During the internship, your work is reinforced with intensive classes, workshops, and team-based mock trading sessions. These will expose you to many of the dynamics we observe in real markets, illustrate the role that we play in making markets more efficient, and help build intuition for how we think about both trading and collaborating.
As a quantitative trading intern, you’ll also have the opportunity to participate in one “elective” based on your interests. Electives consist of targeted classes and immersive activities, and are designed to give you a deeper and more nuanced look into one of the many aspects of what quantitative trading can look like at Jane Street:
Machine Learning, Modeling, and Data Science
You’ll learn how Jane Street applies advanced machine learning and statistical techniques to make models and predictions using large datasets of both real and simulated market data. You’ll learn how to train and use a variety of ML models, and gain an understanding of the differences between textbook machine learning and its application to noisy and complex financial data.
Algorithmic Trading and Market Microstructure
You’ll learn the end-to-end process of developing an algorithmic trading strategy. You’ll analyze market data to develop a tradable fair value and implement a trading strategy in Python. Your algorithmic strategy will connect directly to simulated markets with different market structures, and you will learn how to optimize your strategy given the unique attributes of each market. You will discover how various market dynamics affect strategy behavior and learn how real-world trading differs from simulation.
Trading Strategy and Scenarios
You’ll be introduced to a rotating set of new trading scenarios inspired by real events on a particular trading desk. You’ll work in teams on multiple mock trading sessions related to each scenario and use the time between sessions to refine your strategies, write recaps, and hear how the story played out in real life from our seasoned full-time traders who lived through it.