Multi-LLM Paper-Trading Bot with Claude Opus as Lead Engineer and Gemini as Strategist: Architecture Breakdown
A developer has shared an autonomous paper-trading bot running on Alpaca that uses a multi-LLM architecture with bounded roles and a documented veto process. The project spans ~4,900 LOC across five Python modules and is fully open-source on GitHub.
Architecture: Three Roles, Bounded Scope
- Commander (human): Capital authority and thesis sign-off. Anything touching money requires human approval.
- Strategist (Gemini Pro): Scope-limited to thesis adjudication only. Not allowed to make implementation choices, pick the broker SDK, or decide architecture.
- Lead Engineer (Claude Opus 4): Writes all code. Audits Strategist directives and has veto power over directives that don't survive contact with engineering reality. Vetoes are logged on the record.
No party can deploy autonomously. Every disagreement is logged in a "Strategist Codex" document that now has 270+ entries. The codex never hides reversals — if a principle gets superseded later, both versions remain with dates.
Why Multi-LLM Works Here
The creator argues that a single LLM has no incentive to disagree with itself. Two LLMs from different vendors with bounded scopes and a documented veto path produce something closer to a real engineering review process. The friction forces disagreement into the design phase instead of the post-mortem.
Real Example: Broker SDK Field Disagreement
Strategist directive: anchor a 14-day position-decay clock to Position.created_at from the broker SDK. Claude (Engineer) checked dir(Position) against the live Alpaca SDK and found the field doesn't exist. Implemented a state-side ledger instead and logged the doctrine update with rationale: "broker did not in fact provide the field the original adjudication assumed." During architect review, Claude further refactored because the first pass held a state lock across N broker calls. Both passes are recorded in the Codex.
Who This Is For
Developers building multi-LLM agent workflows, especially for financial automation or any domain where audit trails and adversarial review are critical. Also relevant for anyone exploring coordinated multi-agent systems with explicit veto logging.
Repo and full 9-page architecture paper: https://github.com/ALGEM-hub/Whitepaper
📖 Read the full source: r/ClaudeAI
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