課程大綱： Machine Learning for Finance (with R)培訓
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
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