Machine Learning for Finance (with R) Training Course

Course Code

mlfinancer

Duration

28 hours (usually 4 days including breaks)

Requirements

  • Programming experience with any language
  • Basic familiarity with statistics and linear algebra

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.

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 will be used as the programming 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

  • 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

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

Testimonials

★★★★★
★★★★★

Some of our clients

is growing fast!

We are looking to expand our presence in the UAE!

As a Business Development Manager you will:

  • expand business in the UAE
  • recruit local talent (sales, agents, trainers, consultants)
  • recruit local trainers and consultants

We offer:

  • Artificial Intelligence and Big Data systems to support your local operation
  • high-tech automation
  • continuously upgraded course catalogue and content
  • good fun in international team

If you are interested in running a high-tech, high-quality training and consulting business.

Apply now!