Suspected wash trading and data opacity spark a 10% ASTER price drop — and fresh questions about market integrity.
DeFi analytics giant DefiLlama has officially removed Aster’s perpetual futures trading metrics, citing lack of transparency and potential wash trading, triggering a sharp ~10% price decline in ASTER.
According to DefiLlama, Aster’s reported futures volumes were “highly correlated” with Binance activity, raising red flags about data legitimacy and market integrity.
Critical trading data — such as order placement, execution timestamps, and transaction provenance — remains inaccessible, preventing independent verification of Aster’s claimed liquidity.
“Correlation alone isn’t proof of manipulation,” DefiLlama clarified, “but it does erode confidence in the data quality and fairness of reported markets.”
The controversy has reignited scrutiny of Aster’s governance, particularly given the advisory presence of Binance co-founder Changpeng Zhao (CZ).
Observers argue that decentralized exchanges must meet the same transparency standards as on-chain DeFi protocols if they expect continued legitimacy in analytics rankings and institutional dashboards.
“DEXs live and die by their data. If users can’t verify volume, they can’t trust it,” said one analyst.
DefiLlama stated it will reinstate Aster’s metrics only after the project enables verifiable data feeds that meet open-access and anti-manipulation standards.
Until then, Aster will remain excluded from aggregate leaderboard calculations — potentially hurting visibility and trader confidence.
Meanwhile, developers and validators within the Aster ecosystem are urging the team to adopt on-chain proofs for trade matching and volume reporting, similar to models used by Hyperliquid and dYdX v4.
Transparency is the backbone of decentralized finance. The Aster episode underscores an emerging truth in DeFi: “if it’s not verifiable, it’s not DeFi.”
DefiLlama’s decision signals that analytics platforms are tightening standards as DeFi matures, prioritizing auditable, open data over hype-driven volume metrics.
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