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How I built an evolutionary factor mining system that uses Claude Code to autonomously discover, backtest, and evolve commodity futures trading factors — and what it found.

How to build a multi-agent trading firm with OpenClaw — specialized analyst agents running in parallel, adversarial bull/bear debate, dedicated risk management gates, sandboxed execution, and persistent memory that compounds institutional knowledge over time.

How to turn OpenClaw into a full-stack AI investment analyst — parsing sell-side research PDFs, building code-based financial models, running 24/7 market monitoring with heartbeat alerts, and conducting deep research with parallel subagents.

A deep dive into Microsoft's RD-Agent framework — the first multi-agent system that automates the full quant research pipeline from hypothesis generation to backtesting, achieving 2x higher annualized returns than classical factor libraries while using 70% fewer factors.

A deep dive into OpenClaw's architecture — how it runs persistent AI agents across messaging platforms with lane-based queuing, session persistence, context compaction, and a built-in heartbeat system for proactive monitoring.