Automated stock trading system using deep reinforcement learning and price and sentiment prediction modules.

• Ano de defesa: 2021
• Autor(a) principal: Silva, Roberto Fray da
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Análise de sentimentos; Aprendizagem por reforço profunda; Aprendizagem profunda; Deep learning; Deep reinforcement learning; Inteligência artificial; Negociação de Ações; Previ são de preços; Price prediction; Sentiment analysis; Stock trading
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/3/3141/tde-10082021-160557/

The artificial intelligence models are considered state of the art in several domains.The deep reinforcement learning models, one of the main categories of artificial intelligence\'s models, have a high potential for being applied on domains with high complexity, nonlinearities, and the existence of autocorrelation, seasonal and cyclical components,and noise. One highly relevant domain that presents these characteristics is stock markettrading. Recent works were conducted in this domain using deep reinforcement learning. Nevertheless, these did not consider integrating other relevant components such as price time series prediction and market sentiment analysis. Another critical gap is the lack of comparison of different deep reinforcement learning models in different stock trading scenarios. Besides being an important developing market, the Brazilian stock market is one of the 20 biggest markets in the world. A critical problem for all the investors in this stock market is how to improve the strategies and systems used for improving returns, considering their associated risks. This research aims to investigate and propose a system for automatic asset trading considering multiple features, time series prediction, sentiment analysis, and deep reinforcement learning models. The methodology used was a simulation of the market environment simulation, considering one asset and the evaluation of two relevant scenarios. Eight versions of the proposed system were implemented and evaluated, considering six relevant domain metrics and the buy-and-hold strategy, the main baseline model in the literature. For the first scenario, which simulated a cycle with upward and downward trends, the system\'s configuration that presented the best results used the price prediction component obtained from a recurrent neural network with a maximum order size of 200 stocks. It obtained better results than the baseline model. For the second scenario, which simulated a deep downward trend, all the system configurations presented better results than the baseline model. The configuration using a recurrent neural network for price prediction and a maximum order size of 10 stocks presented the best results. The main contribution of this research for the deep reinforcement learning area was the proposal of a system that uses additional time series analysis and sentiment analysis features extracted with deep learning models. The main contribution of this research for stock market trading was to propose the use of deep reinforcement learning considering as features: market prices, volume traded, technical indicators, and price and market sentiment predictions obtained using deep learning models. The proposed system can be used in different markets and assets and adapted to other sub-domains.