quantskills

Use when an agent needs a verified library of directional OHLCV alpha factor Skills for trend, breakout, reversal, channel-position, or price-direction research across A-share and US equity samples.

Use when computing the 5D Z-Scored Return Momentum factor from user-supplied real OHLCV data or reviewing its bundled real-data validation metrics.

Use when computing the 5D Z-Scored Skip-Period Momentum factor from user-supplied real OHLCV data or reviewing its bundled real-data validation metrics.

Use when computing the 5D Z-Scored Return Reversal factor from user-supplied real OHLCV data or reviewing its bundled real-data validation metrics.

Use when computing the 5D Z-Scored SMA Gap factor from user-supplied real OHLCV data or reviewing its bundled real-data validation metrics.

Use when computing the 5D Z-Scored EMA Gap factor from user-supplied real OHLCV data or reviewing its bundled real-data validation metrics.

Use when computing the 5D Z-Scored Dual EMA Gap factor from user-supplied real OHLCV data or reviewing its bundled real-data validation metrics.

Use when computing the 5D Z-Scored SMA Slope factor from user-supplied real OHLCV data or reviewing its bundled real-data validation metrics.

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

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.

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

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

Use when tasks need PandaData/PandaAI market data, reference data, adjustment factors, futures tick downloads, or symbol conversion.

Use when tasks need HTML reports, Markdown strategy reports, PNG chart helpers, or report files under the research reports directory.

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

Use when tasks need local Parquet market data storage, pool management, research artifacts, backtest records, or model metadata.

Generate A-share end-of-day market review reports with Pandadata data, covering trade-date checks, index performance and valuation, market breadth, limit-up/down sentiment, industries/concepts, 龙虎榜, block trades, margin financing, northbound holdings, risk notes, and optional scheduled after-close automation. Use when the user asks for 今日复盘, 收盘总结, 每日市场报告, A股复盘, 龙虎榜复盘, 北向资金动向, or to set up an automated daily market review.

Pandadata/panda_data Python SDK API reference skill for selecting, calling, and troubleshooting Pandadata data interfaces from the bundled Chinese 接口文档. Use when the user asks to query Pandadata data, choose the right panda_data.get_* method, write or validate panda_data Python examples, inspect request/response fields, or install/load/use this skill in Claude Code, Codex, Hermes, OpenClaw, Cursor, or WorkBuddy agents.

Reconstruct Serenity-style investment research logic from public X/Twitter posts, saved threads, exported post datasets, public summaries, and ticker histories. Use when an agent needs to analyze @aleabitoreddit/Serenity, decompose AI or semiconductor supply-chain investment theses at the smallest practical unit, build a structured signal dataset, evaluate public calls versus later price action, or produce a reusable Serenity-style research model for portable agent platforms such as Claude Code, OpenClaw, Codex-style skill systems, or other local AI agent runtimes.

基于 Pandadata 期货 DeepView 接口做期货品种、主力合约和跨期结构的综合研判，覆盖席位净持仓保证金、席位建仓、盈亏排行、多空比、资金流、基差、期限结构、仓单库存、虚实盘比、现货利润和跨期套利。Use when the user asks for 期货席位分析、持仓博弈、主力席位动向、基差或期限结构监控、仓单库存跟踪、虚实盘比、跨期套利扫描、期货品种综合分析, or a futures DeepView report for a variety or contract.

Run a self-contained FactorMAD-style LLM multi-agent debate workflow for mining interpretable code-based stock alpha factors from OHLCV market data. Use when an agent needs to generate, debate, validate, score, and export candidate alpha factor code for quantitative research.

One sentence on what this skill does, with concrete capabilities. Use when an agent needs to <trigger scenario 1>, <trigger scenario 2>, or <trigger scenario 3> on portable agent platforms such as Claude Code, OpenClaw, or Codex-style skill systems.

Build trader-specific research-model agent skills from public X/Twitter post datasets, exported timelines, saved threads, signal CSVs, and quote-aware review files. Use when an agent needs to reverse-engineer an X trader's investment logic, separate forward-looking thesis posts from quote-only or retrospective performance posts, generate clean signal datasets, derive a high-quality thesis template, or prepare a reusable trader-specific agent skill for platforms such as Claude Code, OpenClaw, Codex-style skill systems, or other local AI agent runtimes.

Build and apply a Gaetano / Crux Capital-style photonics, optical networking, Physical AI, and AI infrastructure research model from public X posts, public Substack pages, company coverage notes, earnings transcripts, technical papers, and market datasets. Use when an agent needs to map the photonics stack, identify AI infrastructure chokepoints, separate public thesis work from paid/private content, or create a trader-specific research skill for portable agent platforms such as Claude Code, OpenClaw, Codex-style skill systems, or other local AI agent runtimes.

Build, repair, query, and maintain a local Pandadata market-data warehouse backed by DuckDB and Parquet for repeat quant research, backtests, factor studies, and batch reports. Use when the user asks to cache Pandadata or panda_data results locally, create or update a local market-data database layer, perform incremental data refreshes, query downloaded data with DuckDB SQL, design table partitions and watermarks, validate local data against Pandadata APIs, or avoid repeated Pandadata API calls.

SSQuant Agent workflow for Chinese futures strategies, data_server diagnostics, CTP/SIMNOW runtime checks, and Chinese backtest reporting. Use when an agent needs to locate an SSQuant project, write or repair SSQuant futures strategies, validate StrategyAPI usage, run evidence-backed backtests, diagnose local/remote data behavior, inspect reports, or plan guarded live/CTP operations.