Deep reinforcement learning for financial trading using multi-modal features

Abstract

Deep Reinforcement Learning (DRL) has several successful applications in various fields. One of these fields is financial trading, in which an agent interacts with its environment in order to maximize profit by purchasing and selling financial assets. Besides price-related features, sentiment-related features have recently been used for this task. Sentiment information reflects the public emotional reaction to these assets. This type of information has been shown to have a beneficial impact on the performance of the agents in recent works. However, directly incorporating such information into trading agents is not always trivial, often leading to sub-optimal results, e.g., due to overfitting. In this work, we propose a novel multi-modal financial embedding-based approach, as an easy and efficient way of combining various modalities from multiple sources of information, such as price and sentiment data. In addition to effectively combining multi-modal information, the proposed method allows for (a) quantifying the impact of different modalities in the decisions of the model, as well as (b) altering the importance of them on-the-fly without performing retraining. We evaluate the agents’ performance using the Profit and Loss (PnL) metric and figure that the impact of sentiment utilization in combination with embeddings is advantageous for DRL training.