Artikel
Encoding candlesticks as images for pattern classification using convolutional neural networks
Candlestick charts display the high, low, opening, and closing prices in a specific period. Candlestick patterns emerge because human actions and reactions are patterned and continuously replicate. These patterns capture information on the candles. According to Thomas Bulkowski's Encyclopedia of Candlestick Charts, there are 103 candlestick patterns. Traders use these patterns to determine when to enter and exit. Candlestick pattern classification approaches take the hard work out of visually identifying these patterns. To highlight its capabilities, we propose a two-steps approach to recognize candlestick patterns automatically. The first step uses the Gramian Angular Field (GAF) to encode the time series as different types of images. The second step uses the Convolutional Neural Network (CNN) with the GAF images to learn eight critical kinds of candlestick patterns. In this paper, we call the approach GAF-CNN. In the experiments, our approach can identify the eight types of candlestick patterns with 90.7% average accuracy automatically in real-world data, outperforming the LSTM model.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Financial Innovation ; ISSN: 2199-4730 ; Volume: 6 ; Year: 2020 ; Issue: 1 ; Pages: 1-19 ; Heidelberg: Springer
- Klassifikation
-
Management
- Thema
-
Convolutional Neural Networks (CNN)
Gramian Angular Field (GAF)
Candlestick
Patterns Classification
Time-Series
Financial Vision
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Chen, Jun-Hao
Tsai, Yun-Cheng
- Ereignis
-
Veröffentlichung
- (wer)
-
Springer
- (wo)
-
Heidelberg
- (wann)
-
2020
- DOI
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doi:10.1186/s40854-020-00187-0
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Chen, Jun-Hao
- Tsai, Yun-Cheng
- Springer
Entstanden
- 2020
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