Claude Skills · Finance

Quant Validation

avelikiy/great_cto

The methods a financial-ML result has to survive before it is evidence — purged cross-validation with an embargo, triple-barrier labelling, sample uniqueness under overlapping labels, fractional differentiation, meta-labelling, and multiple-testing correction. Written because the invariants were required of quant-researcher and nothing in the project explained how to satisfy them: a rule without a method produces either an invention or a block. Applied whenever a backtest, a feature or a label is being designed or judged.

★ 89 Synced 2 hours ago View SKILL.md

At a glance

Finance Manual install Node.js Actively maintained

This skill is for Finance and helps you validate machine learning results for trading, design backtests with proper controls, and implement fractional differentiation for features.

Install git clone --depth 1 https://github.com/avelikiy/great_cto cp -r great_cto/skills/quant-validation ~/.claude/skills/quant-validation
Can use ReadWriteSearch

Setup, runtime and requirements describe avelikiy/great_cto, the repo this skill ships in.

machine-learningbacktestingStatistical ValidationQuant ResearchCross ValidationFinancial Modeling

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