Financial trading decisions based on deep fuzzy self-organizing map

The volatility features of financial data would considerably change in different periods, that is one of the main factors affecting the applications of machine learning in quantitative trading. Therefore, to effectively distinguish fluctuation patterns of financial markets can provide meaningful information for the trading decision. In this article, a novel intelligent trading system based on deep fuzzy self -organizing map (DFSOM) companied with GRU networks is proposed, where DFSOM is utilized for the clustering of financial data to acquire multiple fluctuation patterns in an unsupervised way. Firstly, in order to capture the trend features and evade the effect of high noises in financial data, the images of extended candlestick charts instead of raw data are processed and the obtained features are applied for the following unsupervised learning, where candlestick charts are produced with both price and volume information. Secondly, by using the candlestick features, a two-layer deep fuzzy self-organizing map is constructed to carry out the clustering, where two-layer models carry out the clustering in multiple time scales to improve the processing of time-dependent information. Thirdly, GRU networks are used to implement the prediction task, based on which an intelligent trading model is constructed. The feasibility and effectiveness of the proposed method are verified by using various real financial datasets.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

In this article, a novel intelligent trading system based on deep fuzzy self-organizing map (DFSOM) and GRU networks is proposed for the classification and prediction of financial data. The feasibility and effectiveness of the method are verified by using various real financial datasets.