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Let's say we have an OHLCV dataset for a universe of stocks. We want to create features based on these price data. Since each stock may have a very different price range from the other if we just take log-delta (eg. open-close) a stock that has a price range around 1000-2000 would look very different from another that has a price range of around 1-10. This will happen also for stock (or any instrument) that has a drastic shift in prices over time. For instance, BTC recent steep rise in value.

What would be a good way to standardize or normalize these features across different stocks of various price ranges?

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Log returns are usually sufficient to place different stocks on the same scale. Yes, some stocks may have 100% returns when others have 1%. And you can standardize them (mean=0, stdev=1). But, isn't this a feature that you want your model to capture rather than remove by normalization or standardization?

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