A Chinese-language analysis tool for futures markets, where contracts set terms for buying or selling an asset later. It uses Pandadata's DeepView interface to examine positions, money flows, inventories, prices, and related contracts.
Pandadata/pandadata 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 pandadata.get method, write or validate pandadata 0.0.12 Python examples, inspect request/response…
A workflow for improving one existing stock or futures trading factor in place. It tests important time-period settings, removes components to measure their contribution, refines the best version, and checks whether the results hold up.
A Chinese-language workflow for improving an existing stock or futures trading factor. It covers testing different time periods, removing components to measure their value, refining the factor, and checking whether the results are reliable.
A portable loader file for agents or development tools that do not have built-in support for skills. It is intended for systems such as Hermes or OpenClaw.
Mine A-share quantitative factors with PandaAI in two modes: AI-led blind discovery using PandaAI data and iterative factor-analysis feedback, or evidence-grounded extraction from papers, reports, PDFs, DOCX files, and text. Use when asked to discover new factors autonomously, translate research formulas into…
Use this model-neutral loader with any AI runtime that does not natively discover SKILL.md folders. The runtime only needs to read files and execute local commands. If native skill discovery is available, install the full folder unchanged and load SKILL.md directly.
Use when an agent needs a disciplined quantitative factor mining workflow for forming one hypothesis at a time, implementing a factor, running validation, recording iteration notes, accepting improvements, or rolling back weak experiments.
A set of rules for a quantitative factor-mining process. It emphasizes testing one hypothesis at a time, recording iterations, and checking correlations before continuing.
An agent package for loading and using factor-mining material, including prompts, decision guidance, usage notes, and supporting references. Factor mining is the search for measurable patterns that may help explain or predict market behavior.
Pandadata options volatility analysis skill for option-chain snapshots, implied volatility versus historical/realized volatility, IV percentiles, term structure, skew/smile diagnostics, volatility premium, and Chinese options-volatility reports. Use when the user asks for 期权波动率分析, IV 历史分位, 波动率溢价, 期权链查询, 波动率曲面/偏度…
Use when an agent needs to review an existing factor library, summarize experiment logs, quantify acceptance rates and score dynamics, analyze factor families and correlations, and recommend the next research direction.
Pandadata index valuation and A-share industry rotation analysis skill for building PE/PB percentile dashboards, valuation temperature tables, broad-index fixed-investment references, industry momentum rankings, and rotation summaries. Use when the user asks for 指数估值分位, 现在贵不贵, 估值温度计, 宽基指数定投参考, 行业轮动, 行业动量排名, 行业强弱切换, or…
Use the Index Valuation Rotation skill for Pandadata index valuation percentiles, valuation dashboards, fixed-investment references, and A-share industry rotation or momentum-ranking analysis.