Machine Learning for Finance (with Python)

• Basic experience with Python programming
• Basic familiarity with statistics and linear algebra

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.

In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry.

Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.

By the end of this training, participants will be able to:

• Understand the fundamental concepts in machine learning
• Learn the applications and uses of machine learning in finance
• Develop their own algorithmic trading strategy using machine learning with Python

Audience

• Developers
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Introduction

• Difference between statistical learning (statistical analysis) and machine learning
• Adoption of machine learning technology and talent by finance companies

Understanding Different Types of Machine Learning

• Supervised learning vs unsupervised learning
• Iteration and evaluation
• Bias-variance trade-off
• Combining supervised and unsupervised learning (semi-supervised learning)

Understanding Machine Learning Languages and Toolsets

• Open source vs proprietary systems and software
• Python vs R vs Matlab
• Libraries and frameworks

Understanding Neural Networks

Understanding Basic Concepts in Finance

• Understanding Stocks Trading
• Understanding Time Series Data
• Understanding Financial Analyses

Machine Learning Case Studies in Finance

• Signal Generation and Testing
• Feature Engineering
• Artificial Intelligence Algorithmic Trading
• Quantitative Trade Predictions
• Robo-Advisors for Portfolio Management
• Risk Management and Fraud Detection
• Insurance Underwriting

Hands-on: Python for Machine Learning

• Setting Up the Workspace
• Obtaining Python machine learning libraries and packages
• Working with Pandas
• Working with Scikit-Learn

Importing Financial Data into Python

• Using Pandas
• Using Quandl
• Integrating with Excel

Working with Time Series Data with Python

• Exploring Your Data
• Visualizing Your Data

Implementing Common Financial Analyses with Python

• Returns
• Moving Windows
• Volatility Calculation
• Ordinary Least-Squares Regression (OLS)    

Developing an Algorithmic Trading Strategy Using Supervised Machine Learning with Python

• Understanding the Momentum Trading Strategy
• Understanding the Reversion Trading Strategy
• Implementing Your Simple Moving Averages (SMA) Trading Strategy

Backtesting Your Machine Learning Trading Strategy

• Learning Backtesting Pitfalls
• Components of Your Backtester
• Using Python Backtesting Tools
• Implementing Your Simple Backtester

Improving Your Machine Learning Trading Strategy

• KMeans
• K-Nearest Neighbors (KNN)
• Classification or Regression Trees
• Genetic Algorithm
• Working with Multi-Symbol Portfolios
• Using a Risk Management Framework
• Using Event-Driven Backtesting

Evaluating Your Machine Learning Trading Strategy's Performance

• Using the Sharpe Ratio
• Calculating a Maximum Drawdown
• Using Compound Annual Growth Rate (CAGR)
• Measuring Distribution of Returns
• Using Trade-Level Metrics
• Summary

Troubleshooting

Closing Remarks