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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.
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篇名 Meta-Learning of Evolutionary Strategy for Stock Trading
来源期刊 数据分析和信息处理(英文) 学科 文学
关键词 META-LEARNING MAML REPTILE Machine Learning NATURAL EVOLUTIONARY Strategy STOCK TRADING
年,卷(期) 2020,(2) 所属期刊栏目
研究方向 页码范围 86-98
页数 13页 分类号 H31
字数 语种
DOI
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研究主题发展历程
节点文献
META-LEARNING
MAML
REPTILE
Machine
Learning
NATURAL
EVOLUTIONARY
Strategy
STOCK
TRADING
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
数据分析和信息处理(英文)
季刊
2327-7211
武汉市江夏区汤逊湖北路38号光谷总部空间
出版文献量(篇)
106
总下载数(次)
0
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