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option pricing and risk managementsimple model for market dynamicsQ-learning using financial problemsoptimal tradingPortfolio Optimization
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 3번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
고급 단계
완료하는 데 약 17시간 필요
영어
자막: 영어

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New York University 로고

New York University

강의 계획 - 이 강좌에서 배울 내용

1

1

완료하는 데 4시간 필요

MDP and Reinforcement Learning

완료하는 데 4시간 필요
14개 동영상 (총 107분), 2 개의 읽기 자료, 1 개의 테스트
14개의 동영상
Prerequisites7m
Welcome to the Course5m
Introduction to Markov Decision Processes and Reinforcement Learning in Finance9m
MDP and RL: Decision Policies9m
MDP & RL: Value Function and Bellman Equation7m
MDP & RL: Value Iteration and Policy Iteration4m
MDP & RL: Action Value Function9m
Options and Option pricing7m
Black-Scholes-Merton (BSM) Model8m
BSM Model and Risk9m
Discrete Time BSM Model7m
Discrete Time BSM Hedging and Pricing8m
Discrete Time BSM BS Limit6m
2개의 읽기 자료
Jupyter Notebook FAQ10m
Hedged Monte Carlo: low variance derivative pricing with objective probabilities10m
2

2

완료하는 데 4시간 필요

MDP model for option pricing: Dynamic Programming Approach

완료하는 데 4시간 필요
7개 동영상 (총 59분), 2 개의 읽기 자료, 1 개의 테스트
7개의 동영상
Action-Value Function5m
Optimal Action From Q Function6m
Backward Recursion for Q Star8m
Basis Functions8m
Optimal Hedge With Monte-Carlo8m
Optimal Q Function With Monte-Carlo10m
2개의 읽기 자료
Jupyter Notebook FAQ10m
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds10m
3

3

완료하는 데 4시간 필요

MDP model for option pricing - Reinforcement Learning approach

완료하는 데 4시간 필요
8개 동영상 (총 71분), 3 개의 읽기 자료, 1 개의 테스트
8개의 동영상
Batch Reinforcement Learning9m
Stochastic Approximations8m
Q-Learning8m
Fitted Q-Iteration10m
Fitted Q-Iteration: the Ψ-basis9m
Fitted Q-Iteration at Work11m
RL Solution: Discussion and Examples11m
3개의 읽기 자료
Jupyter Notebook FAQ10m
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds and The QLBS Learner Goes NuQLear10m
Course Project Reading: Global Portfolio Optimization10m
4

4

완료하는 데 5시간 필요

RL and INVERSE RL for Portfolio Stock Trading

완료하는 데 5시간 필요
10개 동영상 (총 82분), 2 개의 읽기 자료, 1 개의 테스트
10개의 동영상
Introduction to RL for Trading12m
Portfolio Model8m
One Period Rewards6m
Forward and Inverse Optimisation10m
Reinforcement Learning for Portfolios9m
Entropy Regularized RL8m
RL Equations10m
RL and Inverse Reinforcement Learning Solutions10m
Course Summary3m
2개의 읽기 자료
Jupyter Notebook FAQ10m
Multi-period trading via Convex Optimization10m

검토

REINFORCEMENT LEARNING IN FINANCE의 최상위 리뷰

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Machine Learning and Reinforcement Learning in Finance 특화 과정 정보

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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