Sequoia-X: quantitative stock selection for China
Sequoia-X V2 is a quantitative stock-selection system for China鈥檚 A-share market. It is rebuilt in Python with object-oriented architecture, vectorized calculations, and incremental data updates; it uses baostock for historical and daily data and SQLite for local storage. After market close it can run the selection process and send results to a Feishu group.
The project provides two operating modes: a daily mode that updates data and runs strategies with parallel processing, and a backfill mode for the initial historical load. The README documents strategies including TurtleTrade, moving-average and volume breakouts, High Tight Flag, post-limit confirmation, and RPS Breakout, making it an extensible base for experimenting with selection rules.
Its main strength is bringing data acquisition, persistence, strategies, and notifications into a reproducible local workflow. The README also spells out requirements鈥擯ython 3.10 or newer, environment-based configuration, and an initial historical backfill鈥攚hich helps users study or modify the system.
It is not a profitability guarantee or a system that should be used without validation: strategies depend on data quality, assumptions, and the Chinese market. Before making financial decisions, users should review the code, test out of sample, account for costs and biases, and evaluate the maintenance of baostock. Verdict: a useful educational quantitative starting point, with real risk if treated as advice.