Liquidations are not emotional events.
They are inevitable results produced by price feeds, update intervals, and aggregation rules.
In leveraged markets, fairness is rarely settled at the trading interface itself; rather, it is resolved at much deeper layers, within the oracle's workflow.
Leverage amplifies the sensitivity of the oracle
In spot trading, small price differences are often of no real impact.
In leveraged trading, the same differences can become a liquidation spark.
At high leverage levels (20x, 50x, 100x), the margin of error becomes tight to fractions of a point. At this level, oracle behavior is as important as market direction itself.
Filtering engines typically depend on:
Index Price
Mark Price
Funding Reference price
And all these prices depend, directly or indirectly, on oracle inputs.
What does 'oracle accuracy' really mean?
Oracle accuracy is often misunderstood as merely a 'correct price.'
But in reality, it is a system of interconnected design decisions, including:
1. Source composition
Number of reference pricing platforms and the nature of their interconnection.
2. Update frequency
Rapid updates reduce delays but increase short-term noise.
3. Aggregation logic
Mean, median, TWAP, or hybrid models, each with its behavior under pressure.
4. Handling outlier values
Are extreme prices excluded, weighted down, or passed through?
5. Emergency mechanisms
How does the system behave when one data source deteriorates?
According to official oracle dashboards and platform documents, most unexpected liquidations are linked to aggregation behaviors in edge cases, not to erroneous raw prices.
Time factor: More sensitive than traders think
Based on technical disclosures published by major platforms and oracle providers:
Oracle updates typically occur every 100 to 500 milliseconds under normal conditions.
During high volatility, price smoothing may lead to a temporary deviation from the last traded price.
Filtering engines operate based on verified oracle states, not based on 'chart candles.'
These characteristics are not design errors, but conscious trade-offs in risk management.
Liquidation fairness as a three-layered system
Liquidation outcomes are the result of interactions among three main layers:
Layer What does it control
Trader Leverage, margin, risk management
Platform Liquidation limits, insurance logic
Oracle Price timing, aggregation, data integrity
The only layer completely outside the trader's control is: the oracle.
This explains why identical positions may be liquidated at different times on multiple platforms, even when the displayed prices seem similar.
Neutral analytical note
Overall, oracle architectures are designed to achieve one of two main objectives:
Low latency response
Quick response to market changes
→ Higher sensitivity to transient fluctuations
Smoothed stability
Reducing liquidation resulting from noise
→ Relative delay in rapid movements
There is no 'best' option absolutely.
Each model reflects a different definition of fairness under pressure.
According to official data, platforms adjust these parameters based on liquidity depth, leverage usage patterns, and insurance fund sizes.
A common misconception among traders
A recurring assumption states:
> 'The price did not touch the liquidation level on the chart, so the liquidation is unfair.'
This assumption ignores significant differences:
The difference between the index price and the last price
Oracle update timing
Differences in the weights of reference platforms
The chart displays market activity.
Liquidation is executed based on a verified oracle state.
Analytical note
This assertion does not accuse any platform or oracle provider.
Oracle systems operate under probabilistic constraints, especially during extreme volatility, and no design can eliminate all edge cases without creating new risks.
Why does this issue become more important?
With leverage remaining widely available, oracle accuracy shifts from a technical detail to a systemic risk factor.
Fairness in leveraged markets does not mean preventing liquidation, but ensuring it occurs due to real economic reality, not due to data distortions.
Visual position 1:
[Diagram: Oracle ← Index Price ← Mark Price ← Filtering Engine under normal conditions]
Visual position 2:
[Comparing Oracle aggregation behavior during high volatility versus calm market]
Methodical summary
In modern leveraged trading, trust is not built at the user interface.
Instead, they are encoded in how price data is selected, aggregated, and timed—long before the liquidation logic is executed.
Sources:
Technical documents for platforms, oracle provider papers, and notes based on official dashboards.
