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Course Outline
AI in the Trading and Asset Management Landscape
- Current trends in algorithmic and AI-based trading
- Overview of workflows in quantitative finance
- Essential tools, platforms, and data sources
Managing Financial Data with Python
- Processing time series data utilizing Pandas
- Data cleaning, transformation, and feature engineering
- Deriving financial indicators and constructing signals
Supervised Learning for Trading Signals
- Regression and classification models for market forecasting
- 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 uncovering patterns
- 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 optimization methods
- Dynamic rebalancing incorporating predictive inputs
Backtesting and Strategy Evaluation
- Utilizing Backtrader or custom frameworks
- 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 grasp of fundamental statistics and financial market mechanics
- Proficiency in Python programming
- Familiarity with time series data structures
Intended Audience
- Quantitative analysts
- Trading professionals
- Portfolio managers
21 Hours
Testimonials (1)
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