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ML4T Ecosystem

Shared standards, qualification evidence, documentation rules, and coordinated work management for the stable ML4T Python libraries.

Library Package Responsibility
Data ml4t-data Market data acquisition, validation, storage, and access
Engineer ml4t-engineer Feature engineering, labeling, and dataset construction
Backtest ml4t-backtest Event-driven simulation, execution, risk, and accounting
Specs ml4t-specs Shared runtime-neutral lifecycle and trading contracts
Live ml4t-live Paper and live execution using the shared contracts
Diagnostic ml4t-diagnostic Statistical validation, splitters, evaluation, and diagnostics
Models ml4t-models Model training, selection, persistence, and inference support

This repository does not contain library source code and is not a runtime dependency. Start with the ecosystem documentation or the tracked documents under standards/, status/, reviews/, and decisions/.

Stable-release policy

ML4T supports every stable CPython release from Python 3.12 through the latest stable version on Linux, macOS, and Windows. Each supported combination must pass installation, import, tests, type checking, and package build checks before release. The next CPython prerelease becomes a blocking compatibility target after beta 1 without being advertised as stable.

See Compatibility and Release qualification for the normative criteria.

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Shared standards, qualification evidence, and workflow management for ML4T libraries

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