Designing AI Trading Agents with Research and Execution Controls
Summary
The document explains how to build trading agents within a strategy framework. It describes a workflow in which research agents gather market evidence and a dedicated trading agent can submit orders, while deterministic Python can enforce fixed execution and risk rules. Agents can use built-in account, market, history, and order tools, as well as custom tools that retrieve external data through APIs or compatible servers.
It emphasizes historical-data safeguards: check timestamps and revisions, bind data to the simulated date, and remember that a model may know facts from later periods. Cached outputs are not independent decisions, and a successful run does not demonstrate profitability. The page describes verification using simulated broker fills, but supplies no performance results or evidence that any example is profitable. Its practical advice concerns agent architecture, tool access, and auditability; model credentials, data availability, broker setup, and external tool behavior remain implementation constraints.
Key ideas
- Separate research roles from agents that are authorized to place or change orders.
- Use deterministic code for risk limits that must remain mechanically fixed.
- Check data timestamps and revisions because a model may use information from after the historical period.
- Treat cached agent responses as reused outputs rather than independent strategy validation.
- A successful backtest or simulated fill does not establish that a strategy is profitable.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.