Reinforcement Learning for Trading Strategies

This course is part of Machine Learning for Trading Specialization

Instructor: Jack Farmer

What you'll learn

  •   Understand the structure and techniques used in reinforcement learning (RL) strategies.
  •   Understand the benefits of using RL vs. other learning methods.
  •   Describe the steps required to develop and test an RL trading strategy.
  •   Describe the methods used to optimize an RL trading strategy.

    • Skills you'll gain

  •   Applied Machine Learning
  •   Financial Market
  •   Time Series Analysis and Forecasting
  •   Markov Model
  •   Deep Learning
  •   Financial Trading
  •   Machine Learning Software
  •   Machine Learning Methods
  •   Reinforcement Learning
  •   Artificial Neural Networks
  •   Machine Learning
  •   Artificial Intelligence and Machine Learning (AI/ML)
  •   Portfolio Management

    • There are 3 modules in this course

    To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).

    Neural Network Based Reinforcement Learning

    Portfolio Optimization

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    Overview of Advanced Methods of Reinforcement Learning in Finance

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