Financial News Visualization in AlphaDesk
A structured interface for fast, sentiment-aware market insight
AlphaDesk includes a financial news viewer that consolidates recent articles for a selected stock and enhances them with sentiment analysis. The goal is to help users quickly interpret news context and prioritize what to read.
Function Overview
Each news article is displayed with the following:
Title: Clickable headline linking to the original article
Sentiment score: A numeric polarity value ranging from –1 (strongly negative) to +1 (strongly positive)
Sentiment bar: A horizontal visual indicator whose width and color reflect the sentiment score
Expandable content: Clicking the title reveals the article's publication date and a short excerpt
This structured format allows users to scan through multiple articles efficiently while focusing attention on items with significant sentiment values.
Data Source
This feature is powered by the EODHD Financial News API, which provides structured news data in JSON format. Each article includes:
date: ISO 8601 timestamptitle: Headline textcontent: Full article bodylink: Direct URL to the articlesymbols: Ticker symbols mentionedtags: Optional topic tags (e.g., "dividend payments", "earnings results")sentiment: Object with keys likepolarity,pos,neg,neu
The sentiment values are included in the API response and are used directly in the interface.
Technical Implementation
The front end is built with React and Tailwind CSS. State is managed locally to support collapsible article rows. Sentiment is color-coded as follows:
Green: polarity > 0.2 (positive)
Red: polarity < –0.2 (negative)
Gray: otherwise (neutral)
A numerical score is shown beside the headline, along with a proportional sentiment bar rendered using Tailwind utility classes.
Use Case
This feature is designed for users who need to quickly evaluate the tone and potential impact of financial news without reading full articles. By surfacing only the most relevant headlines and sentiment signals, it helps reduce information overload and supports faster decision-making. Because the information is persistently stored in structured JSON format, it can also serve as a foundation for future agentic workflows — enabling cross-sectional studies, news-based screening, and contextual triggers for automated reasoning agents.
To be continued
S.M.L.