Using Machine Learning in Trading and Finance
- Offered byCoursera
Using Machine Learning in Trading and Finance at Coursera Overview
Duration | 19 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Using Machine Learning in Trading and Finance at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 2 of 3 in the Machine Learning for Trading Specialization
- 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
Using Machine Learning in Trading and Finance at Coursera Course details
- 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).
Using Machine Learning in Trading and Finance at Coursera Curriculum
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
Understand Quantitative Strategies
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
Pairs Trading Strategy concepts
Using Machine Learning in Trading and Finance at Coursera Admission Process
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