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Course Outline
The Role of AI in Trading and Asset Management
- Emerging trends in algorithmic and AI-driven trading
- An overview of workflows in quantitative finance
- Essential tools, platforms, and data sources
Managing Financial Data with Python
- Processing time-series data using Pandas
- Data cleaning, transformation, and feature engineering
- Constructing financial indicators and trading signals
Supervised Learning for Trading Signals
- Applying regression and classification models for market forecasting
- Assessing predictive performance (e.g., accuracy, precision, Sharpe ratio)
- Case study: Developing an ML-based signal generator
Unsupervised Learning and Market Regimes
- Clustering to identify volatility regimes
- Dimensionality reduction for pattern recognition
- Applications in basket trading and risk categorisation
AI-Driven Portfolio Optimisation
- The Markowitz framework and its inherent constraints
- Risk parity, Black-Litterman, and ML-based optimisation methods
- Dynamic rebalancing using predictive inputs
Backtesting and Strategy Assessment
- Utilising Backtrader or custom-built frameworks
- Analysing risk-adjusted performance metrics
- Mitigating overfitting and look-ahead bias
Deploying AI Models in Live Trading
- Integrating with trading APIs and execution platforms
- Continuous model monitoring and re-training cycles
- Ethical, regulatory, and operational considerations
Summary and Subsequent Steps
Requirements
- Foundational knowledge of basic statistics and financial markets
- Proficiency in Python programming
- Familiarity with time-series data
Target Audience
- Quantitative Analysts
- Trading Professionals
- Portfolio Managers
21 Hours
Testimonials (1)
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