Research on Machine Learning High-Frequency Trading Strategy of Cryptocurrency Based on Transaction Cost-Aware Filtering
TL;DR
The empirical results verify that the core bottleneck of high-frequency quantitative trading is not model prediction accuracy but the cost adaptation mechanism of signal transformation, and a simple and efficient transaction cost filtering strategy is far more valuable than blindly iterating complex time-series models.
Abstract
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 trading of BTC/USDT perpetual contracts as the research scenario, this paper constructs a quantitative trading system covering three heterogeneous time-series models (XGBoost, LSTM, and iTransformer) under the constraint of 10 bps full-dimensional transaction costs. A Cost-Aware Execution Filter (CA) is introduced, combined with the 27-fold non-anchored Walk-Forward Optimization (WFO) framework and a three-layer nested feature engineering system for empirical research. The results show that all three machine learning models can achieve excess returns in the frictionless scenario, with the annualized return of iTransformer reaching 181.76%. However, when 10 bps real trading frictions are introduced, all naive unfiltered strategies incur comprehensive losses, with annualized returns collapsing ranging from -64.00% to -98.00%. The CA filtering mechanism substantially reduces the strategy turnover rate by two orders of magnitude and effectively repairs strategy returns. The optimal strategy achieves an annualized return of 65.40% with a Sharpe ratio of 1.09. The empirical results verify that the core bottleneck of high-frequency quantitative trading is not model prediction accuracy but the cost adaptation mechanism of signal transformation, and a simple and efficient transaction cost filtering strategy is far more valuable than blindly iterating complex time-series models. This study provides an important reference for the research and development, real-market implementation, and standardized backtesting system construction of cryptocurrency high-frequency quantitative strategies.