Objective
Make AutoPredict a trustworthy forecast-evaluation and shadow-deployment framework with one canonical path:
record/ingest -> validate -> evaluate -> report -> shadow -> promote
The product should prove that a user-provided forecast adds held-out value over the market baseline before real-money execution is exposed.
Prioritized issue groups
P0 — repository and evaluation integrity
P1 — credible end-to-end product
P2/P3 — defensible learning and product clarity
Implementation sequence
- Repository cleanup and release-integrity CI.
- Canonical schema and evaluation CLI.
- Forecast-provider boundary.
- Polymarket recorder and deterministic replay.
- Real shadow trading with durable state.
- Statistical promotion hardening.
- Live-safety readiness review.
Definition of done
- Source and installed-wheel integrity checks pass.
- One versioned data-to-report workflow is documented and implemented.
- Shadow and future live execution share normalization, decision, risk, and logging paths.
- Promotion uses aggregate held-out evidence against the market baseline.
- Live submission remains gated until reconciliation and operational safety requirements pass.
Detailed evidence, acceptance criteria, and work packets are included in the repository-consolidation specification package on the cleanup branch:
Objective
Make AutoPredict a trustworthy forecast-evaluation and shadow-deployment framework with one canonical path:
record/ingest -> validate -> evaluate -> report -> shadow -> promoteThe product should prove that a user-provided forecast adds held-out value over the market baseline before real-money execution is exposed.
Prioritized issue groups
P0 — repository and evaluation integrity
P1 — credible end-to-end product
P2/P3 — defensible learning and product clarity
Implementation sequence
Definition of done
Detailed evidence, acceptance criteria, and work packets are included in the repository-consolidation specification package on the cleanup branch: