  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outdated in 2022)
