Recent advances in the machine learning field have given rise to efficient ensemble methods that accurately forecast time-series. In this paper, we use the Quantopian algorithmic stock market trading simulator to assess ensemble methods performance in daily prediction and trading. The ensemble methods used are Extremely Randomized Trees, Random Forest, and Gradient Boosting. All methods are trained using multiple technical indicators and automatic stock selection is used. Simulation results show significant returns relative to the benchmark and large values of alpha are produced from all methods. These results strengthen the role of ensemble method based machine learning in automated stock market trading.

中文翻译：

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使用集成机器学习方法研究算法股票市场交易

机器学习领域的最新进展催生了可以准确预测时间序列的高效集成方法。在本文中，我们使用 Quantopian 算法股票市场交易模拟器来评估集成方法在日常预测和交易中的性能。使用的集成方法是极度随机化的树、随机森林和梯度提升。所有方法都使用多个技术指标进行训练，并使用自动选股。模拟结果显示相对于基准的显着回报，并且所有方法都产生了较大的 alpha 值。这些结果加强了基于集成方法的机器学习在自动化股票市场交易中的作用。