Agent skill for Pandadata-based A-share after-close daily market review reports.
-
Updated
Jul 16, 2026 - Python
Agent skill for Pandadata-based A-share after-close daily market review reports.
Audit split and cash-dividend consistency across raw and adjusted equity prices before research or backtesting.
Run reproducible index addition, deletion, and weight-change event studies around announcement or effective-date anchors.
Audit continuous futures contract selection, roll events, same-day price gaps, and adjustment ledgers before research or backtesting.
Audit normalized intraday OHLCV data for timestamp, gap, price, volume, and trading-date defects before research or backtesting.
A-share ????+????+????????,25?????+4????+LLM????
Auditable Q58 five-session A-share short-term reversal factor research with PandaData, point-in-time controls, cost-aware backtesting, and reproducible validation.
Audit normalized intraday OHLCV data for timestamp, gap, price, volume, and trading-date defects before research or backtesting.
Point-in-time, fail-closed Buffett moat hard screener for Shanghai and Shenzhen A-shares using PandaData.
Audit point-in-time universe membership, security lifecycles, stable identities, and missing delisting returns before backtesting.
Stress portfolio liquidation capacity, pro-rata redemption shortfalls, and spread plus square-root impact costs.
To associate your repository with the pandadata topic, visit your repo's landing page and select "manage topics."