Bots and humans in the studies (exploratory): the method

Of the 323 accounts the studies measured, 22 look like bots, 151 like people using a web interface, 150 are unclear. For likely bots nothing robust can be said yet; for likely humans the protection results hold. All the studies.

The classifier

Its rules are in the protocol, fixed before any outcome was split. In short:

Bot evidence:

• client order ids on orders not routed through a builder (+2, or +1 above 10%);
• Ioc market orders off builder routes (+1);
• post-only-heavy order flow (+1);
• sub-second sequences of separate actions (+2, or +1);
• trading round the clock (+1);
• ≥ 100 actions per active day (+1);
• actions on the minute (+1);
• ≥ 90% of orders cancelled (+1).

Human evidence:

• the official web app’s market orders, tif: "FrontendMarket" (−2, or −1);
• a third-party front end, i.e. a builder fee on most fills (−1);
• a daily sleep gap (−1);
• under 180 filled orders in 180 days (−1).

Classes: score ≥ +2 is likely bot, ≤ −2 likely human, anything else unclear. Strict cut-off: ±3.

• The official web app sets no client order id. 1.5% of 123,638 FrontendMarket orders carry one.
• Third-party front ends do. Accounts trading mostly through a builder have a median cloid share of 59%, and they send Ioc market orders. Liquidated accounts trade mostly this way: a median 88% of their filled orders carry a builder fee.

Not usable:

• TWAP: twapId is null on every cached fill.
• Approved API wallets: extraAgents is not in any cache, and fetching it needs the main session’s OK (below).

Coverage

• Insufficient data (under 10 orders): 7 accounts, all in B.
• Not in the caches: none.
• In M, the rules that fired:
  • web-app market orders on ≥ 50% of entries: 42% of accounts;
  • daily sleep gap: 31%;
  • round the clock: 14%;
  • API market orders: 7%;
  • API client ids on ≥ 50%: 4%.

What the classes look like (all study accounts, medians):

Sanity check (descriptive)

The thresholds were set with these groups’ feature distributions in view, so agreement is weak support (protocol).

• Mostly as expected, but the score is conservative.
  • It rarely calls a market maker human (5 of 119), but it calls most of them unclear.
  • Most of these market makers have only one cached page of fills and no order history, so the timing and order-type rules cannot fire.
• Disagreements:
  • 3 of 11 public vaults score human (a sleep gap and web-app orders: vault leaders may trade by hand);
  • 1 low-activity account scores bot.
• Low-activity accounts land in “unclear”, not “human”: a lack of data is not human evidence.
• Not checkable: HLP and other named market-making vaults are not in the caches.

Trial count

• The parents’ counts rise by 12, 56 and 24.
• The Guard-defaults test set has now been looked at twice.
• Multiplicity: with 94 measurements, about five intervals would exclude zero by chance alone. That is one more reason to read every single-cell result here as exploratory.
• Not counted: the context numbers (return change, halts, feature medians) are reported as listed.
• Nothing else was tried: no rule, threshold or cut-off was changed after the protocol’s commit.

Caveats

• No ground truth. The score reads how orders were placed, not who decided.
  • A person can run an API script, and an AI agent can drive a front end.
  • One address can hold both, which is why much of the sample is unclear.
• Unclear is not a residual of noise. Accounts trading plain Gtc limit orders, with no client id and no web-app market orders, could be either. An SDK user who sets no client id and avoids Ioc looks like a manual limit-order trader.
• Agent wallets are invisible here.
  • extraAgents was not fetched.
  • Even if it were, the official app’s one-click trading also signs with an agent, so an approved agent would not prove a bot.
  • Whether the app’s agent can be told apart (for example by name) is untested.
• Builder routes are ambiguous. Wallet apps and Telegram front ends are mostly people; copy-trading and automation platforms also pay builders. H2 is weak for that reason.
• Recent history only: 2,000 orders and 10,000 fills. For active accounts, the order-type and timing features describe the last days to weeks.
• Small cells: 19 likely bots in M, 8 in the test set, 1 in the compliance cohorts.
• Post-hoc subgroups of studies whose headlines were already known; every parent study’s caveats apply (survivorship, what-if replay, main-dex perps, one 180-day period).