In the field of high-frequency quantitative trading for cryptocurrencies, the industry has long fallen into an algorithm arms race centered on deep learning models such as LSTM and Transformer. Researchers excessively pursue marginal improvements in predictive indicators including Mean Squared Error (MSE) and Directional Accuracy, which leads...
Current machine learning-based quantitative high-frequency trading suffers from a significant Prediction-to-Trading Gap. Academic and industrial research overly focuses on the optimization of model prediction errors while ignoring the devastating impact of real-market trading frictions on strategy returns. Taking the hourly high-frequency...
Direct linear regression prediction of index returns has long been recognized as a challenging task in industry time-series timing research. Constrained by the extremely low signal-to-noise ratio and pervasive nonlinear characteristics of financial data, ordinary least squares (OLS) regression suffers from poor out-of-sample performance and...
