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Reinforcement Learning for Trading Strategies

Learners: 212

Instructor: Jack Farmer and Ram Seshadri

Duration:

This course introduces you to the world of Reinforcement Learning (RL) and its application to trading strategies. You will learn how to integrate RL with neural networks and apply LSTMs to time series data. By the end of the course, you will be able to build trading strategies using RL, 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. Experience with SQL and a background in statistics and financial markets is recommended. Click now to learn how to use RL to build trading strategies!▼▲

Course Feature Course Overview Course Provider

Go to class

Course Feature

Cost:

Free

Provider:

Coursera

Certificate:

Paid Certification

Language:

English

Start Date:

17th Jul, 2023

Course Overview

❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [August 18th, 2023]

Skills and Knowledge:
This course will provide students with the skills and knowledge to build trading strategies using reinforcement learning. Students will learn how to differentiate between actor-based policies and value-based policies, and incorporate RL into a momentum trading strategy. Additionally, students will gain an understanding of how RL has been integrated with neural networks and review LSTMs and how they can be applied to time series data. To be successful in this course, students 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. Students should also 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).
Professional Growth:
This course contributes to professional growth by introducing students to reinforcement learning (RL) and its application to trading strategies. Students 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, students 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. This course also provides students with the necessary skills and knowledge to be successful in the field of trading, such as advanced competency in Python programming, familiarity with pertinent libraries for machine learning, experience with SQL, and a background in statistics and foundational knowledge of financial markets.
Further Education:
Yes, this course is suitable for preparing further education. It covers topics such as reinforcement learning, neural networks, LSTMs, and building trading strategies. It also requires advanced competency in Python programming and familiarity with pertinent libraries for machine learning, as well as a background in statistics and foundational knowledge of financial markets. All of these topics are essential for further education in the field of machine learning for trading.