For high-frequency quantitative traders, the lifecycle of a strategy often measures in milliseconds. Once the trading logic is exposed on-chain, copying and sniping follow. This is the core dilemma faced by many quantitative teams.

Imagine this scenario: your AI trading agent detects market anomalies and needs to fetch market depth data from an oracle, combining it with a private arbitrage model for calculations. Under traditional oracle architecture, you must send requests in plaintext—which is equivalent to broadcasting your strategy parameters across the entire network.

APRO changes this gameplay. When the agent initiates a request via the ATTPs protocol, it uses fully homomorphic encryption (FHE) to fully encapsulate the query parameters and strategy logic. The data becomes ciphertext, as if locked inside a black box only you hold the key to, then thrown into the public network.

The most interesting part comes next—APRO’s oracle nodes receive this ciphertext bundle and have no idea what you are querying or what your strategy parameters are. Thanks to the properties of FHE, nodes can perform computations directly on the ciphertext without decrypting. It’s like writing a math problem on paper, sealing it inside a black box, and handing it to the node; the node reaches in, performs the calculation, and returns the ciphertext answer to you. Only you with the private key can open the black box and see the real result.

This "blind computation" mechanism fundamentally cuts off the possibility of strategy leakage. Nodes cannot steal your trading logic, and competitors cannot infer your arbitrage model from on-chain data. For quantitative teams that rely on informational advantages and strategy confidentiality, this is a game-changing development.