This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading. By the end of the course, you will be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, build a pair trading strategy prediction model and back test it, and build a momentum-based trading model and back test it.
This course is part of the Machine Learning for Trading Specialization
Offered By
New York Institute of Finance
Google Cloud
Machine Learning for Trading SpecializationNew York Institute of FinanceAbout this Course
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Learner Career Outcomes
50%
got a tangible career benefit from this course
50%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 2 of 3 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
- Basic competency in Python, familiarity with the Scikit Learn, Statsmodels and Pandas library.
- Familiarity with statistics, financial markets, ML
Approx. 19 hours to complete
English
Subtitles: English
What you will 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 will gain
Algorithmic TradingPython ProgrammingMachine Learning
Learner Career Outcomes
50%
got a tangible career benefit from this course
50%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 2 of 3 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
- Basic competency in Python, familiarity with the Scikit Learn, Statsmodels and Pandas library.
- Familiarity with statistics, financial markets, ML
Approx. 19 hours to complete
English
Subtitles: English
Offered by
New York Institute of Finance
The New York Institute of Finance (NYIF), is a global leader in training for financial services and related industries. Started by the New York Stock Exchange in 1922, it now trains 250,000+ professionals in over 120 countries. NYIF courses cover everything from investment banking, asset pricing, insurance and market structure to financial modeling, treasury operations, and accounting. The institute has a faculty of industry leaders and offers a range of program delivery options, including self-study, online courses, and in-person classes. Its US customers include the SEC, the Treasury, Morgan Stanley, Bank of America and most leading worldwide banks.
Google Cloud
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Syllabus - What you will learn from this course
1 hour to complete
Introduction to Quantitative Trading and TensorFlow
In this module we discuss the key components that are common to every trading strategy, no matter how complex. This foundation will help guide you as you develop more advanced strategies using machine learning techniques.
1 hour to complete
4 videos (Total 23 min), 1 reading, 1 quiz
4 videos
Basic Trading Strategy Entries and Exits Endogenous Exogenous7m
Basic Trading Strategy Building a Trading Model2m
Advanced Concepts in Trading Strategies6m
1 reading
Welcome to Using Machine Learning in Trading and Finance10m
1 practice exercise
Understand Quantitative Strategies
4 hours to complete
Introduction to TensorFlow
4 hours to complete
11 videos (Total 50 min)
11 videos
Overview1m
Introduction to TensorFlow6m
TensorFlow API Hierarchy4m
Components of tensorflow Tensors and Variables8m
Getting Started with Google Cloud Platform and Qwiklabs3m
Lab Intro Writing low-level TensorFlow programs43s
Working in-memory and with files3m
Training on Large Datasets with tf.data API4m
Getting the data ready for model training6m
Embeddings8m
Lab Intro Manipulating data with TensorFlow Dataset API34s
3 hours to complete
Training neural networks with Tensorflow 2 and Keras
3 hours to complete
12 videos (Total 53 min)
12 videos
Overview31s
Activation functions8m
Activation functions: Pitfalls to avoid in Backpropagation 5m
Neural Networks with Keras Sequential API7m
Serving models in the cloud3m
Lab Intro : Keras Sequential API21s
Neural Networks with Keras Functional API9m
Regularization: The Basics4m
Regularization: L1, L2, and Early Stopping5m
Regularization: Dropout5m
Lab Intro: Keras Functional API38s
Recap57s
6 hours to complete
Build a Momentum-based Trading System
Momentum trading is a strategy in which traders buy or sell assets according to the strength of recent price trends. Price momentum is similar to momentum in physics, where mass multiplied by velocity determines the persistence with which an object will follow its current path (like a heavy train on a track). In financial markets, however, momentum is determined by other factors like trading volume and rate of price changes. Momentum traders bet that an asset price that is moving strongly in a given direction will continue to move in that direction until the trend loses strength or reverses. This module teaches you all about momentum trading.
6 hours to complete
12 videos (Total 68 min), 1 reading, 2 quizzes
12 videos
Introduction to Hurst8m
Building a Momentum Trading Model7m
Define the Problem9m
Collect the Data2m
Creating Features3m
Split the Data3m
Selecting a Machine Learning Algorithm3m
Backtest on Unseen Data1m
Understanding the Code: Simple ML Strategies to Generate Trading Signal9m
Lab Intro: Momentum Trading43s
Momentum Trading Lab Solution7m
1 reading
Hurst Exponent and Trading Signals Derived from Market Time Series10m
5 hours to complete
Build a Pair Trading Strategy Prediction Model
In this module, we introduce pairs trading. We will discuss what pairs trading is, and how you can make money doing it. We will discuss what you need to know about the members to form a suitable pair.
5 hours to complete
11 videos (Total 74 min)
11 videos
Picking Pairs4m
Picking Pairs with Clustering8m
How to implement a Pair Trading Strategy9m
Evaluate Results of a Pair Trade6m
Backtesting and Avoiding Overfitting6m
Next Steps: Imrovements to your Pair Strategy5m
Lab Intro: Pairs Trading30s
Lab Solution: Pairs Trading7m
Kalman Filter Introduction11m
Kalman Filter Trading Applications6m
1 practice exercise
Pairs Trading Strategy concepts
Reviews
TOP REVIEWS FROM USING MACHINE LEARNING IN TRADING AND FINANCE
by BYDec 16, 2020
This the best online course I've ever joined, very practical, and could be able to implement in the real world with your own thoughts plus the hints from the course.
by MMApr 30, 2020
This course was great!!! I think they skipped over a lot so it takes a lot of time to learn the details of the skills. But it definitely gives you the tools needed!
by PLFeb 28, 2020
The course is inspiring. It gave me another perspective of learning trading not just for Machine Learning also for day to day trading algorithm.
by MFJan 19, 2020
Very informative. I does not go too much in details but you get a lot of insight about trading and using ML in trading strategies
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.
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