Responsibilities: - Research and develop high-frequency trading strategies, including but not limited to statistical arbitrage, market making, and short-term trend following. - Perform feature extraction, signal construction, factor analysis, and modeling based on high-frequency data. - Build or optimize high-frequency strategy backtesting frameworks to evaluate returns, risk, slippage, and transaction costs. - Continuously iterate strategy parameters and logic to enhance Sharpe ratio, execution efficiency, and stability. - Monitor and assess the latest financial market developments, academic research, and high-frequency trading technologies. Requirements: 1. Master’s degree or above in mathematics, statistics, physics, computer science, electronic engineering, financial engineering, or a related field. 2. Proficiency in at least one of the following languages: Python, C++, C, Java, or Rust; strong code architecture and performance optimization skills. 3. Solid understanding of time series analysis, machine learning, and signal processing, with independent modeling experience. 4. Strong foundation in probability theory, mathematical statistics, and optimization. 5. Prior experience in developing, backtesting, and deploying high-frequency trading strategies is a plus. Preferred Qualifications: 1. Internship or work experience with top domestic or international hedge funds, quantitative private funds, or proprietary high-frequency trading teams at brokerages. 2. Familiarity with the microstructure of A-shares, U.S. equities, futures, or cryptocurrencies. 3. Winners of mathematical modeling or quantitative competitions.