A Risk-Aware Reinforcement Learning Reward for Financial Trading
This work addresses the need for robust multi-objective reward functions in financial trading, though it is incremental as it builds on existing risk-return balancing methods.
The authors tackled the problem of designing reward functions for reinforcement learning in financial trading by proposing a composite reward that balances return and risk using four differentiable terms, achieving a modular and parameterized framework that allows practitioners to encode diverse investor preferences.
We propose a novel composite reward function for reinforcement learning in financial trading that balances return and risk using four differentiable terms: annualized return downside risk differential return and the Treynor ratio Unlike single metric objectives for example the Sharpe ratio our formulation is modular and parameterized by weights w1 w2 w3 and w4 enabling practitioners to encode diverse investor preferences We tune these weights via grid search to target specific risk return profiles We derive closed form gradients for each term to facilitate gradient based training and analyze key theoretical properties including monotonicity boundedness and modularity This framework offers a general blueprint for building robust multi objective reward functions in complex trading environments and can be extended with additional risk measures or adaptive weighting