algotrading-agent-army-resume

A two-agent alpha-generation workflow splits quantitative research into research planning and idea sourcing by the Alpha agent, while the Beta agent handles feature development and tests them against research output.

You can coordinate feature generation and research planning by using a structured repository layout with spawn commands and design references, allowing the Beta agent to implement features tested against Alpha agent research output.

No, you do not need custom tooling to reproduce algorithmic trading experiments. This workflow provides a repeatable blueprint with directory layouts, references, and spawn commands to reproduce experiments directly within the repository.

The best way to outline a research-to-production pipeline for quantitative trading is to consolidate roles, data flows, and transformation steps into a single repeatable blueprint using an orchestrated two-agent workflow.

Yes, the algo trading workflow supports idea sourcing and feature development simultaneously by assigning the Alpha agent to conduct research while the Beta agent implements features and tests them against the output.