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.
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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.
After taking this course, students will be able to
- explain fundamental concepts of finance such as market equilibrium, no arbitrage, predictability,
- discuss market modeling,
- Apply the methods of Reinforcement Learning to high-frequency trading, credit risk peer-to-peer lending, and cryptocurrencies trading.
Course 4 of 4 in the Machine Learning and Reinforcement Learning in Finance Specialization
Syllabus
WEEK 1: Black-Scholes-Merton model, Physics and Reinforcement Learning
WEEK 2: Reinforcement Learning for Optimal Trading and Market Modeling
WEEK 3: Perception - Beyond Reinforcement Learning
WEEK 4: Other Applications of Reinforcement Learning: P-2-P Lending, Cryptocurrency, etc.
