Using Machine Learning in Trading and Finance

Using Machine Learning in Trading and Finance

This course is part of Machine Learning for Trading Specialization

Instructor: Jack Farmer

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

Details to know

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Assessments

2 quizzes

Taught in English

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This course is part of the Machine Learning for Trading Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 5 modules in this course

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.

To be successful in this course, you 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. 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).

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.

What's included

4 videos1 reading1 quiz

4 videosTotal 23 minutes

Introduction to Course6 minutesPreview module
Basic Trading Strategy Entries and Exits Endogenous Exogenous7 minutes
Basic Trading Strategy Building a Trading Model2 minutes
Advanced Concepts in Trading Strategies6 minutes

1 readingTotal 10 minutes

Welcome to Using Machine Learning in Trading and Finance10 minutes

1 quiz

Understand Quantitative Strategies0 minutes

What's included

11 videos2 app items

11 videosTotal 50 minutes

Overview1 minutePreview module
Introduction to TensorFlow6 minutes
TensorFlow API Hierarchy4 minutes
Components of tensorflow Tensors and Variables8 minutes
Getting Started with Google Cloud Platform and Qwiklabs3 minutes
Lab Intro Writing low-level TensorFlow programs0 minutes
Working in-memory and with files3 minutes
Training on Large Datasets with tf.data API4 minutes
Getting the data ready for model training6 minutes
Embeddings8 minutes
Lab Intro Manipulating data with TensorFlow Dataset API0 minutes

2 app itemsTotal 180 minutes

Lab: Writing low-level TensorFlow Programs120 minutes
Lab: Manipulating data with TensorFlow Dataset API60 minutes

What's included

12 videos2 app items

12 videosTotal 53 minutes

Overview0 minutesPreview module
Activation functions8 minutes
Activation functions: Pitfalls to avoid in Backpropagation 5 minutes
Neural Networks with Keras Sequential API7 minutes
Serving models in the cloud3 minutes
Lab Intro : Keras Sequential API0 minutes
Neural Networks with Keras Functional API9 minutes
Regularization: The Basics4 minutes
Regularization: L1, L2, and Early Stopping5 minutes
Regularization: Dropout5 minutes
Lab Intro: Keras Functional API0 minutes
Recap0 minutes

2 app itemsTotal 120 minutes

Lab: Introducing the Keras Sequential API60 minutes
Lab: Introducing the Keras Functional API60 minutes

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.

What's included

12 videos1 reading2 app items1 discussion prompt

12 videosTotal 68 minutes

Introduction to Momentum Trading9 minutesPreview module
Introduction to Hurst8 minutes
Building a Momentum Trading Model7 minutes
Define the Problem9 minutes
Collect the Data2 minutes
Creating Features3 minutes
Split the Data3 minutes
Selecting a Machine Learning Algorithm3 minutes
Backtest on Unseen Data1 minute
Understanding the Code: Simple ML Strategies to Generate Trading Signal9 minutes
Lab Intro: Momentum Trading0 minutes
Momentum Trading Lab Solution7 minutes

1 readingTotal 10 minutes

Hurst Exponent and Trading Signals Derived from Market Time Series10 minutes

2 app itemsTotal 300 minutes

Lab: Momentum Strategies150 minutes
Optional Lab: Improve Momentum Trading strategies using Hurst150 minutes

1 discussion promptTotal 10 minutes

Compare interpretability versus explanatory power of the momentum factor10 minutes

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.