You are working with a dataset of high-frequency stock trades. Design a comprehensive feature engineering pipeline that transforms raw timestamp and price data into input features for a Gradient Boosting model. Include rolling window statistics (e.g., EWMA, volatility), lag features, and Fourier transforms for seasonality detection. Explain how you would handle data leakage during backtesting and ensure stationarity in the features before training.