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Course Outline
The Landscape of AI in Trading and Asset Management
- Emerging trends in algorithmic and AI-driven trading.
- A comprehensive overview of quantitative finance workflows.
- Key tools, platforms, and essential data sources.
Managing Financial Data with Python
- Processing time series data using Pandas.
- Data cleansing, transformation, and feature engineering.
- Constructing financial indicators and trading 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 an ML-based signal generator.
Unsupervised Learning and Market Regimes
- Clustering techniques for identifying volatility regimes.
- Dimensionality reduction for uncovering patterns.
- Applications in basket trading and risk grouping.
AI-Enhanced Portfolio Optimisation
- The Markowitz framework and its inherent limitations.
- Risk parity, Black-Litterman, and ML-based optimisation.
- Dynamic rebalancing utilising predictive inputs.
Backtesting and Strategy Assessment
- Utilising Backtrader or custom frameworks.
- 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 re-training cycles.
- Ethical, regulatory, and operational considerations.
Summary and Next Steps
Requirements
- Foundational knowledge of basic statistics and financial market structures.
- Practical experience with Python programming.
- Familiarity with the nuances of time series data.
Target Audience
- Quantitative analysts.
- Trading professionals.
- Portfolio managers.
21 Hours
Testimonials (1)
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