JPMorgan OnChain Liquidity-Token Money Market Fund (JLTXX) Sentiment & Fear and Greed Index
As of July 11, 2026, JPMorgan OnChain Liquidity-Token Money Market Fund's Ruma Fear & Greed Index is 50 (Neutral), its social sentiment score is 0/100 (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
Latest JPMorgan OnChain Liquidity-Token Money Market Fund insights
JPMorgan's tokenized money market fund, JLTXX, grew 245% in 30 days to $692.7 million. Launched on Ethereum on May 13, the fund's expansion highlights increasing institutional adoption of blockchain-based financial products.
JPMorgan has filed for a new tokenized Treasury fund, named JLTXX, on the Ethereum blockchain. This move represents a significant step in the integration of traditional financial products with blockchain technology. The filing also indicates that 'GENIUS Act reserves' are being built for the fund.
Frequently asked questions
What is JPMorgan OnChain Liquidity-Token Money Market Fund's Fear & Greed Index?
JPMorgan OnChain Liquidity-Token Money Market Fund's Ruma Fear & Greed Index is currently 50 out of 100, which is Neutral. The index blends social sentiment, social interest, price momentum, volatility, and emotional intensity into a single 0–100 sentiment score, updated continuously.
Is JPMorgan OnChain Liquidity-Token Money Market Fund bullish or bearish right now?
JPMorgan OnChain Liquidity-Token Money Market Fund's social sentiment is currently bearish, with a sentiment score of 0/100 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 JPMorgan OnChain Liquidity-Token Money Market Fund sentiment?
Ruma reads every relevant social post about JPMorgan OnChain Liquidity-Token Money Market Fund 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.