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Course Outline
The Role of AI in Trading and Asset Management
- Emerging trends in algorithmic and AI-driven trading
- An overview of workflows in quantitative finance
- Essential tools, platforms, and data sources
Managing Financial Data with Python
- Processing time series data utilizing Pandas
- Data cleaning, transformation, and feature engineering
- Construction of financial indicators and trading signals
Supervised Learning for Generating Trading Signals
- Applying regression and classification models for market prediction
- Assessing predictive models (e.g., accuracy, precision, Sharpe ratio)
- Case study: Creating a machine learning-based signal generator
Unsupervised Learning and Market Regime Analysis
- Using clustering to identify volatility regimes
- Dimensionality reduction for uncovering patterns
- Applications in basket trading and risk segmentation
Optimizing Portfolios with AI
- The Markowitz framework and its associated limitations
- Risk parity, Black-Litterman, and ML-based optimization methods
- Implementing dynamic rebalancing using predictive inputs
Backtesting and Strategy Assessment
- Leveraging Backtrader or custom-built frameworks
- Analyzing risk-adjusted performance metrics
- Mitigating overfitting and look-ahead bias
Integrating AI Models into Live Trading
- Connecting with trading APIs and execution platforms
- Monitoring models and managing re-training cycles
- Addressing ethical, regulatory, and operational aspects
Wrap-up and Future Directions
Requirements
- Foundational knowledge of statistics and financial market mechanics
- Proficiency in Python programming
- Working familiarity with time series data
Target Audience
- Quantitative analysts
- Trading specialists
- Portfolio managers
21 Hours
Testimonials (1)
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