

63.5k ,

Hi Ethan,

This usually means that the hist is empty. This likely occurred because there was no data for the given security, and to prevent this from happening, try adding if len(hist) > 0: as a conditional before any calculations.

Furthermore, please try to move all the logic for calculations and buying/selling of securities to the OnSecuritiesChanged method.

Best Regards,
Shile Wen

John Fogel INVESTOR

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