Fuzzy control has long been applied to industry with several important theoretical results
and successful results. Originally introduced as model-free control design approach, model …

Algorithmic stock trading has become a staple in today's financial market, the majority of
trades being now fully automated. Deep Reinforcement Learning (DRL) agents proved to be …

Can we train the computer to beat experienced traders for financial assert trading? In this
paper, we try to address this challenge by introducing a recurrent deep neural network (NN) …

The increasing complexity and dynamical property in stock markets are key challenges of
the financial industry, in which inflexible trading strategies designed by experienced …

Deep learning (DL) is an emerging and powerful paradigm that allows large-scale task-
driven feature learning from big data. However, typical DL is a fully deterministic model that …

Financial time-series forecasting has long been a challenging problem because of the
inherently noisy and stochastic nature of the market. In the high-frequency trading …

This paper proposes an innovative tuning approach for fuzzy control systems (CSs) with a
reduced parametric sensitivity using the Grey Wolf Optimizer (GWO) algorithm. The CSs …

Algorithmic trading is a continuous perception and decision making problem, where
environment perception requires to learn feature representation from highly nonstationary …

The advent of reinforcement learning (RL) in financial markets is driven by several
advantages inherent to this field of artificial intelligence. In particular, RL allows to combine …

Recurrent reinforcement learning (RRL) techniques have been used to optimize asset
trading systems and have achieved outstanding results. However, the majority of the …