用自编码器+CNN+GAN预测比特币价格波动，提前捕捉交易信号
Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms
- 先用自编码器去噪，再用CNN降维提取关键特征
- GAN生成高维特征，全连接层预测价格大幅涨跌概率
- 实测可实时预警价格突变，适合量化交易开发者
本文利用机器学习算法对金融时间序列进行预测与分析。首先通过去噪自编码器过滤主力合约价格数据中的随机噪声波动；随后使用一维卷积降低数据维度并提取关键信息；将处理后的数据输入GAN网络，其输出作为全连接网络的输入。通过交叉验证训练模型，以捕捉预示重大价格波动的特征。该模型可实时预测价格序列中显著变化的可能性与方向，并在预测准确率高时触发交易。实证结果表明，结合自编码器与卷积对金融数据进行滤波去噪，再融合GANs，能实现一定水平的预测性能，验证了机器学习算法发现金融序列潜在模式的能力。
原文摘要 · Abstract (English)
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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