外汇价格预测的特征重要性概述和叠加模型
Feature importance recap and stacking models for forex price prediction
摘要：
外汇交易是最大的市场在定量交易方面。传统上，交易者参考基于历史数据的技术分析来做出决策和交易。随着人工智能的发展，深度学习在外汇预测中发挥着越来越重要的作用。如何使用深度学习模型来预测未来的价格是大多数研究人员的主要目的。这种预测不仅可以帮助投资者和交易者做出决策，而且可以用于自动交易系统。在这篇文章中，我们提出了一种新的特征选择方法，称为“特征重要性回顾”，它将基于树的模型的特征重要性得分与深度学习模型的性能相结合。为了进一步提高性能，还开发了一种堆叠模型。研究结果表明，适当的特征选择方法可以显著提高模型的性能，并且对于金融数据，某些特征在许多模型中具有较高的重要性得分。堆叠模型的结果表明，将一些模型的预测组合并输入到神经网络中可以进一步提高性能。
英文摘要：
Forex trading is the largest market in terms of qutantitative trading. Traditionally, traders refer to technical analysis based on the historical data to make decisions and trade. With the development of artificial intelligent, deep learning plays a more and more important role in forex forecasting. How to use deep learning models to predict future price is the primary purpose of most researchers. Such prediction not only helps investors and traders make decisions, but also can be used for auto-trading system. In this article, we have proposed a novel approach of feature selection called 'feature importance recap' which combines the feature importance score from tree-based model with the performance of deep learning model. A stacking model is also developed to further improve the performance. Our results shows that proper feature selection approach could significantly improve the model performance, and for financial data, some features have high importance score in many models. The results of stacking model indicate that combining the predictions of some models and feed into a neural network can further improve the performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
