social-media-intelligence

To build trading signals from Twitter and Telegram sentiment data, you aggregate and normalize posts across platforms, compute sentiment scores using VADER or FinBERT, and output time-bucketed metrics suitable for quantitative factor modeling.

Social media sentiment factor backtesting evaluates predictive power by computing IC and ICIR metrics across forward-return horizons. It uses normalized sentiment scores and discussion-buzz z-scores aggregated from platforms like Twitter, Reddit, and Discord.

Yes, you can use FinBERT for finance-aware sentiment scoring on Reddit and Discord posts. The skill supports lightweight VADER, finance-aware FinBERT, and optional LLM-based approaches to compute nuanced sentiment scores for quantitative analysis.

To calculate a fear and greed index from social media buzz, you measure discussion volume anomalies and combine sentiment extremes from collected posts. This supports contrarian or momentum-aware trading decisions based on platform-weighted aggregation.

Yes, this sentiment analysis approach supports platform-weighted aggregation for quantitative modeling. It normalizes content from Twitter/X, Telegram, Discord, and Reddit into a unified feed, outputting normalized scores and time-bucketed metrics for factor construction.

Limitations of using social media buzz signals include relying on noisy, high-volume public posts that require normalization. Sentiment scoring depends on model accuracy, and factor backtesting requires valid forward-return horizons to ensure predictive reliability.