Machine Learning for Finance (with Python) Training Course

Course Code

mlfinancepython

Duration

21 hours (usually 3 days including breaks)

Requirements

  • Basic experience with Python programming
  • 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. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.

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.

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 Python

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

Hands-on: Python for Machine Learning

  • Setting Up the Workspace
  • Obtaining Python machine learning libraries and packages
  • Working with Pandas
  • Working with Scikit-Learn

Importing Financial Data into Python

  • Using Pandas
  • Using Quandl
  • Integrating with Excel

Working with Time Series Data with Python

  • Exploring Your Data
  • Visualizing Your Data

Implementing Common Financial Analyses with Python

  • Returns
  • Moving Windows
  • Volatility Calculation
  • Ordinary Least-Squares Regression (OLS)    

Developing an Algorithmic Trading Strategy Using Supervised Machine Learning with Python

  • Understanding the Momentum Trading Strategy
  • Understanding the Reversion Trading Strategy
  • Implementing Your Simple Moving Averages (SMA) Trading Strategy

Backtesting Your Machine Learning Trading Strategy

  • Learning Backtesting Pitfalls
  • Components of Your Backtester
  • Using Python Backtesting Tools
  • 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
  • Summary

Troubleshooting

Closing Remarks

Testimonials

★★★★★
★★★★★

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