Financial modelling with AI/machine learning

What is the course about?

Leading banks, hedge funds, and fintech firms increasingly depend on machine learning and algorithmic trading to stay competitive. This engaging course delves deeply into advanced financial modelling powered by artificial intelligence, preparing participants to build and deploy dynamic, predictive financial systems. Learners will use Python and top industry tools to develop trading strategies, optimize portfolios, and forecast market trends through hands-on, practical projects.

What will we cover?

Participants will master the full AI-driven financial modelling process—from handling financial data and preprocessing time series to sophisticated forecasting and risk modelling. Specific topics covered include:

• Portfolio Optimization
• Asset Price Modelling
• Financial Data Analysis and Visualization
• Acquiring secret financial data, such as institutional holders and insider trades
• Leveraging state-of-the-art machine learning pipelines using H2O AutoML
• Building interactive pipelines for automating trading strategies via AI agents (agentic workflows)
• Creating interactive visualizations through Streamlit dashboards

Advanced modules further cover reinforcement learning for algorithmic trading bots, NLP-driven sentiment analysis, and cloud-based real-time model deployment using libraries such as XGBoost, LightGBM, TensorFlow, PyTorch, and backtrader.

What will I achieve?
By the end of this course you should be able to...

By the end of the course, students will be able to:

• Design, test, and deploy fully automated, machine learning-driven trading systems.
• Integrate classical technical analysis with advanced machine learning automation.
• Develop and deploy autonomous forecasting models leveraging deep learning techniques.
• Automate complex portfolio optimization and risk management workflows.
• Utilize automated NLP tools and alternative data for enhanced market insights.
• Navigate ethical, regulatory, and interpretability challenges in automated financial AI.
• Build scalable, production-ready automated pipelines for real-world financial applications.

What level is the course and do I need any particular skills?

This course is designed for basic to intermediate learners familiar with Python programming and with foundational knowledge of financial markets or machine learning. Prior experience with pandas, NumPy, and scikit-learn is beneficial but not mandatory. Professionals in finance, fintech, or data science aiming to enhance their AI-driven trading, investing, and risk management capabilities will find this course particularly valuable. Structured guidance and regular reviews ensure confident progression for all participants.

How will I be taught, and will there be any work outside the class?

The course methodology emphasizes practical, project-based learning, combining concise theoretical sessions, live coding demonstrations, and extensive hands-on exercises using industry-standard datasets and tools. Participants will progressively build comprehensive financial models, tailoring projects to personal interests or professional goals. While core learning occurs during class, short assignments and additional strategy refinement exercises outside class will further reinforce skills and understanding.

Are there any other costs? Is there anything I need to bring?

No other costs.

When I've finished, what course can I do next?

City Lit offers a variety of progression courses in this subject area. Please click here to view our Programming and Maths courses

Disclaimer: Use of Third-Party Software
This course might require you to either use your own personal account or create an account for the purposes of this course. City Lit cannot accept any responsibility for any failings of the third party or provide technical support. Whilst using the software you will be responsible for abiding by the providers terms and conditions and maintaining your own work.