Overview of Advanced Methods of Reinforcement Learning in Finance
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Overview of Advanced Methods of Reinforcement Learning in Finance
This course is part of Machine Learning and Reinforcement Learning in Finance Specialization
Instructor: Igor Halperin
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3 assignments
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There are 4 modules in this course
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.
In particular, we will talk about links between Reinforcement Learning, option pricing and physics, implications of Inverse Reinforcement Learning for modeling market impact and price dynamics, and perception-action cycles in Reinforcement Learning. Finally, we will overview trending and potential applications of Reinforcement Learning for high-frequency trading, cryptocurrencies, peer-to-peer lending, and more. After taking this course, students will be able to - explain fundamental concepts of finance such as market equilibrium, no arbitrage, predictability, - discuss market modeling, - Apply the methods of Reinforcement Learning to high-frequency trading, credit risk peer-to-peer lending, and cryptocurrencies trading.
What's included
13 videos1 assignment
13 videosβ’Total 103 minutes
- Welcome to Specializationβ’5 minutes
- Specialization Prerequisitesβ’7 minutes
- Interview with Rossen Roussevβ’15 minutes
- Reinforcement Learning and Ptolemy's Epicyclesβ’5 minutes
- PDEs in Physics and Financeβ’6 minutes
- Competitive Market Equilibrium Models in Financeβ’5 minutes
- I Certainly Hope You Are Wrong, Herr Professor!β’7 minutes
- Risk as a Science of Fluctuationβ’4 minutes
- Markets and the Heat Death of the Universeβ’4 minutes
- Option Trading and RLβ’15 minutes
- Liquidityβ’10 minutes
- Modeling Market Frictionsβ’10 minutes
- Modeling Feedback Frictionsβ’10 minutes
1 assignmentβ’Total 120 minutes
- Assignment 1β’120 minutes
What's included
8 videos1 assignment
8 videosβ’Total 73 minutes
- From Portfolio Optimization to Market Modelβ’9 minutes
- Invisible Handβ’6 minutes
- GBM and Its Problemsβ’9 minutes
- The GBM Model: An Unbounded Growth Without Defaultsβ’9 minutes
- Dynamics with Saturation: The Verhulst Modelβ’7 minutes
- The Singularity is Nearβ’9 minutes
- What are Defaults?β’12 minutes
- Quantum Equilibrium-Disequilibriumβ’11 minutes
1 assignmentβ’Total 120 minutes
- Assignment 2β’120 minutes
What's included
8 videos1 assignment
8 videosβ’Total 60 minutes
- Welcome!!β’4 minutes
- Market Dynamics and IRLβ’6 minutes
- Diffusion in a Potential: The Langevin Equationβ’8 minutes
- Classical Dynamicsβ’8 minutes
- Potential Minima and Newton's Lawβ’4 minutes
- Classical Dynamics: the Lagrangian and the Hamiltonianβ’8 minutes
- Langevin Equation and Fokker-Planck Equationsβ’10 minutes
- The Fokker-Planck Equation and Quantum Mechanicsβ’12 minutes
1 assignmentβ’Total 120 minutes
- Assignment 3β’120 minutes
What's included
9 videos1 peer review1 ungraded lab
9 videosβ’Total 79 minutes
- Welcome!!β’1 minute
- Electronic Markets and LOBβ’9 minutes
- Trades, Quotes and Order Flowβ’8 minutes
- Limit Order Bookβ’9 minutes
- LOB Modelingβ’8 minutes
- LOB Statistical Modelingβ’11 minutes
- LOB Modeling with ML and RLβ’10 minutes
- Other Applications of RLβ’7 minutes
- The Value of Universatilityβ’15 minutes
1 peer reviewβ’Total 60 minutes
- Final Projectβ’60 minutes
1 ungraded labβ’Total 60 minutes
- Final Project: Exploration of non-linear market model dynamicsβ’60 minutes
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Reviewed on Dec 11, 2019
Great refreshment on Stochastic calculus and overall rewind of the specialization!
Reviewed on Dec 8, 2019
It was very difficult to get the peer-graded assignments graded.
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