Comparative analysis of high-frequency algorithms and low-frequency algorithms in quantitative trading

With the rapid development of financial technology and computational capabilities, quantitative trading has become increasingly sophisticated, highlighting the need for a comprehensive understanding of different trading algorithm approaches. This study compares the characteristics and performance of high-frequency and low-frequency algorithms in quantitative trading, aiming to provide systematic insights into their relative advantages and limitations. The research employs empirical analysis on four years of market data from the Shanghai Stock Exchange, examining both tick-level and daily frequency data streams. The article elaborates on the implementation methods of both types of algorithms, including data processing architectures, signal generation mechanisms, and order execution strategies. Through comparative experiments involving over 500 stocks and 10,000 trading instances, the study analyzes the differences in computational complexity, performance metrics, and risk management frameworks. The results indicate that high-frequency algorithms achieve superior short-term returns with an annualized return of 28.5% and Sharpe ratio of 2.1, but require sophisticated infrastructure and face higher operational risks. In contrast, low-frequency algorithms demonstrate greater stability with an annualized return of 22.7% and Sharpe ratio of 1.8, making them more suitable for long-term investment strategies. The low correlation coefficient of 0.35 between these approaches suggests potential benefits from their integration. This research provides practitioners with comprehensive empirical evidence and practical guidelines for algorithm selection based on their specific trading objectives and resource constraints.