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Course Outline
The Role of AI in Trading and Asset Management
- Current trends in algorithmic and AI-assisted trading.
- A comprehensive overview of quantitative finance workflows.
- Essential tools, platforms, and data sources.
Processing Financial Data with Python
- Managing time series data using Pandas.
- Data cleansing, transformation, and feature engineering.
- Construction of financial indicators and trading signals.
Supervised Learning for Generating Trading Signals
- Applying regression and classification models to market forecasting.
- Assessing predictive models using metrics such as accuracy, precision, and Sharpe ratio.
- Case study: Developing a machine learning-based signal generator.
Unsupervised Learning and Identifying Market Regimes
- Utilising clustering techniques to identify volatility regimes.
- Applying dimensionality reduction to uncover hidden patterns.
- Applications in basket trading and risk grouping.
Optimising Portfolios with AI Techniques
- Examining the Markowitz framework and its inherent limitations.
- Implementing risk parity, Black-Litterman, and machine learning-based optimisation.
- Dynamic rebalancing informed by predictive inputs.
Backtesting and Evaluating Strategies
- Employing Backtrader or bespoke frameworks for testing.
- Analysing risk-adjusted performance metrics.
- Mitigating overfitting and look-ahead bias.
Deploying AI Models in Live Trading Environments
- Integrating models with trading APIs and execution platforms.
- Establishing cycles for model monitoring and re-training.
- Addressing ethical, regulatory, and operational considerations.
Course Summary and Subsequent Steps
Requirements
- A solid grasp of fundamental statistics and financial market mechanics.
- Proficiency in Python programming.
- Familiarity with the structure and analysis of time series data.
Target Audience
- Quantitative Analysts
- Trading Professionals
- Portfolio Managers
21 Hours
Testimonials (1)
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