À propos de ce cours : This course aims at introducing the fundamental concepts of Reinforcement Learning (RL), and develop use cases for applications of RL for option valuation, trading, and asset management. Prerequisites are the courses "Guided Tour of Machine Learning in Finance" and "Fundamentals of Machine Learning in Finance". Students are expected to know the lognormal process and how it can be simulated. Knowledge of option pricing is not assumed but desirable.
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 Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course. In addition, students are expected to know the lognormal process and how it can be simulated. Knowledge of option pricing is not assumed but desirable.
Créé par : New York University Tandon School of Engineering
Enseigné par : Igor Halperin
| Informations de base | Cours 3 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. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 1
MDP and Reinforcement Learning
14 vidéos, 2 lectures
- Vidéo: Prerequisites
- Vidéo: Welcome to the Course
- Vidéo: Introduction to Markov Decision Processes and Reinforcement Learning in Finance
- Vidéo: MDP and RL: Decision Policies
- Vidéo: MDP & RL: Value Function and Bellman Equation
- Vidéo: MDP & RL: Value Iteration and Policy Iteration
- Vidéo: MDP & RL: Action Value Function
- Vidéo: Options and Option pricing
- Vidéo: Black-Scholes-Merton (BSM) Model
- Vidéo: BSM Model and Risk
- Vidéo: Discrete Time BSM Model
- Vidéo: Discrete Time BSM Hedging and Pricing
- Vidéo: Discrete Time BSM BS Limit
- Reading: Jupyter Notebook FAQ
- Notebook: Discrete-time Black Scholes model
- Reading: Hedged Monte Carlo: low variance derivative pricing with objective probabilities
Noté: Discrete-time Black Scholes model
SEMAINE 2
MDP model for option pricing: Dynamic Programming Approach
7 vidéos, 2 lectures
- Vidéo: Action-Value Function
- Vidéo: Optimal Action From Q Function
- Vidéo: Backward Recursion for Q Star
- Vidéo: Basis Functions
- Vidéo: Optimal Hedge With Monte-Carlo
- Vidéo: Optimal Q Function With Monte-Carlo
- Reading: Jupyter Notebook FAQ
- Notebook: QLBS Model Implementation
- Reading: QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds
Noté: QLBS Model Implementation
SEMAINE 3
MDP model for option pricing - Reinforcement Learning approach
8 vidéos, 3 lectures
- Vidéo: Batch Reinforcement Learning
- Vidéo: Stochastic Approximations
- Vidéo: Q-Learning
- Vidéo: Fitted Q-Iteration
- Vidéo: Fitted Q-Iteration: the Ψ-basis
- Vidéo: Fitted Q-Iteration at Work
- Vidéo: RL Solution: Discussion and Examples
- Reading: Jupyter Notebook FAQ
- Notebook: Fitted Q-Iteration
- Reading: QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds and The QLBS Learner Goes NuQLear
- Reading: Course Project Reading: Global Portfolio Optimization
Noté: Fitted Q-Iteration
SEMAINE 4
RL and INVERSE RL for Portfolio Stock Trading
10 vidéos, 2 lectures
- Vidéo: Introduction to RL for Trading
- Vidéo: Portfolio Model
- Vidéo: One Period Rewards
- Vidéo: Forward and Inverse Optimisation
- Vidéo: Reinforcement Learning for Portfolios
- Vidéo: Entropy Regularized RL
- Vidéo: RL Equations
- Vidéo: RL and Inverse Reinforcement Learning Solutions
- Vidéo: Course Summary
- Reading: Jupyter Notebook FAQ
- Notebook: IRL Market Model Calibration
- Reading: Multi-period trading via Convex Optimization
Noté: IRL Market Model Calibration
FAQ
Comment cela fonctionne
Coursework
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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.
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
It is a good course to leave the comfort zone of filling model and start creating your own models from research papers. However, I have to say that the last assignment is quite impossible to do without help . It seems that they have some notation problems in the reference paper.
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Excellent overview of reinforcement learning with applications to option pricing and stock trading..To benefit fully from this course, a good command of python and various libraries for machine learning/data science is essential...