Distinguish the indistinguishable: a Deep Reinforcement Learning approach for volatility targeting models

Can an agent efficiently learn to distinguish extremely similar financial models in an environment dominated by noise and regime changes? Standard statistical methods based on averaging or ranking models fail precisely because of regime changes and noisy environments. Additional contextual information in Deep Reinforcement Learning (DRL), helps training an agent distinguish different financial models whose time series are very similar. Our contributions are four-fold: (i) we combine model-based and modelfree Reinforcement Learning (RL). The last model-free RL allows us selecting the different models, (ii) we present a concept, called "walk-forward analysis", which is defined by successive training and testing based on expanding periods, to assert the robustness of the resulting agent, (iii) we present a method based on the importance of features that looks like the one in gradient boosting methods and is based on features sensitivities, (iv) last but not least, we introduce the concept of statistical difference significance based on a two-tailed T-test, to highlight the ways in which our models differ from more traditional ones. Our experimental results show that our approach outperforms the benchmarks in almost all evaluation metrics commonly used in financial mathematics, namely net performance, Sharpe ratio, Sortino, maximum drawdown, maximum drawdown over volatility.