課程信息
3.3
22 個評分
7 個審閱
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....
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Advanced Level

高級

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Approx. 17 hours to complete

建議:6 hours/week...
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English

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Globe

100% 在線課程

立即開始,按照自己的計劃學習。
Calendar

可靈活調整截止日期

根據您的日程表重置截止日期。
Advanced Level

高級

Clock

Approx. 17 hours to complete

建議:6 hours/week...
Comment Dots

English

字幕:English...

教學大綱 - 您將從這門課程中學到什麼

Week
1
Clock
完成時間為 4 小時

MDP and Reinforcement Learning

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Reading
14 個視頻(共 107 分鐘), 2 個閱讀材料, 1 個測驗
Video14 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
Reading2 個閱讀材料
Jupyter Notebook FAQ10分鐘
Hedged Monte Carlo: low variance derivative pricing with objective probabilities10分鐘
Week
2
Clock
完成時間為 4 小時

MDP model for option pricing: Dynamic Programming Approach

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Reading
7 個視頻(共 59 分鐘), 2 個閱讀材料, 1 個測驗
Video7 個視頻
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
Reading2 個閱讀材料
Jupyter Notebook FAQ10分鐘
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds10分鐘
Week
3
Clock
完成時間為 4 小時

MDP model for option pricing - Reinforcement Learning approach

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Reading
8 個視頻(共 71 分鐘), 3 個閱讀材料, 1 個測驗
Video8 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
Reading3 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
Week
4
Clock
完成時間為 5 小時

RL and INVERSE RL for Portfolio Stock Trading

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Reading
10 個視頻(共 82 分鐘), 2 個閱讀材料, 1 個測驗
Video10 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
Reading2 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....
Machine Learning and Reinforcement Learning in Finance

常見問題

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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