Bilateral Autotrading Framework for Stock Prediction

As the core of quantitative trading, indicator effectiveness continuously plays a vital role in stock prediction. The majority of studies are currently dedicated to constructing indicators with high Pearson Correlation Coefficient (CORR) with returns. However, the pursuit of high CORR may ignore some indicators that produce high profits. Therefore, we propose a new Bilateral Correlation Coefficient (BCORR), which can detect some profitable indicators that are previously discarded due to low CORR. BCORR is a weighted correlation coefficient, and the weight is a variable related to the return so that the top and bottom ranked returns have a more significant impact on it. To generate an indicator that has high BCORR with the return, we propose a framework called the Bilateral Autotrading Framework (BAF) based on Bilateral Loss to forecast the cross-sectional rank of stock return, and the prediction is adopted as a bilateral indicator to select stocks to invest. Meanwhile, the positions of the selected stocks are optimized by the Sharpe-oriented optimization to reduce the risk and improve the return. Experiments on real-world stock market datasets show that the BAF can significantly improve performance to deep stock prediction methods, such as Transformer, LSTM, and NBEATS.