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Course Outline
The Role of AI in Trading and Asset Management
- Emerging trends in algorithmic and AI-driven trading.
- An overview of workflows in quantitative finance.
- Essential tools, platforms, and data sources.
Managing Financial Data with Python
- Processing time series data using Pandas.
- Data cleaning, transformation, and feature engineering.
- Constructing financial indicators and trading signals.
Applying Supervised Learning to Trading Signals
- Utilizing regression and classification models for market forecasting.
- Assessing predictive model performance (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, and machine learning-based optimization.
- Dynamic rebalancing informed by predictive inputs.
Backtesting and Strategy Assessment
- Utilizing Backtrader or custom frameworks.
- Analyzing 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.
- Working familiarity with time series data.
Target Audience
- Quantitative Analysts.
- Professional Traders.
- Portfolio Managers.
21 Hours
Testimonials (1)
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