Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
The Role of AI in Trading and Asset Management
- Current trends in algorithmic and AI-driven trading
- Insights into quantitative finance workflows
- Essential tools, platforms, and data sources
Processing Financial Data with Python
- Managing time series data using Pandas
- Data cleaning, transformation, and feature engineering
- Calculation of financial indicators and signal construction
Supervised Learning for Trading Signals
- Utilizing regression and classification models for market prediction
- Assessing predictive model performance (e.g., accuracy, precision, Sharpe ratio)
- Practical case study: Developing an ML-based signal generator
Unsupervised Learning and Market Regimes
- Clustering techniques for identifying volatility regimes
- Dimensionality reduction for pattern discovery
- Applications in basket trading and risk grouping
AI-Driven Portfolio Optimization
- Understanding the Markowitz framework and its constraints
- Implementing risk parity, Black-Litterman, and ML-based optimization
- Dynamic rebalancing strategies using predictive inputs
Backtesting and Strategy Assessment
- Utilizing Backtrader or custom frameworks
- Analysis of risk-adjusted performance metrics
- Strategies to prevent overfitting and look-ahead bias
Deploying AI Models for Live Trading
- Integration with trading APIs and execution platforms
- Continuous model monitoring and re-training cycles
- Ethical, regulatory, and operational considerations
Summary and Future Directions
Requirements
- Foundational knowledge of statistics and financial market dynamics
- Proficiency in Python programming
- Experience working with time series data
Target Audience
- Quantitative analysts
- Professional traders
- Portfolio managers
21 Hours
Testimonials (1)
i already have some reports that i know, i will use some of the prompts that looked at today
