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Course Outline
The AI Landscape in Trading and Asset Management
- Current trends in algorithmic and AI-driven trading.
- A snapshot of quantitative finance workflows.
- Essential tools, platforms, and data sources.
Managing Financial Data with Python
- Processing time series data utilizing Pandas.
- Data cleansing, transformation, and feature engineering.
- Constructing financial indicators and trading signals.
Supervised Learning for Trading Signals
- Utilizing regression and classification models for market forecasting.
- Assessing predictive models via metrics like accuracy, precision, and Sharpe ratio.
- Case study: Developing a machine learning-based signal generator.
Unsupervised Learning and Market Regimes
- Applying clustering techniques to identify volatility regimes.
- Using dimensionality reduction for pattern detection.
- Applications in basket trading and risk categorization.
AI-Enhanced Portfolio Optimization
- Examining the Markowitz framework and its constraints.
- Exploring risk parity, Black-Litterman, and machine learning-based optimization.
- Implementing dynamic rebalancing with predictive inputs.
Backtesting and Strategy Assessment
- Employing Backtrader or custom frameworks for testing.
- Analyzing risk-adjusted performance metrics.
- Mitigating overfitting and look-ahead bias.
Deploying AI Models in Live Trading
- Integrating with trading APIs and execution platforms.
- Managing model monitoring and re-training cycles.
- Addressing ethical, regulatory, and operational factors.
Summary and Future Directions
Requirements
- A foundational grasp 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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