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

2024-12-24

AI Summary

To address the challenges of high noise, weak discriminative features, and distributional shift in cryptocurrency high-frequency price volatility forecasting, this paper proposes an end-to-end deep learning trading strategy. First, a denoising autoencoder (DAE) cleans raw price sequences; second, a 1D convolutional neural network (CNN) performs temporal dimensionality reduction and extracts salient time-series features; third, a generative adversarial network (GAN) synthesizes augmented volatility signals, which—combined with a fully connected network—enables real-time prediction of both direction and probability of large price movements. This work is the first to synergistically integrate DAEs, 1D CNNs, and GANs into a closed-loop “denoise–represent–generate–decide” optimization framework. Empirical evaluation demonstrates a significant improvement in large-volatility prediction accuracy (+12.7% on average over baseline models) and yields robust, positive risk-adjusted returns in live trading, validating the efficacy of multi-module collaborative deep architectures for uncovering latent patterns in financial time series.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies
• Security and Privacy: Cryptocurrency and smart contracts
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Cryptocurrency Trading
• Algorithmic Trading
• Financial Market Prediction

Innovation

• Deep Learning
• Generative Adversarial Networks (GANs)
• Cryptocurrency Price Prediction