ETF Daily Report V2.4

Solving the Performance Bottleneck in Full-Volume ETF Queries

In quantitative analysis or daily market monitoring, fetching real-time data for the entire ETF market (often over 6,000 instruments) usually faces API rate limits, excessive request batches, and slow response times. Traditional scripts processing the full dataset can take over 25 seconds, while a large portion of low-liquidity instruments (with negligible daily turnover) offers little practical value for trading decisions. ETF Daily Report V2.4 addresses this pain point by using smart filtering and concurrency optimization to compress the retrieval time for relevant data to sub-second levels.

Core Capabilities and Processing Pipeline

The skill achieves high-performance data collection and cleaning through the following mechanisms:
- Smart Whitelist Filtering: It queries only about 830 mainstream broad-based, sector, and cross-border ETFs, directly reducing invalid requests by 85%.
- High Concurrency and Retry Logic: It utilizes 20 threads for concurrent requests, combined with a 5-minute TTL smart cache and automatic failure retries (2 times), ensuring stable data retrieval.
- Empty Batch Tracking: It records historical empty batches via empty_batches.json and automatically skips them, further reducing invalid network overhead by 70%.
- Standardized Output: After execution, the script automatically generates timestamped .txt (text reports), .csv (Excel-compatible data), and .json (structured data) files, ready for downstream analytical pipelines.

Boundaries and Considerations

• Data Coverage: The skill focuses on highly liquid mainstream ETFs and automatically filters out instruments with a daily turnover below 5 million CNY. It is not suitable for analyzing micro-cap or highly illiquid ETFs.
• Data Source Dependency: The primary data source relies on the Tencent Finance API (qt.gtimg.cn) using GBK encoding. If the data source API changes, the field mapping logic may require updates.