README

Unreleased breaking change: artifact loading

New source builds introduce a backward-incompatible safety default: recorder
loads are restricted. If you reload saved models/datasets, resume online or delayed
training, or use DDG-DA, verify the artifact writer and storage permissions before
opting in with trusted=True (CLI: --trusted=True). Fresh in-memory training and
supported data-only reads need no opt-in. HIST mappings and high-frequency cache
paths also have migration requirements.

Follow the artifact loading migration guide
before upgrading. Merging into main affects source installs before a new PyPI
release; this change remains unreleased until the first tagged release containing
it. That release's versioned upgrade notes should link to the same guide.

 Upgrading configuration-driven workflows? See the
 configuration migration guide for the
 unreleased source changes: per-component trusted: true for local .py
 imports, restricted expressions, and explicit extension mappings. Package
 imports remain available. Match this guide to your checkout; PR source,
 main, and tagged/PyPI releases may differ.

 :newspaper: What's NEW!     :sparkling_heart: 

Recent released features

 Introducing : LLM-Based Autonomous Evolving Agents for Industrial Data-Driven R&D

We are excited to announce the release of RD-Agent📢, a powerful tool that supports automated factor mining and model optimization in quant investment R&D.

RD-Agent is now available on GitHub, and we welcome your star🌟!

To learn more, please visit the RD-Agent repository. We have prepared several public demo videos for you:

| Scenario | Demo video (English) | Demo video (中文) |
| --                      | ------    | ------    |
| Quant Factor Mining | YouTube | YouTube |
| Quant Factor Mining from reports | YouTube | YouTube |
| Quant Model Optimization | YouTube | YouTube |

- 📃Paper: R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization
- 👾Code: https://github.com/microsoft/RD-Agent/
BibTeX
@misc{li2025rdagentquant,
    title={R\&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization},
    author={Yuante Li and Xu Yang and Xiao Yang and Minrui Xu and Xisen Wang and Weiqing Liu and Jiang Bian},
    year={2025},
    eprint={2505.15155},
    archivePrefix={arXiv},
    primaryClass={cs.AI}
}

!image



| Feature | Status |
| --                      | ------    |
| R&D-Agent-Quant Published | Apply R&D-Agent to Qlib for quant trading | 
| BPQP for End-to-end learning | 📈Coming soon!(Under review) |
| 🔥LLM-driven Auto Quant Factory🔥 | 🚀 Released in ♾️RD-Agent on Aug 8, 2024 |
| KRNN and Sandwich models | :chart_with_upwards_trend: Released on May 26, 2023 |
| Release Qlib v0.9.0 | :octocat: Released on Dec 9, 2022 |
| RL Learning Framework | :hammer: :chart_with_upwards_trend: Released on Nov 10, 2022. 1332, 1322, 1316,1299,1263, 1244, 1169, 1125, 1076|
| HIST and IGMTF models | :chart_with_upwards_trend: Re

Top issues

Latest release

Changes

This release includes security hardening requested by Microsoft. It incorporates 2339 and 2340.

 🔒 Security and reliability

- Restrict loading of executable recorder artifacts. Data-only reads and new in-memory training remain available without an opt-in; loading artifacts from verified, trusted storage now requires explicit trusted=True. See the artifact loading migration guide.
- Keep high-frequency cache paths inside the configured artifact root and replace the bundled HIST example artifact with a data-only format.
- Replace evaluation of feature expressions with a restri