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Course Outline
AI’s Role in Trading and Asset Management
- Current trends in algorithmic and AI-driven trading
- An overview 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 trading signals
Supervised Learning for Trading Signals
- Regression and classification models for market forecasting
- Assessing predictive models (e.g., accuracy, precision, Sharpe ratio)
- Case study: Creating an ML-based signal generator
Unsupervised Learning and Market Regimes
- Clustering techniques for identifying volatility regimes
- Dimensionality reduction for uncovering market patterns
- Applications in basket trading and risk categorization
AI-Driven Portfolio Optimization
- The Markowitz framework and its inherent limitations
- Risk parity, Black-Litterman, and ML-based optimization approaches
- 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
- Continuous model monitoring and re-training cycles
- Ethical, regulatory, and operational factors
Summary and Future Steps
Requirements
- Foundational knowledge of statistics and financial market mechanics
- Proficiency in Python programming
- Working familiarity with time series data
Target Audience
- Quantitative analysts
- Trading specialists
- Portfolio managers
21 Hours
Testimonials (1)
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