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Course Outline
AI in the Trading and Asset Management Landscape
- Emerging trends in algorithmic and AI-driven trading.
- A broad overview of quantitative finance workflows.
- Essential tools, platforms, and critical data sources.
Working with Financial Data in Python
- Managing time series data with Pandas.
- Techniques for data cleaning, transformation, and feature engineering.
- Constructing financial indicators and trading signals.
Supervised Learning for Trading Signals
- Utilizing regression and classification models for market forecasting.
- Assessing predictive models using metrics such as accuracy, precision, and Sharpe ratio.
- Case study: Developing a machine learning-based signal generator.
Unsupervised Learning and Market Regimes
- Applying clustering to identify volatility regimes.
- Using dimensionality reduction to uncover hidden patterns.
- Practical applications in basket trading and risk grouping.
Portfolio Optimization with AI Techniques
- Examining the Markowitz framework and its inherent limitations.
- Exploring risk parity, Black-Litterman, and ML-based optimization methods.
- Implementing dynamic rebalancing using predictive inputs.
Backtesting and Strategy Evaluation
- Employing Backtrader or custom frameworks for testing.
- Analyzing risk-adjusted performance metrics.
- Strategies for avoiding overfitting and look-ahead bias.
Deploying AI Models in Live Trading
- Integrating models with trading APIs and execution platforms.
- Managing model monitoring and re-training cycles.
- Addressing ethical, regulatory, and operational considerations.
Summary and Next Steps
Requirements
- A foundational grasp of basic statistics and financial market dynamics.
- Proficiency in Python programming.
- Working familiarity with time series data.
Target Audience
- Quantitative analysts.
- Trading professionals.
- Portfolio managers.
21 Hours
Testimonials (1)
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