The dynamics of financial markets is subject of much debate among researchers and financial experts trying to understand and explain how financial markets work and traders behave. Diversified explanations result from the complexity of markets, and the hardly observable aspects of price formation mechanisms and of participants' motivation behind trading decisions. In an attempt to provide a better understanding of market dynamics, studies in the realm of agent-based computational economics represent markets from bottom-up. 
The aim of this thesis is to contribute to the understanding of market dynamics by extending the agent-based computational approach. In order to achieve our goal we propose a modular, continuous-time, agent-based trading environment, with individual, autonomous representation of market participants. In order to be able to develop such an environment we first analyze and compare real and artificial stock markets (ASMs). Based on this analysis we propose a conceptual framework to describe real markets. By enriching the framework with design and implementation issues we get a multi-dimensional taxonomy of artificial stock markets. ABSTRACTE, the proposed modular environment is an operational form of these frameworks. ABSTRACTE is aimed to embed the common aspects of real markets that exhibit big variations and are rarely represented in artificial stock markets. This environment provides the user with a flexible mechanism to implement many of the varying and hardly observable aspects of stock markets and traders' behavior. In this way it can contribute to the understanding of market dynamics as it can be used both as a test bed to replicate and evaluate existing market models, and to compare dynamics of multiple ASMs, as well as a tool to conduct experiments with new models and traders