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

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

Bookings, Prices and Enquiries

Guaranteed to run even with a single delegate!

Private Classroom

From £5000

Private Remote

From £4400 (86)

Public Classroom

Cannot find a suitable date? Choose Your Course Date >>Too expensive? Suggest your price

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