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Course Outline
The Role of AI in Trading and Asset Management
- Current trends in algorithmic and AI-driven trading
- 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 processes
- Creating financial indicators and constructing signals
Supervised Learning for Trading Signals
- Regression and classification models for market forecasting
- Assessing predictive models using metrics like accuracy, precision, and 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 market patterns
- Applications in basket trading and risk grouping strategies
AI-Driven Portfolio Optimization
- The Markowitz framework and its inherent limitations
- Risk parity, Black-Litterman models, and ML-based optimization
- Dynamic rebalancing strategies utilizing predictive inputs
Backtesting and Strategy Assessment
- Implementation using Backtrader or custom frameworks
- Evaluation of risk-adjusted performance metrics
- Strategies to avoid overfitting and look-ahead bias
Deploying AI Models in Live Trading Environments
- Integration with trading APIs and execution platforms
- Establishing model monitoring and re-training cycles
- Addressing ethical, regulatory, and operational considerations
Summary and Recommended Next Steps
Requirements
- Foundational knowledge of basic statistics and financial market dynamics
- Proficiency in Python programming
- Working familiarity with time series data analysis
Target Audience
- Quantitative analysts
- Trading professionals
- Portfolio managers
21 Hours
Testimonials (1)
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