AI-native quantitative research framework with reusable skills, strategy domains, and thin orchestration scripts. Use when a coding agent needs to build, test, or review quantitative research workflows, strategy examples, backtests, reports, or reusable quant modules inside QuantSpace.
Use when tasks need factor diagnostics, IC/grouped return analysis, attribution, robustness checks, deterministic factor-mining evaluation, or time-series distribution and stationarity checks.
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.
Use when tasks need PandaData/PandaAI stock, fund, ETF, index, or futures data, reference data, adjustment factors, futures tick downloads, or symbol conversion.
Use when converting OHLCV alpha ideas into QuantSkills organization factor Skills, batch-generating non-duplicate framework-neutral quant factor Skill folders, validating them on cached real market data such as AkShare A-share and Yahoo US data, and writing factor evaluation reports.
Generate a structured Chinese A-share stock due-diligence dossier from Pandadata interfaces, covering company profile, financials, dividends and capital actions, shareholder behavior, pledge/unlock/reduction risks, market funds, and sourced appendices. Use when the user asks for A股个股体检、个股尽调、公司全面分析、股票基本面报告、质押解禁减持风险排查…
Use when an agent needs to neutralize or orthogonalize a quantitative factor against industry, size, style exposures, or an existing factor library before evaluating, combining, or accepting the signal.