Machine Learning for Finance using R Language

Overview

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems. Machine Learning for Finance (with R) Training in Dubai brings the best for the developers and data scientists.

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. R Language will be used as the programming language for Machine Learning for Finance using R Language.

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 R

Audience for Machine Learning for Finance using R Language

Developers
Data scientists

Format of the course

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

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

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

Back-testing 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