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Deep Candlestick Mining

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10635))

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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.

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Notes

  1. 1.

    2 Crows; 3 Black Crows; 3 Inside; 3 Line Strike; 3 Outside; 3 Stars in South; 3 White Soldiers; Abandoned Baby; Advance Block; Belt Hold; Break Away; Closing Marubozu; Conceal Baby Swell; Counter Attack; Dark Cloud Cover; Down Side Gap 3 Methods; Downside Gap 2 Crows; Engulfing; Evening Star; Gap Side White; Hammer; Hanging Man; Harami; High Wave; Hikkake; Hikkake Mod; Homing Pigeon; Identical 3 Crows; In Neck; Inverted Hammer; Ladder Bottom; Long Line; Marubozu; Mat Hold; Matching Low; Morning Star; Piercing; Rise Fall 3 Methods; Separating Lines; Shooting Star; Short Line; Spinning Top; Stalled Pattern; Stick Sandwich; Takuri; Tasuki Gap; Thrusting; Tri Star; Unique 3 River.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University College London, London, WC1E 6BT, UK

    Andrew D. Mann & Denise Gorse

Authors
  1. Andrew D. Mann
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  2. Denise Gorse
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Corresponding author

Correspondence to Andrew D. Mann .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Mann, A.D., Gorse, D. (2017). Deep Candlestick Mining. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_93

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  • DOI: https://doi.org/10.1007/978-3-319-70096-0_93

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70095-3

  • Online ISBN: 978-3-319-70096-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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