DRAM (DRAM) Sentiment & Fear and Greed Index
As of July 4, 2026, DRAM'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
Latest DRAM insights
Solana introduced five 1:1 backed stocks—$SPCX, $MU, $PAXG, $DRAM, and $SNDK—redeemable through a brokerage. The tokens enable 24/7 trading without KYC or geographic restrictions, offering a permissionless way to trade traditional equities on-chain.
Roundhill Investments' $DRAM token has gone live on Solana through a partnership with Sunrise DeFi. The token is issued by Backpack Securities, marking an expansion of traditional financial products into the Solana ecosystem.
Ondo Perps will launch on June 9, as announced in a recent post. The launch follows a market downturn over the past week and weekend, which the poster suggests may present buying opportunities for assets like DRAM ETF and SPCX.
The Memory Chip ETF ($DRAM) has surpassed BlackRock's Bitcoin ETF ($IBIT) to become the fastest-growing ETF in history. $DRAM achieved over $6.5 billion in assets under management within its first 27 days of trading. The ETF further accelerated, reaching $10 billion in AUM just days later, setting a new record.
Frequently asked questions
What is DRAM's Fear & Greed Index?
DRAM'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 DRAM bullish or bearish right now?
Ruma scores DRAM'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 DRAM sentiment?
Ruma reads every relevant social post about DRAM 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.