etf-three-factor-v7

ETF three-factor signal monitoring is automated by collecting market data via akshare, computing volume, direction, and share probabilities, persisting records to SQLite, and generating HTML visualization reports.

Daily ETF analysis reports can be scheduled and dispatched via email by configuring environment-based SMTP settings, enabling automated delivery of the generated HTML visualization and JSON data files.

ETF volume and share probability calculations use Tencent Finance K-line data for market pricing and akshare for Shanghai and Shenzhen stock exchange share data to compute the three-factor probabilities.

Akshare works with SQLite for ETF backtesting by fetching exchange share data into pandas DataFrames, which are then persisted locally in a SQLite database to support deterministic historical backtracking analysis.

In ETF signal analysis, the three-factor probabilities are weighted with volume probability at fifty percent, direction probability at twenty percent, and share probability at thirty percent to generate the final signal.

Using SQLite for ETF market data storage limits concurrent write access during high-frequency scheduling, but it effectively handles deterministic local persistence for daily three-factor monitoring and backtracking.