Blockchain Analytics

Bitcoin on-chain trends and whale influence
Bitcoin On-Chain Demo

Do whale transactions actually move the price?

Real BTC/USD price and on-chain fundamentals (CoinGecko, blockchain.info), a real bounded window of whale transactions scanned directly from recent Bitcoin blocks (blockchain.info rawblock API, no key), a generalized ESD anomaly detector applied to price and active addresses, and a lead-lag correlation test between whale activity and price returns, reported honestly whether or not it turns up a signal.

$77,127
BTC/USD (latest)
+22.4%
30-day price change
696,791 BTC
whale volume, 2-day scan
2177
whale tx (≥50 BTC), 150 blocks scanned
2
price anomalies (ESD test)
1
active-address anomalies
44.1%
annualized volatility
-0.08%
mean daily return

BTC price, with detected anomalies

Red points are days flagged by a generalized ESD test on the residuals of price against its own 7-day moving average (Rosner 1983), not a fixed percentage threshold.

Active addresses trend

Unique active on-chain addresses per day (blockchain.info), the same ESD test applied independently.

Whale activity, real bounded window

Every block in the scan window was fully downloaded and every transaction's total output value checked; transactions moving ≥50 BTC in outputs are counted as whale transactions. This is a real, recent 2-day window, not a full year, scanning a year of blocks (roughly 52,000) block-by-block is not practical for a demo (see README).

Does whale activity lead the price?

Pearson correlation between daily whale BTC volume and price returns at different lags. Positive lag = whale activity that day correlates with a price return `lag` days later.

LagPearson rp-valuen
Not enough overlapping days between whale activity and price data to test correlation.

Why statistical methods, not ML

Data scopeA 2-day whale window and a one-year daily price/on-chain series is far too little data to train a model that generalizes, but plenty to run a well-understood statistical test correctly.
InterpretabilityAn ESD test and a Pearson/Spearman correlation table are auditable line by line. A black-box model's "whale influence score" would not be, and this project's whole point is showing the actual evidence.
Honesty over sophisticationA fancier model fit to this little data would likely just overfit noise and report a confident-looking but meaningless signal, worse than reporting "inconclusive" plainly.

Full pipeline, data sources, and honest limitations: github.com/fborbon/blockchain-analytics.