Reinforcement Learning

Overview

In this course, learners will explore how to design, backtest, and optimize a working reinforcement-based ML trading strategy. This course will introduce popular techniques and indicators used in reinforcement learning-based trading, such as Q-learning, PCA, use of market indicators, assessment of market context, and assessment of the strategy outcomes. This course is designed for hobby traders with a background in data science. By the end of this course, you will be able to build, train, backtest, and optimize a reinforcement learning trading strategy with Python.

Syllabus

Reinforcement Learning in Trading

Introduction to reinforcement learning, Q-learning, and core concepts including how reinforcement learning fits in the trading world.

Representing the Financal Market: State and Action Spaces

Explore the concept of Financial State and Action Spaces. Learn how to define states and extract popular market indicators and conditions with Python and YFinance.

Constructing a Reinforcement Trading Model

Construct a RL trading model using Python including define and running a training loop. Learn key tips for implementation and run test data through the newly created model.

Backtesting and Optimization Techniques

Examine key backtesting concepts, gather important backtesting information on an RL model, and learn how to interpret those results to optimize performance.