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Image processing meets time series analysis: Predicting Forex profitable technical pattern positions
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DOI：10.1016/j.asoc.2021.107460.png)
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摘要
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Using technical price patterns is one of the well-known techniques for predicting future trends in financial markets. Some of these patterns are profitable under certain conditions and some might be non-profitable based on the target market situation and spread. This paper aims to propose a model that works along with the moving average crossover technical pattern. The outputs of the technical price pattern, which are long or short signals, are given as input to the proposed model to predict its profitability. We use a joint model that benefits from two different types of intelligent processing techniques, namely image processing which is applied to candlesticks extracted from price history, and time series analysis which is applied to the numerical features. For the former process, Convolutional Neural Network (CNN) is used and for the latter process, CNN with Long Short-Term Memory (LSTM) is used for the prediction. The proposed model is applied to the data from EUR/USD pairs. The tests were performed for spread values of 0.5, 1, 1.5, and 2. We show that the hybrid model achieves superior results compared to the individual ones, Relative Strength Index (RSI) and Bollinger Bands (BB) technical analysis patterns, as well as two state-of-the-art price prediction models based on CNN-Bidirectional LSTM (BiLSTM) and Phase-State Reconstruction (PSR) with LSTM. (C) 2021 Elsevier B.V. All rights reserved.
Keyword:
Forex prediction
Deep learning
Candlestick processing
Time series analysis
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期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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