Data Augmentation for Deep Candlestick Learner
This addresses a data scarcity issue for professional traders in finance, but it is incremental as it adapts existing data augmentation techniques to a new domain.
The paper tackles the problem of limited labeled data for deep learning in financial trading by proposing a Modified Local Search Attack Sampling method to augment candlestick data, showing it generates high-quality data that is hard for humans to distinguish.
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.