AI for Trading and Asset Management Training Course
Artificial Intelligence comprises a powerful array of techniques designed to create intelligent trading systems capable of analyzing market data, making predictions, and executing strategies autonomously.
This instructor-led live training, available either online or on-site, is tailored for intermediate-level finance professionals aiming to apply AI techniques in trading and asset management. The focus is on signal generation, portfolio optimization, and algorithmic strategies.
Upon completion of this training, participants will be able to:
- Comprehend the role of AI in modern financial markets.
- Utilize Python to construct and backtest algorithmic trading strategies.
- Apply supervised and unsupervised learning models to financial data.
- Optimize portfolios using AI-driven techniques.
Format of the Course
- Interactive lecture and discussion.
- Extensive exercises and practice.
- 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
Open Training Courses require 5+ participants.
AI for Trading and Asset Management Training Course - Booking
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AI for Trading and Asset Management - Consultancy Enquiry
Testimonials (1)
Trainer was very knowledgeable and easy to speak to
Gareth Gird - Teleflex Medical Europe Ltd
Course - Copilot for Finance and Accounting Professionals
Provisional Upcoming Courses (Require 5+ participants)
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