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Course Outline
AI in the Trading and Asset Management Landscape
- Trends in algorithmic and AI-based trading
- Overview of quantitative finance workflows
- Key tools, platforms, and data sources
Working with Financial Data in Python
- Handling time series data using Pandas
- Data cleaning, transformation, and feature engineering
- Financial indicators and signal construction
Supervised Learning for Trading Signals
- Regression and classification models for market prediction
- Evaluating predictive models (e.g. accuracy, precision, Sharpe ratio)
- Case study: building an ML-based signal generator
Unsupervised Learning and Market Regimes
- Clustering for volatility regimes
- Dimensionality reduction for pattern discovery
- Applications in basket trading and risk grouping
Portfolio Optimization with AI Techniques
- Markowitz framework and its limitations
- Risk parity, Black-Litterman, and ML-based optimization
- Dynamic rebalancing with predictive inputs
Backtesting and Strategy Evaluation
- Using Backtrader or custom frameworks
- Risk-adjusted performance metrics
- Avoiding overfitting and look-ahead bias
Deploying AI Models in Live Trading
- Integration with trading APIs and execution platforms
- Model monitoring and re-training cycles
- Ethical, regulatory, and operational considerations
Summary and Next Steps
Requirements
- An understanding of basic statistics and financial markets
- Experience with Python programming
- Familiarity with time series data
Audience
- Quantitative analysts
- Trading professionals
- Portfolio managers
21 Hours
Testimonials (1)
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.
