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Course Outline
AI in the Context of Trading and Asset Management
- Emerging trends in algorithmic and AI-driven trading
- Insights into quantitative finance workflows
- Essential tools, platforms, and data sources
Managing Financial Data with Python
- Processing time series data using Pandas
- Data cleaning, transformation, and feature engineering techniques
- Constructing financial indicators and trading signals
Applying Supervised Learning to Trading Signals
- Using regression and classification models for market forecasting
- Assessing predictive model performance via metrics like accuracy, precision, and Sharpe ratio
- Case study: Developing a machine learning-based signal generator
Unsupervised Learning and Market Regime Analysis
- Employing clustering techniques to identify volatility regimes
- Utilizing dimensionality reduction for pattern discovery
- Applications in basket trading and risk grouping
AI-Driven Portfolio Optimization
- Understanding the Markowitz framework and its constraints
- Implementing risk parity, Black-Litterman, and ML-based optimization methods
- Dynamic rebalancing strategies incorporating predictive inputs
Backtesting and Strategy Assessment
- Leveraging Backtrader or custom frameworks for testing
- 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
- Establishing model monitoring and re-training protocols
- Addressing ethical, regulatory, and operational considerations
Course Summary and Future Directions
Requirements
- A solid foundation in basic statistics and financial market dynamics
- Proficiency in Python programming
- Familiarity with handling time series data
Target Audience
- Quantitative analysts
- Trading professionals
- Portfolio managers
21 Hours
Testimonials (1)
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