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Deep Candlestick Mining

Mann, AD; Gorse, D; (2017) Deep Candlestick Mining. In: Liu, D and Xie, S and Li, Y and Zhao, D and El-Alfy, EM, (eds.) Neural Information Processing: 24th International Conference, ICONIP 2017, Guangzhou, China, November 14-18, 2017, Proceedings, Part II. (pp. pp. 913-921). Springer: Cham, Switzerland. Green open access

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Abstract

A data mining process we name Deep Candlestick Mining (DCM) is developed using Randomised Decision Trees, Long Short Term Memory Recurrent Neural Networks and k-means++, and is shown to discover candlestick patterns significantly outperforming traditional ones. A test for the predictive ability of novel versus traditional candlestick patterns is devised using all significant candlestick patterns within the traditional or deep mined categories. The deep mined candlestick system demonstrates a remarkable ability to outperform the traditional system by 75.2% and 92.6% on the German Bund 10-year futures contract and EURUSD hourly data.

Type: Proceedings paper
Title: Deep Candlestick Mining
Event: 24th International Conference, ICONIP 2017, 14 - 18 November 2017, Guangzhou, China
ISBN-13: 9783319700953
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-70096-0_93
Publisher version: https://doi.org/10.1007/978-3-319-70096-0_93
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Machine Learning, LSTMs, RNNs, Decision Trees, Clustering, Factor Mining, OHLC Data, Candlestick Patterns
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10062933
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