Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 17 of its 50 references that are in the map. First posted on arXiv 24 Dec 2024.
Builds on
17 of 50 references.
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Citing papers per year: 2025: 3.
3 papers.