About this Course

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Intermediate Level

Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.

Approx. 12 hours to complete
English
Subtitles: English

What you will 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 will gain

Reinforcement Learning Model DevelopmentReinforcement Learning Trading Algorithm OptimizationReinforcement Learning Trading Strategy DevelopmentReinforcement Learning Trading Algo Development
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level

Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.

Approx. 12 hours to complete
English
Subtitles: English

Offered by

New York Institute of Finance logo

New York Institute of Finance

Google Cloud logo

Google Cloud

Syllabus - What you will learn from this course

Week
1

Week 1

3 hours to complete

Introduction to Course and Reinforcement Learning

3 hours to complete
10 videos (Total 64 min), 1 reading, 1 quiz
10 videos
What is Reinforcement Learning?9m
History Overview2m
Value Iteration9m
Policy Iteration6m
TD Learning8m
Q Learning6m
Benefits of Reinforcement Learning in Your Trading Strategy6m
DRL Advantages for Strategy Efficiency and Performance7m
Introduction to Qwiklabs3m
1 reading
Idiosyncrasies and challenges of data driven learning in electronic trading10m
Week
2

Week 2

5 hours to complete

Neural Network Based Reinforcement Learning

5 hours to complete
9 videos (Total 39 min)
9 videos
Deep Q Networks - Loss2m
Deep Q Networks Memory2m
Deep Q Networks - Code3m
Policy Gradients4m
Actor-Critic3m
What is LSTM?7m
More on LSTM4m
Applying LSTM to Time Series Data7m
Week
3

Week 3

4 hours to complete

Portfolio Optimization

4 hours to complete
10 videos (Total 54 min)
10 videos
Steps Required to Develop a DRL Strategy7m
Final Checks Before Going Live with Your Strategy5m
Investment and Trading Risk Management4m
Trading Strategy Risk Management4m
Portfolio Risk Reduction4m
Why AutoML?13m
AutoML Vision2m
AutoML NLP3m
AutoML Tables7m

Reviews

TOP REVIEWS FROM REINFORCEMENT LEARNING FOR TRADING STRATEGIES

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About the Machine Learning for Trading Specialization

This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended....
Machine Learning for Trading

Frequently Asked Questions

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

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    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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