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Course Outline

The Landscape of AI in Trading and Asset Management

  • Current trends in algorithmic and AI-driven trading
  • Comprehensive 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 techniques
  • Construction of financial indicators and trading signals

Supervised Learning for Generating Trading Signals

  • Application of regression and classification models for market prediction
  • Evaluation of predictive models (e.g., accuracy, precision, Sharpe ratio)
  • Case study: Development of an ML-based signal generator

Unsupervised Learning and Identifying Market Regimes

  • Utilizing clustering for volatility regime identification
  • Dimensionality reduction techniques for pattern discovery
  • Applications in basket trading and risk grouping strategies

Portfolio Optimization via AI Techniques

  • The Markowitz framework and its inherent limitations
  • Risk parity, Black-Litterman, and ML-based optimization approaches
  • Dynamic rebalancing strategies informed by predictive inputs

Backtesting and Strategy Evaluation

  • Leveraging Backtrader or custom-built frameworks
  • Analysis of risk-adjusted performance metrics
  • Mitigating overfitting and look-ahead bias

Deployment of AI Models in Live Trading Environments

  • Integration with trading APIs and execution platforms
  • Continuous model monitoring and re-training cycles
  • Considerations regarding ethics, regulations, and operations

Summary and Future Steps

Requirements

  • Foundational knowledge of basic 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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