The study introduces an automated trading system for S\&P500 E-mini futures (ES) based on state-of-the-art machine learning. Concretely: we extract a set of scenarios from the tick market data to train the model and further use the predictions to model trading. We define the scenarios from the local extrema of the price action. Price extrema is a commonly traded pattern, however, to the best of our knowledge, there is no study presenting a pipeline for automated classification and profitability evaluation. Our study is filling this gap by presenting a broad evaluation of the approach showing the resulting average Sharpe ratio of 6.32. However, we do not take into account order execution queues, which of course affect the result in the live-trading setting. The obtained performance results give us confidence that this approach is worthwhile.
翻译:这项研究引入了S ⁇ P500 E-mini 期货自动化交易系统,该系统基于最新的机器学习。具体地说,我们从市场数据中提取了一系列设想方案,以培训模型,并进一步将预测用于模式交易。我们从价格行动的当地极限中界定了各种设想方案。但据我们所知,Price extrema是一种经常交易的模式,没有一项研究为自动分类和利润评估提供管道。我们的研究正在填补这一空白,方法是对显示6.32的夏普平均比率的方法进行广泛评估。然而,我们没有考虑到订单执行队列,这当然会影响现场交易的结果。获得的绩效结果使我们相信这种做法是值得的。