Applying AI Agents to Quantitative Strategy Research and Development

Summary

This event outline presents a learning series on using large language models and AI agents in quantitative research workflows, with examples centered on VeighNa strategy development. Topics include agent and tool interaction, model tool calls, MCP, task agents, memory systems, reusable agent skills, and coordinating multi-step research tasks. The first session is planned to cover setting up a development environment, connecting to a model service, streaming responses, and using system prompts to guide behavior.

For strategy coding, the agenda proposes reflection to improve generated code, structured outputs, and an automated process that runs backtests. It also describes a progression from prototypes toward production systems. These are proposed teaching topics, not reported experimental results: the document provides no accuracy, backtest performance, or efficiency measurements. Its practical scope is an introductory event outline, and the methods would need independent validation before relying on generated strategies or automated research workflows.

Key ideas

• The series focuses on applying AI agents to quantitative research and VeighNa strategy development.
• Planned topics include model tool use, MCP, task agents, memory, and reusable agent skills.
• The first session outlines environment setup, model interaction, streaming output, and prompt design.
• Reflection and structured outputs are proposed to improve generated strategy code and enable automated backtests.
• The document is an event agenda and offers no measured results or validation of these methods.

Tags

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