LOF Arbitrage Opportunity Alerts
Paste the following prompt into your AI chat to install this skill:
Please follow https://skillhub.cn/install/skillhub.md to install @user_628666c1/taolitixing
About this skill
Problem
LOF arbitrage depends on the gap between the exchange-traded price and the fund's estimated net value. A single data source may miss tickers, misread subscription status, or have stale quotes. Buying from one high-premium list can lead to unarbitrageable positions.
How It Works
- Runs
yijia_tonghuashun.pyandyijia_sina.pyto load full LOF lists from Tonghuashun and Sina. - Calculates real-time premiums using Sina live prices and Eastmoney
fund_value_estimation_em()estimated NAV. - Filters out invalid prices or NAV and LOFs whose subscription status is suspended, then outputs tradable LOFs with premiums above
3%. - Compares the two lists: tickers appearing in both are marked dual-source confirmed, while single-source entries keep their source label.
Limits
Useful for intraday A-share screening during trading hours, but it should not replace post-close actual NAV verification. Estimated NAV is based on quarterly disclosed holdings and can deviate from true NAV; confirm fees, settlement rules, subscription caps, and daily notices before acting.
Use Cases
- During A-share trading hours, screen all LOFs and find tradable candidates with premiums above 3%.
- Compare Tonghuashun and Sina LOF lists and flag high-premium tickers confirmed by both sources.
- Generate full LOF CSVs for manual review of code, price, estimated NAV, premium, and subscription status.
- Filter suspended-subscription LOFs before trading to avoid unarbitrageable high-premium funds.
Best For
- A-share individual investors tracking LOF market premiums who need tradable candidates during sessions.
- Quant analysts validating fund data who compare Tonghuashun and Sina LOF quote outputs.
- Research assistants running Python screening scripts who need reviewable CSV lists before trading.
- Fund-arbitrage researchers who need full premium and subscription-status fields for monitoring.
Related Skills
Switch AI from a polite executor into a skeptical thinking partner, using assumption checks, pre-mortems, and evidence gates to expose weak ideas early.
Applies research discipline, data lookup, evidence grading, red-team checks, and structured reporting to analyze AI company investment logic.
Uses Qichacha or Tianyancha MCP data and public information to score corporate credit across 12 dimensions and output a Markdown risk report.
Calculates personal injury compensation via CLI and outputs case summary, standards, line items, sources, and risk notes.