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Course Outline
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
Requirements
- Basic experience with Python programming
- Basic familiarity with statistics and linear algebra
21 Hours
