Evolvering av Biologiskt Inspirerade Handelsalgoritmer

Abstract

One group of information systems that have attracted a lot of attention during the past decade are financial information systems, especially systems pertaining to financial markets and electronic trading. Delivering accurate and timely information to traders substantially increases their chances of making better trading decisions. Since the dawn of electronic exchanges the trading community has seen a proliferation of computer-based intelligence within the field, enabled by an exponential growth of processing power and storage capacity due to advancements in computer technology. The financial benefits associated with outperforming the market and gaining leverage over the competition has fueled the research of computational intelligence in financial information systems. This has resulted in a plethora of different techniques. The most prevalent techniques used within algorithmic trading today consist of various machine learning technologies, borrowed from the field of data mining. Neural networks have shown exceptional predictive capabilities time and time again. One recent machine learning technology that has shown great potential is Hierarchical Temporal Memory (HTM). It borrows concepts from neural networks, Bayesian networks and makes use of spatiotemporal clustering techniques to handle noisy inputs and to create invariant representations of patterns discovered in its input stream. In a previous paper [1], an initial study was carried-out where the predictive performance of the HTM technology was investigated within algorithmic trading of financial markets. The study showed promising results, in which the HTM-based algorithm was profitable across bullish-, bearish and horizontal market trends, yielding comparable results to its neural network benchmark. Although, the previous work lacked any attempt to produce near optimal trading models. Evolutionary optimization methods are commonly regarded as superior to alternative methods. The simplest evolutionary optimization technique is the genetic algorithm, which is based on Charles Darwin's evolutionary theory of natural selection and survival of the fittest. The genetic algorithm combines exploration and exploitation in the search for optimal models in the solution space. This paper extends the HTM-based trading algorithm, developed in the previous work, by employing the genetic algorithm as an optimization method. Once again, neural networks are used as the benchmark technology since they are by far the most prevalent modeling technique used for predicting financial markets. Predictive models were trained, validated and tested using feature vectors consisting of technical indicators, derived from the E-mini S&P 500 index futures market. The results show that the genetic algorithm succeeded in finding predictive models with good performance and generalization ability. The HTM models outperformed the neural network models, but both technologies yielded profitable results with above average accuracy.