Unlike traditional stocks, futures, and options markets, crypto markets trade 24/7. 

Therefore, it's necessary to adjust your crypto price data to match the data from exchange traded securities when doing certain equity time series analysis. 

For this video, I use the Yahoo_fin library to get historical equity price data and the CoinGecko API to get historical crypto price data.  

Here's the code from the video 

https://github.com/AdamGetbags/cleaningCryptoTimeSeries/blob/main/adjustingCryptoData.py 

1:17 get equity data from yahoo_fin 

1:34 get crypto data from CoinGecko API 

2:00 reformat dates datatype, set index, other reformatting 

3:57 join equity and crypto data together on the index 

5:50 calculate log returns   

Let me explain. Say you wanted to know if there was a relationship between NYSE/NASDAQ/AMEX exchange traded financial asset and an asset in the crypto market. For example, what is the beta of Riot Blockchain with respect to Bitcoin price. 

It will be helpful to adjust your crypto price data by removing the weekends and US market holidays from your time series.   

It all depends on the analytical task, but for linear regression, correlation, machine learning, and other statistical analysis techniques, this adjustment can be useful.  In regards to the crypto data, there are some dangerous assumptions to be made about removing ~2/7th of all your observations. 

So, be mindful about the specific task you are doing.

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Need some ideas for your project portfolio?

Check out how to get historical option price data in Python from the Polygon API.

https://youtu.be/87O9qxfMJ-g

Here's how to quickly get crypto data in Python from the CoinGecko API

https://youtu.be/4-CC3u4i73k

Here's how to create a SQL database from a Pandas DataFrame in Python

https://youtu.be/9IPlc-nKD1k

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