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.
Этот курс входит в специализацию ''Специализация Machine Learning and Reinforcement Learning in Finance'
Overview of Advanced Methods of Reinforcement Learning in Finance
от партнера
Специализация Machine Learning and Reinforcement Learning in FinanceNew York University Tandon School of Engineering
Программа курса: что вы изучите
Неделя
1Black-Scholes-Merton model, Physics and Reinforcement Learning
13 видео ((всего 103 мин.)), 1 тест
13 видео
Specialization Prerequisites7мин
Interview with Rossen Roussev14мин
Reinforcement Learning and Ptolemy's Epicycles5мин
PDEs in Physics and Finance5мин
Competitive Market Equilibrium Models in Finance5мин
I Certainly Hope You Are Wrong, Herr Professor!7мин
Risk as a Science of Fluctuation3мин
Markets and the Heat Death of the Universe3мин
Option Trading and RL14мин
Liquidity9мин
Modeling Market Frictions9мин
Modeling Feedback Frictions10мин
1 практическое упражнение
Assignment 1мин
Неделя
2Reinforcement Learning for Optimal Trading and Market Modeling
8 видео ((всего 73 мин.)), 1 тест
8 видео
Invisible Hand5мин
GBM and Its Problems9мин
The GBM Model: An Unbounded Growth Without Defaults9мин
Dynamics with Saturation: The Verhulst Model7мин
The Singularity is Near9мин
What are Defaults?11мин
Quantum Equilibrium-Disequilibrium11мин
1 практическое упражнение
Assignment 2мин
Неделя
3Perception - Beyond Reinforcement Learning
8 видео ((всего 60 мин.)), 1 тест
8 видео
Welcome!!4мин
Market Dynamics and IRL5мин
Diffusion in a Potential: The Langevin Equation8мин
Classical Dynamics7мин
Potential Minima and Newton's Law4мин
Classical Dynamics: the Lagrangian and the Hamiltonian7мин
Langevin Equation and Fokker-Planck Equations9мин
The Fokker-Planck Equation and Quantum Mechanics12мин
1 практическое упражнение
Assignment 3мин
Неделя
4Other Applications of Reinforcement Learning: P-2-P Lending, Cryptocurrency, etc.
9 видео ((всего 79 мин.)), 1 тест
9 видео
Welcome!!1мин
Electronic Markets and LOB9мин
Trades, Quotes and Order Flow7мин
Limit Order Book8мин
LOB Modeling8мин
LOB Statistical Modeling10мин
LOB Modeling with ML and RL9мин
Other Applications of RL7мин
The Value of Universatility15мин
О 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.
О специализации ''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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