Top Resources for HPC and Quantitative Finance in 2025
High-Performance Computing (HPC) Resources
Conferences & Workshops
- ISC High Performance 2025: Major annual conference covering the latest in HPC technology, applications, and research[1].
- International HPC Summer School 2025: Intensive summer program focusing on state-of-the-art HPC and big data analytics for computational sciences[2].
- Spring 2025 Manitoba HPC & Cloud Workshop: Hands-on event introducing Linux, SLURM, Open OnDemand, Globus, OpenStack, and Python for AI/ML on HPC systems. Suitable for beginners and intermediates[3].
Access to HPC Facilities
- UKRI HPC Facilities (Spring 2025): Provides computational resources for research projects in the UK, focusing on onboarding and training new users[4].
- Danish e-Infrastructure Consortium (DeiC): National and European HPC resources available through regular calls for applications[5].
Platforms & Reports
- Altair HPC and Cloud Platform 2025: Features AI-assisted user access and enhanced support for AI/ML workloads[6].
- IDTechEx Hardware for HPC, Data Centers, and AI 2025-2035: Market report on HPC and AI hardware trends[7].
Quantitative Finance: Trends and Learning Resources
Key Trends in 2025
- AI and Machine Learning: Integration of advanced AI/ML for asset pricing, risk assessment, and trading strategies[8][9].
- Quantum Computing: Emerging applications in portfolio optimization, derivatives pricing, and high-frequency trading[8][9].
- Advanced Risk Management: Enhanced models for Value at Risk (VaR), stress testing, and portfolio optimization using sophisticated mathematical and computational techniques[10].
University Courses
- Boston University Financial Analytics (Spring 2025): Uses Python and Jupyter Notebook for hands-on assignments in portfolio construction, risk management, and time series analysis. Recommended references include Bodie, Kane, Marcus (Investments), Hull (Options, Futures, and Other Derivatives), and Yan (Python for Finance)[11].
Kaggle, Jupyter, and Google Colab Notebooks
Kaggle Notebooks
- Quantitative Finance Notebooks Collection: Open-source repository with educational notebooks covering probability, regression, time series, stochastic calculus, Black-Scholes, and more. Suitable for both learning and practical applications[12].
- Kaggle Financial Mathematics Notebook: Example notebook on financial mathematics, using Python libraries like NumPy and Matplotlib, easily deployable to Google Colab for further experimentation[13].
- Kaggle Notebooks Platform: Allows free, cloud-based Jupyter notebooks with GPU support for machine learning and finance projects. Notebooks can be shared, collaborated on, and imported into Colab[14].
Jupyter Notebooks
- Python in Finance: Real-Time Data Streaming: Tutorial on using Jupyter for dynamic, real-time financial data visualization and analysis, including live trading signals and Bollinger Bands[15].
- Quotient Platform Integration: Video guide on using Jupyter Notebook within the Quotient platform for advanced financial modeling and visualization[16].
Google Colab
- Google Colab Tutorial 2025: Step-by-step course for using Colab for Python, data analysis, and machine learning, with features for sharing, collaboration, and GitHub integration[17][18].
- IBKR Quant Guide to Google Colab: Practical guide for using Colab in quant finance, suitable for machine learning and data analysis workflows[19].
Recommended Table: Notebooks & Tutorials for Quantitative Finance
| Resource/Notebook | Platform | Focus Area | Year | Notes |
|---|---|---|---|---|
| Quantitative Finance Notebooks Collection[12] | Jupyter/GitHub | Theory & practical finance | 2025 | Covers core quant topics |
| Financial Mathematics Notebook[13] | Kaggle/Colab | Intro to financial math | 2023 | Easily import to Colab |
| Real-Time Data Streaming in Finance[15] | Jupyter | Streaming, visualization | 2024 | Live data, quant use |
| Google Colab Tutorial 2025[17][18] | Colab | Python, ML, data analysis | 2025 | Beginner to advanced |
| BU Financial Analytics Course[11] | Jupyter | Portfolio, risk, time series | 2025 | University course |
How to Get Started
- For HPC: Attend major conferences, apply for national/international HPC resources, and join hands-on workshops to build foundational and advanced skills.
- For Quant Finance: Leverage open-source Jupyter and Kaggle notebooks, follow university-level course materials, and stay updated on AI/ML and quantum computing trends.
- For Notebooks: Start with beginner tutorials on Google Colab or Kaggle, then explore specialized finance notebooks and real-time data analysis examples for practical experience.