- Conduct rigorous quantitative research focused on mid-frequency trading strategies in the commodity futures market, with a particular emphasis on energy, metals, and agricultural commodities.
- Develop and implement statistical models, machine learning algorithms, and other quantitative methodologies to generate alpha signals and forecast price movements.
- Collaborate closely with portfolio managers, traders, and other researchers to design and optimize trading strategies, ensuring their successful integration into our trading systems.
- Analyze large datasets of historical and real-time market data, employing advanced statistical techniques and data analysis tools to uncover patterns and identify profitable trading opportunities.
- Conduct thorough backtesting and performance analysis of trading strategies, assessing their risk-return characteristics and ensuring their robustness and profitability.
- Stay abreast of industry developments, market trends, and relevant academic research to enhance existing strategies and develop new approaches to commodity futures trading.
- Collaborate with risk management teams to assess and manage the risks associated with trading strategies, ensuring compliance with regulatory requirements and internal risk guidelines.
- Effectively communicate research findings, strategy performance, and recommendations to both technical and non-technical stakeholders.
- Advanced degree in a quantitative field such as Mathematics, Statistics, Physics, or related disciplines.
- Experience in quantitative research and mid-frequency trading strategies, with a focus on commodity futures. Proven track record of developing successful trading strategies in the commodity markets.
- Strong programming skills in languages such as Python, R, or MATLAB, with experience in data analysis, statistical modeling, and machine learning libraries.
- Solid understanding of commodity market dynamics, including energy, metals, and agricultural commodities. Familiarity with supply-demand fundamentals, price drivers, and seasonality effects.
- Proficiency in statistical analysis, time series analysis, and econometric modeling techniques.
- Fluency in Chinese required.