利用卷积神经网络对日本烛台模式增强市场趋势预测。

IF 3.5 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
PeerJ Computer Science Pub Date : 2025-02-27 eCollection Date: 2025-01-01 DOI:10.7717/peerj-cs.2719
Edrees Ramadan Mersal, Kürşat Mustafa Karaoğlan, Hakan Kutucu
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引用次数: 0

摘要

本研究讨论使用日本烛台(JC)模式来预测未来金融市场的价格走势。烛台交易的历史可以追溯到17世纪,涉及到对JC交易过程中形成的模式的分析。烛台模式是金融市场交易者技术分析的实用工具。它们可以作为市场情绪和趋势方向潜在变化的交易者文件的指标。本研究旨在使用卷积神经网络(cnn)预测以下基于蜡烛趋势的JC图。为了提高预测后续金融烛台方向运动的准确性,按照结构化的三步流程构建了丰富的数据集,并训练了CNN模型。首先,对数据集进行分析,并使用滑动窗口技术生成子图。随后,使用Ta-lib库来识别窗口内是否存在预定义的模式。第三阶段涉及对每个窗口的方向趋势进行分类,并通过使用各种技术指标来验证趋势的方向。经过数据准备和分析阶段,开发了CNN模型,从子图中提取特征,有效地进行精确预测。实验结果表明,该方法的预测准确率高达99.3%。实现交叉验证技术对于验证模型的可靠性和整体性能至关重要。为了实现这一目标,数据集被分成几个小子集。随后,使用这些子集的不同组合对模型进行多次训练和评估。这种方法通过检查模型在未知数据上的表现,可以更准确地评估模型的预测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing market trend prediction using convolutional neural networks on Japanese candlestick patterns.

This study discusses using Japanese candlestick (JC) patterns to predict future price movements in financial markets. The history of candlestick trading dates back to the 17th century and involves the analysis of patterns formed during JC trading. Candlestick patterns are practical tools for the technical analysis of traders in financial markets. They may serve as indicators of traders' documents of a potential change in market sentiment and trend direction. This study aimed to predict the following candle-trend-based JC charts using convolutional neural networks (CNNs). In order to enhance the accuracy of predicting the directional movement of subsequent financial candlesticks, a rich dataset has been constructed by following a structured three-step process, and a CNN model has been trained. Initially, the dataset was analyzed, and sub-charts were generated using a sliding window technique. Subsequently, the Ta-lib library was used to identify whether predefined patterns were present within the windows. The third phase involved the classification of each window's directional tendency, which was substantiated by employing various technical indicators to validate the direction of the trend. Following the data preparation and analysis phases, a CNN model was developed to extract features from sub-charts and facilitate precise predictions effectively. The experimental results of this approach demonstrated a remarkable predictive accuracy of up to 99.3%. Implementing cross-validation techniques is essential to verify the reliability and overall performance of the model. To achieve this goal, the dataset was divided into several small subsets. Subsequently, the model was trained and evaluated multiple times using different combinations of these subsets. This method allows for a more accurate assessment of the model's predictive capabilities by examining its performance on unseen data.

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来源期刊
PeerJ Computer Science
PeerJ Computer Science Computer Science-General Computer Science
CiteScore
6.10
自引率
5.30%
发文量
332
审稿时长
10 weeks
期刊介绍: PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.
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