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.
本課程是 Machine Learning and Reinforcement Learning in Finance 專項課程 專項課程的一部分
Reinforcement Learning in Finance
提供方
Machine Learning and Reinforcement Learning in Finance 專項課程New York University Tandon School of Engineering
教學大綱 - 您將從這門課程中學到什麼
週
1MDP and Reinforcement Learning
14 個視頻 （總計 107 分鐘）, 2 個閱讀材料, 1 個測驗
14 個視頻
Prerequisites7分鐘
Welcome to the Course5分鐘
Introduction to Markov Decision Processes and Reinforcement Learning in Finance9分鐘
MDP and RL: Decision Policies9分鐘
MDP & RL: Value Function and Bellman Equation7分鐘
MDP & RL: Value Iteration and Policy Iteration4分鐘
MDP & RL: Action Value Function9分鐘
Options and Option pricing7分鐘
Black-Scholes-Merton (BSM) Model8分鐘
BSM Model and Risk9分鐘
Discrete Time BSM Model7分鐘
Discrete Time BSM Hedging and Pricing8分鐘
Discrete Time BSM BS Limit6分鐘
2 個閱讀材料
Jupyter Notebook FAQ10分鐘
Hedged Monte Carlo: low variance derivative pricing with objective probabilities10分鐘
週
2MDP model for option pricing: Dynamic Programming Approach
7 個視頻 （總計 59 分鐘）, 2 個閱讀材料, 1 個測驗
7 個視頻
MDP Formulation10分鐘
Action-Value Function5分鐘
Optimal Action From Q Function6分鐘
Backward Recursion for Q Star8分鐘
Basis Functions8分鐘
Optimal Hedge With Monte-Carlo8分鐘
Optimal Q Function With Monte-Carlo10分鐘
2 個閱讀材料
Jupyter Notebook FAQ10分鐘
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds10分鐘
週
3MDP model for option pricing - Reinforcement Learning approach
8 個視頻 （總計 71 分鐘）, 3 個閱讀材料, 1 個測驗
8 個視頻
Batch Reinforcement Learning9分鐘
Stochastic Approximations8分鐘
Q-Learning8分鐘
Fitted Q-Iteration10分鐘
Fitted Q-Iteration: the Ψ-basis9分鐘
Fitted Q-Iteration at Work11分鐘
RL Solution: Discussion and Examples11分鐘
3 個閱讀材料
Jupyter Notebook FAQ10分鐘
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds and The QLBS Learner Goes NuQLear10分鐘
Course Project Reading: Global Portfolio Optimization10分鐘
週
4RL and INVERSE RL for Portfolio Stock Trading
10 個視頻 （總計 82 分鐘）, 2 個閱讀材料, 1 個測驗
10 個視頻
Introduction to RL for Trading12分鐘
Portfolio Model8分鐘
One Period Rewards6分鐘
Forward and Inverse Optimisation10分鐘
Reinforcement Learning for Portfolios9分鐘
Entropy Regularized RL8分鐘
RL Equations10分鐘
RL and Inverse Reinforcement Learning Solutions10分鐘
Course Summary3分鐘
2 個閱讀材料
Jupyter Notebook FAQ10分鐘
Multi-period trading via Convex Optimization10分鐘
關於 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.
關於 Machine Learning and Reinforcement Learning in Finance 專項課程
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.
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