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NVIDIA xStock (NVDAX) Sentiment & Fear and Greed Index

As of July 5, 2026, NVIDIA xStock's Ruma Fear & Greed Index is 38 (Fear), its social sentiment score is 5/100 (bearish), 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 & Greed38 · Fear
Sentiment5/100
Mindshare0.00%
Price$197.61 +0.4%

Latest NVIDIA xStock insights

Skate AMM v2 Launches TradFi Pools for NVDAx and QQQxJun 30, 2026

Skate AMM v2 launched TradFi trading pools for NVDAx and QQQx, allowing users to trade these assets against USDC across Solana, Ethereum, and BNB Chain. The pools are shared across all three chains, enabling unified liquidity and cross-chain trading.

Real-World Stock Assets Now on Solana; Borrowing EnabledApr 27, 2026

Solana's ecosystem now supports 1:1 backed tokenized real-world stock assets, such as NVDAx and TSLAx. This integration allows users to hold traditional stocks on-chain and borrow against them through platforms like Jupiter Lend. This development marks a significant milestone, as previously, holding and leveraging real-world stocks directly on Solana was considered unfeasible.

Frequently asked questions

What is NVIDIA xStock's Fear & Greed Index?

NVIDIA xStock's Ruma Fear & Greed Index is currently 38 out of 100, which is Fear. The index blends social sentiment, social interest, price momentum, volatility, and emotional intensity into a single 0–100 sentiment score, updated continuously.

Is NVIDIA xStock bullish or bearish right now?

NVIDIA xStock's social sentiment is currently bearish, with a sentiment score of 5/100 based on how bullish or bearish the crypto social conversation is. Sentiment reflects the mood of the market, not price direction or financial advice.

How does Ruma measure NVIDIA xStock sentiment?

Ruma reads every relevant social post about NVIDIA xStock 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.