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. 
 

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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