StockFormer: A Swing Trading Strategy Based on STL Decomposition and
  Self-Attention Networks
Bohan Ma Yiheng Wang Yuchao Lu Tianzixuan Hu Jinling Xu Patrick Houlihan
Amidst ongoing market recalibration and increasing investor optimism, the
U.S. stock market is experiencing a resurgence, prompting the need for
sophisticated tools to protect and grow portfolios. Addressing this, we
introduce "Stockformer," a cutting-edge deep learning framework optimized for
swing trading, featuring the TopKDropout method for enhanced stock selection.
By integrating STL decomposition and self-attention networks, Stockformer
utilizes the S&P 500's complex data to refine stock return predictions. Our
methodology entailed segmenting data for training and validation (January 2021
to January 2023) and testing (February to June 2023). During testing,
Stockformer's predictions outperformed ten industry models, achieving superior
precision in key predictive accuracy indicators (MAE, RMSE, MAPE), with a
remarkable accuracy rate of 62.39% in detecting market trends. In our
backtests, Stockformer's swing trading strategy yielded a cumulative return of
13.19% and an annualized return of 30.80%, significantly surpassing current
state-of-the-art models. Stockformer has emerged as a beacon of innovation in
these volatile times, offering investors a potent tool for market forecasting.
To advance the field and foster community collaboration, we have open-sourced
Stockformer, available at https://github.com/Eric991005/Stockformer.