For sell-side trading desks and market makers
Which clients send you rates and route credit elsewhere. Which client's hit ratio fell from a third to a tenth this quarter. Which sales-trader covers eighty accounts and quotes thirty. From RFQ logs and trade blotters, client IDs only, inside your own tenant.
A trading desk sees the RFQs it receives and the trades it wins. It rarely sees the two together per client, per product, over time. Covirage computes hit ratio and flow share from the logs, values the gap at desk margins, and ranks it per sales-trader.
Won against received, by product and tenor, with the trend that says a client is drifting.
Your share of the client's estimated flow, where you hold benchmark or tender data.
Clients per sales-trader against quotes made, so the list fits a trading day.
Three steps, in this order.
By client, product and sales-trader.
Daily for hit ratio, quarterly for share.
Sales-traders see their clients. The desk head sees the roll-up.
Short answers. The Help centre has the long ones.
That page is salesperson coverage and wallet share across products. This page is flow: RFQs, hit ratios and response, per client and product, for the desk.
No. Enterprise runs in a separate pseudonymised tenant with client IDs only.
Any RFQ log or blotter export: Bloomberg, Tradeweb, MarketAxess exports, or internal systems.
Analytics software for trading, compared · Alternatives to named products
Written for this desk: the measures, the data you already hold, and the arithmetic.
How a trading desk measures whether its axes reach the clients who trade them, from the axe log, the distribution records and the blotter: axes sent per client per product, axes traded within a stated window, the clients who receive many and trade none, the clients who trade the desk's axes but receive few, the salespeople whose distribution lists have not changed in a year, and the identity that trades against axes sit inside the blotter.
16 Sept 20262 min readHow a trading desk tiers its clients from the RFQ log and the blotter rather than from the coverage list: two axes, notional traded and hit ratio, four tiers that follow, the clients with high inquiry volume and low hit ratio who consume pricing effort for little, the clients with high hit ratio and modest flow who would trade more if shown more, and the quarterly re-tiering that moves clients on their own numbers.
16 Sept 20262 min readHow a sell-side desk computes hit ratio and flow share per client and product from its own RFQ logs and blotter, why the two numbers must be read together, and the three drifts that predict a client is leaving before the revenue shows it.
16 Sept 20264 min readWhy a trading desk's hit ratio should be weighted by ticket size as well as counted, how the two diverge when a competitor commits balance sheet, the computation from the RFQ log, and a worked example where a rising count-weighted ratio masks a falling share of volume.
16 Sept 20263 min readThe ten questions a head of sales trading puts to the salespeople and the traders, which clients have gone quiet in a product where the desk has not, what is the size-weighted hit ratio by client, which clients are tiered where by flow and hit ratio, which axes reached the clients who trade them, who is covered by one salesperson only, what does the coverage map look like by client and product, does the blotter reconcile to the benchmark submission, which clients receive many prices and trade none, which salespeople's distribution lists are stale, and what changed, each with the table from the RFQ log, the blotter and the coverage file, and the answer to send back.
16 Sept 20262 min readHow a sales-trading desk finds the clients whose inquiry or trade count has fallen against their own baseline from the RFQ and trade logs, per product, why a client can be silent on one desk and active on another, the quiet score that ranks them by the flow at stake, and the weekly list that reaches the salesperson before the quarter-end review does.
16 Sept 20262 min read