The role of attribute selection in Deep ANNs learning framework for high-frequency financial trading

Abstract

In financial trading, technical and quantitative analysis tools are used for the development of decision support systems. Although these traditional tools are useful, new techniques in the field of machine learning have been developed for time-series forecasting. This paper analyses the role of attribute selection on the development of a simple deep-learning ANN (D-ANN) multi-agent framework to accomplish a profitable trading strategy in the course of a series of trading simulations in the foreign exchange market. The paper evaluates the performance of the D-ANN multi-agent framework over different time spans of high-frequency (HF) intraday asset time-series data and determines how a set of the framework attributes produces effective forecasting for profitable trading. The paper shows the existence of predictable short-term price trends in the market time series, and an understanding of the probability of price movements may be useful to HF traders. The results of this paper can be used to further develop financial decision-support systems and autonomous trading strategies for the financial market.