Common reward designs in financial RL collapse into buy-and-hold or zero-trade policies. We present a decoupled architecture that separates signal generation (RL confidence scores) from a deterministic execution engine handling position sizing, risk rules, and exits. The talk covers failure-mode detection, production Python patterns (Position, Order, RiskManager, ExitPolicy), and lessons learned deploying this system.