AI for Trading and Asset Management Training Course
Artificial Intelligence is a robust set of techniques used to create intelligent trading systems that analyze market data, make predictions, and execute strategies autonomously.
This instructor-led, live training (available online or onsite) is designed for intermediate-level finance professionals who want to apply AI techniques in trading and asset management. The focus will be on signal generation, portfolio optimization, and algorithmic strategies.
By the end of this training, participants will be able to:
- Understand the role of AI in contemporary financial markets.
- Use Python to develop and backtest algorithmic trading strategies.
- Apply both supervised and unsupervised learning models to financial data.
- Optimize portfolios using AI-driven methods.
Format of the Course
- Interactive lecture and discussion.
- Extensive exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
AI in the Trading and Asset Management Landscape
- Trends in algorithmic and AI-based trading
- Overview of quantitative finance workflows
- Key tools, platforms, and data sources
Working with Financial Data in Python
- Handling time series data using Pandas
- Data cleaning, transformation, and feature engineering
- Financial indicators and signal construction
Supervised Learning for Trading Signals
- Regression and classification models for market prediction
- Evaluating predictive models (e.g. accuracy, precision, Sharpe ratio)
- Case study: building an ML-based signal generator
Unsupervised Learning and Market Regimes
- Clustering for volatility regimes
- Dimensionality reduction for pattern discovery
- Applications in basket trading and risk grouping
Portfolio Optimization with AI Techniques
- Markowitz framework and its limitations
- Risk parity, Black-Litterman, and ML-based optimization
- Dynamic rebalancing with predictive inputs
Backtesting and Strategy Evaluation
- Using Backtrader or custom frameworks
- Risk-adjusted performance metrics
- Avoiding overfitting and look-ahead bias
Deploying AI Models in Live Trading
- Integration with trading APIs and execution platforms
- Model monitoring and re-training cycles
- Ethical, regulatory, and operational considerations
Summary and Next Steps
Requirements
- An understanding of basic statistics and financial markets
- Experience with Python programming
- Familiarity with time series data
Audience
- Quantitative analysts
- Trading professionals
- Portfolio managers
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Testimonials (3)
Trainers can answer all questions and accept any queries
Dewi Anggryni - PT Dentsu International Indonesia
Course - Copilot for Finance and Accounting Professionals
The background / theory of LLMs, the exercise
Joanne Wong - IPG HK Limited
Course - Applied AI for Financial Statement Analysis & Reporting
Possible applications /exercises
Estelle De la Fouchardiere - Advanced Bionics AG
Course - Machine Learning & AI for Finance Professionals
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