Research on Machine Learning High-Frequency Trading Strategies Under Transaction Cost

Abstract

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 to a widespread dilemma: high prediction accuracy accompanied by poor live trading performance. Taking hourly high-frequency trading of Bitcoin as the research object, this paper constructs a comprehensive transaction cost system covering explicit handling fees, bid-ask spreads and slippage. Adopting the Walk-Forward dynamic backtesting framework, this study systematically compares the performance of traditional time series models, complex deep learning models and transaction cost-aware filtering strategies. The empirical results show that: first, there is a significant disconnect between model prediction accuracy and actual net returns; marginal improvements in prediction brought by complex models cannot offset profit losses caused by transaction frictions in high-frequency trading. Second, simple signal filtering rules designed based on transaction costs deliver far better profitability improvements than iterative optimization of deep learning architectures. Third, although the target strategies achieve impressive returns in single-path backtesting, their returns are extremely unevenly distributed across time intervals with weak statistical significance, indicating prominent stability risks in live trading. The conclusions of this research provide theoretical basis and practical references for the R&D of cryptocurrency quantitative strategies, the construction of standardized backtesting systems and live trading risk control.