Multi-Agent Trading Council System Using GPT-5.1 and Claude 4.6
System Architecture and Components
A developer has implemented a multi-agent trading system designed to force rigorous justification of trading setups before execution. The system uses ZagiHQ for orchestration, specifically chosen for its agent locking capabilities and avoidance of manual VM provisioning or container management. Internally, multi-agent coordination is handled by OpenFang, which automates communication between nodes.
Workflow Breakdown
The system operates through four distinct stages:
- The Scouts: Three parallel agents gather data: one scrapes high-signal X (Twitter) accounts, one monitors macro news from Bloomberg/Reuters feeds, and one pulls technical indicators from TradingView. All data is normalized into a shared schema before processing.
- The Council: Three models analyze the data simultaneously: GPT-5.1 handles pattern recognition, Claude 4.6 Opus handles macro/technical reasoning, and Claude 4.6 Sonnet checks logic consistency.
- The Judge: This gatekeeper kills any trade where there's meaningful disagreement on Entry, Stop Loss, Take Profit, or Risk-to-Reward ratio. There is no override mechanism.
- Human-in-the-loop: Surviving setups trigger a Telegram alert with full model breakdowns. All trades require manual approval, and the system is currently paper trading only.
Initial Observations and Current Challenges
The developer reports that single models tend to anchor on initial interpretations and rationalize them, while the multi-agent approach with reasoning layers filters out significant noise. Setups that pass through feel "sturdier" and harder to critique.
Three specific problems are being addressed:
- X (Twitter) sentiment value: Questioning whether the signal-to-noise ratio justifies the overhead, even with LLM pre-filtering. Considering deprioritization unless clear macro catalysts drive chatter.
- Consensus trap: Concern that three-way agreement might reflect shared training biases rather than genuine signal. Considering adding a "Devil's Advocate" agent specifically prompted to find reasons not to take trades.
- Judge enhancement: Currently only kills trades based on direction and R:R. Considering adding volatility regime checks and minimum conviction thresholds (possibly via logprobs if reliable).
The developer is willing to share schemas and discuss the workflow with others building similar systems.
📖 Read the full source: r/clawdbot
👀 See Also
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YantrikClaw Fork Adds Cognitive Memory, Companion Mode, and Tier-Aware Tools to ZeroClaw
YantrikClaw is a fork of ZeroClaw that introduces three major features: Cognitive Memory with YantrikDB for persistent semantic recall, Companion Mode with bond tracking and proactive behavior, and tier-aware tool selection that adapts to model size from Raspberry Pi to large clusters.
Qwen Meetup Draft: Function Calling Harness 2 Boosts CoT Compliance from 9.91% to 100% via Structured Schemas
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