🤖 AI Summary
This study addresses the low automation and insufficient interpretability in quantitative multi-factor research by proposing an end-to-end framework based on multi-agent collaboration. The framework uniquely integrates reflective planning, chained tool invocation, and an MCP (Model-Controller-Provider) server architecture, incorporating a panel data analysis toolkit to emulate real-world research team workflows. It supports factor replication, novel signal development, and standardized report generation. Departing from purely dynamic code generation, the approach leverages traceable computational chains and narrative-driven analysis to significantly enhance research efficiency, result transparency, and empirical performance on large-scale panel datasets.
📝 Abstract
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.