Aprendizaje Automático

Este curso forma parte de Programa Especializado - Machine Learning and Reinforcement Learning in Finance

Reinforcement Learning in Finance

ofrecido por

Programa Especializado - Machine Learning and Reinforcement Learning in Finance

New York University Tandon School of Engineering

Acerca de este Curso

21,669 vistas recientes

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. 

By the end of this course, students will be able to
- Use reinforcement learning to solve classical problems of Finance such as portfolio optimization, optimal trading, and option pricing and risk management.
- Practice on valuable examples such as famous Q-learning using financial problems.
- Apply their knowledge acquired in the course to a simple model for market dynamics that is obtained using reinforcement learning as the course project.

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.

Curso 3 de 4 en

Programa Especializado - Machine Learning and Reinforcement Learning in Finance

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. 21 horas para completar

Sugerido: 5 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Habilidades que obtendrás

option pricing and risk managementsimple model for market dynamicsQ-learning using financial problemsoptimal tradingPortfolio Optimization

Curso 3 de 4 en

Programa Especializado - Machine Learning and Reinforcement Learning in Finance

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. 21 horas para completar

Sugerido: 5 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana

4 horas para completar

MDP and Reinforcement Learning

...

14 videos (Total 107 minutos), 2 readings, 1 quiz

14 videos

Introduction to the Specialization4m

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

Semana

4 horas para completar

MDP model for option pricing: Dynamic Programming Approach

...

7 videos (Total 59 minutos), 2 readings, 1 quiz

7 videos

MDP Formulation10m

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

Semana

4 horas para completar

MDP model for option pricing - Reinforcement Learning approach

...

8 videos (Total 71 minutos), 3 readings, 1 quiz

8 videos

Week Introduction2m

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

Semana

5 horas para completar

RL and INVERSE RL for Portfolio Stock Trading

...

10 videos (Total 82 minutos), 2 readings, 1 quiz

10 videos

Week Welcome Video2m

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

Instructor

Igor Halperin

Acerca de 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....

Acerca del 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?
Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.
¿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?
¿Hay ayuda económica disponible?

¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.

Coursera brinda acceso universal a la mejor educación del mundo, al asociarse con las mejores universidades y organizaciones, para ofrecer cursos en línea.