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Course Outline
The Role of AI in Trading and Asset Management
- Emerging trends in algorithmic and AI-powered trading
- Insight into 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 performance (e.g., accuracy, precision, Sharpe ratio)
- Case study: Developing an ML-based signal generator
Unsupervised Learning and Market Regimes
- Clustering techniques for volatility regimes
- Dimensionality reduction for pattern identification
- Applications in basket trading and risk categorization
AI-Enhanced Portfolio Optimization
- The Markowitz framework and its constraints
- Risk parity, Black-Litterman, and ML-based optimization approaches
- Dynamic rebalancing utilizing predictive inputs
Backtesting and Strategy Assessment
- Utilizing Backtrader or bespoke frameworks
- Risk-adjusted performance evaluation metrics
- Mitigating overfitting and look-ahead bias
Deploying AI Models in Live Trading
- Integrating with trading APIs and execution platforms
- Monitoring models and managing re-training cycles
- Ethical, regulatory, and operational considerations
Conclusion and Recommended Next Steps
Requirements
- Foundational knowledge of basic statistics and financial markets
- Proficiency in Python programming
- Familiarity with time series data analysis
Target Audience
- Quantitative analysts
- Trading professionals
- Portfolio managers
21 Hours
Testimonials (1)
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