Separating AI Market Research from Trade Execution with Hard Guardrails

Summary

This article outlines an architecture for crypto trading agents that separates market research from order execution. A read-only market data MCP supplies prices, order books, candles, and context for an agent's analysis; a REST trading API carries out orders and bot management. Between them, ordinary code validates the agent's proposed action against fixed rules, then logs both the reasoning and the execution result.

The article applies this pattern to thesis-driven entries, opportunistic swing trades, exchange routing, risk-off signals, and configuring existing bots. It recommends limits such as position caps, drawdown rules, approved pairs and venues, and human review for large orders. These are design recommendations and illustrative use cases, not tested strategies or evidence of profitability. The discussion is specific to the named crypto platform and says its execution MCP is not yet available; it also advocates keeping validation and audit controls when that changes.

Key ideas

• Use market data tools for analysis and a separate API for deterministic order execution.
• Validate proposed trades in code with fixed risk limits before submitting them.
• Log the agent's reasoning alongside the execution response to support auditing.
• Possible applications include trade selection, venue routing, risk-off actions, and bot configuration.
• The article gives architecture guidance and examples, not backtest or live performance evidence.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.