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Course Outline
The Role of AI in Trading and Asset Management
- Emerging trends in algorithmic and AI-driven trading
- Overview of standard workflows in quantitative finance
- Essential tools, platforms, and data resources
Managing Financial Data with Python
- Processing time series data utilizing Pandas
- Techniques for data cleaning, transformation, and feature engineering
- Constructing financial indicators and trading signals
Supervised Learning for Generating Trading Signals
- Utilizing regression and classification models for market forecasting
- Assessing predictive model performance (e.g., accuracy, precision, Sharpe ratio)
- Case study: Developing a machine learning-based signal generator
Unsupervised Learning and Market Regime Analysis
- Applying clustering techniques to identify volatility regimes
- Using dimensionality reduction for pattern recognition
- Applications in basket trading and risk categorization
AI-Enhanced Portfolio Optimization
- Analyzing the Markowitz framework and its inherent constraints
- Exploring risk parity, Black-Litterman, and machine learning-based optimization
- Implementing dynamic rebalancing using predictive inputs
Strategy Backtesting and Evaluation
- Utilizing Backtrader or custom-built frameworks for testing
- Analyzing risk-adjusted performance metrics
- Mitigating overfitting and look-ahead bias in models
Deploying AI Models in Live Trading Environments
- Integrating models with trading APIs and execution platforms
- Managing model monitoring and re-training cycles
- Addressing ethical, regulatory, and operational factors
Course Summary and Recommended Next Steps
Requirements
- Foundational knowledge of basic statistics and financial market dynamics
- Proficiency in Python programming
- Familiarity with time series data structures
Target Audience
- Quantitative analysts
- Trading professionals
- Portfolio managers
21 Hours
Testimonials (1)
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