Using Machine Learning in Trading and Finance
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.
Monthly Subscription Starting at AED 99 + VAT
- Level Professional
- Duration 19 hours
- Course by New York Institute of Finance
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Offered by
About
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).Modules
Introduction to TensorFlow, Trading, ML
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1
Videos
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1
Readings
1
Videos
- Introduction to Course
1
Readings
- Welcome to Using Machine Learning in Trading and Finance
Understand Quantitative Trading Strategies
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3
Videos
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1
Quiz
3
Videos
- Basic Trading Strategy Entries and Exits Endogenous Exogenous
- Basic Trading Strategy Building a Trading Model
- Advanced Concepts in Trading Strategies
1
Quiz
- Understand Quantitative Strategies
Introduction to TensorFlow
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11
Videos
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2
ExternalTool
2
External Tool
- Lab: Writing low-level TensorFlow Programs
- Lab: Manipulating data with TensorFlow Dataset API
11
Videos
- 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
Overview of Neural Networks and Introduction to Keras APIs
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12
Videos
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2
ExternalTool
2
External Tool
- Lab: Introducing the Keras Sequential API
- Lab: Introducing the Keras Functional API
12
Videos
- 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
Identify momentum-based factors
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2
Videos
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1
Readings
2
Videos
- Introduction to Momentum Trading
- Introduction to Hurst
1
Readings
- Hurst Exponent and Trading Signals Derived from Market Time Series
Build a trading model that uses momentum factors
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10
Videos
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1
Discussion
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2
ExternalTool
2
External Tool
- Lab: Momentum Strategies
- Optional Lab: Improve Momentum Trading strategies using Hurst
1
Discussions
- Compare interpretability versus explanatory power of the momentum factor
10
Videos
- 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
Picking Pairs
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3
Videos
3
Videos
- Introduction to Pair Trading
- Picking Pairs
- Picking Pairs with Clustering
Trading Strategy
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2
Videos
2
Videos
- How to implement a Pair Trading Strategy
- Evaluate Results of a Pair Trade
Backtesting and Avoiding Overfitting
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4
Videos
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1
ExternalTool
1
External Tool
- Lab: Pairs Trading Strategy
4
Videos
- Backtesting and Avoiding Overfitting
- Next Steps: Improvements to your Pairs Strategy
- Lab Intro: Pairs Trading
- Lab Solution: Pairs Trading
Optimize momentum trading model to minimize costs
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2
Videos
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1
Quiz
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1
ExternalTool
1
External Tool
- Optional Lab: Estimate parameters using Kalman Filters
2
Videos
- Kalman Filter Introduction
- Kalman Filter Trading Applications
1
Quiz
- Pairs Trading Strategy concepts
Auto Summary
Unlock advanced trading strategies with the "Using Machine Learning in Trading and Finance" course. Designed for professionals in the Data Science & AI domain, this Coursera course dives deep into the application of machine learning techniques to develop sophisticated trading models. Led by expert instructors, you'll explore key trading strategies like quantitative trading, pairs trading, and momentum trading. The hands-on curriculum includes designing basic quantitative strategies, building machine learning models with Keras and TensorFlow, and back-testing pair trading and momentum-based models. Over 1140 minutes of intensive learning, the course demands advanced Python proficiency and familiarity with libraries like Scikit-Learn, StatsModels, and Pandas. A solid grasp of statistics and financial markets, along with some SQL experience, will ensure you can fully engage with the material. Ideal for professionals ready to elevate their trading skills, the course offers flexible subscription options, including Starter and Professional tiers. Join now to master the intersection of machine learning and finance.
Jack Farmer