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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
Requirements
- Basic experience with Python programming
- Basic familiarity with statistics and linear algebra
21 Hours
Testimonials (2)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
