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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 quantitative finance workflows
- Essential tools, platforms, and data resources
Managing Financial Data with Python
- Processing time series data utilizing Pandas
- Data cleansing, transformation, and feature engineering
- Calculating financial indicators and constructing signals
Supervised Learning for Trading Signals
- Applying regression and classification models for market forecasting
- Assessing predictive model performance (e.g., accuracy, precision, Sharpe ratio)
- Case study: developing an ML-based signal generator
Unsupervised Learning and Market Regimes
- Clustering methods for identifying volatility regimes
- Dimensionality reduction techniques for pattern recognition
- Applications in basket trading and risk categorization
AI-Enhanced Portfolio Optimization
- The Markowitz framework and its inherent constraints
- Risk parity, Black-Litterman, and machine learning-based optimization
- Dynamic rebalancing strategies utilizing predictive inputs
Backtesting and Strategy Assessment
- Utilizing Backtrader or proprietary frameworks
- Analyzing risk-adjusted performance metrics
- Mitigating overfitting and look-ahead bias
Deploying AI Models in Live Trading Environments
- Integrating models with trading APIs and execution platforms
- Monitoring model health and managing re-training cycles
- Addressing ethical, regulatory, and operational considerations
Concluding Summary and Future Directions
Requirements
- A foundational grasp of 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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