Impact
- Shows how to design a risk-gated research system without exposing private trading logic or making live-trading claims.
- Turns volatile event research into replay, audit, dashboard, and report artifacts a reviewer can inspect safely.
Paper-only market replay research lab
Agentic Trading Research Lab
Paper ResearchNo Live TradingRisk-GatedActive Development
Agentic Trading Research Lab is a paper-only market-event research platform. It is presented as an engineering and research system, not as an execution product: public-safe fixtures, event-harsh replay, deterministic safety gates, offline simulation, JSONL audit trails, strategy comparison, walk-forward validation, dashboard artifacts, and explicit human approval boundaries before any future escalation.
What it proves
Shows how to design a risk-gated research system without exposing private trading logic or making live-trading claims.
What it proves
Turns volatile event research into replay, audit, dashboard, and report artifacts a reviewer can inspect safely.
Problem
Volatile market-event streams can move quickly, but a serious automation project needs controlled replay, deterministic safety checks, and reviewable evidence before any discussion of real execution.
Approach
The platform treats the first milestone as research infrastructure: collect or replay market events, normalize them into bounded inputs, generate candidate recommendations, run fail-closed risk checks, simulate outcomes offline, and write audit/report artifacts that can be inspected later.
Current status
Active private research. The public portfolio shows only sanitized positioning and curated visuals. The source repository, detailed research exports, credentials, local paths, and research artifacts are intentionally not exposed.
Architecture / workflow
- Data/replay layer for fixture-backed and public-data research inputs.
- Strategy layer that produces structured candidate recommendations instead of direct actions.
- Risk layer with deterministic gates for position sizing, stale data, drawdown, activity limits, and stop conditions.
- Offline simulation layer that models outcomes without authenticated market actions.
- Audit/report layer that writes JSONL events, summary files, dashboards, and daily review artifacts.
Safety boundaries
- The portfolio page does not expose source code, raw research artifacts, credentials, private configuration, or local machine paths.
- No real funds, authenticated exchange operations, or execution claims are part of this public presentation.
- The system is described as active research and remains private until a separate sanitized public release is prepared.
- AI or agent layers, if added later, may support research and review but do not bypass deterministic risk gates.
Research pipeline
- Load curated fixture or public market-event inputs.
- Replay volatile scenarios with stricter event-order and data-quality checks.
- Generate candidate recommendations through deterministic strategy modules.
- Apply risk gates before any offline simulation step.
- Write audit logs, summary metrics, and dashboard/report artifacts for review.
Next steps
- Expand public-data replay coverage and scenario diversity.
- Add clearer strategy comparison reports across market regimes.
- Prepare a sanitized public repository only if it can be separated from private research artifacts.
- Keep any future release path behind explicit safety review and separate approval.
