À propos de ce cours : 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.
Les destinataires de ce cours : The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance A pre-requisite to this course is the course “Reinforcement Learning in Finance”. Familiarity with main financial models such as Markowitz portfolio model, capital asset pricing model (CAPM) and the Black-Scholes model is desirable not not neccessary.
Créé par : New York University Tandon School of Engineering
Enseigné par : Igor Halperin
| Informations de base | Cours 4 de 4 dans Machine Learning and Reinforcement Learning in Finance Specialization |
| Niveau | Avancées |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
Programme de cours
SEMAINE 1
Black-Scholes-Merton model, Physics and Reinforcement Learning
13 vidéos
- Vidéo: Specialization Prerequisites
- Vidéo: Interview with Rossen Roussev
- Vidéo: Reinforcement Learning and Ptolemy's Epicycles
- Vidéo: PDEs in Physics and Finance
- Vidéo: Competitive Market Equilibrium Models in Finance
- Vidéo: I Certainly Hope You Are Wrong, Herr Professor!
- Vidéo: Risk as a Science of Fluctuation
- Vidéo: Markets and the Heat Death of the Universe
- Vidéo: Option Trading and RL
- Vidéo: Liquidity
- Vidéo: Modeling Market Frictions
- Vidéo: Modeling Feedback Frictions
Noté: Assignment 1
SEMAINE 2
Reinforcement Learning for Optimal Trading and Market Modeling
8 vidéos
- Vidéo: Invisible Hand
- Vidéo: GBM and Its Problems
- Vidéo: The GBM Model: An Unbounded Growth Without Defaults
- Vidéo: Dynamics with Saturation: The Verhulst Model
- Vidéo: The Singularity is Near
- Vidéo: What are Defaults?
- Vidéo: Quantum Equilibrium-Disequilibrium
Noté: Assignment 2
SEMAINE 3
Perception - Beyond Reinforcement Learning
8 vidéos
- Vidéo: Market Dynamics and IRL
- Vidéo: Diffusion in a Potential: The Langevin Equation
- Vidéo: Classical Dynamics
- Vidéo: Potential Minima and Newton's Law
- Vidéo: Classical Dynamics: the Lagrangian and the Hamiltonian
- Vidéo: Langevin Equation and Fokker-Planck Equations
- Vidéo: The Fokker-Planck Equation and Quantum Mechanics
Noté: Assignment 3
SEMAINE 4
Other Applications of Reinforcement Learning: P-2-P Lending, Cryptocurrency, etc.
9 vidéos
- Vidéo: Electronic Markets and LOB
- Vidéo: Trades, Quotes and Order Flow
- Vidéo: Limit Order Book
- Vidéo: LOB Modeling
- Vidéo: LOB Statistical Modeling
- Vidéo: LOB Modeling with ML and RL
- Vidéo: Other Applications of RL
- Vidéo: The Value of Universatility
- Notebook: Final Project: Exploration of non-linear market model dynamics
Noté: Final Project
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Créateurs
New York University Tandon School of Engineering
Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.
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