In the last course of our specialization, Overview of Advanced Methods of Reinforcement Learning in Finance, we will take a deeper look into topics discussed in our third course, Reinforcement Learning in Finance.
In particular, we will talk about links between Reinforcement Learning, option pricing and physics, implications of Inverse Reinforcement Learning for modeling market impact and price dynamics, and perception-action cycles in Reinforcement Learning. Finally, we will overview trending and potential applications of Reinforcement Learning for high frequency trading, cryptocurrencies, peer-to-peer lending, and more.
Este curso faz parte do Programa de cursos integrados Machine Learning and Reinforcement Learning in Finance
Overview of Advanced Methods of Reinforcement Learning in Finance
oferecido por
Programa de cursos integrados Machine Learning and Reinforcement Learning in FinanceInstituto Politécnico da Universidade de Nova Iorque
Programa - O que você aprenderá com este curso
Semana
14 horas para concluir
Black-Scholes-Merton model, Physics and Reinforcement Learning
13 vídeos (total de (Total 103 mín.) min), 1 teste
13 videos
Specialization Prerequisites7min
Interview with Rossen Roussev14min
Reinforcement Learning and Ptolemy's Epicycles5min
PDEs in Physics and Finance5min
Competitive Market Equilibrium Models in Finance5min
I Certainly Hope You Are Wrong, Herr Professor!7min
Risk as a Science of Fluctuation3min
Markets and the Heat Death of the Universe3min
Option Trading and RL14min
Liquidity9min
Modeling Market Frictions9min
Modeling Feedback Frictions10min
1 exercício prático
Assignment 1s
Semana
23 horas para concluir
Reinforcement Learning for Optimal Trading and Market Modeling
8 vídeos (total de (Total 73 mín.) min), 1 teste
8 videos
Invisible Hand5min
GBM and Its Problems9min
The GBM Model: An Unbounded Growth Without Defaults9min
Dynamics with Saturation: The Verhulst Model7min
The Singularity is Near9min
What are Defaults?11min
Quantum Equilibrium-Disequilibrium11min
1 exercício prático
Assignment 2s
Semana
33 horas para concluir
Perception - Beyond Reinforcement Learning
8 vídeos (total de (Total 60 mín.) min), 1 teste
8 videos
Welcome!!4min
Market Dynamics and IRL5min
Diffusion in a Potential: The Langevin Equation8min
Classical Dynamics7min
Potential Minima and Newton's Law4min
Classical Dynamics: the Lagrangian and the Hamiltonian7min
Langevin Equation and Fokker-Planck Equations9min
The Fokker-Planck Equation and Quantum Mechanics12min
1 exercício prático
Assignment 3s
Semana
44 horas para concluir
Other Applications of Reinforcement Learning: P-2-P Lending, Cryptocurrency, etc.
9 vídeos (total de (Total 79 mín.) min), 1 teste
9 videos
Welcome!!1min
Electronic Markets and LOB9min
Trades, Quotes and Order Flow7min
Limit Order Book8min
LOB Modeling8min
LOB Statistical Modeling10min
LOB Modeling with ML and RL9min
Other Applications of RL7min
The Value of Universatility15min
Sobre Instituto Politécnico da Universidade de Nova Iorque
Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.
Sobre o Programa de cursos integrados Machine Learning and Reinforcement Learning in Finance
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.
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