LeverUp now runs on Pyth Pro — the institutional tier of the Pyth oracle network. Measured across 370 parallel samples on Monad mainnet, price staleness dropped from 1.676 seconds (Pyth Core) to 0.086 seconds (Pyth Pro). That's a 19.5× improvement in feed freshness.
On a perp platform where every open, close, and liquidation references the oracle feed for pricing and mark-to-market, that gap is the gap between referencing an older market snapshot and referencing a much fresher one.
Why Oracle Freshness Matters for Perp Execution
On LeverUp, oracle-referenced pricing is load-bearing infrastructure. LeverUp's execution, mark-to-market, and liquidation logic all depend on timely oracle-referenced pricing. There's no separate matching layer that can absorb or smooth out a late price.
When the feed is fresh, execution quality reflects current market conditions. When it's stale, every trade in that window references a snapshot of reality that's already moved.
If that feed is 1.6 seconds behind the market, positions are marked against prices that may not reflect the market at the moment of execution. On a calm market, that's a minor friction. On a volatile move — exactly when most liquidations happen — a 2-second lag in price data means liquidation triggers may reference prices that didn't reflect the live market at that moment.
This is what traders loosely call "slippage." It usually hides inside the fill. The execution was worse than expected, the liquidation hit at a price that didn't quite make sense, the PnL drifted in a direction that was hard to explain. On high leverage, oracle staleness is often the largest hidden cost most traders never measure.
Pyth Core vs. Pyth Pro: The Two Tiers
Pyth offers two service levels.
Pyth Core is the original public price feed, widely used across DeFi and sufficient for most applications. It publishes frequently enough for the majority of on-chain use cases.
Pyth Pro is the institutional tier: lower latency, higher publish frequency, tighter timing guarantees from the same first-party publishers. Same underlying market, same source network, different service grade.
For a perp venue operating at high leverage, the gap between these two tiers is the difference between adequate and accurate.
The Data
We ran 370 parallel samples of Pyth Pro and Pyth Core on BTC/USD and ETH/USD, side by side on Monad mainnet. Same pairs, same window, same chain. The only variable was the oracle tier.
| Metric | Pyth Core | Pyth Pro |
|---|---|---|
| Average staleness | 1.676 seconds | 0.086 seconds |
| Improvement | 19.5× | |
| % samples with zero staleness | 91.4% |
91.4% of Pyth Pro samples had zero staleness. In those samples, the on-chain read matched the latest available published price with no measured staleness. Core samples generally showed roughly 1.5–2 seconds of staleness across the window.
Consistency Across the Window
One good average doesn't mean much if it breaks down at the edges. Oracles tend to degrade when they're needed most: volatile moves, sudden spikes, heavy publisher load. We split the sampling window into 20 segments and checked whether Pro's lead held up across all of them.
It held. Pro stayed flat near zero across all 20 segments. Across this sampling window, Core remained around 1.5–2 seconds and did not close the gap.
This is the important finding. Liquidations don't happen in calm windows. They happen when the market is moving fast, exactly when a 2-second lag in price data causes the most damage.
Pro Isn't Showing a Different Price
A reasonable concern: if Pro shows different numbers than Core, is it more accurate or just biased in one direction?
We checked. The average absolute divergence between Pro and Core prices is 1.35 basis points. 59.2% of samples are within 1 bps of each other. And the directional split is essentially 50/50: Pro prints higher 50.8% of the time, lower 47.0% of the time.
Pro isn't systematically higher or lower. It's tracking the same market Core tracks, sampled with tighter timing. The improvement comes from less lag, not different prices.
What Changes for Traders
Opens and closes reference fresher market data. LeverUp uses oracle-referenced pricing for every trade execution and mark-to-market. A 19× improvement in feed freshness flows through to the prices those calculations reference, with no middle layer absorbing the benefit.
Liquidations reference fresher market data. The "I got liquidated on a wick that wasn't really there" problem comes from stale feeds triggering against prices the market never actually printed at that moment. With Pro, liquidation checks reference a feed with substantially lower measured staleness.
Tighter effective execution. On orderbook venues, the bid-ask spread is where market makers price in the risk of operating on imperfect data. LeverUp's protocol-managed virtual liquidity system works differently: trades reference oracle-based pricing, so improvements in feed freshness can directly reduce stale-pricing risk in execution rather than being absorbed elsewhere.
A large part of what traders experience as unexpected execution on oracle-referenced platforms is simply oracle lag. Compressing measured staleness by 19× helps reduce that source of execution drag.
Why Monad Makes This Work End-to-End
A fast oracle feeding a slow chain is wasted capacity.
If Pyth Pro publishes a new price every 100ms but block confirmation takes 2 seconds, the freshness is lost before it reaches settlement. You'd be paying for Pro-grade data and getting Core-grade execution.
Monad's low-latency execution environment helps preserve more of the benefit from fresher oracle updates before settlement. The chain keeps up with the oracle. That's why the end-to-end trading experience actually reflects what Pro is doing, rather than being diluted by execution latency downstream.
Pyth Pro supplies the data. Monad supplies the execution. LeverUp is the venue that runs on both.
The Methodology
370 parallel samples across BTC/USD and ETH/USD. 17-minute live window on Monad mainnet. Staleness measured as time between price publish and on-chain observation. Divergence measured in basis points against Pyth Core. Monitoring is ongoing.
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