Friends who have done quantitative trading have definitely encountered this awkward situation: at the same time, one leading exchange reports BTC price at 95,000, another major platform reports 95,200, and some smaller exchanges even shout out 96,000. Looking at these price differences, if you choose a price based on intuition to develop a strategy, nine times out of ten you’ll get sniped by arbitrageurs. But building your own system to aggregate these data and handle anomalies? The cost is simply too high and not feasible.

The situation on-chain is even more dangerous. Liquidations in DeFi protocols, options settlements, and prediction market judgments all hinge on the prices provided by oracles. Once the price is wrong, it could lead to millions of dollars in erroneous operations. Therefore, data aggregation by oracles is far from just calculating a simple average.

Some oracle solutions are already fetching price information from 161 different data sources. That number might sound intimidating at first, but the real challenge is: how to aggregate these 161 prices into a final price that is both trustworthy, resistant to manipulation, unaffected by outliers, and sufficiently real-time?

Why is it necessary to have so many data sources? In theory, just using the prices from two or three top exchanges should be enough for liquidity. But in reality, the performance of different assets varies greatly across platforms. Some altcoins have excellent liquidity on certain exchanges but are not listed elsewhere. Stablecoins, for example, are often more accurately priced on some DEXs than on centralized exchanges. This fragmented market structure requires oracles to collect data broadly to avoid local information biases.