课程信息

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高级
完成时间大约为17 小时
英语(English)
字幕:英语(English)

您将获得的技能

option pricing and risk managementsimple model for market dynamicsQ-learning using financial problemsoptimal tradingPortfolio Optimization
可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
可灵活调整截止日期
根据您的日程表重置截止日期。
高级
完成时间大约为17 小时
英语(English)
字幕:英语(English)

提供方

New York University 徽标

New York University

教学大纲 - 您将从这门课程中学到什么

1

1

完成时间为 4 小时

MDP and Reinforcement Learning

完成时间为 4 小时
14 个视频 (总计 107 分钟), 2 个阅读材料, 1 个测验
14 个视频
Prerequisites7分钟
Welcome to the Course5分钟
Introduction to Markov Decision Processes and Reinforcement Learning in Finance9分钟
MDP and RL: Decision Policies9分钟
MDP & RL: Value Function and Bellman Equation7分钟
MDP & RL: Value Iteration and Policy Iteration4分钟
MDP & RL: Action Value Function9分钟
Options and Option pricing7分钟
Black-Scholes-Merton (BSM) Model8分钟
BSM Model and Risk9分钟
Discrete Time BSM Model7分钟
Discrete Time BSM Hedging and Pricing8分钟
Discrete Time BSM BS Limit6分钟
2 个阅读材料
Jupyter Notebook FAQ10分钟
Hedged Monte Carlo: low variance derivative pricing with objective probabilities10分钟
2

2

完成时间为 4 小时

MDP model for option pricing: Dynamic Programming Approach

完成时间为 4 小时
7 个视频 (总计 59 分钟), 2 个阅读材料, 1 个测验
7 个视频
Action-Value Function5分钟
Optimal Action From Q Function6分钟
Backward Recursion for Q Star8分钟
Basis Functions8分钟
Optimal Hedge With Monte-Carlo8分钟
Optimal Q Function With Monte-Carlo10分钟
2 个阅读材料
Jupyter Notebook FAQ10分钟
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds10分钟
3

3

完成时间为 4 小时

MDP model for option pricing - Reinforcement Learning approach

完成时间为 4 小时
8 个视频 (总计 71 分钟), 3 个阅读材料, 1 个测验
8 个视频
Batch Reinforcement Learning9分钟
Stochastic Approximations8分钟
Q-Learning8分钟
Fitted Q-Iteration10分钟
Fitted Q-Iteration: the Ψ-basis9分钟
Fitted Q-Iteration at Work11分钟
RL Solution: Discussion and Examples11分钟
3 个阅读材料
Jupyter Notebook FAQ10分钟
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds and The QLBS Learner Goes NuQLear10分钟
Course Project Reading: Global Portfolio Optimization10分钟
4

4

完成时间为 5 小时

RL and INVERSE RL for Portfolio Stock Trading

完成时间为 5 小时
10 个视频 (总计 82 分钟), 2 个阅读材料, 1 个测验
10 个视频
Introduction to RL for Trading12分钟
Portfolio Model8分钟
One Period Rewards6分钟
Forward and Inverse Optimisation10分钟
Reinforcement Learning for Portfolios9分钟
Entropy Regularized RL8分钟
RL Equations10分钟
RL and Inverse Reinforcement Learning Solutions10分钟
Course Summary3分钟
2 个阅读材料
Jupyter Notebook FAQ10分钟
Multi-period trading via Convex Optimization10分钟

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关于 Machine Learning and Reinforcement Learning in Finance 专项课程

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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