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.
提供方
Machine Learning and Reinforcement Learning in Finance 專項課程New York University課程信息
19,718 次近期查看
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高級
完成時間大約為17 小時
英語（English）
字幕：英語（English）
您將獲得的技能
option pricing and risk managementsimple model for market dynamicsQ-learning using financial problemsoptimal tradingPortfolio Optimization
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
可靈活調整截止日期
根據您的日程表重置截止日期。
高級
完成時間大約為17 小時
英語（English）
字幕：英語（English）
提供方
New York University
Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 4 小時
MDP and Reinforcement Learning
完成時間為 4 小時
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
完成時間為 4 小時
MDP model for option pricing: Dynamic Programming Approach
完成時間為 4 小時
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
完成時間為 4 小時
MDP model for option pricing - Reinforcement Learning approach
完成時間為 4 小時
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
完成時間為 5 小時
RL and INVERSE RL for Portfolio Stock Trading
完成時間為 5 小時
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
審閱
來自REINFORCEMENT LEARNING IN FINANCE的熱門評論
由 LC 提供2019年6月5日
Excellent course. The peer reviewed evaluation is very interisting and it is definitely worth the time to do it in detail but does not take two hours with luck a week.
由 MR 提供2020年5月7日
The content discussed in the course is very interesting and innovative. However, it could have more practical content through numerical examples.
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.
常見問題
我什么时候能够访问课程视频和作业？
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
- The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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退款政策是如何规定的？
如果订阅，您可以获得 7 天免费试听，在此期间，您可以取消课程，无需支付任何罚金。在此之后，我们不会退款，但您可以随时取消订阅。请阅读我们完整的退款政策。
有助学金吗？
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完成课程后，我会获得大学学分吗？
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