English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Candlestick charts display the high, low, opening, and closing prices in aspecific period. Candlestick patterns emerge because human actions andreactions are patterned and continuously replicate. These patterns captureinformation on the candles. According to Thomas Bulkowski's Encyclopedia ofCandlestick Charts, there are 103 candlestick patterns. Traders use thesepatterns to determine when to enter and exit. Candlestick patternclassification approaches take the hard work out of visually identifying thesepatterns. To highlight its capabilities, we propose a two-steps approach torecognize candlestick patterns automatically. The first step uses the GramianAngular Field (GAF) to encode the time series as different types of images. Thesecond step uses the Convolutional Neural Network (CNN) with the GAF images tolearn eight critical kinds of candlestick patterns. In this paper, we call theapproach GAF-CNN. In the experiments, our method can identify the eight typesof candlestick patterns with 90.7% average accuracy automatically in real-worlddata, outperforming the LSTM model.

Encoding Candlesticks as Images for Patterns Classification Using Convolutional Neural Networks