Abstract

The fractal market hypothesis highlights multi-scale dynamics in financial time series and provides a theoretical foundation for pattern-based analysis. This study proposes a model-free visual pattern mining framework that transforms high-frequency market data into image representations to support intelligent decision-making. By converting 1-minute KOSPI200 futures data into candlestick chart and Bollinger band images, the method effectively captures structural patterns and volatility dynamics. The framework applies similarity metrics and Intersection over Union (IoU)-based visual comparison to identify historically similar patterns and generate intelligent trading signals without model training or complex parameter tuning. Experimental results demonstrate that combining visual features of candlestick and Bollinger bands achieves a cumulative return of 11.676% and a maximum drawdown of 4.283%, with a payoff ratio of 1.322 and a profit factor of 1.135. These findings suggest that visual pattern mining with similarity metrics offers a practical, interpretable, and robust approach to intelligent decision-making in high-frequency market environments.

Department(s)

Engineering Management and Systems Engineering

Comments

Soonchunhyang University, Grant None

Keywords and Phrases

Bollinger bands; Candlestick chart; Model-free trading; Similarity metrics; Visual pattern mining

International Standard Serial Number (ISSN)

1573-7497; 0924-669X

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Springer, All rights reserved.

Publication Date

01 Apr 2026

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