Machine Learning for Finance (with R) Training Cursus

Course Code



28 hours (usually 4 days including breaks)


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


Machine learning is een tak van kunstmatige intelligentie waarbij computers kunnen leren zonder expliciet te worden geprogrammeerd. R is een populaire programmeertaal in de financiële sector. Het wordt gebruikt in financiële toepassingen variërend van kernhandelprogramma's tot risicobeheersystemen.

In deze live training onder leiding van een instructeur leren deelnemers hoe ze technieken en hulpmiddelen voor machine learning kunnen toepassen om echte problemen in de financiële sector op te lossen. R wordt gebruikt als programmeertaal.

Deelnemers leren eerst de belangrijkste principes en brengen hun kennis vervolgens in de praktijk door hun eigen machine learning-modellen te bouwen en deze te gebruiken om een aantal teamprojecten te voltooien.

Aan het einde van deze training kunnen deelnemers:

  • Begrijp de fundamentele concepten in machine learning
  • Leer de toepassingen en toepassingen van machine learning in financiën
  • Ontwikkel hun eigen algoritmische handelsstrategie met behulp van machine learning met R


  • ontwikkelaars
  • Data wetenschappers

Formaat van de cursus

  • Deelcollege, deelbespreking, oefeningen en zware praktijkoefeningen

Machine Translated

Course Outline


  • 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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