About the Role
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Algorithmic Trader Intern
Chicago, Illinois, United States
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Who We Are
Founded in 1980 as Aardvark Financial, TransMarket Group (TMG) is a privately held global markets proprietary trading firm in Chicago. We use next-generation technology to capture opportunities around the world and manage risk in financial markets. Our mission is to bless others through the services we provide and through the generous stewardship of the wealth we create.
TMG is a well-established and profitable business as a respected member of the global financial system for over 40 years, but also is like a startup because the potential of our group is at least one order of magnitude greater than what we are currently producing. We have an entrepreneurial culture and collaboratively develop our business with patience and discipline; we work hard, learn constantly, and relentlessly improve our expertise.
Description
As an Algorithmic Trader Intern, you will have the opportunity to learn and experience relative value arbitrage trading paired with innovative software. You will work closely with Junior and Senior Traders to assist and learn all facets of automated trading. From researching and analyzing high-frequency tick data and trading performance, to building mathematical models and performing time series analysis, you will experience a deep dive into real-time trading and gain exposure to build your expertise. The ideal candidate is intellectually curious, technically analytical, and has the desire to learn and apply programming skills in a fast-paced environment (Python, C++).
Responsibilities
Work with Junior and Senior Algorithmic Traders to assist and learn all facets of automated trading
Develop expertise in market microstructure, tick data, and automated trading systems
Build and maintain trading quantitative model tools and analytics
Learn and master programming languages
Develop, code, maintain and support production-quality automated trading strategies
Engage in dynamic research projects and formalized internship educational program
Requirements
Pursuing a Bachelor's, Master's, or Doctorate in a technical field or pertinent industry experience such as but not limited to STEM or Finance with a graduation date between December 2026 and Spring 2027
Required courses: Data structures, Probability, Statistics and Linear Algebra
Minimum major GPA of 3.5/4 or equivalent scale
Proficiency and experience in object-oriented programming languages, C++, Python and/or Java preferred
Proficiency and experience in Linux scripting are a plus
Demonstrated passion for markets, finance, and trading such as, but not limited to personal trading, participation in trading competitions, attendance at firm discover days, industry related student groups or clubs and/or prior internship experience preferred
Project or internship experience in data analysis, quantitative modeling and/or machine learning
Passion for math and software applied to markets and trading
Strong understanding of computing, math, probability, statistics, and finance
Strong work ethic and willingness to do what it takes to get the job done
Excellent verbal and written communication skills
Ability to work within a team in a fast-paced environment
This position requires physical presence and is onsite at our office in Chicago, IL
View our resources to prepare for the interview process.
Benefits
We offer one of the most generous profit sharing programs in the industry because we believe our employees should be able to take part in our rapid growth and success. We are proud to offer more world-class benefits for our full-time employees and their families.
TransMarket Group is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, gender, sexual orientation, gender identity or expression, religion, national origin, marital status, age, disability, veteran status, genetic information, or any other protected status.
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Tech Stack
PythonC++JavaData structuresProbabilityStatisticsLinear AlgebraData analysisQuantitative modelingMachine learning