文章基本信息

标题：Meta-Learning of Evolutionary Strategy for Stock Trading
本地全文：下载
作者：Erik Sorensen ; Ryan Ozzello ; Rachael Rogan 等
期刊名称：Journal of Data Analysis and Information Processing
印刷版ISSN：2327-7211
电子版ISSN：2327-7203
出版年度：2020
卷号：8
期号：2
页码：86-98
DOI：10.4236/jdaip.2020.82005
出版社：Scientific Research Publishing
摘要：Meta-learning algorithms learn about the learning process itself so it can speed up subsequent similar learning tasks with fewer data and iterations. If achieved, these benefits expand the flexibility of traditional machine learning to areas where there are small windows of time or data available. One such area is stock trading, where the relevance of data decreases as time passes, requiring fast results on fewer data points to respond to fast-changing market trends. We, to the best of our knowledge, are the first to apply meta-learning algorithms to an evolutionary strategy for stock trading to decrease learning time by using fewer iterations and to achieve higher trading profits with fewer data points. We found that our meta-learning approach to stock trading earns profits similar to a purely evolutionary algorithm. However, it only requires 50 iterations during test, versus thousands that are typically required without meta-learning, or 50% of the training data during test.
关键词：Meta-Learning;MAML;Reptile;Machine Learning;Natural Evolutionary Strategy;Stock Trading