Investigating Market Strength Prediction with CNNs on Candlestick Chart Images

TL;DR

This study explores predicting market strength using CNNs on candlestick chart images without time-series data, finding that candlestick patterns do not significantly enhance model performance, highlighting limitations of visual signals alone.

Contribution

It introduces a CNN-based approach for market prediction from candlestick images and evaluates the impact of pattern detection, revealing the limited value of candlestick patterns in this context.

Findings

Candlestick patterns do not improve prediction accuracy.

Pure image-based models achieve around 0.7 accuracy.

Patterns add little value over raw chart images.

Abstract

This paper investigates predicting market strength solely from candlestick chart images to assist investment decisions. The core research problem is developing an effective computer vision-based model using raw candlestick visuals without time-series data. We specifically analyze the impact of incorporating candlestick patterns that were detected by YOLOv8. The study implements two approaches: pure CNN on chart images and a Decomposer architecture detecting patterns. Experiments utilize diverse financial datasets spanning stocks, cryptocurrencies, and forex assets. Key findings demonstrate candlestick patterns do not improve model performance over only image data in our research. The significance is illuminating limitations in candlestick image signals. Performance peaked at approximately 0.7 accuracy, below more complex time-series models. Outcomes reveal challenges in distilling sufficient predictive power from visual shapes alone, motivating the incorporation of other data modalities. This research clarifies how purely image-based models can inform trading while confirming patterns add little value over raw charts. Our content is endeavored to be delineated into distinct sections, each autonomously furnishing a unique contribution while maintaining cohesive linkage. Note that, the examples discussed herein are not limited to the scope, applicability, or knowledge outlined in the paper.