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Course Outline

AI in the Trading and Asset Management Landscape

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

Working with Financial Data in Python

  • Managing time series data using Pandas.
  • Data cleaning, transformation, and feature engineering.
  • Construction of financial indicators and signals.

Supervised Learning for Trading Signals

  • Application of regression and classification models for market prediction.
  • Evaluation of predictive models (e.g., accuracy, precision, Sharpe ratio).
  • Case study: developing 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 inherent limitations.
  • Risk parity, Black-Litterman, and ML-based optimization approaches.
  • Dynamic rebalancing incorporating predictive inputs.

Backtesting and Strategy Evaluation

  • Utilising Backtrader or custom frameworks.
  • Risk-adjusted performance metrics.
  • Mitigating 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

  • A foundational understanding of statistics and financial markets.
  • Practical experience with Python programming.
  • Familiarity with time series data.

Audience

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

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