Futures Strats are responsible for all aspects of the futures electronic trading business, providing sophisticated execution-related services to the firm’s clients, with a particular focus on automated execution algorithms. They are responsible for market microstructure research, pre and post trade analytics as well as design, implementation, testing and support of high-performance algorithmic trading systems and strategies for the firm’s futures trading businesses. The team interfaces on a regular basis with clients, sales-trading, technology, and other Strats teams.
Responsibilities:
- Design, build and maintain complex, scalable, low latency and high-capacity quantitative models for real time algorithmic trading, order state management, risk management, and other execution functions.
- Design and implement novel trading algorithms and approaches to provide generalizable solutions to complex, high-dimensional problems, ensuring efficiency and scalability across different markets.
- Build state of the art execution and market making algos using statistical and mathematical approaches and develop new models to leverage trading capabilities.
- Work with super-large datasets to extract data and turn data into tradable information.
- Provide quantitative analysis and analyze noisy data. Generate ideas to build complex signals and design overall strategies. Combine methods of theoretical physics and artificial intelligence to generate predictive mathematical models.
- Engineer software applications for high frequency trading and develop logical theories for trade execution.
- Develop and implement feedback mechanisms to continuously improve the accuracy and effectiveness of the models.
- Communicate complex technical concepts and findings to non-technical stakeholders in a clear and concise manner. Collaborate with cross-functional teams to understand business requirements and translate them into actionable solutions.
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Requirements:
- A bachelor’s degree in Computer Science, Operations Research, Math, Physics or Statistics.
- Proficiency in programming languages like Python, Java or C++ and the ability to write efficient, clean, and maintainable code. Background in Probability, Statistics, Machine Learning, Natural Language Processing, Reinforcement Learning, Large Language Models is desirable.
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