quant-swarm MCP server

quant-swarm

Risk-first pre-trade checks that gate live capital — point-in-time data, invariant and leakage audits, regime probes — plus the Shadow Tournament: score your strategy genome against a live, self-evolving trading swarm's champion on identical market paths.

<!-- mcp-name: io.github.blink1217/quant-swarm -->

Built by a Principal Quantitative Technologist

Authored by the Principal Quantitative Technologist at The 1.21 Initiative — the same code path that gates live promotion and execution decisions in the trading swarm. Every tool exists because production trading systems fail in two repeatable ways: lookahead leakage in research and an oversized position in production. quant-swarm ships the checkers that prevent both, so you can audit your own work with the exact logic that guards deployed capital.

Your backtest is static. The swarm is not.

Behind this package is a swarm that does not hand-tune strategies. A champion genome runs live; every cycle the swarm breeds challengers (tier-capped mutations), replays champion and challenger on the identical episode-seed matrix across five labelled market regimes (high-vol, drawdown, chop, melt-up, gap-heavy), and records the paired outcome in an ELO ledger. A surrogate prior pre-scores candidates so expensive replays are spent where they matter. Nothing is promoted on a single good backtest: promotion requires MINEPISODES=20 paired episodes, a Wilcoxon-significant delta, a positive worst-regime margin (maximin, not mean), a deflated-Sharpe margin against the monotonic count of every trial ever attempted, and a PBO cap. This is the champion/challenger, self-play-style loop popularised by AlphaZero-style systems, applied honestly to markets — with the two things that do not* transfer stated plainly: the market does not react to us, so there is no true self-play, and there is no learned dynamics model or tree search; selection is done by paired replay, ELO and statistical gates.

Replay is only half of it. Every Sunday the swarm also runs a forward league: each league genome — the champion, the top-ELO challengers, contributed genomes, and a planner entrant whose weekend moves are chosen by a learned model — seals its positions for the coming week from Friday's causal features, and the SHA-256 commitment is stored before the week happens. The next cycle marks every sealed position against the realised closes (net of each genome's own cost model), ranks the field on the identical real week, and updates ELO on that. Sealed forecasts cannot be overfit to the week that follows; that is the swarm's ground truth, and it feeds a replay buffer where every position is decomposed into its component moves (screen, entry, size, weekend) so credit is assigned per move and regime, not to the strategy as a blob. The forward league started on 2026-09-02; the first realised settlements land mid-September, and the swarm runs untouched for eight weeks to collect them.

Shortened. The full README is on GitHub.