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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.covUsing 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!
Brad Hallett
Is there anyone able to assist with this?
Brad Hallett
I believe I may have found a solution...
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) self.history = self.History(self.tickers, 22, Resolution.Daily) self.history['log_ret'] = np.log(1 + self.history.close.pct_change()) self.Log(self.history['log_ret'].unstack(level=0).dropna()) cov_matrix = np.cov(self.history['log_ret'].unstack(level=0).dropna()) self.Log(cov_matrix)Please let me know if you have any feedback or recommendations to improve this code...
Thanks!
Brad Hallett
I'm thinking there must be an error in my code... The shape of the covariance matrix appears off...
Brad Hallett
I believe I have found a solution - posting it in case it can be helpful:
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) self.history = self.History(self.tickers, 22, Resolution.Daily) self.history['log_ret'] = np.log(1 + self.history.close.pct_change()) self.Log(self.history['log_ret'].unstack(level=0).dropna().cov())Please let me know if you have any feedback or recommendations to improve this code...
Yiyun Wu
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!
Brad Hallett
Thank you!
Brad Hallett
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