We study trading systems using reinforcement learning with three newly proposed methods
to maximize total profits and reflect real financial market situations while overcoming the …

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

Portfolio traders strive to identify dynamic portfolio allocation schemes that can allocate their
total budgets efficiently through the investment horizon. This study proposes a novel portfolio …

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

Generating asset-specific trading signals based on the financial conditions of the assets is
one of the challenging problems in automated trading. Various asset trading rules are …

Nowadays, investors seek more sophisticated decision-making tools that maximize their
profit from investing in the financial markets by suitably determining the optimal position …

Many researchers have tried to optimize pairs trading as the numbers of opportunities for
arbitrage profit have gradually decreased. Pairs trading is a market‐neutral strategy; it profits …

Reinforcement learning (RL) for dynamic asset allocation is an emerging field of study. Total
return, the common performance metric, is useful for comparing algorithms but does not help …

Computers and algorithms are widely used to help in stock market decision making. A few
questions with regards to the profitability of algorithms for stock trading are can computers …

The many success stories of reinforcement learning (RL) and deep learning (DL) techniques
have raised interest in their use for detecting patterns and generating constant profits from …