A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning
- PDF / 2,970,879 Bytes
- 17 Pages / 595.224 x 790.955 pts Page_size
- 10 Downloads / 302 Views
A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning Salvatore Carta1 · Andrea Corriga1 · Anselmo Ferreira1 Diego Reforgiato Recupero1
· Alessandro Sebastian Podda1 ·
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The adoption of computer-aided stock trading methods is gaining popularity in recent years, mainly because of their ability to process efficiently past information through machine learning to predict future market behavior. Several approaches have been proposed to this task, with the most effective ones using fusion of a pile of classifiers decisions to predict future stock values. However, using prices information in single supervised classifiers has proven to lead to poor results, mainly because market history is not enough to be an indicative of future market behavior. In this paper, we propose to tackle this issue by proposing a multi-layer and multi-ensemble stock trader. Our method starts by pre-processing data with hundreds of deep neural networks. Then, a reward-based classifier acts as a meta-learner to maximize profit and generate stock signals through different iterations. Finally, several metalearner trading decisions are fused in order to get a more robust trading strategy, using several trading agents to take a final decision. We validate the effectiveness of the approach in a real-world trading scenario, by extensively testing it on the Standard & Poor’s 500 future market and the J.P. Morgan and Microsoft stocks. Experimental results show that the proposed method clearly outperforms all the considered baselines (which still performs very well in the analysed period), and even the conventional Buy-and-Hold strategy, which replicates the market behaviour. Keywords Deep learning · Deep reinforcement learning · Intraday stock trading
1 Introduction The growing popularization of stock market trading has become a rich field to be explored by powerful and fast computing techniques. These tools have been widely explored, to provide better trading strategies and to maximize performance metrics such as, among others, profit, economic utility, or risk-adjusted return. Indeed, machine learning solutions have been proposed to stock trading even before the popularization of computers [15, 20, 21] and have potential to distribute wealth among investors with a reduced need for human intervention. The research performed in this paper has been supported by the “Bando ”Aiuti per progetti di Ricerca e Sviluppo“-POR FESR 2014-2020—Asse 1, Azione 1.1.3, Strategy 2- Program 3, Project AlmostAnOracle - AI and Big Data Algorithms for Financial Time Series Forecasting” Salvatore Carta
[email protected]
Extended author information available on the last page of the article.
Financial forecasting techniques are usually divided into the branches of Technical Analysis (TA), which uses data acquired from the past to indicate future behavior of the market, and the Fundamentalist Analysis (FA), which analyzes economic metrics that may infl
Data Loading...
