Covariance Matrix in Python by Brad Hallett

Hello everyone,

I am new to QC and was hoping to get some help. Trying to adapt an algorithm to QC, I am trying to create a covariance matrix but am having a hard time with the QC History Dataframes.

Here is my attempt:

hist = self.History(self.stocks, self.lookback+1, Resolution.Daily).unstack(level=0).close
hist = hist[symbols].pct_change()[1:]
covar_matrix = hist.cov

Using this code, I receive the following error while attempting to call covar_matrix.shape[0] (to determine the number of symbols being analyzed):

QuantConnect.Scheduling.ScheduledEventException: In Scheduled Event 'SPY: MonthEnd: SPY: 15 min before MarketClose', ---> System.Exception: AttributeError : 'function' object has no attribute 'shape' ---> Python.Runtime.PythonException: AttributeError : 'function' object has no attribute 'shape'
at Python.Runtime.PyObject.Invoke (Python.Runtime.PyObject[] args) [0x00035] in <945e8bed1d5b4491a3fcef472d4dd5dc>:0
at QuantConnect.Scheduling.ScheduleManager+<>c__DisplayClass15_0.b__0 (System.String name, System.DateTime time) [0x00006] in <08e06e9d6cbe44de803fb98b16360adf>:0
at QuantConnect.Scheduling.ScheduledEvent.OnEventFired (System.DateTime triggerTime) [0x00029] in <08e06e9d6cbe44de803fb98b16360adf>:0
--- End of inner exception stack trace ---
--- End of inner exception stack trace ---

Can you help me solve this error and correct the format of the covariance matrix?

Any assistance is greatly appreciated. Thank you!

Hi Brad,

I am glad you solve the problem. Yes, the code works. If you just need log return, you can try the code below. If you need the open, high, low, close price for further usage, then your code is fine.

import numpy as np
import pandas as pd

class CovarianceMatrixTest(QCAlgorithm):
    
    def Initialize(self):
        self.SetStartDate(2019,9,1)   # Set Start Date
        self.SetCash(10000)           # Set Strategy Cash
        
        self.tickers = [
            "SPY",
            "QQQ",
            "TLT"
        ]
        
        for symbol in self.tickers:
            self.AddEquity(symbol, Resolution.Minute)
        
        returns = np.log(self.History(self.tickers, 22, Resolution.Daily).\
                  close.unstack(level=0).pct_change()+1).dropna()
        self.Log(returns.cov())

Hope this helps!

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