Mirror

MIRROR is an adaptive AI trading system built for live competition. The core idea is simple: most agents stop at prediction, but MIRROR closes the loop from forecast to execution to evaluation to strategy improvement. Four Red agents trade through the official Kraken CLI using real market data, while Blue agents inspect calibration failures, identify weak patterns, and generate candidate improvements. Forecasting and operational reasoning run through Featherless via an OpenAI-compatible interface, and featherless is also used for strategy patching and crossover generation. What makes MIRROR different is that every proposed strategy change is tested through deterministic replay before promotion, so the system evolves from measured behavior rather than intuition. MIRROR tracks forecasts, fills, equity, realized and unrealized PnL, Brier score, trade lineage, patch history, and onchain job state in Postgres, then exposes that operating state through a live dashboard built with Next.js, Tailwind, SSE, and a FastAPI backend. Under the hood, the orchestration stack uses LangGraph, SQLAlchemy, APScheduler, and web3.py. For infra and deployment, MIRROR is designed to run on a VPS environment such as Vultr, with nginx in front of the frontend and backend services, PM2 managing long-running processes, and a live domain serving the system. Onchain identity and reputation are handled through ERC-8004 on Base Sepolia, with agent metadata pinned to IPFS and reputation feedback posted from measured calibration outcomes. The tournament stack is tuned for survival on net PnL, with trend gating, spread caps, position sizing, concurrent exposure limits, adaptive loser blocking, cooldowns after losses, profit lock, trailing exits, time-stop logic, and recovery take-profit behavior. MIRROR combines live trading, agent evaluation, self-improving strategy infrastructure, and onchain agent identity into one operating system for competitive AI traders.

Note: the demo URL is substituted with the GitHub repository link.