We investigated the use of Empirical Mode Decomposition (EMD) combined with Gaussian Mixture Models (GMM), feature engineering and machine learning algorithms to optimize trading decisions. We used five, two, and one year samples of hourly candle data for GameStop, Tesla, and XRP (Ripple) markets respectively. Applying a 15 hour rolling window for each market, we collected several features based on a linear model and other classical features to predict the next hour's movement. Subsequently, a GMM filtering approach was used to identify clusters among these markets. For each cluster, we applied the EMD algorithm to extract high, medium, low and trend components from each feature collected. A simple thresholding algorithm was applied to classify market movements based on the percentage change in each market's close price. We then evaluated the performance of various machine learning models, including Random Forests (RF) and XGBoost, in classifying market movements. A naive random selection of trading decisions was used as a benchmark, which assumed equal probabilities for each outcome, and a temporal cross-validation approach was used to test models on 40%, 30%, and 20% of the dataset. Our results indicate that transforming selected features using EMD improves performance, particularly for ensemble learning algorithms like Random Forest and XGBoost, as measured by accumulated profit. Finally, GMM filtering expanded the range of learning algorithm and data source combinations that outperformed the top percentile of the random baseline.

翻译：本研究探讨了结合经验模态分解（EMD）与高斯混合模型（GMM）、特征工程及机器学习算法以优化交易决策的方法。我们分别采用GameStop、特斯拉和XRP（瑞波币）市场为期五年、两年及一年的小时K线数据样本。对每个市场应用15小时滚动窗口，基于线性模型及其他经典特征提取了若干特征以预测下一小时的价格走势。随后采用GMM滤波方法识别这些市场间的聚类结构。针对每个聚类，应用EMD算法从各特征中提取高频、中频、低频及趋势分量。通过简单阈值算法根据各市场收盘价百分比变化对市场走势进行分类。我们评估了包括随机森林（RF）和XGBoost在内的多种机器学习模型在市场走势分类中的表现。以朴素随机交易决策作为基准（假设各结果出现概率相等），并采用时间序列交叉验证方法在数据集40%、30%和20%的样本上测试模型。结果表明，通过EMD对选定特征进行变换能提升模型性能——以累计收益衡量，尤其对随机森林和XGBoost等集成学习算法效果显著。最终，GMM滤波扩展了优于随机基准最高百分位数的学习算法与数据源组合的范围。