Reinforcement Learning for Trading Strategies (Coursera)

Reinforcement Learning for Trading Strategies (Coursera)

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Descripción de “Reinforcement Learning for Trading Strategies (Coursera)”

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.

Course 3 of 4 in the Machine Learning and Reinforcement Learning in Finance Specialization.

 

Syllabus

 

WEEK 1: MDP and Reinforcement Learning

WEEK 2: MDP model for option pricing: Dynamic Programming Approach

WEEK 3: MDP model for option pricing – Reinforcement Learning approach

WEEK 4: RL and INVERSE RL for Portfolio Stock Trading

 

Especificaciones: Reinforcement Learning for Trading Strategies (Coursera)

Curso ofrecido por
Disponibilidad

✔ Disponible

Plataforma

Universidad

Impartido por

Jack Farmer Ram Seshadri

País

USA

Nivel, duración y fechas
Nivel

Avanzado

Fecha

04/05/2020

Duración

4 semanas

Tiempo necesario

4 horas/semana

Idioma del curso
Idioma vehicular

Inglés

Subtítulos

Inglés

Exámenes y Certificados
Certificados

Certificado de Pago

Exámenes/Proyectos

Con Examen/Proyecto Final de pago

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Reinforcement Learning for Trading Strategies (Coursera)
Reinforcement Learning for Trading Strategies (Coursera)

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