论文标题
加速机器学习培训时间限制订单簿预测
Accelerating Machine Learning Training Time for Limit Order Book Prediction
论文作者
论文摘要
金融公司有兴趣模拟，以发现涉及金融机器学习的给定算法是否会盈利。尽管研究人员最近发表了许多此类算法的版本，但由于可解释的性质和高频市场数据的可用性，这里的重点是在特定的机器学习培训项目上。对于此任务，预计硬件加速会加快金融机学习研究人员获得结果所需的时间。由于大部分时间都可以用于分类器培训，因此对更快的培训步骤感到兴趣。我们的主题是一本发布的限制顺序算法，用于预测股票市场方向，机器学习培训过程可能是耗时的，尤其是在考虑模型开发的迭代性质时。为了解决这个问题，我们在数据中心提供了由NVIDIA生产的图形处理单元（GPU），该数据中心可用于计算机体系结构以并行高速算术操作。在研究的配置中，这导致训练时间明显更快，从而使模型开发更加高效，更广泛。
Financial firms are interested in simulation to discover whether a given algorithm involving financial machine learning will operate profitably. While many versions of this type of algorithm have been published recently by researchers, the focus herein is on a particular machine learning training project due to the explainable nature and the availability of high frequency market data. For this task, hardware acceleration is expected to speed up the time required for the financial machine learning researcher to obtain the results. As the majority of the time can be spent in classifier training, there is interest in faster training steps. A published Limit Order Book algorithm for predicting stock market direction is our subject, and the machine learning training process can be time-intensive especially when considering the iterative nature of model development. To remedy this, we deploy Graphical Processing Units (GPUs) produced by NVIDIA available in the data center where the computer architecture is geared to parallel high-speed arithmetic operations. In the studied configuration, this leads to significantly faster training time allowing more efficient and extensive model development.
