DIA vs OpenLedger — how do they compare? DIA trades at Rp2,253 (market cap Rp270,35M, Rp48,08M 24h volume), while OpenLedger trades at Rp3,263 (market cap Rp1,04T, Rp121,2M 24h volume). The key difference: OpenLedger is far larger — about 3846.9× DIA's market cap, and DIA's circulating supply is 119,7M / 200M DIA (60%) versus 319M / 1B OPEN (32%) for OpenLedger. Which is the better fit depends on your goals — on Pluang, investors hold DIA for 24 Days and OpenLedger for 23 Days on average.
| DIA | OPEN | |
|---|---|---|
Market Cap | Rp270,35M | Rp1,04T |
Volume (24h) | Rp48,08M | Rp121,2M |
Circulating Supply | 119,7M / 200M DIA (60%) | 319M / 1B OPEN (32%) |
Typical Hold Time | 24 Days | 23 Days |
Signals from Pluang's Aura AI — not financial advice
DIA is currently trading at Rp 2,257 with a market cap of Rp 269.38 million, showing bullish technical signals overall. The asset maintains a 60% circulating supply rate with an average hold time of 24 days. Technical indicators show mixed signals with moving averages bullish but RSI suggesting potential overbought conditions near-term.
The overall outlook remains cautiously optimistic with strong technical momentum but requires monitoring of overbought conditions. Key opportunities include continued ecosystem growth, while risks involve typical crypto volatility and the need for increased network adoption to sustain current levels.
No Aura AI signal available yet.
What Pluang investors did over the last 30 days
DIA (Decentralised Information Asset) is an open-source oracle platform that enables market actors to source, supply, and share trustable data. DIA aims to be an ecosystem for open financial data in a financial smart contract ecosystem, to bring together data analysts, data providers, and data users. In general, DIA provides a reliable and verifiable bridge between off-chain data from various sources and on-chain smart contracts that can be used to build a variety of financial DApps.
Read more on DIA →OpenLedger is an AI blockchain that unlocks liquidity for monetizing data, models, applications, and agents. It facilitates the training, deployment, and on-chain tracking of specialized AI models and data, addressing critical challenges related to transparency, attribution, and verifiability in AI.
Read more on OPEN →