Qlib packages quant ML stack for developers

Qlib is more useful as research infrastructure than as a plug-and-play trading machine, which is exactly why developers should treat it seriously.

• –It gives ML engineers a full quant workflow scaffold instead of forcing them to stitch together data loaders, factor pipelines, trainers, and backtesters by hand
• –Built-in support for supervised models, reinforcement learning, and market adaptation makes it broader than a narrow stock-prediction demo repo
• –The learning curve and finance-specific assumptions are real, so casual users may bounce before reaching useful experiments
• –RD-Agent integration pushes Qlib toward automated factor mining and model optimization, which is the most interesting AI-native angle for quant developers