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.
Este curso forma parte de Programa Especializado - Machine Learning and Reinforcement Learning in Finance
ofrecido por
Acerca de este Curso
18,115 vistas recientes
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel avanzado
Aprox. 17 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
Habilidades que obtendrás
option pricing and risk managementsimple model for market dynamicsQ-learning using financial problemsoptimal tradingPortfolio Optimization
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel avanzado
Aprox. 17 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
ofrecido por
New York University
Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.
Programa - Qué aprenderás en este curso
4 horas para completar
MDP and Reinforcement Learning
4 horas para completar
14 videos (Total 107 minutos), 2 lecturas, 1 cuestionario
14 videos
Prerequisites7m
Welcome to the Course5m
Introduction to Markov Decision Processes and Reinforcement Learning in Finance9m
MDP and RL: Decision Policies9m
MDP & RL: Value Function and Bellman Equation7m
MDP & RL: Value Iteration and Policy Iteration4m
MDP & RL: Action Value Function9m
Options and Option pricing7m
Black-Scholes-Merton (BSM) Model8m
BSM Model and Risk9m
Discrete Time BSM Model7m
Discrete Time BSM Hedging and Pricing8m
Discrete Time BSM BS Limit6m
2 lecturas
Jupyter Notebook FAQ10m
Hedged Monte Carlo: low variance derivative pricing with objective probabilities10m
4 horas para completar
MDP model for option pricing: Dynamic Programming Approach
4 horas para completar
7 videos (Total 59 minutos), 2 lecturas, 1 cuestionario
7 videos
Action-Value Function5m
Optimal Action From Q Function6m
Backward Recursion for Q Star8m
Basis Functions8m
Optimal Hedge With Monte-Carlo8m
Optimal Q Function With Monte-Carlo10m
2 lecturas
Jupyter Notebook FAQ10m
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds10m
4 horas para completar
MDP model for option pricing - Reinforcement Learning approach
4 horas para completar
8 videos (Total 71 minutos), 3 lecturas, 1 cuestionario
8 videos
Batch Reinforcement Learning9m
Stochastic Approximations8m
Q-Learning8m
Fitted Q-Iteration10m
Fitted Q-Iteration: the Ψ-basis9m
Fitted Q-Iteration at Work11m
RL Solution: Discussion and Examples11m
3 lecturas
Jupyter Notebook FAQ10m
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds and The QLBS Learner Goes NuQLear10m
Course Project Reading: Global Portfolio Optimization10m
5 horas para completar
RL and INVERSE RL for Portfolio Stock Trading
5 horas para completar
10 videos (Total 82 minutos), 2 lecturas, 1 cuestionario
10 videos
Introduction to RL for Trading12m
Portfolio Model8m
One Period Rewards6m
Forward and Inverse Optimisation10m
Reinforcement Learning for Portfolios9m
Entropy Regularized RL8m
RL Equations10m
RL and Inverse Reinforcement Learning Solutions10m
Course Summary3m
2 lecturas
Jupyter Notebook FAQ10m
Multi-period trading via Convex Optimization10m
Revisiones
Principales revisiones sobre REINFORCEMENT LEARNING IN FINANCE
por LC5 de jun. de 2019
Excellent course. The peer reviewed evaluation is very interisting and it is definitely worth the time to do it in detail but does not take two hours with luck a week.
por MR7 de may. de 2020
The content discussed in the course is very interesting and innovative. However, it could have more practical content through numerical examples.
Acerca de Programa especializado: Machine Learning and Reinforcement Learning in Finance
The main goal of this specialization is to provide the knowledge and practical skills necessary to develop a strong foundation on core paradigms and algorithms of machine learning (ML), with a particular focus on applications of ML to various practical problems in Finance.
The specialization aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include:
(1) mapping the problem on a general landscape of available ML methods,
(2) choosing particular ML approach(es) that would be most appropriate for resolving the problem, and
(3) successfully implementing a solution, and assessing its performance.
The specialization is designed for three categories of students:
· Practitioners working at financial institutions such as banks, asset management firms or hedge funds
· Individuals interested in applications of ML for personal day trading
· 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.
The modules can also be taken individually to improve relevant skills in a particular area of applications of ML to finance.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
- The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Cuál es la política de reembolsos?
Si estás suscrito, obtienes una prueba gratis de 7 días, que podrás cancelar cuando desees sin ningún tipo de penalidad. Una vez transcurrido ese tiempo, no realizamos reembolsos. No obstante, puedes cancelar tu suscripción cuando quieras. Consulta nuestra política completa de reembolsos.
¿Hay ayuda económica disponible?
Sí, Coursera ofrece ayuda económica a los estudiantes que no pueden pagar la tarifa. Solicítala haciendo clic en el enlace de Ayuda económica que está debajo del botón “Inscribirse” a la izquierda. Se te pedirá que completes una solicitud. Recibirás una notificación en caso de que se apruebe. Deberás completar este paso para cada uno de los cursos que forman parte del Programa especializado, incluido el proyecto final. Obtén más información.
¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.
