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Event-Driven LSTM For Forex Price Prediction

Author

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  • Ling Qi
  • Matloob Khushi
  • Josiah Poon

Abstract

The majority of studies in the field of AI guided financial trading focus on purely applying machine learning algorithms to continuous historical price and technical analysis data. However, due to non-stationary and high volatile nature of Forex market most algorithms fail when put into real practice. We developed novel event-driven features which indicate a change of trend in direction. We then build long deep learning models to predict a retracement point providing a perfect entry point to gain maximum profit. We use a simple recurrent neural network (RNN) as our baseline model and compared with short-term memory (LSTM), bidirectional long short-term memory (BiLSTM) and gated recurrent unit (GRU). Our experiment results show that the proposed event-driven feature selection together with the proposed models can form a robust prediction system which supports accurate trading strategies with minimal risk. Our best model on 15-minutes interval data for the EUR/GBP currency achieved RME 0.006x10^(-3) , RMSE 2.407x10^(-3), MAE 1.708x10^(-3), MAPE 0.194% outperforming previous studies.

Suggested Citation

  • Ling Qi & Matloob Khushi & Josiah Poon, 2021. "Event-Driven LSTM For Forex Price Prediction," Papers 2102.01499, arXiv.org.
    • Handle: RePEc:arx:papers:2102.01499
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    File URL: http://arxiv.org/pdf/2102.01499
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    References listed on IDEAS

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    1. Taewook Kim & Ha Young Kim, 2019. "Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-23, February.
    2. Yun-Cheng Tsai & Jun-Hao Chen & Jun-Jie Wang, 2018. "Predict Forex Trend via Convolutional Neural Networks," Papers 1801.03018, arXiv.org.
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    Cited by:

    1. Jaideep Singh & Matloob Khushi, 2021. "Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating," Papers 2103.09106, arXiv.org.

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