01. Introduction to Reinforcement Learning

AI For Trading C5 L1 A01 Introduction V2

Introduction to Reinforcement Learning and Trading

The course offers an overview of applying reinforcement learning (RL) in trading. It is structured into four lessons designed to build understanding and practical skills.

Course Structure:

  1. Foundation Concepts

    • Understanding RL in finance through core mathematical principles.
    • Explore key components of reinforcement learning interacting with financial markets.

  2. External System Components

    • Representation of market dynamics.
    • Study of actions such as buying, selling, and holding stocks.

  3. Internal Trading Components

    • Analyze the roles of neural networks.
    • Delve into trading policies and the learning process.

  4. System Backtesting and Optimization

    • Techniques for testing and enhancing trading systems.

Final Project:

  • Application of learned concepts to build a basic RL system for trading a stock.

Key Concepts Reviewed:

  • Supervised Learning: Involves labelled datasets where models predict accurate outputs.
  • Unsupervised Learning: Deals with unlabelled data, uncovering hidden patterns.
  • Reinforcement Learning distinguishes by using feedback from action outcomes rather than data understanding alone.

By completing the course, students will be equipped with fundamental skills for creating personalized trading systems, utilizing personal market insights.

What is the primary goal of supervised learning?
SOLUTION: To map inputs to correct outputs using labelled data

How does unsupervised learning differ from supervised learning?
SOLUTION: It focuses on uncovering hidden patterns in unlabelled data

Which of the following best describes the feedback mechanism in reinforcement learning?
SOLUTION: Delayed feedback in the form of rewards or penalties from actions

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