quantskills/agent-quantspace

analyze

Use when tasks need factor diagnostics, IC/grouped return analysis, attribution, robustness checks, deterministic factor-mining evaluation, or time-series distribution and stationarity checks.

원문 언어: 영어

compute

Use when tasks need strategy-agnostic OHLCV indicators, math utilities, generic factor examples, regime slicing, resampling, or label makers.

원문 언어: 영어

factor-mining

Use when tasks need AI multi-agent factor mining research boundaries, versioned ResearchBrief/FactorSpec contracts, evaluation/review/decision objects, or cross-platform role task protocols without implementing compute/analyze algorithms here.

원문 언어: 영어

ml

Use when tasks need optional PyCaret model training, ML factor generation, inference wrappers, feature importance, or sparse LASSO weight generation.

원문 언어: 영어

report

Use when tasks need complete HTML research reports, HTML dashboards, PNG chart helpers, or files under the research reports directory.

원문 언어: 영어

strategy

Use when tasks need reusable strategy contracts, cross-sectional selection types, or time-series signal-to-weight helpers.

원문 언어: 영어

backtest

Use when tasks need vectorized strategy execution, portfolio weighting, portfolio-level filters, transaction cost helpers, exit A/B analysis, overlay metrics, or multi-strategy return blending.

원문 언어: 영어

ingest

Use when tasks need PandaData/PandaAI stock, fund, ETF, index, or futures data, reference data, adjustment factors, futures tick downloads, or symbol conversion.

원문 언어: 영어

store

Use when tasks need local market Parquet data, factor artifacts, backtest data, or model files through DataManager.

원문 언어: 영어

research

Use when tasks need reusable research pipeline templates, factor screening, or parameter sensitivity sweeps.

원문 언어: 영어