Trading and investing

Trading Feature Engineering

Feature construction from market data for ML trading models including price, volume, on-chain, and microstructure features. Trading analysis or dry-run only; live data and broker access require separately available host tools.

Works with the context you provideVersion 1.0.0

Trading Feature Engineering

First read and apply `references/chiefos-runtime.md`. Choose the source method below that matches the requested outcome and available evidence; do not execute every alternate provider path.

agiprolabs: feature-engineering

Load `references/agiprolabs/feature-engineering/SKILL.md` for the complete method, required inputs, ordered decisions, and output contract. Resolve its local resources beneath the same directory.

For upstream sibling names, resolve the canonical skill and source entrypoint with `references/source-routing.json`. Load sibling resources from that canonical skill; do not assume the original directory name is independently installed.

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Related methods

Also known as

feature engineering

About this method

ChiefOS runtime and authority wrapper around retained MIT (TraderMonty/staskh) or Apache-2.0 (NinjaTrader) source methods, with original licenses. Upstream subgroups and integrations are retained in references/source-index.json. Script execution requires the full pinned source checkout; import does not establish live integrations or behavioral validation.