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Duration 21 hours (3 days)
Course Outline
The Role of AI in Trading and Asset Management
- Emerging trends in algorithmic and AI-driven trading
- A comprehensive view of 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 signals
Supervised Learning for Generating Trading Signals
- Regression and classification models for market forecasting
- Assessing predictive models (e.g., accuracy, precision, Sharpe ratio)
- Case study: Developing a machine learning-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
AI-Driven Portfolio Optimization
- The Markowitz framework and its inherent limitations
- Risk parity, Black-Litterman models, and ML-based optimization
- Dynamic rebalancing informed by predictive inputs
Backtesting and Strategy Assessment
- 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
- Foundational knowledge of statistics and financial markets.
- Proficiency in Python programming.
- Familiarity with time series data structures.
Target Audience
- Quantitative analysts.
- Professional traders.
- Portfolio managers.
Testimonials (1)
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