Machine Learning for Finance (with R)

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

Introduction to R

• Installing the RStudio IDE
• Loading R Packages
• Data Structures
• Vectors
• Factors
• Lists
• Data Frames
• Matrices and Arrays

Importing Financial Data into R

• Databases, Data Warehouses, and Streaming Data
• Distributed Storage and Processing with Hadoop and Spark
• Importing Data from a Database
• Importing Data from Excel and CSV

Implementing Regression Analysis with R

• Linear Regression
• Generalizations and Nonlinearity

Evaluating the Performance of Machine Learning Algorithms

• Cross-Validation and Resampling
• Bootstrap Aggregation (Bagging)
• Exercise

Developing an Algorithmic Trading Strategy with R

• Setting Up Your Working Environment
• Collecting and Examining Stock Data
• Implementing a Trend Following Strategy

Backtesting Your Machine Learning Trading Strategy

• Learning Backtesting Pitfalls
• Components of Your Backtester
• 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

Extending your Company's Capabilities

• Developing Models in the Cloud
• Using GPUs to Accelerate Deep Learning
• Applying Deep Learning Neural Networks for Computer Vision, Voice Recognition, and Text Analysis

Summary and Conclusion