QRAFTI: An Agentic Framework for Empirical Research in Quantitative Finance
For quantitative finance researchers, QRAFTI aims to automate parts of empirical research, but the results are preliminary and lack concrete performance numbers.
The paper introduces QRAFTI, a multi-agent framework for quantitative finance research that integrates panel data tools and MCP servers to replicate factors, test signals, and generate reports. It suggests that chained tool calls with reflection-based planning may outperform dynamic code generation on multi-step tasks.
We introduce a multi-agent framework intended to emulate parts of a quantitative research team and support equity factor research on large financial panel datasets. QRAFTI integrates a research toolkit for panel data with MCP servers that expose data access, factor construction, and custom coding operations as callable tools. It can help replicate established factors, formulate and test new signals, and generate standardized research reports accompanied by narrative analysis and computational traces. On multi-step empirical tasks, using chained tool calls and reflection-based planning may offer better performance and explainability than dynamic code generation alone.