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Course Outline
The Role of AI in Trading and Asset Management
- Current trends in algorithmic and AI-powered 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 cleaning, transformation, and feature engineering.
- Constructing financial indicators and trading signals.
Supervised Learning for Trading Signals
- Employing regression and classification models for market prediction.
- Assessing predictive models using metrics like accuracy, precision, and Sharpe ratio.
- Case study: Developing a machine learning-based signal generator.
Unsupervised Learning and Market Regimes
- Using clustering to identify volatility regimes.
- Applying dimensionality reduction for pattern discovery.
- Applications in basket trading and risk grouping.
AI-Driven Portfolio Optimization
- Examining the Markowitz framework and its limitations.
- Exploring risk parity, Black-Litterman, and ML-based optimization.
- Implementing dynamic rebalancing with 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
- Integrating with trading APIs and execution platforms.
- Establishing model monitoring and re-training cycles.
- Addressing ethical, regulatory, and operational considerations.
Wrap-up and Future Directions
Requirements
- A solid foundation in basic statistics and financial market dynamics.
- Proficiency in Python programming.
- Familiarity with time series data analysis.
Target Audience
- Quantitative analysts.
- Professional traders.
- Portfolio managers.
21 Hours
Testimonials (1)
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