<ul><li>This paper introduces a cryptocurrency trading strategy based on machine learning algorithms, including autoencoders, CNN, and GANs.</li><li>The process involves using a denoising autoencoder to filter out noise fluctuations from financial time series data.</li><li>One-dimensional convolution is then applied to reduce dimensionality and extract essential information from the filtered data.</li><li>By utilizing GANs and a fully connected network, the model predicts significant price changes in real-time sequences with promising accuracy.</li></ul>

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

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