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Duration 21 hours (3 days)
Course Outline
AI in the Trading and Asset Management Landscape
- Current trends in algorithmic and AI-driven trading
- An overview of quantitative finance workflows
- Essential tools, platforms, and data sources
Managing Financial Data in Python
- Processing time-series data using Pandas
- Data cleaning, transformation, and feature engineering
- Constructing financial indicators and trading signals
Supervised Learning for Trading Signals
- Employing regression and classification models for market forecasting
- Assessing predictive models (e.g., accuracy, precision, Sharpe ratio)
- Case study: Developing a machine learning-based signal generator
Unsupervised Learning and Market Regimes
- Clustering techniques for identifying volatility regimes
- Dimensionality reduction for pattern discovery
- Applications in basket trading and risk grouping
Portfolio Optimization with AI Techniques
- The Markowitz framework and its inherent limitations
- Risk parity, Black-Litterman, and ML-based optimization methods
- Dynamic rebalancing strategies utilizing predictive inputs
Backtesting and Strategy Evaluation
- Utilizing Backtrader or custom frameworks
- Risk-adjusted performance metrics
- Mitigating overfitting and look-ahead bias
Deploying AI Models in Live Trading
- Integrating with trading APIs and execution platforms
- Model monitoring and re-training cycles
- Ethical, regulatory, and operational considerations
Summary and Next Steps
Requirements
- A foundational understanding of basic statistics and financial market dynamics
- Proficiency in Python programming
- Familiarity with handling time-series data
Target Audience
- Quantitative Analysts
- Trading Professionals
- Portfolio Managers
Testimonials (1)
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