Course Outline
Artificial Intelligence in the Trading and Asset Management Landscape for Government
- Current trends in algorithmic and AI-based trading strategies
- Overview of quantitative finance workflows and methodologies
- Key tools, platforms, and data sources utilized in the industry
Working with Financial Data in Python for Government
- Techniques for handling time series data using Pandas
- Methods for data cleaning, transformation, and feature engineering
- Construction of financial indicators and signals
Supervised Learning for Trading Signals for Government
- Application of regression and classification models for market prediction
- Evaluation of predictive models using metrics such as accuracy, precision, and Sharpe ratio
- Case study: development of an ML-based signal generator
Unsupervised Learning and Market Regimes for Government
- Use of clustering techniques to identify volatility regimes
- Dimensionality reduction methods for pattern discovery in financial data
- Applications of unsupervised learning in basket trading and risk grouping
Portfolio Optimization with AI Techniques for Government
- Review of the Markowitz framework and its limitations
- Advanced optimization techniques such as risk parity, Black-Litterman, and machine learning-based approaches
- Implementation of dynamic rebalancing using predictive inputs
Backtesting and Strategy Evaluation for Government
- Utilization of backtesting frameworks such as Backtrader or custom solutions
- Assessment of risk-adjusted performance metrics
- Strategies to avoid overfitting and look-ahead bias in model evaluation
Deploying AI Models in Live Trading for Government
- Integration with trading APIs and execution platforms for real-time operations
- Ongoing monitoring and re-training of models to maintain accuracy
- Ethical, regulatory, and operational considerations in deploying AI models
Summary and Next Steps for Government
Requirements
- A foundational knowledge of statistics and financial markets for government applications
- Experience with Python programming for data analysis tasks
- Familiarity with handling time series data for government
Audience
- Quantitative analysts in the public sector
- Trading professionals working in governmental financial roles
- Portfolio managers within government agencies
Testimonials (3)
The background / theory of LLMs, the exercise
Joanne Wong - IPG HK Limited
Course - Applied AI for Financial Statement Analysis & Reporting
it has opened my mind to new tool that can help me in creating automation
Alessandra Parpajola - Advanced Bionics AG
Course - Machine Learning & AI for Finance Professionals
I very much appreciated the way the trainer presented everything. I understood everything even if Finance is not my area, he made sure that every participant was on the same page, while keeping up with the time left. The exercises were placed at good intervals. Communication with the participants was always there. The material was perfect, not too much, not too little. He elaborated very well on a bit more complicated subjects so that it can be understood by everyone.
