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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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Medium |
| Canonical URL | https://medium.com/@DolphinDB_Inc/building-a-real-time-stock-momentum-ranking-system-with-dolphindb-stream-processing-8ee9d9c484b4 |
| Publication time | Wed, 27 May 2026 09:24:01 +0000 |
| Retrieval time | 2026-05-27T09:37:57.389Z |
| Last seen | 2026-05-27T09:37:57.389Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 4i3GkyYLsie4 · 4 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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.
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