trade-sentiment

Stock sentiment aggregation combines news headlines, social media chatter, and institutional activity to compute a 0-100 Sentiment Score for a given ticker. This normalizes diverse market data into actionable trading signals.

Sentiment analysis for pre-trade briefings works by running a scoring engine across real-time news and social data, returning sub-scores and narrative themes alongside the main 0-100 score. This provides immediate risk awareness and informs thesis building before execution.

Building a holistic stock sentiment score requires web search modules to gather news headlines, social media chatter, analyst opinions, institutional activity, insider trading, and short interest. Data normalization processes these inputs into a structured 0-100 score.

You can monitor real-time sentiment across a stock universe by invoking the scoring engine for multiple tickers. The system supports real-time monitoring, daily research briefs, and pre-trade checks, returning structured output with sub-scores and narrative themes for each symbol.

The best way to combine social media sentiment with institutional trading data is through a data normalization engine that aggregates both into a unified 0-100 score. This approach merges on-chain-like institutional signals with social chatter to produce sub-scores for comprehensive thesis building.