Master advanced reinforcement learning techniques for financial applications, from market modeling to high-frequency trading and cryptocurrencies.
Master advanced reinforcement learning techniques for financial applications, from market modeling to high-frequency trading and cryptocurrencies.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Machine Learning and Reinforcement Learning in Finance Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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English
پښتو, বাংলা, اردو, 2 more
What you'll learn
Apply reinforcement learning to high-frequency trading strategies
Model market dynamics using advanced mathematical frameworks
Implement trading strategies for cryptocurrencies and P2P lending
Analyze market equilibrium and price dynamics
Develop models for market impact and liquidity analysis
Skills you'll gain
This course includes:
5.2 Hours PreRecorded video
3 assignments, 1 peer review
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This advanced course explores sophisticated applications of reinforcement learning in finance, connecting concepts from physics, market equilibrium, and option pricing. The curriculum covers market modeling, high-frequency trading, and cryptocurrency markets, examining topics like Black-Scholes-Merton models and limit order books. Students learn to apply reinforcement learning techniques to real-world financial scenarios, including market impact analysis, peer-to-peer lending, and cryptocurrency trading strategies.
Black-Scholes-Merton model, Physics and Reinforcement Learning
Module 1 · 3 Hours to complete
Reinforcement Learning for Optimal Trading and Market Modeling
Module 2 · 3 Hours to complete
Perception - Beyond Reinforcement Learning
Module 3 · 3 Hours to complete
Other Applications of Reinforcement Learning: P-2-P Lending, Cryptocurrency, etc.
Module 4 · 3 Hours to complete
Fee Structure
Instructor
Research Professor of Financial Machine Learning at NYU Tandon School of Engineering
Igor Halperin is a former Research Professor of Financial Machine Learning at NYU Tandon School of Engineering, specializing in applying advanced methods from reinforcement learning, information theory, neuroscience, and physics to financial problems. His research focuses on areas such as portfolio optimization, dynamic risk management, and the inference of sequential decision-making processes of financial agents. With extensive industrial experience in statistical and financial modeling, Igor has worked in areas like option pricing, credit portfolio risk modeling, and operational risk modeling. He previously held the position of Executive Director of Quantitative Research at JPMorgan and served as a quantitative researcher at Bloomberg LP. Igor has published widely in finance and physics journals and is a frequent speaker at financial conferences. He is also the co-author of Credit Risk Frontiers, published by Bloomberg LP. Holding a Ph.D. in theoretical high energy physics from Tel Aviv University and an M.Sc. in nuclear physics from St. Petersburg State Technical University, Igor advises several fintech and data science start-ups as well as risk management firms.
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