| Author: | Cheung, Yee Chung |
| Title: | Trade with AI pattern recognition |
| Advisors: | Wei, Steven (AF) |
| Degree: | DFinTech |
| Year: | 2025 |
| Subject: | Investments Investment analysis Artificial intelligence Hong Kong Polytechnic University -- Dissertations |
| Department: | Faculty of Business |
| Pages: | 228 pages : color illustrations |
| Language: | English |
| Abstract: | Price patterns and technical indicators are pivotal tools in technical analysis, guiding trading decisions such as entry points, stop-loss levels, and profit targets. However, accurately identifying these patterns remains challenging, often relying on traders’ expertise and manual efforts. This study introduces a novel one-shot candlestick price pattern recognition system leveraging computer vision to enhance efficiency and accuracy in financial markets. We propose the CandSiNet model, a Siamese network architecture with a CNN backbone, representing the first application of one-shot classification to candlestick price patterns. Two key innovations underpin this work: (1) the use of Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF) techniques to transform time series data into feature-rich images, and (2) a time-shifted resampling method to address data scarcity issues during training. Experiments confirm that the CandSiNet model successfully achieves accurate one-shot classification, highlighting its robustness and practical utility in technical analysis. This study contributes to the emerging field of applying computer vision to financial modeling, offering a framework for future exploration of technical pattern classification. By automating the recognition of intricate price patterns, our findings empower traders to enhance decision-making, risk assessment, and profitability in dynamic financial markets. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 8375.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.91 MB | Adobe PDF | View/Open |
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