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Course Outline
AI in the Context of Trading and Asset Management
- Emerging trends in algorithmic and AI-powered trading.
- Insights into quantitative finance workflows.
- Essential tools, platforms, and data sources.
Managing Financial Data with Python
- Processing time series data utilizing Pandas.
- Data cleansing, transformation, and feature engineering.
- Calculation of financial indicators and signal formulation.
Supervised Learning for Generating Trading Signals
- Utilizing regression and classification models for market forecasting.
- Assessing predictive models via metrics such as accuracy, precision, and Sharpe ratio.
- Case study: Developing an ML-based signal generator.
Unsupervised Learning and Market Regimes
- Applying clustering techniques to identify volatility regimes.
- Using dimensionality reduction for pattern recognition.
- Practical applications in basket trading and risk grouping.
Portfolio Optimization Leveraging AI Techniques
- The Markowitz framework and its inherent constraints.
- Risk parity, Black-Litterman models, and ML-based optimization approaches.
- Dynamic rebalancing informed by predictive inputs.
Backtesting and Strategy Assessment
- Utilizing Backtrader or bespoke frameworks.
- Analysis of risk-adjusted performance metrics.
- Mitigating overfitting and look-ahead bias.
Deploying AI Models in Live Trading Environments
- Integration with trading APIs and execution platforms.
- Continuous model monitoring and re-training cycles.
- Addressing ethical, regulatory, and operational factors.
Summary and Recommended Next Steps
Requirements
- Foundational knowledge of 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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