Automate ML meta-labeling for trading with triple-barrier labeling and XGBoost calibration.
npx skills add https://github.com/bitandbytes/Argus --skill ml-meta-labeler
Please help me install this Agent Skill. Skill: ml-meta-labeler Source: https://github.com/bitandbytes/Argus/tree/main/.claude/skills/ml-meta-labeler Command: npx skills add https://github.com/bitandbytes/Argus --skill ml-meta-labeler
This skill automates the meta-labeling layer (Layer 3) of a multi-model trading cascade, enabling selective action on quant-engine signals to improve precision and reduce false positives.
Provide a history of quant signals and features, then run the meta-model training workflow to produce a calibrated classifier.
High-intent search queries and answers about installing and using this skill.
Triple-barrier labeling generates robust binary or meta labels for training by setting profit-taking, stop-loss, and time barriers around each trade entry. This mechanism captures the actual outcome dynamics of trading signals to produce realistic target variables.
To train an XGBoost meta-model, assemble a feature matrix from quant and regime inputs, then apply embargo-based purged K-fold cross-validation. This prevents data leakage during training and maintains realistic out-of-sample evaluation for robust production models.
You must provide a historical dataset of quant signals and a feature matrix assembled from quant features, market regime inputs, and sentiment data. This historical signal and feature history is required to run the meta-model training workflow and produce a calibrated classifier.
Purged cross-validation with embargo prevents data leakage by removing correlated data points around training folds, which limits the effective training dataset size. This approach requires sufficient historical data to maintain statistical significance for robust out-of-sample evaluation.
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