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 forma parte de Programa Especializado - Machine Learning and Reinforcement Learning in Finance
Overview of Advanced Methods of Reinforcement Learning in Finance
ofrecido por
Programa Especializado - Machine Learning and Reinforcement Learning in FinanceNew York University Tandon School of Engineering
Programa - Qué aprenderás en este curso
Semana
14 horas para completar
Black-Scholes-Merton model, Physics and Reinforcement Learning
13 videos (Total 103 minutos), 1 quiz
13 videos
Specialization Prerequisites7m
Interview with Rossen Roussev14m
Reinforcement Learning and Ptolemy's Epicycles5m
PDEs in Physics and Finance5m
Competitive Market Equilibrium Models in Finance5m
I Certainly Hope You Are Wrong, Herr Professor!7m
Risk as a Science of Fluctuation3m
Markets and the Heat Death of the Universe3m
Option Trading and RL14m
Liquidity9m
Modeling Market Frictions9m
Modeling Feedback Frictions10m
1 ejercicio de práctica
Assignment 1s
Semana
23 horas para completar
Reinforcement Learning for Optimal Trading and Market Modeling
8 videos (Total 73 minutos), 1 quiz
8 videos
Invisible Hand5m
GBM and Its Problems9m
The GBM Model: An Unbounded Growth Without Defaults9m
Dynamics with Saturation: The Verhulst Model7m
The Singularity is Near9m
What are Defaults?11m
Quantum Equilibrium-Disequilibrium11m
1 ejercicio de práctica
Assignment 2s
Semana
33 horas para completar
Perception - Beyond Reinforcement Learning
8 videos (Total 60 minutos), 1 quiz
8 videos
Market Dynamics and IRL5m
Diffusion in a Potential: The Langevin Equation8m
Classical Dynamics7m
Potential Minima and Newton's Law4m
Classical Dynamics: the Lagrangian and the Hamiltonian7m
Langevin Equation and Fokker-Planck Equations9m
The Fokker-Planck Equation and Quantum Mechanics12m
1 ejercicio de práctica
Assignment 3s
Semana
44 horas para completar
Other Applications of Reinforcement Learning: P-2-P Lending, Cryptocurrency, etc.
9 videos (Total 79 minutos), 1 quiz
9 videos
Electronic Markets and LOB9m
Trades, Quotes and Order Flow7m
Limit Order Book8m
LOB Modeling8m
LOB Statistical Modeling10m
LOB Modeling with ML and RL9m
Other Applications of RL7m
The Value of Universatility15m
Acerca de New York University Tandon School of Engineering
Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.
Acerca del programa especializado 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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