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.
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 FinanceNew York University Tandon School of Engineering
Programa - Qué aprenderás en este curso
Semana
14 horas para completar
MDP and Reinforcement Learning
14 videos (Total 107 minutos), 2 readings, 1 quiz
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
Semana
24 horas para completar
MDP model for option pricing: Dynamic Programming Approach
7 videos (Total 59 minutos), 2 readings, 1 quiz
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
Semana
34 horas para completar
MDP model for option pricing - Reinforcement Learning approach
8 videos (Total 71 minutos), 3 readings, 1 quiz
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
Semana
45 horas para completar
RL and INVERSE RL for Portfolio Stock Trading
10 videos (Total 82 minutos), 2 readings, 1 quiz
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
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.
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