kalshi-bias-engine
Automated trading system exploiting behavioral mispricing on Kalshi by comparing
quoted contract prices against an independently-modeled fair value, executing
entries and exits when calibrated edge clears fees and risk checks.
V1 trades crypto contracts only. Architecture is domain-agnostic from day one —
sports, cross-venue event arb, and other categories get added later as plug-in
modules, not rewrites.
Layout
engine/ — Python trading engine (long-running process, Docker linux/arm64).dashboard/ — Next.js dashboard on Vercel, reads Neon Postgres.ops/ — deploy notes and infra scripts.
Topology
- Engine — Phase 1: local. Phase 2: Oracle Cloud Always Free ARM VM.
- Neon Postgres — source of truth AND control plane (kill-switch, phase
gates, heartbeat). Vercel-Neon integration.
- Dashboard — read-mostly, only writes control-plane rows. Never touches
the Kalshi order API.
Build phase
Phase 1 (paper). The execution/live module is import-gated and intentionally
inert. Order placement unlocks per-domain when out-of-sample calibration passes
the phase-gate checks in the calibration store — not by flipping a config flag.
Engine runtime loops
The engine process runs the following concurrently. All writes flow through
the local SQLite spool, so Neon transience never stalls the trading loop.
- Trading loop (
_discover_and_trade_once) — every loop_interval_sec
discovers open Kalshi markets, upserts contracts, snapshots quotes,
generates signals via oracle + bias model, and asks the
PaperExecutionPolicy to decide. On a FILLED paper open an
attached_fill piggybacks on the paper_order write so order + fill
land in the same Neon transaction.
- Kraken ingest (WebSocket) — streams trades into the multi-horizon
realized-vol store the oracle reads.
- Settlement ingest (
settlement_poll_interval_sec, default 60s) —
finds contracts past settlement_time without a settlements row and
spools settlement writes from Kalshi's per-market endpoint.
- Refit loop (
calibration_refit_interval_sec, default 15m) — pulls
settled records, rebuilds the per-band per-domain calibration report,
refits each bias feature with a 70/30 time-based in-sample/OOS split,
spools calibration_snapshot + bias_params, and hot-loads the fresh
params into the in-memory bias model. evidence_ok (min OOS sample AND
min Brier improvement) is what actually toggles a feature on at runtime.
- Heartbeat (
heartbeat_interval_sec) — dashboard reads staleness
for the dead-man's-switch.
