  Autonomous trading robots have been studied in ar-tificial intelligence area
for quite some time. Many AI techniqueshave been tested in finance field
including recent approaches likeconvolutional neural networks and deep
reinforcement learning.There are many reported cases, where the developers are
suc-cessful in creating robots with great performance when executingwith
historical price series, so called backtesting. However, whenthese robots are
used in real markets or data not used intheir training or evaluation frequently
they present very poorperformance in terms of risks and return. In this paper,
wediscussed some fundamental aspects of modelling autonomoustraders and the
complex environment that is the financialworld. Furthermore, we presented a
framework that helps thedevelopment and testing of autonomous traders. It may
also beused in real or simulated operation in financial markets. Finally,we
discussed some open problems in the area and pointed outsome interesting
technologies that may contribute to advancein such task. We believe that mt5b3
may also contribute todevelopment of new autonomous traders.
