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Course Outline
AI in the Trading and Asset Management Landscape
- Emerging trends in algorithmic and AI-based trading
- An overview of quantitative finance workflows
- Essential tools, platforms, and data sources
Working with Financial Data in Python
- Managing time series data using Pandas
- Data cleaning, transformation, and feature engineering
- Calculating financial indicators and constructing signals
Supervised Learning for Trading Signals
- Regression and classification models for market prediction
- 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 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 optimisation
- Dynamic rebalancing using predictive inputs
Backtesting and Strategy Evaluation
- Utilising Backtrader or custom frameworks
- Analysing 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
- A solid understanding of fundamental statistics and financial markets
- Practical experience with Python programming
- Familiarity with time series data analysis
Target Audience
- Quantitative analysts
- Trading professionals
- Portfolio managers
21 Hours
Testimonials (1)
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