Backtesting
Mattheus turns AI research into draft strategy artifacts, backtest requests, workflow validation nodes, and proposal-first changes before production execution.
Queue historical backtests, review results, and inspect trade-level details before promotion.
AutoResearch is described as proposal-first and remains gated until real production backtest flags are enabled.
Backtest plans can include metrics, baselines, failure cases, and approval gates for production deployment.
Use the assistant to analyze markets, explain protocols, create artifacts, and prepare workflows without enabling live execution.
Generate execution previews and approval requests. Live actions stay blocked until user approval and policy gates pass.
Run deployed algorithms only after configured risk limits, signer checks, venue gates, stale-data stops, and approval policies pass.
The product narrative should encourage simulation and approval evidence before production changes, especially for strategies touching real capital.
No. The backend blocks AutoResearch unless both AutoResearch and the production backtest engine flags are enabled.
Mattheus can help draft research-backed strategy artifacts and conditions for review. Users remain responsible for reviewing assumptions, risk limits, and approvals before any live execution.
No. Past or simulated results do not indicate future outcomes.
Mattheus provides research, simulation, automation, and execution tooling for on-chain markets. It does not promise outcomes or provide individualized investment advice.