Corvil Venue Performance Analysis

Do you use this solution for venue performance analysis? If yes, please explain your use case with examples. Does this help you improve order routing decisions? If yes, how?

Senior Network Engineer at a financial services firm with 501-1,000 employees
In addition, we do venue performance analysis. A good example is FX pricing. We take all the OTC pricing from various sources - JP Morgan, HSBC, and so forth. There are about ten venues that generate prices that we have to look at. Key metrics for us with FX are things like sending-time latencies. We look at that. We always knew anecdotally that our that Morgan Stanley feed, for example, was really poor when it came to latency. But we didn't have the numbers to prove it. We could just tell by looking at the quotes from the others to know how far out the Morgan Stanley ones were and that the latency was really bad. Corvil helped us show that information in a nice, graphical manner and gave us some metrics behind that. We definitely it for per-venue analysis. We fixed the problem with Morgan Stanley. It's good. People say, "Could you supply us with quote ID where you observed these issues," maybe, in some respects, thinking it would take us a long time to get that information. But we can literally, in two clicks, export the spreadsheet and send it to them. Having that information helps us improve order routing decisions. Some of our trading is done based on latency, for the hedging side. What would happen is, if a lot of clients are betting one way, and it exceeds our risk profile, we'll have to go out and hedge against that in the market. A lot of that is automated. It's not that the Corvil tool is used to feed those automated tools, but it has created other metrics for that. But if there is an issue, it allows them to pull out the data, or they might ask me to do it, and we can say, "Oh, yeah, this venue had a problem at this time," or, "We're seeing some kind of issue with this venue." That's why we advise you not to trade on this particular venue. So that's definitely helpful. We never had that kind of correlation in the past. It was a case of a trader coming up to us and saying, "Did we have a problem with XYZ at this particular time?" We'd have to dig out multiple logs from the networks and try to figure out what was going on at that time. It allows us to narrow down on the problem a lot quicker. We've got a copy of all the messages, we know what went on at each point. We can say, "Oh, look, we didn't see any particular messages at this particular point," which means there's either an issue with our monitoring of the structure - unlikely - or more likely some kind of issue with the vendor.
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Network Operations at a financial services firm with 1,001-5,000 employees
We do measure latency of our market data feeds coming down from the exchanges. If it breaches a threshold, we do contact the venues, then they make necessary corrections. This helps us improve our order routing decisions, because if it is too high of a latency, we just go through another venue.
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User with 10,001+ employees
If we have the venue round trip time from the time it leaves the application, we can just go back to the exchange, discuss this, and say, "Why has it taken so much time?" Maybe there has been scenarios where the exchange or market comes back saying they did some type of configuration changes at that site during that particular time, and that's why there was an increase in latency. Or, they needed some type of changes at their site to improve the latency. This helps in our venue performance analysis. For different venues, depending upon the application, we have different requirements. For example, for certain application, we target the time that it takes for the acknowledgement to come back, or for the request to go to the exchange, it should be seamless. So, we use different statistic for different markets. Based on that, we can work with different markets or exchanges to match the timings. Or, we use a different routing logic within our application to be able to process the order at the same time. Based on this analysis and the statistics that we have, we can use it to match or change the routing logic that we have. There have been a few scenarios where we have done this.
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