An Autonomous Trader Agent for the Stock Market Based on Online Sequential Extreme Learning Machine Ensemble

Financial markets are very important to the economical and social organization of modern society. In this kind of market, the success of an investor depends on the quality of the information he uses to trade in the market, and on how fast he is able to take decisions. In the literature, several statistical and soft computing mechanisms have been proposed in order to support investors decision in the financial market. In this work we propose an autonomous trader agent that is able to compute technical indicators of the stock market and take decisions on buying or selling stocks. Our trader agent is based on a single hidden layer feedforward (SLFN) ensemble trained with online sequential extreme learning machine (OS-ELM), a variant of ELM that is able to learn data one-by-one and dynamically accommodate changes in the market. In addition, we propose a set of trading rules that guides the trader agent in order to improve the potential profit. Experimental results on real dataset from Brazilian stock market showed that our proposed trader agent based on OS-ELM ensemble is able to increase the financial gain when compared with other approaches proposed in literature.