Overview of Advanced Methods of Reinforcement Learning in Finance
- 3.8
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Approx. 13 hours to complete
Course Summary
This course focuses on advanced reinforcement learning methods and their applications in finance. Students will learn how to apply these methods to portfolio optimization, derivative pricing, and risk management.Key Learning Points
- Learn advanced reinforcement learning methods for finance
- Apply these methods to portfolio optimization, derivative pricing, and risk management
- Understand the limitations and challenges of using reinforcement learning in finance
Job Positions & Salaries of people who have taken this course might have
- Quantitative Researcher
- USA: $135,000
- India: ₹1,000,000
- Spain: €65,000
- Risk Manager
- USA: $110,000
- India: ₹800,000
- Spain: €50,000
- Portfolio Manager
- USA: $120,000
- India: ₹900,000
- Spain: €55,000
Related Topics for further study
Learning Outcomes
- Ability to apply advanced reinforcement learning methods in finance
- Understanding of the limitations and challenges of using reinforcement learning in finance
- Hands-on experience with portfolio optimization, derivative pricing, and risk management
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of programming and finance
- Familiarity with reinforcement learning concepts
Course Difficulty Level
AdvancedCourse Format
- Online
- Self-paced
Similar Courses
- Machine Learning for Trading
- Algorithmic Trading Strategies
- Trading Strategies in Emerging Markets
Related Education Paths
Notable People in This Field
- Andrew Ng
- David Silver
Related Books
Description
In the last course of our specialization, Overview of Advanced Methods of Reinforcement Learning in Finance, we will take a deeper look into topics discussed in our third course, Reinforcement Learning in Finance.
Outline
- Black-Scholes-Merton model, Physics and Reinforcement Learning
- Welcome to Specialization
- Specialization Prerequisites
- Interview with Rossen Roussev
- Reinforcement Learning and Ptolemy's Epicycles
- PDEs in Physics and Finance
- Competitive Market Equilibrium Models in Finance
- I Certainly Hope You Are Wrong, Herr Professor!
- Risk as a Science of Fluctuation
- Markets and the Heat Death of the Universe
- Option Trading and RL
- Liquidity
- Modeling Market Frictions
- Modeling Feedback Frictions
- Assignment 1
- Reinforcement Learning for Optimal Trading and Market Modeling
- From Portfolio Optimization to Market Model
- Invisible Hand
- GBM and Its Problems
- The GBM Model: An Unbounded Growth Without Defaults
- Dynamics with Saturation: The Verhulst Model
- The Singularity is Near
- What are Defaults?
- Quantum Equilibrium-Disequilibrium
- Assignment 2
- Perception - Beyond Reinforcement Learning
- Welcome!!
- Market Dynamics and IRL
- Diffusion in a Potential: The Langevin Equation
- Classical Dynamics
- Potential Minima and Newton's Law
- Classical Dynamics: the Lagrangian and the Hamiltonian
- Langevin Equation and Fokker-Planck Equations
- The Fokker-Planck Equation and Quantum Mechanics
- Assignment 3
- Other Applications of Reinforcement Learning: P-2-P Lending, Cryptocurrency, etc.
- Welcome!!
- Electronic Markets and LOB
- Trades, Quotes and Order Flow
- Limit Order Book
- LOB Modeling
- LOB Statistical Modeling
- LOB Modeling with ML and RL
- Other Applications of RL
- The Value of Universatility
Summary of User Reviews
Learn advanced methods of reinforcement learning for finance through this course on Coursera. Students have praised the course for its comprehensive approach to the subject matter and the practical applications of the concepts being taught.Key Aspect Users Liked About This Course
The course is highly comprehensive and teaches practical applications of reinforcement learning in finance.Pros from User Reviews
- In-depth coverage of advanced methods in reinforcement learning
- Practical applications of the concepts being taught
- Experienced instructors with industry expertise
- Engaging and interactive course materials
- Flexible learning options with self-paced schedule
Cons from User Reviews
- Some students found the course to be too technical and challenging
- Limited focus on traditional finance concepts
- Course materials and lectures can be lengthy and time-consuming
- Not suitable for beginners with no prior knowledge of reinforcement learning or finance
- Some students found the course to be too theoretical and lacking in practical examples
Igor Halperin
3.8 Raiting
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