Об этом курсе: 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.
Для кого этот курс: 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.
Автор: New York University Tandon School of Engineering
Преподаватели: Igor Halperin
| Основные сведения | |
| Уровень | Продвинутые функции |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
MDP and Reinforcement Learning
14 видео, 2 материала для самостоятельного изучения
- Видео: Prerequisites
- Видео: Welcome to the Course
- Видео: Introduction to Markov Decision Processes and Reinforcement Learning in Finance
- Видео: MDP and RL: Decision Policies
- Видео: MDP & RL: Value Function and Bellman Equation
- Видео: MDP & RL: Value Iteration and Policy Iteration
- Видео: MDP & RL: Action Value Function
- Видео: Options and Option pricing
- Видео: Black-Scholes-Merton (BSM) Model
- Видео: BSM Model and Risk
- Видео: Discrete Time BSM Model
- Видео: Discrete Time BSM Hedging and Pricing
- Видео: 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
Оцениваемый: Discrete-time Black Scholes model
НЕДЕЛЯ 2
MDP model for option pricing: Dynamic Programming Approach
7 видео, 2 материала для самостоятельного изучения
- Видео: Action-Value Function
- Видео: Optimal Action From Q Function
- Видео: Backward Recursion for Q Star
- Видео: Basis Functions
- Видео: Optimal Hedge With Monte-Carlo
- Видео: Optimal Q Function With Monte-Carlo
- Reading: Jupyter Notebook FAQ
- Notebook: QLBS Model Implementation
- Reading: QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds
Оцениваемый: QLBS Model Implementation
НЕДЕЛЯ 3
MDP model for option pricing - Reinforcement Learning approach
8 видео, 3 материала для самостоятельного изучения
- Видео: Batch Reinforcement Learning
- Видео: Stochastic Approximations
- Видео: Q-Learning
- Видео: Fitted Q-Iteration
- Видео: Fitted Q-Iteration: the Ψ-basis
- Видео: Fitted Q-Iteration at Work
- Видео: 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
Оцениваемый: Fitted Q-Iteration
НЕДЕЛЯ 4
RL and INVERSE RL for Portfolio Stock Trading
10 видео, 2 материала для самостоятельного изучения
- Видео: Introduction to RL for Trading
- Видео: Portfolio Model
- Видео: One Period Rewards
- Видео: Forward and Inverse Optimisation
- Видео: Reinforcement Learning for Portfolios
- Видео: Entropy Regularized RL
- Видео: RL Equations
- Видео: RL and Inverse Reinforcement Learning Solutions
- Видео: Course Summary
- Reading: Jupyter Notebook FAQ
- Notebook: IRL Market Model Calibration
- Reading: Multi-period trading via Convex Optimization
Оцениваемый: IRL Market Model Calibration
Часто задаваемые вопросы
Как это работает
Trabajo del curso
Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.
Ayuda de tus compañeros
Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.
Certificados
Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.
Авторы
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.
Стоимость
| Приобрести курс | |
|---|---|
| Получить доступ к материалам курса | Доступен |
| Получить доступ к оцениваемым материалам курса | Доступен |
| Получить итоговую оценку | Доступен |
| Получите сертификат о прохождении курса, ссылкой на который можно делиться с другими людьми | Доступен |
Рейтинги и отзывы
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.
Great delivery
Coursera делает лучшее в мире образование доступным каждому, предлагая онлайн-курсы от ведущих университетов и организаций.
© Coursera Inc., 2018 Все права защищены.
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...