Using Machine Learning in Trading and Finance
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Course Summary
This course teaches you how to apply machine learning techniques to financial markets and trading. You will learn how to use Python to collect and analyze financial data, build and evaluate trading models, and develop profitable trading strategies.Key Learning Points
- Learn how to collect and analyze financial data using Python
- Build and evaluate trading models using machine learning techniques
- Develop profitable trading strategies based on your models
- Understand the risks and limitations of machine learning in finance
Related Topics for further study
Learning Outcomes
- Collect and analyze financial data using Python
- Build and evaluate trading models using machine learning techniques
- Develop profitable trading strategies based on your models
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Basic understanding of financial markets and trading
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
- Hands-on projects
Similar Courses
- Quantitative Trading and Finance
- Financial Markets and Investment Strategy
Related Education Paths
Notable People in This Field
- Andrew Ng
- Elon Musk
Related Books
Description
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.
Knowledge
- 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.
Outline
- Introduction to Quantitative Trading and TensorFlow
- Introduction to Course
- Basic Trading Strategy Entries and Exits Endogenous Exogenous
- Basic Trading Strategy Building a Trading Model
- Advanced Concepts in Trading Strategies
- Welcome to Using Machine Learning in Trading and Finance
- Introduction to TensorFlow
- Overview
- Introduction to TensorFlow
- TensorFlow API Hierarchy
- Components of tensorflow Tensors and Variables
- Getting Started with Google Cloud Platform and Qwiklabs
- Lab Intro Writing low-level TensorFlow programs
- Working in-memory and with files
- Training on Large Datasets with tf.data API
- Getting the data ready for model training
- Embeddings
- Lab Intro Manipulating data with TensorFlow Dataset API
- Training neural networks with Tensorflow 2 and Keras
- Overview
- Activation functions
- Activation functions: Pitfalls to avoid in Backpropagation
- Neural Networks with Keras Sequential API
- Serving models in the cloud
- Lab Intro : Keras Sequential API
- Neural Networks with Keras Functional API
- Regularization: The Basics
- Regularization: L1, L2, and Early Stopping
- Regularization: Dropout
- Lab Intro: Keras Functional API
- Recap
- Build a Momentum-based Trading System
- Introduction to Momentum Trading
- Introduction to Hurst
- Building a Momentum Trading Model
- Define the Problem
- Collect the Data
- Creating Features
- Split the Data
- Selecting a Machine Learning Algorithm
- Backtest on Unseen Data
- Understanding the Code: Simple ML Strategies to Generate Trading Signal
- Lab Intro: Momentum Trading
- Momentum Trading Lab Solution
- Hurst Exponent and Trading Signals Derived from Market Time Series
- Build a Pair Trading Strategy Prediction Model
- Introduction to Pair Trading
- Picking Pairs
- Picking Pairs with Clustering
- How to implement a Pair Trading Strategy
- Evaluate Results of a Pair Trade
- Backtesting and Avoiding Overfitting
- Next Steps: Imrovements to your Pair Strategy
- Lab Intro: Pairs Trading
- Lab Solution: Pairs Trading
- Kalman Filter Introduction
- Kalman Filter Trading Applications
Summary of User Reviews
Discover how to use machine learning for trading in finance with this comprehensive course on Coursera. Learners have praised the course for its practical approach, interactive exercises and real-world examples.Key Aspect Users Liked About This Course
Real-world examplesPros from User Reviews
- Practical approach
- Interactive exercises
- Real-world examples
- Excellent instructor
- Great insights
Cons from User Reviews
- Requires some prior knowledge
- Can be challenging at times
- Could benefit from more explanations
- Course material is dense
- Not suitable for beginners
Jack Farmer
4 Raiting
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