Blog · Industry
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.
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 readHow desks should choose analytics software: start from the questions, check the data you hold, ask vendors ten questions, avoid the traps.
24 Sept 20264 min readThe complete axe distribution calculation on ten axes sent by one desk, small enough to check by hand: each axe's instrument, side and the clients it was sent to, the blotter's trades in those instruments within the window, the match rule, axes received and traded per client, the axe hit rate, the client on every list who traded none, the client who traded the desk's axed instrument without being sent the axe, the salesperson's list age, and the assertion that matched trades are a subset of the blotter, so a reader can reproduce every figure and then run it on their own axe log.
17 Sept 20263 min readThe ten client KPIs a sales and trading desk should run on, each with its formula, the export it comes from and what it tells you: hit ratio by client and product, size-weighted hit ratio, inquiry share, the quiet client list, client tiering by flow and profitability, axe hit rate, traded unsent, coverage recency by client value, revenue concentration, and response time on inquiries. Also the three measures most desks miss, the figures to drop, the identities, and who owns what.
17 Sept 20264 min readThe complete hit ratio calculation on ten RFQs from two clients in one product, small enough to check by hand: each inquiry's notional and outcome, the count-weighted ratio, the size-weighted ratio, why the two diverge when a client trades the small tickets and leaves the large, the tier the two clients land in on flow and hit ratio, and the assertion that traded notional never exceeds inquired, so a reader can reproduce every figure and then run it on their own RFQ log.
17 Sept 20262 min readThe honest answer to what hit ratio a sales trading desk should run: the 20 to 40 percent figures quoted on credit and rates desks depend on the product, on whether the ratio is by inquiry count or by size, and on the client mix, because a desk that is in competition on every inquiry from a large client will run lower than one that sees only the inquiries it is likely to win. This page gives the ranges by product, the three measurable things that set the right figure for one desk, and the table to compute before anyone quotes a percentage.
17 Sept 20263 min readClient flow analytics for trading desks. Which clients show you the flow, and which show it to the street.
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