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 Specialization
Start Your Career in Machine Learning for Trading. Learn the machine learning techniques used in quantitative trading.
Instructor: Jack Farmer
Included with 
(1,111 reviews)
Recommended experience
(1,111 reviews)
Recommended experience
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
- Market Trend
- Securities Trading
- Reinforcement Learning
- Artificial Intelligence and Machine Learning (AI/ML)
- Supervised Learning
- Technical Analysis
- Artificial Neural Networks
- Portfolio Management
- Data Pipelines
- Financial Trading
- Applied Machine Learning
- Time Series Analysis and Forecasting
- Machine Learning Algorithms
- Financial Market
- Deep Learning
- Machine Learning
- Google Cloud Platform
- Statistical Machine Learning
Tools you'll learn
What’s included
Add to your LinkedIn profile
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
Compare with similar products
| Rating | ||||
|---|---|---|---|---|
| Level | ||||
| Skills | ||||
| Tools | ||||
| Last updated | ||||
| Number of practice exercises | ||||
| Degree eligibility | ||||
| Part of Coursera Plus |
You might also like
Why people choose Coursera for their career
Open new doors with Coursera Plus
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy
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.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
More questions
Financial aid available,