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Course Outline
AI in the Trading and Asset Management Ecosystem
- Emerging trends in algorithmic and AI-driven trading.
- An overview of quantitative finance workflows.
- Essential tools, platforms, and 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 using metrics such as accuracy, precision, and the Sharpe ratio.
- Case study: Developing an ML-based signal generator.
Unsupervised Learning and Market Regimes
- Clustering techniques for identifying volatility regimes.
- Dimensionality reduction for pattern detection.
- Applications in basket trading and risk grouping.
Portfolio Optimisation Using AI Techniques
- The Markowitz framework and its inherent limitations.
- Risk parity, Black-Litterman models, and ML-based optimisation.
- Dynamic rebalancing incorporating predictive inputs.
Backtesting and Strategy Assessment
- Utilising Backtrader or custom-built 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 Recommended Next Steps
Requirements
- A solid grasp of fundamental statistics and financial market dynamics.
- Proficiency in Python programming.
- Knowledge of time-series data structures.
Target Audience
- Quantitative Analysts.
- Trading Professionals.
- Portfolio Managers.
21 Hours
Testimonials (1)
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