如何在 Python 交易策略中实现步进回测以防止过拟合

Overfitting is a common issue in machine learning models, where a model performs exceptionally well on training data but fails to generalize to new, unseen data, leading to poor performance and losses in live trading. Walk-forward backtesting is a technique used to prevent overfitting by evaluating a trading strategy's performance on out-of-sample data. This method involves segmenting historical data for backtesting and incrementally updating model parameters to ensure effectiveness under varying market conditions. Using the TradeSight framework as an example, the implementation details and methodologies for walk-forward backtesting can be explored, enhancing the reliability and stability of trading strategies.