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Duration 21 hours
Course Outline
AI Within the Trading and Asset Management Ecosystem
- Emerging trends in algorithmic and AI-driven trading.
- A comprehensive overview of quantitative finance workflows.
- Essential tools, platforms, and data sources.
Managing Financial Data with Python
- Processing time series data utilizing Pandas.
- Data cleaning, transformation, and feature engineering techniques.
- Deriving financial indicators and constructing signals.
Supervised Learning for Trading Signals
- 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 methods for identifying volatility regimes.
- Dimensionality reduction techniques for pattern detection.
- Applications in basket trading and risk grouping.
Portfolio Optimization via AI Techniques
- The Markowitz framework and its inherent limitations.
- Risk parity, Black-Litterman, and ML-based optimization approaches.
- Dynamic rebalancing strategies informed by predictive inputs.
Backtesting and Strategy Assessment
- Utilizing Backtrader or custom frameworks for testing.
- Risk-adjusted performance metrics.
- Mitigating overfitting and look-ahead bias.
Deploying AI Models in Live Trading
- Integration with trading APIs and execution platforms.
- Model monitoring and continuous re-training cycles.
- Ethical, regulatory, and operational considerations.
Summary and Recommended Next Steps
Requirements
- Foundational knowledge of basic statistics and financial market dynamics.
- Practical experience with Python programming.
- Proficiency in handling time series data.
Target Audience
- Quantitative analysts.
- Trading professionals.
- Portfolio managers.
Testimonials (1)
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