claude

fin-ml

Machine learning on financial data without the leaks that make it look like it works - fractional differentiation, triple-barrier labels, meta-labeling, sample weights for overlapping outcomes, feature importance that survives correlated features, bet sizing, and structural-break tests. One skill per technique, source-verified, each with an executable script and the measured size of the trap it prevents.

1 on GitHubv0.1.0MITupdated today
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Plugin fin-ml from marketplace fin-skills in howard-lynn-ye/Fin-RSI. Commands read from the repo's manifest — today.

What it installs

Skills (7)

  • bet-sizing
  • feature-importance-financial
  • fractional-differentiation
  • meta-labeling
  • sample-weights-and-uniqueness
  • structural-breaks
  • triple-barrier-labeling
View source on GitHub howard-lynn-ye.github.io

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FEDERATED - the claims inside are not verified by this repo. HKUDS' 33k-star trading agent, taken for its skill tree alone - the richest algorithm content this sweep found: the Kakushadze 101, GTJA 191 and Qlib 158 alpha zoos, factor IC/IR research, execution models (square-root impact, VWAP/TWAP), microstructure (VPIN, Kyle's lambda, Amihud), SABR and local-vol options, convertible bonds, credit, Brinson attribution, GARCH/ADF and a Pine/vnpy exporter. The skills are coupled to the app's data router and Python toolkit, so test them standalone before trusting them; data keys go in a .env, and live orders are reachable only through a separate gated connector with a kill switch, not from these skills. 90 skills is about 4.5x the discovery budget. Installs disabled.

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