Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms

• URL: http://arxiv.org/abs/2412.18202v4
• Date: Sat, 01 Feb 2025 07:22:53 GMT
• Title: Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms
• Authors: Zhuohuan Hu, Richard Yu, Zizhou Zhang, Haoran Zheng, Qianying Liu, Yining Zhou,
• Abstract summary: This paper leverages machine learning algorithms to forecast and analyze financial time series. Process begins with a denoising autoencoder to filter out random noise fluctuations from the main contract price data. One-dimensional convolution reduces the dimensionality of the filtered data and extracts key information.
• Score: 1.9447509748918168
• License:
• Abstract: This paper leverages machine learning algorithms to forecast and analyze financial time series. The process begins with a denoising autoencoder to filter out random noise fluctuations from the main contract price data. Then, one-dimensional convolution reduces the dimensionality of the filtered data and extracts key information. The filtered and dimensionality-reduced price data is fed into a GANs network, and its output serve as input of a fully connected network. Through cross-validation, a model is trained to capture features that precede large price fluctuations. The model predicts the likelihood and direction of significant price changes in real-time price sequences, placing trades at moments of high prediction accuracy. Empirical results demonstrate that using autoencoders and convolution to filter and denoise financial data, combined with GANs, achieves a certain level of predictive performance, validating the capabilities of machine learning algorithms to discover underlying patterns in financial sequences. Keywords - CNN;GANs; Cryptocurrency; Prediction.

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