WaveLSFromer

WaveLSFromer is a research codebase for long-sequence financial time-series forecasting. It extends the Informer/Stockformer style transformer stack with stock-specific training objectives, PyTorch Lightning experiment loops, config-driven model runs, and learnable wavelet front-end components for low/high frequency feature extraction.

Paper: A Learnable Wavelet Transformer for Long-Short Equity Trading and Risk-Adjusted Return Optimization.

The repository includes:

transformer, Informer, DLinear, LSTM, and MLP model baselines;
learnable 1D wavelet filters with frequency-domain regularization;
PyTorch Lightning training, validation, prediction, and checkpoint workflows;
stock-return metrics and differentiable trading-oriented loss functions;
YAML experiment configs for financial and benchmark time-series datasets;
notebooks and scripts for data collection, preparation, and result analysis.

Thanks to polygon.io for being our financial data provider.