Real-Time News & Sentiment Feed for a Quant Trading Desk

The Client

A systematic trading desk at a mid-sized fund running event-driven and sentiment-augmented strategies across US and European equities. Their models were hungry for one input they couldn't buy off the shelf in the shape they needed: broad, low-latency news and web-sentiment coverage, ticker-mapped, with point-in-time integrity.

The Challenge

Commercial news APIs covered the major wires well — but the desk's research showed alpha decayed fastest in exactly the sources those APIs covered thinnest:

• Long-tail coverage gaps. Regional business press, trade publications, regulatory notice boards, and company newsroom pages often moved prices minutes to hours before wire pickup — and weren't in any packaged feed.
• Latency mismatch. Their strategies needed minutes-level detection on breaking items; batch-daily alternative data products were useless for the event-driven book.
• Entity resolution. Raw scraped news names companies inconsistently ("the Bengaluru-based delivery giant"); models need tickers. Mapping text mentions to tradable entities — including subsidiaries and recent renames — was half the problem.
• Backtest integrity. Every record needed an as-collected timestamp, immutable once written. Any feed that couldn't reproduce "what was knowable at 09:42 on a given date" couldn't be backtested honestly.

The Actowiz Solution

1. Source universe engineering.

Together with the desk's researchers, we mapped a 1,400+ source universe: regional and trade press, company newsrooms and press-release pages, regulatory and exchange notice boards, and selected high-signal public web sources — weighted to their coverage universe of ~2,000 tickers.

2. Event-driven collection.

Rather than uniform polling, sources were tiered by signal half-life: exchange notices and newsrooms on minutes-level cadence, trade press on sub-hourly, long-tail sources hourly. Our agentic scrapers held coverage through site redesigns without gaps — critical for continuous time series.

3. Extraction & structuring.

Each item parsed into a typed record: headline, body text, publication timestamp, collection timestamp, source tier, language, and detected event type (earnings, guidance, M&A, regulatory, litigation, product, management change).

4. Entity mapping.

A resolution layer matched company mentions to tickers using a maintained entity graph (legal names, brands, subsidiaries, aliases), with confidence scores. Ambiguous matches shipped flagged rather than guessed — the desk's explicit requirement.

5. Sentiment & novelty scoring.

Each record carried model-generated sentiment scores and a novelty score (similarity against the trailing item stream), letting the desk separate genuinely new information from echo coverage — a major noise reduction for event models.

6. Point-in-time delivery.

Records streamed to the client via low-latency API and landed in append-only Parquet archives — collection-timestamped, never revised. Corrections shipped as new records referencing the original, preserving the as-known-when history.

The Outcome

The desk integrated the feed into two production strategies. The researchers' own attribution highlighted the long-tail sources — the regional and trade coverage arriving ahead of wire pickup — as the feed's differentiating slice, and the novelty score as the biggest noise filter. Operationally, the fund's data team retired an internal patchwork of one-off scrapers, and the engagement later expanded to a second workstream: regulatory-filing and tender-notice extraction for the credit book.

Just as importantly for their diligence process: the feed passed vendor review because governance was built in — public sources only, no paywall circumvention, PII masked at the edge, and full per-record lineage.

Why This Pattern Repeats in Finance

BFSI is the anchor segment of web-data demand, with funds, lenders, and insurers feeding models with scraped news, job postings, and consumer sentiment. The recurring lesson from these engagements: packaged feeds commoditize fast; the edge lives in source universes tailored to your book, latency matched to your signal half-life, and point-in-time discipline that survives a backtest audit.

Frequently Asked Questions

How fast can scraped news reach a trading model?

Tiered collection puts high-signal sources (exchange notices, newsrooms) on minutes-level cadence; median collection-to-delivery latency in this engagement ran under four minutes for Tier-1 sources.

How is entity mapping handled for ambiguous company mentions?

Against a maintained entity graph with confidence scoring — and ambiguous matches are delivered flagged, not silently guessed, so models can weight or exclude them.

Can the source universe be customized to our coverage list?

Yes — the universe is engineered per client around your tickers, sectors, and geographies, and evolves as your book changes.

Is this compliant for institutional use?

The pipeline collects public sources only, respects paywalls, masks PII at the edge, and ships per-record lineage — documentation designed for institutional vendor diligence. Contact Actowiz Solutions to scope a pilot feed.