RUMA
/

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

Shielded Labs (SHIELDED) Sentiment & Fear and Greed Index

As of July 4, 2026, Shielded Labs'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 Shielded Labs insights

Zcash Ironwood Upgrade Faces Delay Over Infrastructure ReadinessJul 3, 2026

Shielded Labs announced the Zcash Ironwood upgrade, designed to prevent creation of unlimited fake ZEC, may be delayed because exchanges, wallets, and mining pools need more time to complete their transition to Z3 software, due to infrastructure readiness issues among key network participants.

Zcash Patches Major Bug, Proposes Shielded Pool RebuildJun 4, 2026

Zcash patched its most significant bug in years, addressing a critical vulnerability. Following the fix, Shielded Labs proposed rebuilding the shielded pool from scratch using turnstile accounting to enhance security and reliability.

Frequently asked questions

What is Shielded Labs's Fear & Greed Index?

Shielded Labs'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 Shielded Labs bullish or bearish right now?

Ruma scores Shielded Labs'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 Shielded Labs sentiment?

Ruma reads every relevant social post about Shielded Labs 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.