A desk that allocates flow on relationship history and a vague sense that one venue "feels better" has no answer when a mandate holder asks why. Scoring venues on their printed tape is the right instinct, because a tape is evidence: it exists whether or not it flatters anyone, it covers the intervals you did not trade as well as the ones you did, and it can be recomputed by a sceptic.
The scorecard is worth building. It just cannot be built from the module in the title, and the reason is worth thirty seconds of your attention before you brief anyone to go and do it.
The page you would reach for, and what it holds instead
Blockcircle's Market Trades page describes itself as a live execution feed across crypto, stocks and commodities. That phrasing points at time and sales. What the page actually carries is trade signals and positions posted by Blockcircle analysts: authored, timestamped entries with their own trade action, filtered by horizon rather than by venue.
Look at the filter row in the screenshot: ALL, SHORT-TERM, MID-TERM, LONG-TERM. Those are horizon tabs. A venue study needs to slice by venue, and there is no venue dimension on this page because the page is not organised around execution locations. The posts themselves can carry rich detail about an idea, including exit prices, durations and strategy statistics as displayed on the day, but a post is a description of a trade thesis and a print is a record of a transaction. The scorecard consumes the second kind of object.
So the sourcing is the first project. Venue tapes come from the venues, through their own market data products or their public feeds, and your own executions come from your execution records keyed on venue identifiers. That is the input. Everything below assumes you have it.
The five statistics a print quality scorecard should carry
Resist the urge to compute forty metrics. Five, computed identically across venues, will change decisions. Forty will produce a dashboard nobody opens.
Print size distribution is the first, and report it as a distribution rather than a mean. The median print tells you what ordinary activity looks like. The upper decile tells you whether size ever trades there at all. A venue where the ninetieth percentile print is barely above the median is a venue that will not absorb an institutional order without a schedule, however healthy its headline volume looks.
Inter-print gap distribution is the second, and specifically its tail. Median time between prints is close to useless. What you want is the ninety fifth percentile: how long the tape goes quiet in the worst five percent of intervals. That number is what determines whether a child order sitting there is exposed for seconds or for minutes.
Clustering is the third. Prints arriving in bursts separated by silence describe a different market from prints arriving steadily at the same daily total. Burstiness is measurable simply, as the variance of print counts across fixed intervals divided by the mean, and it matters because a bursty tape punishes fixed-rate schedules.
Price continuity is the fourth. Measure the distribution of price changes between consecutive prints in ticks. A venue where consecutive prints routinely jump several ticks is telling you that the book between those levels is thin, regardless of what the depth snapshot claims.
Realised size at your reference order is the fifth, and it is the one that translates the other four into your terms. Take the notional you actually work in that instrument, then measure how much printed volume accumulates in the interval you are willing to work over. That is the venue's answer to your specific question, and it is the only one of the five that a portfolio manager will read without translation.
Normalising so you are not just ranking venues by size
The failure mode of every venue scorecard is that it reproduces the volume table. The largest venue wins on every metric, which you knew before you started, and the study adds nothing.
Three normalisations do most of the work. Compare venues only over identical wall-clock intervals, so a venue is never scored during its home session against one scored outside it. Compare within instrument rather than across, because print size distributions differ more between instruments than between venues. And compute each metric conditional on a market state variable you choose in advance, volatility bucket being the obvious one, because venue rankings that hold in calm conditions often invert in stressed ones and the stressed ranking is the one that costs money.
The conditional view is the part worth the extra engineering. A venue that scores second in normal conditions and first when volatility is in its top decile is not the second best venue. It is the venue you route to on the days that matter, and no unconditional average will ever reveal that.
The half of venue quality no tape can show
Print statistics describe what happened after trades occurred. A meaningful part of execution quality happens before that, and none of it is visible in a tape.
Reject behaviour is invisible. So is the treatment of your resting orders, latency from your gateway, queue position, and whether the venue's matching behaves as documented under load. Fee schedules, including any maker rebate that inverts the ranking entirely, are not in the tape. Neither is uptime, and a venue with excellent prints that is unreachable for forty minutes during a repricing event has a quality problem no print statistic will capture.
For anything on the crypto side there is a further dimension that outranks all of the above, and it is not an execution question at all. Assets sitting at a venue are exposed to that venue. The scorecard should carry a separate column for custody and credit posture, and the operating rules that go with it are not negotiable: keys scoped to trading only, withdrawal permissions never granted to an execution key, custody held apart from the venue you route through, and credentials never shared with anyone including support staff who ask. A venue that scores well on print quality and requires a withdrawal-enabled key to integrate has failed the review before the tape statistics are opened.
Turning a scorecard into an allocation rule that survives review
The scorecard's purpose is to make the flow decision reproducible, which means it has to terminate in a rule rather than a discussion.
Write the rule with the weights fixed in advance and recorded before the data is refreshed. Any process that lets the weights be chosen after the scores are visible is a process that will rationalise the venue somebody already wanted. State the review cadence, quarterly is defensible for most books, and state what a venue has to do to be removed as well as what it has to do to be added. Removal criteria are the half that most policies omit and the half a regulator asks about.
Then keep a live measurement running rather than relying on the periodic study. Randomise a small share of child orders across eligible venues continuously, so that you are always accumulating comparable fills at each one rather than only at whichever venue the current rule favours. Without that, your own fill data becomes a record of the rule rather than a test of it, and next quarter's study will confirm this quarter's decision for reasons that have nothing to do with venue quality.