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Course Outline

The Landscape of AI in Trading and Asset Management

  • Current trends in algorithmic and AI-driven trading.
  • Overview of quantitative finance workflows.
  • Key tools, platforms, and data sources.

Managing Financial Data with Python

  • Processing time series data using Pandas.
  • Data cleaning, transformation, and feature engineering.
  • Developing financial indicators and constructing signals.

Supervised Learning for Trading Signals

  • Regression and classification models for market prediction.
  • Evaluating predictive models (e.g., accuracy, precision, Sharpe ratio).
  • Case study: Building an ML-based signal generator.

Unsupervised Learning and Market Regimes

  • Clustering techniques for identifying volatility regimes.
  • Dimensionality reduction for pattern discovery.
  • Applications in basket trading and risk grouping.

Portfolio Optimization with AI Techniques

  • The Markowitz framework and its limitations.
  • Risk parity, Black-Litterman models, and ML-based optimization.
  • Dynamic rebalancing using predictive inputs.

Backtesting and Strategy Evaluation

  • Utilizing Backtrader or custom frameworks.
  • Risk-adjusted performance metrics.
  • Strategies to avoid 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.
  • Practical experience with Python programming.
  • Familiarity with time series data analysis.

Target Audience

  • Quantitative analysts.
  • Trading professionals.
  • Portfolio managers.
 21 Hours

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