Fear and Greed Index

Sentiment:

Notable Calls

Calls:

Mindshare:

Intelligence

Emotions

Social Momentum

Feed:

Conviction Index

Followers

Sentiment Timeframes

Charles Schwab (SCHW) Sentiment & Fear and Greed Index

As of August 5, 2026, Charles Schwab's Nebula Fear & Greed Index is 0 (Extreme Fear), it holds 0.00% of crypto social mindshare. These signals are computed by Nebula from social posts across crypto Twitter/X and other sources, scored with large language models rather than keyword counts.

Updated continuously · Source: Nebula

Fear & Greed0 · Extreme Fear
Mindshare0.00%

Latest Charles Schwab insights

Charles Schwab Warns Bitcoin Clarity Act May CollapseAug 4, 2026

Charles Schwab warned the Bitcoin Clarity Act is on the verge of collapsing due to a hang-up over its ethics clause, emphasizing time is running out as the Senate appears to delay progress.

Charles Schwab Warns Bitcoin Clarity Act May FailAug 4, 2026

Charles Schwab warned the Bitcoin Clarity Act may fail due to a hang-up over its ethics clause, stating time is running out with a decision expected by the end of this week.

Charles Schwab Launches 24/7 Bitcoin Futures TradingJun 3, 2026

Charles Schwab, the largest publicly traded U.S. investment firm with $12.6 trillion in client assets, has launched 24/7 Bitcoin futures trading.

Frequently asked questions

What is Charles Schwab's Fear & Greed Index?

Charles Schwab's Nebula 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 Charles Schwab bullish or bearish right now?

Nebula scores Charles Schwab'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 Nebula measure Charles Schwab sentiment?

Nebula reads every relevant social post about Charles Schwab 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.