I Built an AI That Analyzes Chinese A-Share Stocks Using a Three-Pillar System — Here's What It Caught Today

A developer open-sourced an AI-powered stock analysis system for the Chinese A-share market that integrates technical, fundamental, and sentiment analysis into a unified scoring model. The system, called A-Share Stock Analysis Expert, runs three independent pipelines and triggers buy signals only for stocks scoring 12+ out of 30 points. It spotted several interesting moves during the July 30 trading session, including a breakout in 中国中免 and a trend in 隆基绿能.

Last week, I open-sourced an AI-powered stock analysis system for the Chinese A-share market. Today I want to show you what it actually does — with real data from the July 30 trading session. Most A-share analysis tools fall into one camp: purely technical MACD/KDJ/RSI overlays , purely fundamental PEG/ROE screens , or purely sentiment-based social media buzz . None of them talk to each other. Retail investors are left stitching three separate dashboards together. The system called A-Share Stock Analysis Expert runs three independent analysis pipelines on every stock in the watchlist: Python-native MACD, KDJ, RSI, Bollinger Bands, and moving average systems — all matching Tongdaxin 通达信 calculation standards. No external dependencies, no API keys needed. DuPont ROE decomposition, PEG valuation, and Buffett-style "three ratios" screening with industry-specific weighting. Majors capital flow tracking, Xueqiu sentiment, dragon-and-tiger list monitoring, institutional position changes, and policy timing. Total score: 30 points. Only stocks scoring 12+ trigger a buy signal. Today was a rough session — Shanghai Composite -1.15%, Shenzhen Component -3.79%. But the system spotted several interesting moves: Technical score: 4/10 — Bullish MACD, golden cross on KDJ, touching upper Bollinger. The system flagged the bullish alignment before the breakout. Scored 5/10 technically — bullish MACD, bullish KDJ, neutral-bullish RSI. Not an explosive play, but steady accumulation in an otherwise red market. The intelligence layer flagged "4 consecutive up days" 连涨4天 — a classic exhaustion signal. The system was already on watch for a reversal. From the training pipeline 36 samples collected across 5 patterns : | Pattern | Example | Signal | |---|---|---| | Breakout | 中国中免 +3.37% | Bullish breakout with volume — buy on confirmation | | Trend | 隆基绿能 +1.03% | Healthy uptrend channel — hold and add on dips | | Pullback | 北方华创 -1.20% | Low-volume pullback — accumulation zone | The system runs a daily cron job at 15:10 market close +10min to collect real-time quotes via the free Sina Finance API for 12 core A-share stocks. Each day's data is classified into one of 5 pattern types: The goal: accumulate 200+ labeled samples for QLoRA fine-tuning. The complete source code and tools are available: Whether you trade A-shares or just want to understand how AI agents can analyze financial markets, I hope this gives you a practical starting point. Pull requests and ideas welcome