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Course Outline

The Landscape of AI in Trading and Asset Management

  • Emerging trends in algorithmic and AI-driven 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
  • Developing financial indicators and constructing signals

Supervised Learning for Generating Trading Signals

  • Applying regression and classification models for market prediction
  • Assessing predictive model performance (e.g., accuracy, precision, Sharpe ratio)
  • Case study: developing an ML-based signal generator

Unsupervised Learning and Market Regimes

  • Utilizing clustering to identify volatility regimes
  • Employing dimensionality reduction for pattern discovery
  • Applications in basket trading and risk grouping

Portfolio Optimization Using AI Techniques

  • Exploring the Markowitz framework and its limitations
  • Implementing risk parity, Black-Litterman, and ML-based optimization strategies
  • Dynamic rebalancing informed by predictive inputs

Backtesting and Strategy Evaluation

  • Utilizing Backtrader or custom frameworks for backtesting
  • Analyzing risk-adjusted performance metrics
  • Strategies to prevent overfitting and look-ahead bias

Deploying AI Models in Live Trading Environments

  • Integrating with trading APIs and execution platforms
  • Establishing model monitoring and re-training cycles
  • Addressing 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

Target Audience

  • Quantitative analysts
  • Trading professionals
  • Portfolio managers
 21 Hours

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