Integrating Piecewise Linear Representation and Deep Learning for Trading Signals Forecasting

Trading signals forecasting is an interesting but challenging research topic in the field of financial investment, since the financial market is a nonlinearity and high volatility system influenced by too many factors, and a small improvement in forecasting performance can bring profits. To realize trading signals detection, this paper presents a novel method which integrates piecewise linear representation (PLR) with a deep learning framework to predict the financial trading points. Firstly, we utilize PLR to generate a number of turning points (valleys or peaks) from trading data and formulate the trading points prediction as a three-class classification problem. Then, the framework combined a convolutional neural network (CNN) for spatial features extraction and a long short-term memory (LSTM) network for temporal domain features extraction (CNN-LSTM) is used to learn the prediction model between the trading points and the financial time series data. Finally, we conduct a series of experiments among PLR-CNN-LSTM, PLR-CNN-TA and PLR-LSTM on companies of US, Turkey and daily Exchange-Traded Fund (ETFs) to test the performance of our established method. The experiment results show that our proposed method has better model performance and profitability with different investment strategies.

Automated summary

This paper presents a novel method that combines piecewise linear representation (PLR) with a deep learning framework to predict financial trading points. The method utilizes PLR to generate turning points from trading data and formulates the prediction problem as a three-class classification. The proposed method uses a CNN for spatial features extraction and an LSTM network for temporal domain features extraction. Experimental results demonstrate that the method outperforms other approaches in terms of model performance and profitability with different investment strategies.