Quantum is a research-driven proprietary trading firm, uncovering new market opportunities with innovative short and mid-term directional strategies.
Empowering
the Next Generation of Traders
Empowering the Next Generation
of Traders
We are committed to ensuring that every capable trader gets the resources, mentorship, and technology needed
to compete at the highest level.
We believe talent should define opportunity — not background.
Students & Early Careers
Why students and early careers should join Quantum?
04
Learn from elite quantitative researchers and systematic traders from top universities and leading financial firms. Gain exclusive insights and guidance to accelerate your trading journey.
World-Class Mentorship
Your success is directly rewarded — compensation is tied to performance, allowing top traders to out-earn even senior professionals.
Performance-Based Earnings
02
Internship Opportunities
Stand out as an exceptional intern and secure pre-placement offers for a seamless transition into a thriving trading career. Gain hands-on experience and open doors to top NYC hedge funds and leading firms.
01
Provided by
We are commited to ensuring your professional growth with Lime Prime both as an individual or a group of individuals.
Opportunity to grow
03
Rustem Kalmetev
Limex Quantum Head of Research & Development
Nicole Königstein
Chief AI Officer & Head of Quantitative Research at Quantmate
What we offer
Research consulting, seminars, and much more
03
Individualized setup and flexible success rates
02
Long-term capital introduction
Up to a six-figure USD amount in the first year
01
How is it like to join Quantum?
Work globally, stay local
Achieve on your own terms
Operate from your home country while accessing global markets, ensuring a balanced and rewarding career.
Experience a workplace where your success is determined solely by your performance, free from office politics.
Continuous Education by Quantum
AlgoTrading Mastery: Strategy & Systems
Future Trading Strategies Automation with LLM & AI
Our programs equip traders of all levels with the skills and tools to master algorithmic trading on global futures markets
Advanced Algorithmic
Futures Trading: Master Futures Strategies
Algo Trading / Quants / Futures / Beginner-friendly
Rustem Kalmetev
Limex Quantum Head of Research & Development
New updated course with more examples and a guide on building portfolio systematic strategies for US stocks and global futures markets coming soon!
Built by Quants, for everyone
Got a game-changing idea?
Team up with Quantum and bring your trading product to life!
Requirements to join Quantum
Testimonials from our students
The hands-on assignments in the course were exceptional, particularly Assignment 4.1. I explored various types of regularization and demonstrated that L2 regularization performed best in my example. The strategy encouraged me to think beyond the basics, incorporating feature engineering, testing new targets, and experimenting with stop loss and take profit mechanisms. The course's practical approach and focus on real-world applications have significantly enhanced my understanding and skills in algorithmic trading.
Alice A.
Math and probability theory student
I have never encountered such a deep dive into the methodology of developing algorithmic strategies as part of free content from industry professionals. The course challenged me with its depth and precision, pushing me to understand complex concepts like the momentum smile, contango/backwardation adjustments, and pair trading risk neutralization. Whenever I faced challenges, the Limex Quantum team was incredibly supportive, answering my questions and providing valuable academic resources. This experience has inspired me to study further and prepare a results portfolio to join their team of external research consultants
Robert L.
Math and CS student
The lectures are highly engaging, and the additional research tasks in the Python notebooks are both challenging and rewarding. I’ve successfully completed several tests, but one particularly thought-provoking problem stood out: calculating the maximum Sharpe ratio under specific conditions. This question encouraged me to think critically about the independence of returns and explore scenarios with dependent returns, where the coefficient could exceed the given solution. The depth of these challenges makes the course an excellent resource for aspiring algorithmic traders.
Matthew S.
Math and CS undergraduate
The course materials and Python notebooks have been incredibly well-structured and insightful. Although I’ve been busy with university exams, I’m looking forward to fully engaging with the assignments starting in January-February. The quality of the content has already given me confidence that this course will significantly enhance my understanding of algorithmic trading methodologies.
Geoffrey B.
Math and CS student
The Algorithmic Trading course has been an invaluable learning experience. While academic commitments limited my time recently, I’ve still managed to prepare and submit my analysis for Module 1, which I hope meets the high standards of the program. The course has fueled my enthusiasm for the field, and I’m eager to dedicate more time to the materials after my exams. I truly believe this opportunity aligns perfectly with my skills and aspirations, and I’m excited to contribute further in the near future.
Nihar P.
Math and CS student
I have never encountered such a deep dive into the methodology of developing algorithmic strategies as part of free content from industry professionals. The course challenged me with its depth and precision, pushing me to understand complex concepts like the momentum smile, contango/backwardation adjustments, and pair trading risk neutralization. Whenever I faced challenges, the Limex Quantum team was incredibly supportive, answering my questions and providing valuable academic resources. This experience has inspired me to study further and prepare a results portfolio to join their team of external research consultants
Robert L.
Math and CS student
The hands-on assignments in the course were exceptional, particularly Assignment 4.1. I explored various types of regularization and demonstrated that L2 regularization performed best in my example. The strategy encouraged me to think beyond the basics, incorporating feature engineering, testing new targets, and experimenting with stop loss and take profit mechanisms. The course's practical approach and focus on real-world applications have significantly enhanced my understanding and skills in algorithmic trading.
Alice A.
Math and probability theory student
The lectures are highly engaging, and the additional research tasks in the Python notebooks are both challenging and rewarding. I’ve successfully completed several tests, but one particularly thought-provoking problem stood out: calculating the maximum Sharpe ratio under specific conditions. This question encouraged me to think critically about the independence of returns and explore scenarios with dependent returns, where the coefficient could exceed the given solution. The depth of these challenges makes the course an excellent resource for aspiring algorithmic traders.
Matthew S.
Math and CS undergraduate
The course materials and Python notebooks have been incredibly well-structured and insightful. Although I’ve been busy with university exams, I’m looking forward to fully engaging with the assignments starting in January-February. The quality of the content has already given me confidence that this course will significantly enhance my understanding of algorithmic trading methodologies.
Geoffrey B.
Math and CS student
The Algorithmic Trading course has been an invaluable learning experience. While academic commitments limited my time recently, I’ve still managed to prepare and submit my analysis for Module 1, which I hope meets the high standards of the program. The course has fueled my enthusiasm for the field, and I’m eager to dedicate more time to the materials after my exams. I truly believe this opportunity aligns perfectly with my skills and aspirations, and I’m excited to contribute further in the near future.
Nihar P.
Math and CS student
Apply
to join Quantum
Apply to join Quantum