Computational Visual Analysis of the Order Book Dynamics for Creating High-frequency Foreign Exchange Trading Strategies

By Javier Sandoval, G. Hernández. 2015

10 citations recorded by the source corpus.

Abstract This paper presents a Hierarchical Hidden Markov Model used to capture the USD/COP market sentiment dynamics choosing from uptrend or downtrend latent regimes based on observed feature vector realizations calculated from transaction prices and wavelet-transformed order book volume dynamics. The HHMM learned a natural switching buy/uptrend sell/downtrend trading strategy using a training-validation framework over one month of market data. The model was tested on the following two months, and its performance was reported and compared to results obtained from randomly classified market states and a feed-forward Neural Network. This paper also separately assessed the contribution to the model's performance of the order book information and the wavelet transformation.

Subfields: Economics and Econometrics, Management Science and Operations Research, Signal Processing

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Citations in the corpus

Tensor representation in high-frequency financial data for price change prediction (2017, methodology): “All kinds of deep architectures have been proposed, ranging from traditional multilayer feed-forward models [6], [7], [8] to Convolutional Neural Network (CNN) [9], Recurrent Neural Network (RNN) [10], [11], [12], Deep Belief Networks [13], [14], [15].”
Benchmark Dataset for Mid-Price Prediction of Limit Order Book data (2017, unknown): “Sandoval and Hernandez [44] create a profitable trading strategy by combining Hierarchical Hidden Markov Models (HHMM) where they considered wavelet based LOB information ﬁltering.”