Combining NeuroEvolution and Principal Component Analysis to trade in the financial markets

When investing in the financial market, determining a trading signal that can fulfill the financial performance demands of an investor is a difficult task and a very popular research topic in the financial investment area. This paper presents an approach combining the principal component analysis (PCA) with the NeuroEvolution of Augmenting Topologies (NEAT) to generate a trading signal capable of achieving high returns and daily profits with low associated risk. The proposed approach is tested with real daily data from four financial markets of different sectors and with very different characteristics. Three different fitness functions are considered in the NEAT algorithm and the most robust results are produced by a fitness function that measures the mean daily profit obtained by the generated trading signal. The results achieved show that this approach outperforms the Buy and Hold (B&H) strategy in the markets tested (in the S&P 500 index this system achieves a rate of return of 18.89% while the B&H achieves 15.71% and in the Brent Crude futures contract this system achieves a rate of return of 37.91% while the B&H achieves −9.94%). Furthermore, it's concluded that the PCA method is vital for the good performance of the proposed approach.