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Reinforcement Learning for Trading Strategies
  • Level Professional
  • Duration 12 hours
  • Course by New York Institute of Finance
  • Offered by

About

In the final course from the Machine Learning for Trading specialization, you will be introduced to reinforcement learning (RL) and the benefits of using reinforcement learning in trading strategies. You will learn how RL has been integrated with neural networks and review LSTMs and how they can be applied to time series data. By the end of the course, you will be able to build trading strategies using reinforcement learning, differentiate between actor-based policies and value-based policies, and incorporate RL into a momentum trading strategy. 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).

Modules

Introduction to Course

    • 2 Videos
Show info about module content

2 Videos

  • Introduction to Course
  • What is Reinforcement Learning?

Working with MDPs

    • 3 Videos
Show info about module content

3 Videos

  • History Overview
  • Value Iteration
  • Policy Iteration

Online Learning

    • 5 Videos
    • 1 Readings
    • 1 ExternalTool
Show info about module content

1 External Tool

  • Early Reinforcement Learning

5 Videos

  • TD Learning
  • Q Learning
  • Benefits of Reinforcement Learning in Your Trading Strategy
  • DRL Advantages for Strategy Efficiency and Performance
  • Introduction to Qwiklabs

1 Readings

  • Idiosyncrasies and challenges of data driven learning in electronic trading

Q-Networks

    • 4 Videos
    • 1 ExternalTool
Show info about module content

1 External Tool

  • Reinforcement Learning DQN

4 Videos

  • TD-Gammon
  • Deep Q Networks - Loss
  • Deep Q Networks Memory
  • Deep Q Networks - Code

Policy Gradients

    • 2 Videos
    • 1 ExternalTool
Show info about module content

1 External Tool

  • Policy Gradients and Actor-to-Critic

2 Videos

  • Policy Gradients
  • Actor-Critic

What is LSTM and How to Apply It

    • 3 Videos
Show info about module content

3 Videos

  • What is LSTM?
  • More on LSTM
  • Applying LSTM to Time Series Data

Investment and Trading Portfolio Optimization

    • 6 Videos
Show info about module content

6 Videos

  • How to Develop a DRL Trading System
  • Steps Required to Develop a DRL Strategy
  • Final Checks Before Going Live with Your Strategy
  • Investment and Trading Risk Management
  • Trading Strategy Risk Management
  • Portfolio Risk Reduction

AutoML

    • 4 Videos
    • 1 ExternalTool
Show info about module content

1 External Tool

  • Machine Learning for Finance Freestyle

4 Videos

  • Why AutoML?
  • AutoML Vision
  • AutoML NLP
  • AutoML Tables

Auto Summary

Discover the power of reinforcement learning in trading strategies with the "Reinforcement Learning for Trading Strategies" course, part of the Machine Learning for Trading specialization. This professional-level course delves into the integration of reinforcement learning (RL) with neural networks, emphasizing its application in financial markets. You'll explore Long Short-Term Memory (LSTM) networks for time series data and learn to build sophisticated trading strategies using RL. Led by industry experts on Coursera, this comprehensive course spans 720 minutes of in-depth content. It covers actor-based and value-based policies, equipping you to incorporate RL into momentum trading strategies effectively. Ideal for professionals with advanced Python programming skills and familiarity with machine learning libraries like Scikit-Learn, StatsModels, and Pandas, this course also recommends experience with SQL and a solid grounding in statistics and financial market fundamentals. Enroll in the "Starter" subscription plan to gain access and elevate your trading strategy expertise with cutting-edge reinforcement learning techniques. This course is meticulously designed for data scientists and AI enthusiasts aiming to leverage their skills in the dynamic world of financial trading.

Jack Farmer
Instructor

Jack Farmer