A financial research firm needed alternative data providers to blend market and social signals from over 30,000 sources into daily trade recommendations, refreshed every 5 to 10 minutes.
Client Overview
A financial research firm built its business on signaling investment options to a subscribed audience, sending trade recommendations for systematic equity investing on a daily basis. The firm wanted to build a recommendation engine that blended traditional market data with social signals, treating alternative data providers as a core part of its own intelligence stack rather than a nice to have.
The ambition was broad by design. Rather than watching a handful of financial news sources, the firm wanted visibility into anything relevant moving across news, blogs, articles, and social media, more than 30,000 source websites in total. At that scale, and in a market where prices and sentiment shift by the minute, the firm needed a partner that could treat data availability and coverage as seriously as the firm treated its own trading signals.
Client Requirements
The firm’s brief to PromptCloud reflected how fast financial signals actually move:
- Data blended from market sources and social platforms across more than 30,000 websites
- A dynamic list of keywords and sources that could shift as the market did
- Low latency delivery, refreshed every 5 to 10 minutes
- Alerts when a source went dead, so crawl results stayed accurate
- Delivery in JSON format through a queryable search API
Challenges
Thirty thousand sources is a different kind of scale problem than crawling a fixed list of known publications. Some sources published constantly, others rarely, and treating all 30,000 the same way would have wasted resources on dormant sites while potentially under-serving the active ones actually driving market moving news.
The latency requirement compounded this. A 5 to 10 minute refresh window across tens of thousands of sources left very little room for a crawl architecture that could not distinguish urgency, and any source that went offline or changed structure needed to be caught immediately, since acting on stale or missing data in systematic equity investing carries real financial consequences for the firm’s subscribers.
Solutions
PromptCloud built this around its DaaS platform’s mass scale, low latency crawl offering, tuning the system specifically to the realities of a 30,000-plus source list.
Adaptive Crawling That Tells Active From Dormant
Rather than crawling all 30,000-plus sources on the same schedule, the system was tuned to crawl adaptively, checking active sources more frequently than ones that rarely published anything new. This kept the firm’s compute and bandwidth focused where financial signals were actually likely to appear, the same discipline that separates real alternative data providers from a simple crawler pointed at a source list. Alerts flagged dead sources automatically, so a source going offline showed up as a fix rather than a silent gap in the firm’s data feed.
Built for a 5 to 10 Minute Latency Window
Meeting a latency requirement this tight across tens of thousands of sources meant adding components specifically built for that level of computation, rather than relying on general purpose crawling infrastructure. This is the kind of engineering question worth raising when comparing PromptCloud’s approach to other data providers, since a systematic equity investing signal is only as good as how current the data behind it actually is, and a slow pipeline undermines the entire premise of real-time trade signals.
A Dynamic Keyword and Source List
The list of keywords and sources was never fixed. As the market moved and new sources became relevant, the list adjusted dynamically rather than requiring a manual update project each time the firm’s focus shifted. That flexibility mattered as much as the raw scale, since a static source list would have started falling behind the market within weeks of going live.
Indexed Data the Firm Could Query in Real Time
Crawled data was indexed through a hosted indexing component, with a search API the firm could query every few minutes to pull current results in JSON format. That combination meant the firm’s own recommendation engine could pull fresh data on its own schedule rather than waiting for a batch delivery, which is what made a 5 to 10 minute latency window actually usable for daily trade signals rather than just an impressive number on paper.
Finance Data Feed, Before and After PromptCloud
| Area | Before | After |
| Source handling | Same crawl treatment for all 30,000+ sources | Adaptive crawling, active sources prioritized over dormant ones |
| Latency | No infrastructure built for a 5 to 10 minute refresh | Purpose built components for real time delivery |
| Dead sources | Would silently break data coverage | Flagged automatically through alerts |
| Data access | No existing pipeline at this scale | Indexed and queryable via search API in JSON |
Benefits to the Client
The firm’s recommendation engine could finally draw on data from more than 30,000 sources without needing to build or maintain that kind of infrastructure itself, the kind only a small number of alternative data providers can actually deliver at this scale. A 5 to 10 minute latency window meant trade signals sent to the firm’s subscribed audience reflected market and social conditions close to the moment they changed, not a stale snapshot from earlier in the day.
Adaptive crawling meant compute effort tracked where real activity actually was, rather than being spread evenly across a source list where most sites published rarely. Automated alerts on dead sources meant the firm’s data coverage stayed reliable without its own team having to notice a gap first, and a queryable search API let the firm pull fresh results on its own schedule rather than waiting on a fixed delivery window.
Alternative Data Providers Are Only as Useful as Their Latency
A recommendation engine sending daily trade signals cannot run on data that arrives late or unevenly across its source list. Alternative data providers only earn their place in a systematic investing stack if the numbers behind them are current enough to act on, across all 30,000-plus sources, not just the easy ones.
That is what changed the outcome here, adaptive crawling that respects how differently sources actually behave, a latency window built for real time use, and a query layer the firm could pull from on its own terms. Together, that is what turned a sprawling, uneven source list into a dependable signal feed.
