What is the best way to deal with NaN values in price data?

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What is the best way to deal with NaN values in price data?

posted Jun 20, 2020

Seeking Help Getting Started Using Data Pipeline

I've spent quite a bit of time researching this on the forum but have yet to find a clear cut answer. The solutions seem to break down as follows:

Remove any offending rows - this removes 'good data' for other stocks on that day
Remove any offending stocks - getting rid of an entire stock seems drastic
Mark as zero - this would screw up any factors calculated on that window
Forward fill - might not that be representative
Refill NaN values with the mean, e.g. .apply(lambda x: x.fillna(x.mean()),axis=0) - best I've seen...?

Is there a Quantopian recommended way to deal with NaN values in price data?

4 responses

You fill in gaps with simulated values derived from a random number generator, I'm not sure how this is done, but I've heard this is a solution many professionals use.

Thanks Jason. Will throw it into the mix :-)

Use a proxy, thats the best solution. If you use random data, even if fitted with the stocks' moments, this would not work. Imagine you want to compute correlation for example, using simulated value would result in an enormous error.

Mathieu M , could explain more about it giving an example ? thanks

Other solution , in my mind we can make a regression to fill up the NaN's values...to check...

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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.