JDAPP: Reinforcement learning with phaseshift jump diffusion and Fourier randomization for robust financial agent training
Year: 2025
Type: article
Abstract: We propose a reinforcement learning framework that enhances trading robustness through advanced synthetic data generation. The method integrates APARCH-based volatility modeling, jump-diffusion dynamics, and a novel Fourier-Block Randomization technique, termed JDAPP, to create diverse, non-repetitive return paths. A Double Deep Q-Network (DDQN) agent trained solely on these scenarios is evaluated against real S&P 500 data. Even with a binary long-or-short regime, the agent significantly outperforms a random baseline. Beyond performance, JDAPP enables stress testing of strategies under extreme market conditions, supporting risk management, regulatory oversight, and the development of more robust AI-based trading systems.
Source: Finance research letters
Authors: Michael Stutz, Ivilina Popova
Institution: Texas State University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,730
OpenAlex ID: W4416426883