In the past two weeks, I connected the arbitrage bot in the lab to @Pyth Network the Blue Ocean ATS post-market data stream. My first impression is that Pyth Pro's layered latency model finally allows me to test low-latency strategies without burning the budget: the free base layer runs backtests, and after upgrading, I can pull millisecond-level post-market quotes. The most intuitive gain is that I no longer have to wait for the morning market open to update the reference price for US stocks on the hedge contracts I deployed on Solana—the real-time post-market data fills this gap, and the floating losses on the strategy immediately narrow down.
@Pyth Network By binding data publishers and $PYTH together through Oracle Integrity Staking, I watched as the newly listed Hong Kong stock pricing attracted additional staking within hours; this 'locked responsibility' design makes institutional clients less likely to ask, 'Who exactly is responsible for your SLA?' Even better, Pyth Pro's access logs can be exported for auditing, which saved me a lot of talking when coordinating with the compliance team. The vision of 'completing US stock prices during Asian hours' mentioned in #PythRoadmap is now confirmed one by one by the green lights on my Dashboard; even if there are occasional node delays, cross-chain redundancy can fill in within seconds, providing a sense of security comparable to when I first used a hardware firewall. $PYTH
