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.
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Machine Learning for Trading Specialization
Start Your Career in Machine Learning for Trading. Learn the machine learning techniques used in quantitative trading.
Instructor: Jack Farmer
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What you'll learn
Understand the structure and techniques used in machine learning, deep learning, and reinforcement learning (RL) strategies.
Describe the steps required to develop and test an ML-driven trading strategy.
Describe the methods used to optimize an ML-driven trading strategy.
Use Keras and Tensorflow to build machine learning models.
Overview
Skills you'll gain
- Artificial Neural Networks
- Reinforcement Learning
- Securities Trading
- Data Pipelines
- Supervised Learning
- Applied Machine Learning
- Machine Learning
- Statistical Machine Learning
- Deep Learning
- Google Cloud Platform
- Financial Market
- Portfolio Management
- Artificial Intelligence and Machine Learning (AI/ML)
- Financial Trading
- Market Trend
- Machine Learning Algorithms
- Time Series Analysis and Forecasting
- Technical Analysis
Tools you'll learn
What’s included
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Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from Google Cloud
Specialization - 3 course series
What you'll learn
Understand the fundamentals of trading, including the concepts of trend, returns, stop-loss, and volatility.
Define quantitative trading and the main types of quantitative trading strategies.
Understand the basic steps in exchange arbitrage, statistical arbitrage, and index arbitrage.
Understand the application of machine learning to financial use cases.
Skills you'll gain
What you'll learn
Design basic quantitative trading strategies
Use Keras and Tensorflow to build machine learning models
Build a pair trading strategy prediction model and back test it.
Build a momentum-based trading model and back test it.
Skills you'll gain
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
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Instructor
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Frequently asked questions
To be successful in this course, you should have a basic competency in Python programming and familiarity with the Scikit Learn, Statsmodels and Pandas library. 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).
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
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Financial aid available,