PA Agent is a desktop application (PyQt6) that combines Al Brooks' price action theory with large language models (DeepSeek, MiMo, QClaw, etc.) to generate two-stage trading analysis: Stage 1 diagnoses market structure (cycle, pattern, direction), Stage 2 produces a trading decision (order type, entry, stop-loss, take-profit). The system ingests K-line data from multiple sources (MT5, TradingView, East Money, Tushare, AkShare) and provides real-time charting, AI reasoning streaming, decision tree visualization, and demo replay. It includes deterministic fallback nodes for data validation, direction voting, and pattern routing, reducing reliance on LLM output consistency. The architecture is modular with a data source abstraction, AI provider abstraction, event bus, incremental indicator computation, and a two-stage orchestration pipeline with retry and validation. While the codebase is well-structured and heavily tested (unit, integration, property-based), it suffers from complexity due to many provider connectors, hardcoded normalizer heuristics, and prompt engineering files that require ongoing maintenance.