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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 quantitative finance workflows
- Essential tools, platforms, and data sources
Manipulating Financial Data with Python
- Processing time series data utilizing Pandas
- Techniques for data cleaning, transformation, and feature engineering
- Constructing financial indicators and signals
Leveraging Supervised Learning for Trading Signals
- Applying regression and classification models for 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 Market Regime Analysis
- Identifying volatility regimes through clustering
- Applying dimensionality reduction for pattern detection
- Use cases in basket trading and risk grouping
Advanced Portfolio Optimization with AI
- Examining the Markowitz framework and its inherent limitations
- Implementing risk parity, Black-Litterman, and ML-based optimization strategies
- Achieving dynamic rebalancing through predictive inputs
Backtesting and Strategy Assessment
- Utilizing Backtrader or developing custom backtesting frameworks
- Analyzing risk-adjusted performance metrics
- Mitigating overfitting and look-ahead bias
Deploying AI Models for Live Trading
- Integrating models with trading APIs and execution platforms
- Managing model monitoring and re-training cycles
- Addressing ethical, regulatory, and operational considerations
Summary and Path Forward
Requirements
- Foundational knowledge of statistics and financial market mechanics
- Proficiency in Python programming
- Experience working with time series data
Target Audience
- Quantitative analysts
- Trading professionals
- Portfolio managers
21 Hours
Testimonials (1)
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