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We design multi-horizon forecasting models for limit order book (LOB) data by using deep learning techniques.
Model-based reinforcement learning for predictions and control for limit order books
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Transformers for limit order books
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DeepFolio: Convolutional neural networks for portfolios with limit order book data
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Extending deep learning models for limit order books to quantile regression
Zhang, Z., S. Zohren, and S. Roberts (2019b) · 2019
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Deep learning modeling of the limit order book: A comparative perspective
Briola, A., J. Turiel, and T. Aste (2020) · 2020
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Using deep learning for price prediction by exploiting stationary limit order book features
Tsantekidis, A., N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis (2020) · 2020
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Deep reinforcement learning for active high frequency trading
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Goodfellow, I., Y. Bengio, and A. Courville (2016) · 2016
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Forecasting stock prices from the limit order book using convolutional neural networks
Tsantekidis, A., N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis (2017a) · 2017
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Using deep learning to detect price change indications in financial markets
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Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods
Ntakaris, A., M. Magris, J. Kanniainen, M. Gabbouj, and A. Iosifidis (2018) · 2018
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Temporal attention-augmented bilinear network for financial time-series data analysis
Tran, D. T., A. Iosifidis, J. Kanniainen, and M. Gabbouj (2018) · 2018
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BDLOB: Bayesian deep convolutional neural networks for limit order books
Zhang, Z., S. Zohren, and S. Roberts (2018) · 2018
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