AI Agent Skills

Detect and prevent overfitting to historical data via multiple testing corrections and pre-registration. Use when evaluating strategy variants to ensure performance is n…

Aggregate tick data into time, volume, and dollar bars. Use when resampling raw tick data into regular or information-driven bars.

Trading calendar awareness for correct date alignment and rolling windows. Use when aligning data across markets or computing holiday-aware windows.

Standardized data schema across all financial datasets. Use when defining or enforcing column names, types, and index conventions.

Filesystem and artifact-contract pattern for reproducible case studies. Use when organizing a research project for reproducibility and collaboration.

Validate causal claims using DAG adjustment sets, bad-control detection, and refutation tests. Use when distinguishing genuine factor effects from confounded association…

Systematic feature computation across multiple assets with group-aware operations. Use when computing technical or fundamental features for a panel of securities.

Build roll-adjusted continuous futures series without artificial price jumps. Use when backtesting futures strategies that span contract rollovers.

Export financial data in efficient columnar formats with schema enforcement. Use when persisting datasets for reproducible research or cross-pipeline sharing.

Prevent train-test contamination, target leakage, and temporal leakage. Use when splitting data, fitting preprocessors, or engineering features for time-series ML.

Define point-in-time tradeable universes with liquidity filters. Use when constructing the investable asset set that avoids survivorship and liquidity bias.

Decompose portfolio into factor, sector, and concentration exposures. Use when checking for unintended bets or risk concentrations.

Five families of financial features - momentum, mean-reversion, volatility, carry, and value. Use when designing a feature set to ensure coverage across complementary ma…

Select informative features using IC ranking, mutual information, or RFE - always within CV folds. Use when reducing feature dimensionality before training.

Organize computed features in versioned storage with schema enforcement and point-in-time retrieval. Use when persisting features for reproducible ML experiments or shar…

Validate features before training - IC significance, stability, redundancy, and contamination checks. Use when auditing feature quality before model fitting.