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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
  • Development of financial indicators and signal construction

Supervised Learning for Trading Signals

  • Utilizing 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

  • Clustering techniques for identifying volatility regimes
  • Dimensionality reduction for uncovering patterns
  • Applications in basket trading and risk grouping

Portfolio Optimization with AI Techniques

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

Backtesting and Strategy Evaluation

  • Utilizing Backtrader or custom backtesting frameworks
  • Analyzing 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
  • Proficiency in Python programming
  • Familiarity with time series data analysis

Target Audience

  • Quantitative analysts
  • Trading professionals
  • Portfolio managers
 21 Hours

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