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Fear and Greed Index

SENTIMENT:

KOL CALLS

Long/Short Calls

MINDSHARE:

Intelligence

Emotions

Social Momentum

FEED:

Cultiness Index

Followers

Volume ($)

Volatility

Mcap vs BTC

Sentiment Timeframes

Barclays (BCS) Sentiment & Fear and Greed Index

As of July 8, 2026, Barclays's Ruma Fear & Greed Index is 0 (Extreme Fear), it holds 0.00% of crypto social mindshare. These signals are computed by Ruma from social posts across crypto Twitter/X and other sources, scored with large language models rather than keyword counts.

Updated continuously · Source: Ruma

Fear & Greed0 · Extreme Fear
Mindshare0.00%

Latest Barclays insights

Barclays Initiates Overweight Rating on MicroStrategyJul 8, 2026

Barclays, a global investment bank with $3 trillion in assets under management, issued an Overweight (buy) rating on MicroStrategy (MSTR). The rating signals that institutions recognize and value Bitcoin Yield, and expect MicroStrategy and CEO Michael Saylor to sustain a high Bitcoin acquisition strategy.

Barclays CEO Reportedly Plans Bitcoin, Crypto AdoptionApr 11, 2026

A recent report indicates Barclays' CEO stated during a live Fox interview that the $2 trillion bank plans to adopt Bitcoin and other cryptocurrencies. This news, shared via a recent tweet, suggests a significant move by a major financial institution into the crypto space.

Frequently asked questions

What is Barclays's Fear & Greed Index?

Barclays's Ruma Fear & Greed Index is currently 0 out of 100, which is Extreme Fear. The index blends social sentiment, social interest, price momentum, volatility, and emotional intensity into a single 0–100 sentiment score, updated continuously.

Is Barclays bullish or bearish right now?

Ruma scores Barclays's social sentiment as bullish, bearish, or mixed based on LLM analysis of the crypto social conversation. Sentiment reflects market mood, not financial advice.

How does Ruma measure Barclays sentiment?

Ruma reads every relevant social post about Barclays across crypto Twitter/X and other sources and scores it with large language models — capturing bullish/bearish tone, emotion, and who is speaking (from retail to smart money) — rather than counting keywords.