Data Augmentation for Deep Candlestick Learner

2020/05/14 by Chia-Ying Tsao, Tsao, Chia-Ying, Jun Hao Chen +5
Computer Science · Decision Sciences · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Stock Market Forecasting Methods

paper · pdf · doi:10.48550/arxiv.2005.06731

openalex publication_date 2020/05/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

To successfully build a deep learning model, it will need a large amount of labeled data. However, labeled data are hard to collect in many use cases. To tackle this problem, a bunch of data augmentation methods have been introduced recently and have demonstrated successful results in computer vision, natural language and so on. For financial trading data, to our best knowledge, successful data augmentation framework has rarely been studied. Here we propose a Modified Local Search Attack Sampling method to augment the candlestick data, which is a very important tool for professional trader. Our results show that the proposed method can generate high-quality data which are hard to distinguish by human and will open a new way for finance community to employ existing machine learning techniques even if the dataset is small.

Citations

• DRAW: A Recurrent Neural Network For Image Generation
• ImageNet classification with deep convolutional neural networks
• Auxiliary Deep Generative Models
• Towards Deeper Understanding of Variational Autoencoding Models
• Encoding Candlesticks as Images for Patterns Classification Using Convolutional Neural Networks

Related

• Explainable Deep Convolutional Candlestick Learner
• Dynamic Deep Convolutional Candlestick Learner
• Deep Learning Models Meet Financial Data Modalities
• Evaluating data augmentation for financial time series classification
• Dynamic Datasets and Market Environments for Financial Reinforcement Learning