このコースについて

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上級レベル
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英語
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option pricing and risk managementsimple model for market dynamicsQ-learning using financial problemsoptimal tradingPortfolio Optimization
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
上級レベル
約17時間で修了
英語
字幕:英語

提供:

New York University ロゴ

New York University

シラバス - 本コースの学習内容

1

1

4時間で修了

MDP and Reinforcement Learning

4時間で修了
14件のビデオ (合計107分), 2 readings, 1 quiz
14件のビデオ
Prerequisites7 分
Welcome to the Course5 分
Introduction to Markov Decision Processes and Reinforcement Learning in Finance9 分
MDP and RL: Decision Policies9 分
MDP & RL: Value Function and Bellman Equation7 分
MDP & RL: Value Iteration and Policy Iteration4 分
MDP & RL: Action Value Function9 分
Options and Option pricing7 分
Black-Scholes-Merton (BSM) Model8 分
BSM Model and Risk9 分
Discrete Time BSM Model7 分
Discrete Time BSM Hedging and Pricing8 分
Discrete Time BSM BS Limit6 分
2件の学習用教材
Jupyter Notebook FAQ10 分
Hedged Monte Carlo: low variance derivative pricing with objective probabilities10 分
2

2

4時間で修了

MDP model for option pricing: Dynamic Programming Approach

4時間で修了
7件のビデオ (合計59分), 2 readings, 1 quiz
7件のビデオ
Action-Value Function5 分
Optimal Action From Q Function6 分
Backward Recursion for Q Star8 分
Basis Functions8 分
Optimal Hedge With Monte-Carlo8 分
Optimal Q Function With Monte-Carlo10 分
2件の学習用教材
Jupyter Notebook FAQ10 分
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds10 分
3

3

4時間で修了

MDP model for option pricing - Reinforcement Learning approach

4時間で修了
8件のビデオ (合計71分), 3 readings, 1 quiz
8件のビデオ
Batch Reinforcement Learning9 分
Stochastic Approximations8 分
Q-Learning8 分
Fitted Q-Iteration10 分
Fitted Q-Iteration: the Ψ-basis9 分
Fitted Q-Iteration at Work11 分
RL Solution: Discussion and Examples11 分
3件の学習用教材
Jupyter Notebook FAQ10 分
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds and The QLBS Learner Goes NuQLear10 分
Course Project Reading: Global Portfolio Optimization10 分
4

4

5時間で修了

RL and INVERSE RL for Portfolio Stock Trading

5時間で修了
10件のビデオ (合計82分), 2 readings, 1 quiz
10件のビデオ
Introduction to RL for Trading12 分
Portfolio Model8 分
One Period Rewards6 分
Forward and Inverse Optimisation10 分
Reinforcement Learning for Portfolios9 分
Entropy Regularized RL8 分
RL Equations10 分
RL and Inverse Reinforcement Learning Solutions10 分
Course Summary3 分
2件の学習用教材
Jupyter Notebook FAQ10 分
Multi-period trading via Convex Optimization10 分

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Machine Learning and Reinforcement Learning in Finance専門講座について

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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