Sequoia-X is a quantitative stock selection system for the A-share market. It automates daily stock data synchronization from baostock (via SQLite), runs multiple vectorized trading strategies (e.g., Turtle Trade, MA Volume, RPS Breakout), and pushes results to Feishu groups. The system operates in two modes: daily (incremental update + strategy execution + notification) and backfill (full historical data download). Architecture features modular separation: core (config/logging), data engine (SQLite/baostock sync), strategy (abstract base + concrete implementations), and notification (Feishu webhook). Key strengths include robust incremental sync with multiprocessing, fault-tolerant backfill with retries/reconnections, and strategy-specific webhook routing via environment variables.
This repository is a comprehensive knowledge base and research platform for quantitative finance, primarily focused on Chinese A-share markets. It contains hundreds of blog posts, course materials, and technical articles covering factor investing, machine learning strategies, backtesting frameworks, and data engineering. The codebase includes reusable data pipelines (ParquetUnifiedStorage, CalendarModel), a custom backtesting framework (Moonshot), and numerous factor analysis scripts. It also hosts a sophisticated set of AI-assisted academic research skills (paper writing, review, deep research) defined as Claude skill files. The repository serves as both an educational resource and a personal research lab, with content spanning from beginner Python tutorials to advanced factor construction and model training. While the code is functional and demonstrates solid data engineering patterns, it is tightly coupled to Chinese financial data APIs (Tushare, akshare) and contains hardcoded paths, limiting portability. The repository is actively maintained but shows signs of technical drift in some dependencies and content duplication.