Summary of Developing Cryptocurrency Trading Strategy Based on Autoencoder-cnn-gans Algorithms, by Zhuohuan Hu et al.
Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms
by Zhuohuan Hu, Richard Yu, Zizhou Zhang, Haoran Zheng, Qianying Liu, Yining Zhou
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistical Finance (q-fin.ST)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning-based approach is proposed to forecast and analyze financial time series by leveraging denoising autoencoders, one-dimensional convolutional neural networks (CNN), generative adversarial networks (GANs), and fully connected networks. The method begins with a denoising autoencoder filtering out random noise fluctuations from the main contract price data, followed by dimensionality reduction using CNN and feature extraction. The filtered data is then fed into GANs to generate synthetic price sequences, which are used as input for a fully connected network. Cross-validation is employed to train the model and capture features that precede large price fluctuations. The trained model predicts the likelihood and direction of significant price changes in real-time price sequences, allowing for trades to be placed at moments of high prediction accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to use machine learning to predict stock prices and patterns is developed. It uses special techniques like denoising autoencoders and GANs to clean up noisy data and find important features. The method can predict when big price changes will happen and even make trades based on that prediction. This approach shows promise in discovering hidden patterns in financial data. |
Keywords
» Artificial intelligence » Autoencoder » Cnn » Dimensionality reduction » Feature extraction » Likelihood » Machine learning » Time series
