Financial trading has been widely analyzed for decades with market participants and academics always looking for advanced methods to improve trading performance. Deep reinforcement learning (DRL), a recently reinvigorated method with significant success in multiple domains, still has to show its...

Convolutional neural networks (CNN) and long short-term memory (LSTM) networks have become a staple of sequence learning. Due to the well-established fact that financial time series data exhibit exceptionally noisy characteristics, capital market anomalies are virtually impossible to detect. We...

Is there an informational gain by training a Deep Reinforcement Learning agent for automated stock trading using other time series than the one to be traded? In this work, we implement a DRL algorithm in a solid framework within a model-free and actor-critic approach and learn it with 21 global...