Understanding the learning dynamics in multiagent systems is an important and challenging
task. Past research on multi-agent learning mostly focuses on two-agent settings. In this …

Algorithmic trading based on machine learning is a develo** and promising field of
research. Financial markets have a complex, uncertain, and dynamic nature, making them …

The paper presents aspects related to develo** methods for financial time series
forecasting using deep learning in relation to multi-agent stock trading system, called A …

A lot of strategies for Take Profit and Stop Loss functionalities have been propounded and
scrutinized over the years. In this paper, we examine various strategies added to a simple …

Occupant behaviour is now widely recognized as a major factor in the disparity between
predicted and measured building performance. Stochastic models are a convenient way to …

The authors of this paper present an approach to trading strategy design for a multi-agent
system which supports investment decisions on the stock market. The individual …

The article presents the performance analysis issues of buy-sell decisions agents' in a-
Trader system. The system allows for supporting of investment decision on FOREX market …

The paper presents issues related to develo** methods for fundamental analysis used to
expand capabilities of multi-agent trading system, to better predict the financial market. The …

In modern economy, enterprises often have to make decisions in a risk environment.
Consequently, decision makers need to be cautious and at the same time be able to prevent …

The article presents the problem related to evaluation of Forex trading strategies in multi-
agent systems. The ratios based on financial measures cannot be assumed to be only …