# Data Augmentation for Deep Candlestick Learner

arXiv: https://arxiv.org/abs/2005.06731 · HTML: https://mlsanity.com/paper/2005.06731/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2020.

## Built on by (most cited first, 1 of 1)

- [White Matter Hyperintensities Segmentation Using Probabilistic TransUNet](https://mlsanity.com/paper/2305.03912/) (2023)

## Abstract (arXiv)

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.