NYU - Overview of Advanced Methods of Reinforcement Learning in Finance
- Offered byCoursera
Overview of Advanced Methods of Reinforcement Learning in Finance at Coursera Overview
Duration | 13 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Overview of Advanced Methods of Reinforcement Learning in Finance at Coursera Highlights
- This Course Plus the Full Specialization.
- Shareable Certificates.
- Graded Programming Assignments.
Overview of Advanced Methods of Reinforcement Learning in Finance at Coursera Course details
- 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.
Overview of Advanced Methods of Reinforcement Learning in Finance at Coursera Curriculum
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
Overview of Advanced Methods of Reinforcement Learning in Finance at Coursera Admission Process
Important Dates
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