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Alpha Lab: research in the open.

Research notes, the signal library and model leaderboard — with methodology published alongside every result.

Research notes

Latest from the lab.

Digital assets8 min read · publishing soon

Exchange inflows as an early-warning signal for Bitcoin drawdowns

On-chain flow shocks compared with volatility regimes as a risk overlay, tested walk-forward.

Methods6 min read · publishing soon

Common sources of bias in crypto backtests — and how point-in-time data helps address them

Survivorship, look-ahead and listing bias, examined across hundreds of cryptoassets.

Sentiment5 min read · publishing soon

LLM sentiment embeddings vs. keyword scores

How each may relate to next-day market behaviour, and how that relationship may change over time.

Macro7 min read · publishing soon

Rates, liquidity and the crypto beta

How global liquidity conditions shift the correlation between digital assets and equities.

Risk6 min read · publishing soon

Volatility analysis for 24/7 markets

Applying volatility analytics to assets that never close.

Market integrity9 min read · publishing soon

Anatomy of a coordinated market spike

Research into market patterns around coordinated spikes.

Signal library

Signals, with their receipts.

Each signal includes a model card and out-of-sample statistics from walk-forward testing. Signals stay “In research” until they pass our promotion criteria.

SignalUniverseHorizonPublished at releaseStatus
ETH 7d mean-reversionETH, SOL, AVAX7 daysPublished at releaseIn research
BTC exchange-inflow risk overlayBTC1–5 daysPublished at releaseIn research
Funding-rate analysisSelected cryptoassets1 dayPublished at releaseIn research
Sentiment momentumTop 50 by volume3 daysPublished at releaseIn research
Volatility regime classifierBTC, ETHDailyPublished at releaseIn research
Listing anomaly alertNew listingsIntradayPublished at releaseIn research
Model leaderboard

Comparing models on out-of-sample results.

Transformers, gradient-boosted trees, recurrent networks and ensembles are ranked on the same out-of-sample folds, using error, calibration and directional accuracy — never on in-sample fit.

How we evaluate models
TrainTest (scored)

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