Evolving neural networks for static single-position automated trading

This paper presents an approach to single-position, intraday automated trading based on a neurogenetic algorithm. An artificial neural network is evolved to provide trading signals to a simple automated trading agent. The neural network uses open, high, low, and close quotes of the selected financial instrument from the previous day, as well as a selection of the most popular technical indicators, to decide whether to take a single long or short position at market open. The position is then closed as soon as a given profit target is met or at market close. Experimental results indicate that, despite its simplicity, both in terms of input data and in terms of trading strategy, such an approach to automated trading may yield significant returns

Evolving Artificial Neural Networks Through Evolutionary Programming

Artificial neural network (ANN) architecture design has been one of the most tedious and difficult tasks in ANN applications due to the lack of satisfactory and systematic methods of designing a near optimal architecture. Evolutionary algorithms have been shown to be very effective in evolving novel ANN architectures for various problems. This paper proposes a new automatic method for simultaneously evolving ANN architectures and weights. The method has been applied to four realworld data sets in the medical domain and achieved very good results. 1 Introduction Artificial neural networks (ANNs) have been used widely in many application areas in recent years. Most applications use feed-forward ANNs and the back-propagation (BP) training algorithm. There are numerous variants of the classical BP algorithm and other training algorithms, but these algorithms assume a fixed ANN architecture. They only train connection weights (including biases) in a fixed architecture that includes both co..