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Reinforcement Learning for Trading Strategies

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Reinforcement Learning for Trading Strategies

This course is part of Machine Learning for Trading Specialization

Instructor: Jack Farmer

20,937 already enrolled

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

250 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.

250 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).

Module details

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 videosTotal 64 minutes

Introduction to Course2 minutes
What is Reinforcement Learning?9 minutes
History Overview3 minutes
Value Iteration10 minutes
Policy Iteration7 minutes
TD Learning8 minutes
Q Learning7 minutes
Benefits of Reinforcement Learning in Your Trading Strategy6 minutes
DRL Advantages for Strategy Efficiency and Performance8 minutes
Introduction to Qwiklabs4 minutes

1 readingTotal 10 minutes

Idiosyncrasies and challenges of data driven learning in electronic trading10 minutes

1 app itemTotal 120 minutes

Early Reinforcement Learning120 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 videosTotal 39 minutes

TD-Gammon4 minutes
Deep Q Networks - Loss3 minutes
Deep Q Networks Memory2 minutes
Deep Q Networks - Code3 minutes
Policy Gradients5 minutes
Actor-Critic3 minutes
What is LSTM?7 minutes
More on LSTM4 minutes
Applying LSTM to Time Series Data8 minutes

2 app itemsTotal 270 minutes

Reinforcement Learning DQN120 minutes
Policy Gradients and Actor-to-Critic150 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 videosTotal 54 minutes

How to Develop a DRL Trading System2 minutes
Steps Required to Develop a DRL Strategy7 minutes
Final Checks Before Going Live with Your Strategy5 minutes
Investment and Trading Risk Management5 minutes
Trading Strategy Risk Management5 minutes
Portfolio Risk Reduction4 minutes
Why AutoML?13 minutes
AutoML Vision3 minutes
AutoML NLP3 minutes
AutoML Tables7 minutes

1 app itemTotal 180 minutes

Machine Learning for Finance Freestyle180 minutes

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Instructor

Instructor ratings

(38 ratings)

Jack Farmer

New York Institute of Finance

13 Courses182,603 learners

Offered by

New York Institute of Finance

Google Cloud

Learner reviews

5 stars
33.60%
4 stars
22.40%
3 stars
17.60%
2 stars
8.80%
1 star
17.60%

Showing 3 of 250

Reviewed on Feb 2, 2021

After the first two courses, this one grabs you into the reinforcement learning spectrum. This topic has been revealing to me and its applications to trading

Reviewed on Jul 13, 2021

Provide the idea and method of RL for trading, but seems like less practice knowledge for the trading. hope can add more detail for for the trading build up. overall the course are good.

Reviewed on Jul 12, 2021

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

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