Checks whether a trading backtest survives its own statistics: deflated Sharpe, multiple-testing correction against a best-of-N-noise benchmark, minimum track record length, and fill realism. Takes no market data and no API keys, and cannot recommend a trade — it only reports that a result is weaker than claimed or not yet provable.
Eval-integrity statistics for AI benchmark claims — multiple-testing correction, power/MDE for model gaps, judge-bias and leaderboard-rank checks. Catches a
benchmark number that won't survive a second look.
Most trading signals are noise. AlphaAssay puts them on trial — deflated Sharpe, out-of-sample, leakage forensics — and returns signed pass/fail verdicts anyone can verify. Methodology audits, not investment advice.
Provides cryptographic governance receipts for AI agents, enabling pre-execution evaluation and signed verdicts (EXECUTE/BLOCK/REVIEW/SHADOW) with offline-verifiable audit trails.
The accountability layer for AI agents — a named human's signed yes before an agent does anything irreversible (payment, record change, deploy), then an offline-verifiable Trust Receipt. Apache-2.0, formally verified.
Portfolio & trading-strategy stress diagnostics for AI agents: multi-asset stress with hedge-break detection, a daily preregistered regime outlook and deflated-Sharpe backtest-integrity checks. Remote streamable-HTTP endpoint with a free tier (no key); this repo is the public server card.
