Risk Score algorithm

The Risk Score is a number between 0.0 and 10.0 that summarizes how rug-pull-y a token looks based on on-chain evidence. It’s glass-box: the full list of signals that fired, with their weights and individual contributions, is in the API response. You can re-compute the score yourself.

Formula

score = min(10.0, sum(signal.contribution for signal in fired_signals) * 10 / 5000)

The divisor 5000 is the calibration target — a raw contribution sum of 5,000 normalizes to 10.0. Real-world rug setups often reach raw sums of 20,000+; they all clip to 10.0.

Level bands

The level field bands the numeric score into four categories for UI:

`level`	Score range
`safe`	0.0 ≤ score < 2.5
`caution`	2.5 ≤ score < 5.0
`warning`	5.0 ≤ score < 7.5
`danger`	7.5 ≤ score ≤ 10.0

Status

`status`	Meaning
`ready`	All 12 signal inputs available; score is fully computed.
`partial_data`	At least one signal couldn’t be evaluated (the underlying data isn’t onboarded yet). The score still reflects the signals that were available; `missing_signals[]` lists which weren’t ready.
`no_data`	None of the signal inputs are available — typically because the mint isn’t yet in `tracked_mints`. `score` is `null`.

A partial_data score is a true lower bound — every signal in missing_signals[] is one that could have added to the score had its input been available.

Signals

Twelve signals across six categories. “Graded” signals scale their contribution with the magnitude of the underlying value (e.g. dev_held_high contributes more when the creator holds 30% vs 6%).

Holder concentration (graded)

`code`	Trigger	Weight	Notes
`single_holder_50pct`	top holder owns > 50% of supply	7000	Graded across 50–100%. Pool wallets are excluded from the “top holder” calculation.
`top10_high`	top-10 sum > 50% of supply	5000	Graded across 50–70%. Pool-excluded.
`top10_very_high`	top-10 sum > 70% of supply	2500	Graded across 70–100%. Stacks on top of `top10_high`.

LP + authority (boolean)

`code`	Trigger	Weight	Notes
`lp_not_burnt`	LP tokens not burnt/locked	4000	Returns `None` (signal sits in `missing_signals`) until the LP-lock state has been checked — avoids a false-positive during the warm-up window. Lock state is written in real time from on-chain burn/lock events and refined by a polling detector; the `/tokens/{mint}/audit` `lock` object surfaces the `mechanism` (burned vs locked) and a `verified` confidence flag. Bonding-curve venues (PumpFun, Meteora DBC) have no fungible LP, so this signal stays `None` there.
`mint_authority_active`	mint authority not revoked	2500	Empty or `null` authority counts as revoked.
`freeze_authority_active`	freeze authority not revoked	7500	The heaviest single signal in the catalog — token holders can be frozen out of their positions.

Sniper concentration (graded)

`code`	Trigger	Weight	Notes
`snipers_count_high`	≥ 10 wallets bought within first 30 slots	3500	Graded by count (10 → 0.1, 50+ → 1.0). A fire never yields 0 contribution.
`snipers_pct_high`	snipers hold > 30% of supply	7500	Graded across 30–50%.

Insider concentration (graded)

`code`	Trigger	Weight	Notes
`insiders_pct_high`	insiders hold > 30% of supply	5000	Graded across 30–50%. An insider is a holder who never appears as a swapper on-DEX.

Creator behavior (graded)

`code`	Trigger	Weight	Notes
`dev_held_high`	creator holds > 5% of supply	3000	Graded across 5–30%. Only fires when `creator_source` is non-empty — i.e. we know who the creator is.
`dev_held_very_high`	creator holds > 30% of supply	5000	Graded across 30–100%. Stacks on top of `dev_held_high`.

Metadata (boolean)

`code`	Trigger	Weight	Notes
`no_socials`	no twitter / telegram / website	2000	All three fields must be empty or null.

Total maximum raw contribution: 53,500 (every signal triggered at full strength).

Why this design

Glass-box so you can audit our judgment. If you disagree with a weight or threshold, you can subtract that signal’s contribution from the raw score and renormalize. The value field on each signal is the concrete measurement, not an opaque score — verifiable against the underlying on-chain data. Stacking thresholds (e.g. top10_high + top10_very_high) compound penalties for severe concentration without making any single threshold a cliff. Graded contribution keeps small differences in the underlying data from causing large score jumps. A token at 51% top-10 concentration doesn’t suddenly leap past one at 49%. Phase 0 strict precedence for dev_held_*: we only fire these when tokens.creator_source is populated. We never fall back to “largest holder is probably the creator” — that produced false positives in pre-Phase-0 testing on tokens where the largest holder was a CEX hot wallet or the AMM pool itself.

Re-computing the score

The response is glass-box on purpose. If you want to weight signals differently:

# Subtract signals you don't care about
penalty = sum(s["contribution"] for s in signals if s["code"] != "no_socials")
# Renormalize against your own divisor
my_score = min(10.0, penalty * 10 / 5000)

Or if you want a fully custom model: walk signals[*].value, ignore the weight/contribution, and feed the raw values into whatever you’d prefer.

Getting started

Concepts

Streaming

Examples

Risk Score algorithm

Formula

Level bands

Status

Signals

Holder concentration (graded)

LP + authority (boolean)

Sniper concentration (graded)

Insider concentration (graded)

Creator behavior (graded)

Metadata (boolean)

Why this design

Re-computing the score

See also

​Formula

​Level bands

​Status

​Signals

​Holder concentration (graded)

​LP + authority (boolean)

​Sniper concentration (graded)

​Insider concentration (graded)

​Creator behavior (graded)

​Metadata (boolean)

​Why this design

​Re-computing the score

​See also

Formula

Level bands

Status

Signals

Holder concentration (graded)

LP + authority (boolean)

Sniper concentration (graded)

Insider concentration (graded)

Creator behavior (graded)

Metadata (boolean)

Why this design

Re-computing the score

See also