论文标题
通过GAN进行进行进行回测的交易策略，以避免过度拟合
Backtesting Trading Strategies with GAN To Avoid Overfitting
论文作者
论文摘要
许多作品表明，仅基于良好的交易策略的过度拟合危险是（在样本中）绩效。但是，他们中的大多数仅显示出这种现象而没有提供避免这种现象的方法。我们提出了一种避免过度拟合的方法：良好（意思是不拟合的）交易策略仍应在根据历史数据的分布生成的路径上很好地工作。我们将GAN与LSTM一起学习或适合历史时间序列的分布。然后，GAN产生的路径避免过度拟合。（本文是论文的tanslated英语版本（10.6342/NTU201801645），该版本最初于2018年用中文编写，其中某些声明和声明在2022年均超过了）。
Many works have shown the overfitting hazard of selecting a trading strategy based only on good IS (in sample) performance. But most of them have merely shown such phenomena exist without offering ways to avoid them. We propose an approach to avoid overfitting: A good (meaning non-overfitting) trading strategy should still work well on paths generated in accordance with the distribution of the historical data. We use GAN with LSTM to learn or fit the distribution of the historical time series . Then trading strategies are backtested by the paths generated by GAN to avoid overfitting.(This paper is an tanslated English version of a thesis (10.6342/NTU201801645) which was originally written in Chinese in 2018, where some statements and claims are outdated in 2022)
