for-liangshuyuan-tasks
claudeMulti-agent collaboration framework for Liangshuyuan tasks, organizing quantitative tools, build workflows, and task roles.
11 skills · 1 agent
5
claude
quantspace
AI-native quantitative research framework for reusable skills, strategy workflows, backtests, and reports.
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Plugin quantspace from marketplace quantskills in quantskills/registry. Commands read from the repo's manifest — today.
What it installs
Skills (9)
analyzeUse when tasks need factor diagnostics, IC/grouped return analysis, attribution, robustness checks, deterministic factor-mining evaluation, or time-series distribution and stationarity checks.
backtestUse 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.
computeUse when tasks need strategy-agnostic OHLCV indicators, math utilities, regime slicing, resampling, or label makers.
factor_miningUse 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.
ingestUse when tasks need PandaData/PandaAI stock, fund, ETF, index, or futures data, reference data, adjustment factors, futures tick downloads, or symbol conversion.
mlUse when tasks need optional PyCaret model training, ML factor generation, inference wrappers, feature importance, or sparse LASSO weight generation.
reportUse when tasks need complete HTML research reports, HTML dashboards, PNG chart helpers, or files under the research reports directory.
storeUse when tasks need local market Parquet data, factor artifacts, backtest data, or model files through DataManager.
strategyUse when tasks need reusable strategy contracts, cross-sectional selection types, or time-series signal-to-weight helpers.
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