本課程是 Machine Learning and Reinforcement Learning in Finance 專項課程專項課程的一部分

Igor Halperin

提供方

Machine Learning and Reinforcement Learning in Finance 專項課程New York University

課程信息

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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.

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第 3 門課程（共 4 門）

Machine Learning and Reinforcement Learning in Finance 專項課程

可靈活調整截止日期

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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。

第 3 門課程（共 4 門）

Machine Learning and Reinforcement Learning in Finance 專項課程

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）

講師

授課教師評分

3.6/5 （5 個評分）

Igor Halperin

34,524 個學生

4 門課程

提供方

New York University

New York University is a leading global institution for scholarship, teaching, and research. Based in New York City with campuses and sites in 14 additional major cities across the world, NYU embraces diversity among faculty, staff and students to ensure the highest caliber, most inclusive educational experience.

教學大綱 - 您將從這門課程中學到什麼

週

週 1

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

Introduction to the Specialization4分鐘

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

週 2

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

週

週 3

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

Week Introduction2分鐘

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

週 4

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

Week Welcome Video2分鐘

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 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

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Reinforcement Learning in Finance