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Context score computation

posted Feb 17, 2019

I am trying to compute the contest score using the data from get_backtest().daily_performance["returns"] and the formula given in the contest rules https://www.quantopian.com/contest/rules, but there it is not specified what is meant by volatility:
Is it the a)returns volatility or the b)portfolio equity volatility?
According to my calculations the score published on the 8/2/2019 leaderboard (I think it has not been updated for few days so the could be a bug there) is not obtained from neither a) or b), but in this notebook https://www.quantopian.com/posts/contest-constraint-check-notebook-with-compact-output it is computed using a). Is there any public code with the exact calculation of the score?

By the way I don't think the function in volatility_adjusted_daily_return in that notebook is consistent with the mathematical definition give at https://www.quantopian.com/contest/rules, because it uses the annualized volatility instead of the 63 trailing volatility.

Thanks
JC

1 response

Hi,
I am surprised nobody has replied this. How can we participate to the contest if the score computation is not clear?
I have done some more checks and I can confirm the recent scores on the leaderboard are not correct. If some stuff member could confirm with few lines of code how is the score computed it would be very useful for everybody. As I already stated the contest score definition leaves some ambiguity in regard to what is meant by volatility.

This is how I compute them in research:

import numpy as np
import pandas as pd
bt = get_backtest('your backtest ID')

ret=bt.daily_performance["returns"]
cum=bt.cumulative_performance["returns"]

Nd=6 # number of days for which the score is compute

print(ret[-Nd-1:])

vol=np.maximum(ret.rolling(63).std(),0.02) # volatility of returns

vol=np.maximum(cum.rolling(63).std(),0.02) # this is for the case of computing the volatility of the portfolio equity

score=ret/vol
dict={'ret': ret , 'score': score, 'vol': vol}
df=pd.DataFrame(data=dict)
print "cumulative return : ",cum[-1],"\n", df[-Nd:]

print vol[-Nd:],"\n",ret[-Nd:],"\n", score[-Nd:],"\n", score[-Nd:].sum()

score[-Nd:].sum()

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The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian.

In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.