Constellation Quant: A Quantitative Tool for Predicting S&P 500 Stock Performance Using Graph Neural Networks

Constellation Quant is a graph neural network (GNN)-based quantitative tool. Its core innovation lies in modeling S&P 500 constituent stocks as an interconnected graph structure (stocks as nodes, correlations/sector affiliations as edges), capturing relational patterns that traditional strategies struggle to detect, generating long-short portfolio signals, and helping investors convert complex data into actionable stock ranking lists.

Traditional quantitative strategies mostly focus on individual stock fundamental or technical factors, assuming that stock performance is isolated. However, the actual market is an interconnected network: a rise in tech stocks may affect financial stocks, supply chain companies rise and fall together, and sector rotation has a high degree of correlation. Constellation Quant was created to break through the limitations of such isolated analysis.

The tech stack of Constellation Quant includes: Data layer uses yfinance to obtain real-time/historical data; Model layer processes graph structures (stocks as nodes, correlations as edges) via GNN, integrating individual stock time series and graph features to generate prediction scores; Application layer is a desktop program that provides visual rankings. Data flow process: Pull data → Build correlation matrix → GNN runs to generate scores → Sort to form ranking list.

The ranking list generated by the tool is a source of long-short signals: top-ranked stocks are candidates for going long, while bottom-ranked ones are for shorting. Advantages of the long-short framework: market neutrality (hedging overall volatility), capital efficiency (shorting frees up capital for additional positions), and risk diversification (different correlation structures). It should be noted that real-world factors such as transaction costs and shorting restrictions need to be considered to convert into actual positions.

Constellation Quant has a zero-code threshold, providing a Windows executable file that is ready to use out of the box. System requirements: Win10/11, 8GB+ RAM, 500MB storage space, stable network. The interface is simple; the main dashboard displays Rank/Ticker/Score/Trend, and supports sorting, industry filtering, and table export.

Tool limitations: Single data source (only yfinance), model black box (difficult to explain scores), no backtesting function, limited real-time performance (due to free API restrictions). Recommended usage scenarios: Screening tool to narrow down research scope, signal source combined with other factors, teaching tool for GNN financial applications, prototype to verify the feasibility of GNN strategies.

Regarding privacy: Analysis runs locally, and search history is not uploaded to the server. Conclusion: Constellation Quant promotes the democratization of quantitative tools, allowing investors without a deep learning background to access AI strategies. Although it is not a complete trading system, it provides a new way to understand the market from a network perspective. Project address: https://github.com/ingoodtaste-rapsession220/constellation-quant