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

TL;DR

This paper presents a novel cryptocurrency trading strategy that combines autoencoders, CNNs, and GANs to denoise, extract features, and predict significant price movements for real-time trading decisions.

Contribution

It introduces an integrated machine learning framework using autoencoders, CNNs, and GANs for improved financial time series prediction in cryptocurrency markets.

Findings

Autoencoders effectively denoise financial data.

CNNs extract key features from filtered data.

GANs improve prediction of large price fluctuations.

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.