Trading Strategies in the Peruvian Foreign Exchange Market with Dynamic Optimization: A Hybrid HMM–Deep Reinforcement Learning Approach (#1211)

Authors

Aradiel Castañeda, Hilario

Mas Azahuanche, Guillermo Antonio

Arteaga Cortez, Humberto Urbano

Castillo Paredes, Omar Tupac Amaru

Reinoso Palacios, Artemio Ruben

Vilchez Inga, César

Carpena Velasquez, Enrique Wilfredo

Abstract

Furthermore, this study develops and evaluates a hybrid architecture for algorithmic trading in the USD/PEN foreign exchange market, where a Gaussian Hidden Markov Model (HMM) infers latent market regimes and a deep reinforcement learning (DRL) agent optimizes Buy/Sell/Hold actions within an MDP formulation. Moreover, a reproducible data pipeline is implemented to integrate heterogeneous financial and macroeconomic sources, producing a consolidated dataset with 4,126 observations and 1,113 variables spanning 2010-02-02 to 2025-12-05, with continuity checks to support stable learning. The inferred regime signal (e.g., bearish/volatile, sideways, bullish) is incorporated as exogenous context to mitigate market non-stationarity and improve policy learning. Finally, out-of-sample benchmarking shows that the RL-based strategy outperforms supervised baselines and Buy & Hold, achieving the highest cumulative return (28.64%) and improved risk-adjusted performance when regime context is included (Sharpe 0.116 for RL+HMM vs 0.081 for RL-only), while remaining robust in adverse periods where the passive benchmark incurs losses (-14.56%). In addition, training dynamics suggest PPO yields more stable convergence than alternative DRL methods in a noisy financial environment. Keywords: USD/PEN; Hidden Markov Models; market regimes; deep reinforcement learning; PPO; algorithmic trading; risk management; CRISP-DM..