Building a Volume Anomaly Scanner for Crypto Markets

If you've been building trading signals, you've probably noticed that raw volume numbers are useless. A 100M volume candle means something completely different on a 10B market cap token versus a 100M market cap token. I've been working on this problem: How do you detect genuine market anomalies versus noise? The answer isn't volume alone -- it's the relationship between volume and market cap. The Core Idea When a token's 24h volume exceeds 10-15% of its market cap, something unusual is happening. Either: Legitimate whale accumulation or distribution Coordinated trading activity (pump scheme or liquidation cascade) Major catalyst event driving real interest Natural volatility spike Most of us catch these by eye, checking charts periodically. But scalable anomaly detection requires automation. The Method Volume-to-market-cap ratio analysis works like this: Pull 24h volume and current market cap from CoinGecko (or your data source) Calculate the ratio: ratio = volume_24h / market_cap Comp