ABSTRACT
This chapter describes a Genetic Algorithm-based implementation of adaptive trading models, specifically designed for trading of multi-currency trading portfolios. The chapter describes the aspects of the decision-making process that must be incorporated into the trading model in order to accurately simulate the decisions a human portfolio trader is required to make in this position. The chapter describes the different types of learning processes for market timing and risk management and how these can be incorporated into the same GA-based learning process.
The basic concept of a distributed, object-oriented learning process is demonstrated, as well as specific fitness value calculations to increase consistency and predictability of portfolio trading performance, which the reader can implement in their own testing and development procedure. Two different concepts of designing the adaptive process are shown, with the effect described in the model portfolio described below.
The performance of such an adaptive system is demonstrated using a diversified foreign exchange trading portfolio, which yields acceptable levels of risk-adjusted return, under realistic assumptions of portfolio constraints and transaction costs.
