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Duration 21 hours (3 days)
Course Outline
AI in the Context of Trading and Asset Management
- Current trends in algorithmic and AI-assisted trading.
- A look at quantitative finance workflows.
- Essential tools, platforms, and data sources.
Managing Financial Data with Python
- Processing time series data via Pandas.
- Data cleaning, transformation, and feature engineering.
- Constructing financial indicators and trading signals.
Supervised Learning for Generating Trading Signals
- Applying regression and classification models for market forecasting.
- Assessing predictive models (e.g., accuracy, precision, Sharpe ratio).
- Practical example: Developing a machine learning-based signal generator.
Unsupervised Learning and Market Regimes
- Utilizing clustering to identify volatility regimes.
- Employing dimensionality reduction for pattern recognition.
- Use cases in basket trading and risk categorization.
AI-Driven Portfolio Optimization
- The Markowitz framework and its inherent limitations.
- Strategies involving risk parity, Black-Litterman, and ML-based optimization.
- Dynamic rebalancing informed by predictive inputs.
Backtesting and Strategy Assessment
- Leveraging Backtrader or bespoke frameworks.
- Risk-adjusted performance indicators.
- Mitigating overfitting and look-ahead bias.
Deploying AI Models in Live Trading
- Connecting with trading APIs and execution platforms.
- Cycles of model monitoring and re-training.
- Ethical, regulatory, and operational factors.
Recap and Future Directions
Requirements
- A foundational grasp of basic statistics and financial market mechanics.
- Proficiency in Python programming.
- Acclimation to working with time series data.
Target Audience
- Quantitative analysts.
- Trading specialists.
- Portfolio managers.
Testimonials (1)
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