Reinforcement Learning for Trading Strategies

Reinforcement Learning for Trading Strategies

This course is part of Machine Learning for Trading Specialization

Instructor: Jack Farmer

Access provided by SGCSRC

20,728 already enrolled

3 modules

Gain insight into a topic and learn the fundamentals.

248 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

248 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

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

Details to know

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Taught in English

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This course is part of the Machine Learning for Trading Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 3 modules in this course

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).

In this module, reinforcement learning is introduced at a high level. The history and evolution of reinforcement learning is presented, including key concepts like value and policy iteration. Also, the benefits and examples of using reinforcement learning in trading strategies is described. We also introduce LSTM and AutoML as additional tools in your toolkit to use in implementing trading strategies.

What's included

10 videos1 reading1 app item

10 videos Total 64 minutes

Introduction to Course 2 minutes
What is Reinforcement Learning? 9 minutes
History Overview 3 minutes
Value Iteration 10 minutes
Policy Iteration 7 minutes
TD Learning 8 minutes
Q Learning 7 minutes
Benefits of Reinforcement Learning in Your Trading Strategy 6 minutes
DRL Advantages for Strategy Efficiency and Performance 8 minutes
Introduction to Qwiklabs 4 minutes

1 reading Total 10 minutes

Idiosyncrasies and challenges of data driven learning in electronic trading 10 minutes

1 app item Total 120 minutes

Early Reinforcement Learning 120 minutes

In the previous module, reinforcement learning was discussed before neural networks were introduced. In this module, we look at how reinforcement learning has been integrated with neural networks. We also look at LSTMs and how they can be applied to time series data.

What's included

9 videos2 app items

9 videos Total 39 minutes

TD-Gammon 4 minutes
Deep Q Networks - Loss 3 minutes
Deep Q Networks Memory 2 minutes
Deep Q Networks - Code 3 minutes
Policy Gradients 5 minutes
Actor-Critic 3 minutes
What is LSTM? 7 minutes
More on LSTM 4 minutes
Applying LSTM to Time Series Data 8 minutes

2 app items Total 270 minutes

Reinforcement Learning DQN 120 minutes
Policy Gradients and Actor-to-Critic 150 minutes

In this module we discuss the practical steps required to create a reinforcement learning trading system. Also, we introduce AutoML, a powerful service on Google Cloud Platform for training machine learning models with minimal coding.

What's included

10 videos1 app item

10 videos Total 54 minutes

How to Develop a DRL Trading System 2 minutes
Steps Required to Develop a DRL Strategy 7 minutes
Final Checks Before Going Live with Your Strategy 5 minutes
Investment and Trading Risk Management 5 minutes
Trading Strategy Risk Management 5 minutes
Portfolio Risk Reduction 4 minutes
Why AutoML? 13 minutes
AutoML Vision 3 minutes
AutoML NLP 3 minutes
AutoML Tables 7 minutes

1 app item Total 180 minutes

Machine Learning for Finance Freestyle 180 minutes

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Instructor

Instructor ratings

(38 ratings)

Jack Farmer

New York Institute of Finance

13 Courses 176,472 learners

Offered by

New York Institute of Finance

Google Cloud

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Showing 3 of 248

Reviewed on Mar 14, 2020

Good course introducing concepts in RL. Wish course provided more examples of using RL in stock prediction.

Reviewed on Mar 5, 2020

It was easy to follow but not easy. I learned a lot and I now have the confidence to implement Reinforcement learning to my own FX trading strategies. Thank you so much.

Reviewed on Jul 12, 2021

A touhg and very advanced course, with an amazing Google Cloud Platform !!!!

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