LAUSR

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.