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План на курса
AI in the Trading and Asset Management Landscape
- Trends in algorithmic and AI-based trading
- Overview of quantitative finance workflows
- Key tools, platforms, and data sources
Working with Financial Data in Python
- Handling time series data using Pandas
- Data cleaning, transformation, and feature engineering
- Financial indicators and signal construction
Supervised Learning for Trading Signals
- Regression and classification models for market prediction
- Evaluating predictive models (e.g. accuracy, precision, Sharpe ratio)
- Case study: building an ML-based signal generator
Unsupervised Learning and Market Regimes
- Clustering for volatility regimes
- Dimensionality reduction for pattern discovery
- Applications in basket trading and risk grouping
Portfolio Optimization with AI Techniques
- Markowitz framework and its limitations
- Risk parity, Black-Litterman, and ML-based optimization
- Dynamic rebalancing with predictive inputs
Backtesting and Strategy Evaluation
- Using Backtrader or custom frameworks
- Risk-adjusted performance metrics
- Avoiding 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
Изисквания
- An understanding of basic statistics and financial markets
- Experience with Python programming
- Familiarity with time series data
Audience
- Quantitative analysts
- Trading professionals
- Portfolio managers
21 Часа
