RD-Agent

RD-Agent automates the research-and-development loop itself, and its quantitative-finance scenarios point that loop at Qlib: an agent proposes alpha factors and forecasting models, implements them as code, and backtests them on real market data. Four entry points are present in the current CLI — fin_quant, fin_factor, fin_model and fin_factor_report --report-folder=<path> — and the implementation lives under rdagent/scenarios/qlib/, with factor and model coder and runner modules, a scenario Dockerfile, and Qlib factor and model config templates.

The companion paper is R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization (arXiv:2505.15155), a poster at the NeurIPS 2025 Datasets and Benchmarks Track rather than the main conference track. It describes a Research stage that forms hypotheses, a Development stage where a code-generation agent called Co-STEER implements them for real-market backtests, and a feedback stage whose multi-armed bandit scheduler decides what to explore next. The performance figures quoted in the paper and README are the authors’ own and unaudited.

Quant is one scenario among several in the same repository — Kaggle, data science, RL and LLM fine-tuning scenarios share the codebase — so what is described here is the fin_* half of the project. Docker is a hard prerequisite for most scenarios, the quant loops included. MIT licensed and published by Microsoft; the PyPI release (rdagent 0.8.0, uploaded 2025-11-03) lags the default branch, which had commits through 2026-08-04.