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.
Este curso faz parte do Programa de cursos integrados Machine Learning and Reinforcement Learning in Finance
Reinforcement Learning in Finance
oferecido por
Programa de cursos integrados Machine Learning and Reinforcement Learning in FinanceInstituto Politécnico da Universidade de Nova Iorque
Informações sobre o curso
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Habilidades que você terá
option pricing and risk managementsimple model for market dynamicsQ-learning using financial problemsoptimal tradingPortfolio Optimization
Programa - O que você aprenderá com este curso
Semana
14 horas para concluir
MDP and Reinforcement Learning
14 vídeos ((Total 107 mín.)), 2 leituras, 1 teste
14 videos
Prerequisites7min
Welcome to the Course5min
Introduction to Markov Decision Processes and Reinforcement Learning in Finance9min
MDP and RL: Decision Policies9min
MDP & RL: Value Function and Bellman Equation7min
MDP & RL: Value Iteration and Policy Iteration4min
MDP & RL: Action Value Function9min
Options and Option pricing7min
Black-Scholes-Merton (BSM) Model8min
BSM Model and Risk9min
Discrete Time BSM Model7min
Discrete Time BSM Hedging and Pricing8min
Discrete Time BSM BS Limit6min
2 leituras
Jupyter Notebook FAQ10min
Hedged Monte Carlo: low variance derivative pricing with objective probabilities10min
Semana
24 horas para concluir
MDP model for option pricing: Dynamic Programming Approach
7 vídeos ((Total 59 mín.)), 2 leituras, 1 teste
7 videos
MDP Formulation10min
Action-Value Function5min
Optimal Action From Q Function6min
Backward Recursion for Q Star8min
Basis Functions8min
Optimal Hedge With Monte-Carlo8min
Optimal Q Function With Monte-Carlo10min
2 leituras
Jupyter Notebook FAQ10min
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds10min
Semana
34 horas para concluir
MDP model for option pricing - Reinforcement Learning approach
8 vídeos ((Total 71 mín.)), 3 leituras, 1 teste
8 videos
Batch Reinforcement Learning9min
Stochastic Approximations8min
Q-Learning8min
Fitted Q-Iteration10min
Fitted Q-Iteration: the Ψ-basis9min
Fitted Q-Iteration at Work11min
RL Solution: Discussion and Examples11min
3 leituras
Jupyter Notebook FAQ10min
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds and The QLBS Learner Goes NuQLear10min
Course Project Reading: Global Portfolio Optimization10min
Semana
45 horas para concluir
RL and INVERSE RL for Portfolio Stock Trading
10 vídeos ((Total 82 mín.)), 2 leituras, 1 teste
10 videos
Introduction to RL for Trading12min
Portfolio Model8min
One Period Rewards6min
Forward and Inverse Optimisation10min
Reinforcement Learning for Portfolios9min
Entropy Regularized RL8min
RL Equations10min
RL and Inverse Reinforcement Learning Solutions10min
Course Summary3min
2 leituras
Jupyter Notebook FAQ10min
Multi-period trading via Convex Optimization10min
Sobre Instituto Politécnico da Universidade de Nova Iorque
Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.
Sobre o Programa de cursos integrados 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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
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