How RL Agents Learn Trading Execution
Unlike supervised learning which predicts static future prices, Reinforcement Learning agents learn dynamic control policies. The agent observes the current market state (volatility, spread, position size), takes an action (buy, sell, hold, adjust stop), and receives reward signals based on risk-adjusted returns (Sharpe ratio).
In platforms like Astra Quant AI, RL agents manage continuous position adjustment, trailing stop-losses, and drawdown risk mitigation on 24/7 VPS execution nodes.