Reward Function Design

The reward function is the core component of a reinforcement learning system that defines the goal by assigning numerical values to the outcomes of the agent's actions. In trading, this function must be carefully designed to balance profit generation against risk metrics like maximum drawdown and Sharpe ratio.

A poorly constructed reward function can lead to perverse incentives, such as an agent taking excessive leverage to achieve short-term gains while ignoring systemic risk. By incorporating penalties for high transaction costs or excessive slippage, the reward function guides the agent toward sustainable, risk-adjusted performance.

It effectively translates the trader's objectives into a language the algorithm can optimize.