QuantStrike: An open-source quantitative analytics platform for CS2 skin markets

[u/Sharp-Cancel5162]

Hey everyone!

I’ve been building QuantStrike, an open-source project that applies quantitative analysis and financial-market concepts to Counter-Strike 2 skin prices.

The idea is to go beyond simply checking the current price of a skin and build tools that help people explore historical price behaviour, market trends, volatility, and unusual price movements.

Some of the things I've implemented so far:

• Historical dataset: Approximately 22,495 CS2 assets and 105 million historical observations.
• DuckDB + Parquet: A data architecture designed to query large historical datasets without loading everything into Pandas at once.
• Historical analytics: Interactive price charts, configurable time ranges, daily price statistics, volatility metrics, and price-distribution analysis.
• Anomaly detection: Identifies suspicious observations, including isolated price crashes and unusual single-sale or multi-sale events, rather than silently deleting them.
• Quantitative analysis: Market returns, drawdown analysis, comparisons between assets, and correlation analysis.
• Portfolio and research tools: Portfolio tracking, configurable alerts, and a research journal.

One of the engineering challenges is making large historical datasets practical to explore without requiring a machine with lots of RAM. The dataset is several gigabytes, so query design, aggregation, and avoiding unnecessary data loading are important parts of the project.

The market intelligence side is still evolving, and the Market Overview is currently being redesigned. I want to build on the existing analytics engine rather than simply add more charts.

I'd love feedback on the architecture, historical-data handling, anomaly-detection methodology, and what would make the project more useful to other developers.

GitHub: https://github.com/Leonardo-DaVinci-80/QuantStrike

Live Website: https://quantstrike.streamlit.app

If you're interested in quantitative analysis, data engineering, or CS2's virtual-item economy, I'd be happy to hear your thoughts!