We consider the application of machine learning models for short-term intra-day trading in equities. We envisage a scenario wherein machine learning models are submitted by independent data scientists to predict discretised ten-candle returns every five minutes, in response to five-minute candlestick data provided to them in near real-time. An ensemble model combines these multiple models via a weighted-majority algorithm. The weights of each model are dynamically updated based on the performance of each model, and can also be used to reward model owners. Each model's performance is evaluated according to two different metrics over a recent time window: In addition to accuracy, we also consider a `utility' metric that is a proxy for a model's potential profitability under a particular trading strategy. We present experimental results on real intra-day data that show that our weighted-majority ensemble techniques show improved accuracy as well as utility over any of the individual models, especially using the utility metric to dynamically re-weight models over shorter time-windows.

翻译：本文探讨了机器学习模型在股票短期日内交易中的应用。我们设想一种场景：独立数据科学家提交机器学习模型，以近实时提供的五分钟K线数据为基础，每五分钟预测离散化的十根K线收益率。集成模型通过加权多数算法将这些多模型进行组合。各模型的权重根据其表现动态更新，该权重也可用于奖励模型所有者。每个模型的性能依据近期时间窗口内的两种不同指标进行评估：除了准确率外，我们还引入了一个“效用”指标，该指标可近似反映特定交易策略下模型的潜在盈利能力。基于真实日内数据的实验结果表明，我们的加权多数集成技术在准确率和效用方面均优于任何单一模型，特别是当采用效用指标在较短时间窗口内动态调整模型权重时效果更为显著。