Building a Real-Time Stock Momentum Ranking System
DolphinDB has developed a real-time stock momentum ranking system that addresses the challenges of calculating Price Rate of Change (ROC) for thousands of stocks simultaneously. The system utilizes a Reactive State Engine to maintain state across events and incrementally update calculations without reprocessing entire datasets. This allows for efficient ranking of stocks based on their performance over specified time periods.
- ▪DolphinDB's system can handle real-time calculations for 5,000 stocks simultaneously.
- ▪The Reactive State Engine maintains per-symbol state and updates incrementally, improving efficiency.
- ▪Price Rate of Change (ROC) is calculated based on historical prices relative to current prices, which is complex in real-time scenarios.
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Building a Real-Time Stock Momentum Ranking System with DolphinDB Stream ProcessingDolphinDB5 min read·1 hour ago--ListenSharePress enter or click to view image in full sizeImagine you’re running a quant desk. 5,000 stocks are ticking in real time. Every new trade arrives, and your system needs to — instantly — answer two questions:How much has each stock moved compared to where it was two minutes ago?Across the whole market, which stocks are leading and which are lagging?In a traditional database, question one alone is already painful: you’d need to store historical snapshots, join them against incoming data, and compute the percentage change — for every single symbol, on every single tick.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Medium.
