I’m trying to track volatility across Ethereum, Solana, BNB Chain, and Avalanche at the same time, but structuring the data correctly is…less easy than I thought.

Right now I’m using the CoinGecko API as a baseline because it gives me normalized prices and volume across chains, so it avoids the “exchange outlier” problem. My plan is to aggregate 5m or 1h OHLC data for each token, compute rolling volatility, then visualize it as a sector-level or chain-level heatmap.

But the problem now is with standardization – different chains have different liquidity profiles, update frequencies, and volatility behaviors. I’m worried that mixing raw data will create false spikes or hide real ones.

I’m also not sure whether to index each token to its own baseline (e.g. z-score normalization) or enforce the same scale across all chains.

So how do you NOT mislead volatility readings when aggregating data from multiple ecosystems? And if you’ve done this with CoinGecko or another aggregated feed, how did you structure your pipeline so it stays accurate and doesn’t get overwhelmed?

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