Resources

Tokens & scanner

A token's quant snapshot is everything FomoQuant knows about it at the latest compute tick: scores with drivers, active signals, market and activity context, the feature vector and data freshness. The scanner runs rules over the same snapshots.

Referencing tokens

Every endpoint that takes a token accepts $XYZ, XYZ, the stable id tk_…, <chain>:<address> or a bare address. Symbols can collide across chains; a symbol resolves to the most active token, so store the tk_ id once you have it. Dead tokens (no price or liquidity for ~24h) keep their id and stay in history with status: "dead".

The quant snapshot

Fields

Name	Description
`scores` object	Plain values per score key (null = insufficient data).
`score_details` object	Per score: value, display, drivers (key, label, points, raw, display, percentile, note), confidence (level, sample_size, basis), model_version, summary, stale; divergence adds state.
`signals` signal[]	Active signals on the token.
`divergence_state` enum \| null	See below.
`market` object	price_usd, price_change_1h/24h, market_cap_usd, liquidity_usd, volume_24h_usd, holder_count, top_holder_share, source, updated_at — null when the provider lacks the field.
`activity` object	theses_1h, theses_24h, unique_authors_1h, unique_authors_24h, last_thesis_at.
`features` object \| null	The point-in-time feature vector the scores were computed from (velocity, breadth, concentration, author quality, narrative, cohort and market features).
`analogs` object \| null	Count, median 24h return and worst 24h drawdown of the nearest historical setups.
`model_versions` object	Versions of every model used for this snapshot.
`freshness` object	updated_at, fomo_last_event_at, market_last_at, lag seconds, stale and stale_reasons.

scoresobject
Plain values per score key (null = insufficient data).
score_detailsobject
Per score: value, display, drivers (key, label, points, raw, display, percentile, note), confidence (level, sample_size, basis), model_version, summary, stale; divergence adds state.
signalssignal[]
Active signals on the token.
divergence_stateenum | null
See below.
marketobject
price_usd, price_change_1h/24h, market_cap_usd, liquidity_usd, volume_24h_usd, holder_count, top_holder_share, source, updated_at — null when the provider lacks the field.
activityobject
theses_1h, theses_24h, unique_authors_1h, unique_authors_24h, last_thesis_at.
featuresobject | null
The point-in-time feature vector the scores were computed from (velocity, breadth, concentration, author quality, narrative, cohort and market features).
analogsobject | null
Count, median 24h return and worst 24h drawdown of the nearest historical setups.
model_versionsobject
Versions of every model used for this snapshot.
freshnessobject
updated_at, fomo_last_event_at, market_last_at, lag seconds, stale and stale_reasons.

Divergence states

State	Meaning
`EARLY_SOCIAL` Early social	Social attention is unusually high (z > 1) while price is flat (\|z\| < 0.5).
`CONFIRMED` Confirmed	Social attention and price are both unusually high.
`LAGGING_SOCIAL` Lagging social	Price is moving (z > 1) while thesis activity is not (z < 0.5).
`DIVERGING` Diverging	Price rising while attention decelerates, or attention high while price falls sharply.
`NEUTRAL` Neutral	Social and market activity are in line.
`NO_MARKET_DATA` No market data	No usable market data in the last 48h — divergence is null, never guessed.

Scanner rules

A rule is { field, op, value } with op one of >, >=, <, <=. Rules are AND-ed and a null value never passes. The same evaluator powers the scanner, saved scans, alerts and backtests. Free plans scan snapshots 15 minutes old; Pro and Team scan the latest tick. Custom rules in the app need Pro or Team; presets work everywhere.

Field	Unit	Meaning
`momentum`	0–100	Acceleration of Fomo attention (0–100).
`conviction`	0–100	Sustained vs one-off attention (0–100).
`crowding`	0–100	Concentration of attention among few accounts (0–100, high = concentrated).
`early_quality`	0–100	Historical quality of accounts posting early (0–100).
`narrative_velocity`	0–100	How fast the token's narrative is spreading (0–100).
`divergence`	−100…+100	Social vs market activity (−100…+100, positive = social leading).
`smart_cohort`	0–100	Convergence of a historically strong cohort (0–100).
`theses_1h`	count	Theses posted in the last hour.
`theses_24h`	count	Theses posted in the last 24 hours.
`unique_authors_1h`	count	Distinct authors in the last hour.
`unique_authors_24h`	count	Distinct authors in the last 24 hours.
`thesis_velocity_1h`	change ratio (2.0 = +200%)	Last-hour theses vs the 7-day hourly baseline (2.0 = +200%).
`unique_author_growth_1h`	change ratio (2.0 = +200%)	Distinct authors this hour vs the previous hour (0.5 = +50%).
`attention_acceleration`	change ratio (2.0 = +200%)	Theses this hour vs previous hour.
`repeat_author_ratio`	fraction (0.05 = 5%)	Share of 24h authors who posted on the token more than once in 7 days.
`top1_share`	fraction (0.05 = 5%)	Share of 24h theses from the single most active author.
`high_quality_author_count`	count	Authors active in 24h with a research score ≥ 70 and at least MEDIUM confidence.
`price_change_1h`	fraction (0.05 = 5%)	Price change over the last hour (0.05 = +5%).
`price_change_24h`	fraction (0.05 = 5%)	Price change over 24 hours.
`volume_change_1h`	fraction (0.05 = 5%)	Last-hour volume vs previous hour.
`liquidity_usd`	USD	Pool liquidity in USD.
`market_cap_usd`	USD	Market capitalisation in USD (FDV when circulating supply is unknown).
`volume_24h_usd`	USD	Trading volume over 24 hours in USD.

Presets

Preset	Rules
Early Attention `early-attention`	Momentum > 70 AND Crowding < 40 AND Unique authors 1h ≥ 5 Attention accelerating early, broad and not crowded. Sorted by momentum.
Broad Momentum `broad-momentum`	Momentum > 65 AND Unique authors 24h > 25 AND Crowding < 35 High momentum carried by many independent authors. Sorted by momentum.
High Conviction `high-conviction`	Conviction > 70 AND Repeat author ratio > 30% Authors keep coming back across multiple windows. Sorted by conviction.
Low Crowding `low-crowding`	Crowding < 25 AND Theses 24h ≥ 15 Active tokens where no small group dominates attention. Sorted by crowding.
Smart Cohort `smart-cohort`	Smart Cohort > 60 AND Early Quality > 60 Historically strong cohorts converging. Sorted by smart_cohort.
Cooling Fast `cooling-fast`	Attention acceleration < -50% AND Theses 24h ≥ 10 Attention decelerating sharply versus the prior hour. Sorted by attention_acceleration.
Narrative Breakout `narrative-breakout`	Narrative Velocity > 65 AND Momentum > 40 Narrative spreading faster than usual. Sorted by narrative_velocity.

Try them in the scanner. Scores are explained in Scores.

Endpoints

Example responses use illustrative values ($XYZ, example_rhea, a placeholder address) — not real tokens or authors; every field name and shape is exactly what the API returns. Long sections are collapsed as { … }.

GET/v1/quant/token/{token}

scope tokens:read

Latest point-in-time quant snapshot of a token.

Plain scores (null = insufficient data), score_details with drivers that sum to each score, confidence and model version, active signals, market, activity, divergence_state, the feature vector, analog summary, model_versions and freshness.

Parameters

Name	Description
`token`required path · string	Token reference: `$XYZ`, `XYZ`, `tk_…`, `<chain>:<address>` or `<address>`. Symbol collisions resolve to the most active token.

tokenpath · stringrequired
Token reference: $XYZ, XYZ, tk_…, <chain>:<address> or <address>. Symbol collisions resolve to the most active token.

curl "https://fomoquant.app/v1/quant/token/XYZ" \
  -H "Authorization: Bearer $FOMOQUANT_API_KEY"

GET/v1/quant/token/{token}/analogs

scope tokens:read

Historical setups most similar to the token's current snapshot (analog-v1.0).

Feature-vector distance over momentum, crowding, early quality, narrative velocity, price momentum, volume acceleration and liquidity. Only candidates whose 24h outcome resolved before the snapshot are used, and the same token within ±24h is excluded.

Parameters

Name	Description
`token`required path · string	Token reference: `$XYZ`, `XYZ`, `tk_…`, `<chain>:<address>` or `<address>`. Symbol collisions resolve to the most active token.
`k` query · integer 1–100	Maximum analogs returned, nearest first.Default `40`

tokenpath · stringrequired
Token reference: $XYZ, XYZ, tk_…, <chain>:<address> or <address>. Symbol collisions resolve to the most active token.
kquery · integer 1–100
Maximum analogs returned, nearest first.
Default 40

curl "https://fomoquant.app/v1/quant/token/XYZ/analogs?k=20" \
  -H "Authorization: Bearer $FOMOQUANT_API_KEY"

GET/v1/quant/tokens

scope tokens:read

Tokens with their current scores, cursor-paginated.

Parameters

Name	Description
`sort` query · rule field	Any rule field (score key or feature such as unique_authors_1h).Default `momentum`
`dir` query · asc \| desc	Sort direction.Default `desc`
`limit` query · integer 1–100	Page size.Default `25`
`cursor` query · string	Opaque cursor from the previous page's `next_cursor`.
`narrative` query · slug	Narrative slug, e.g. ai-agents.
`chain` query · enum	One of solana, robinhood, base, bnb, eth, arc.
`q` query · string ≤ 64	Symbol / name search.

sortquery · rule field
Any rule field (score key or feature such as unique_authors_1h).
Default momentum
dirquery · asc | desc
Sort direction.
Default desc
limitquery · integer 1–100
Page size.
Default 25
cursorquery · string
Opaque cursor from the previous page's next_cursor.
narrativequery · slug
Narrative slug, e.g. ai-agents.
chainquery · enum
One of solana, robinhood, base, bnb, eth, arc.
qquery · string ≤ 64
Symbol / name search.

curl "https://fomoquant.app/v1/quant/tokens?sort=momentum&limit=25" \
  -H "Authorization: Bearer $FOMOQUANT_API_KEY"

POST/v1/quant/scan

scope tokens:read

Run a scanner rule set against the latest snapshot of every token.

All rules must pass (AND); a null value never passes. Free plans see a 15-minute delayed scanner (delayed_minutes). Send rules, a preset (early-attention, broad-momentum, high-conviction, low-crowding, smart-cohort, cooling-fast, narrative-breakout), or both.

Parameters

Name	Description
`preset` body · string	Scanner preset id; combined with rules when both are sent.
`rules` body · { field, op, value }[] ≤ 12	Conditions. `op` is one of `>` `>=` `<` `<=`; pct fields are fractions (0.05 = 5%).
`sort` body · rule field	Sort field (defaults to the preset's).
`sort_dir` body · asc \| desc	Sort direction.
`limit` body · integer 1–200	Rows returned.
`chains` body · chain[]	Restrict to chains.
`narrative` body · slug	Restrict to one narrative.

presetbody · string
Scanner preset id; combined with rules when both are sent.
rulesbody · { field, op, value }[] ≤ 12
Conditions. op is one of > >= < <=; pct fields are fractions (0.05 = 5%).
sortbody · rule field
Sort field (defaults to the preset's).
sort_dirbody · asc | desc
Sort direction.
limitbody · integer 1–200
Rows returned.
chainsbody · chain[]
Restrict to chains.
narrativebody · slug
Restrict to one narrative.

curl -X POST "https://fomoquant.app/v1/quant/scan" \
  -H "Authorization: Bearer $FOMOQUANT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"rules":[{"field":"momentum","op":">","value":75},{"field":"crowding","op":"<","value":30}],"limit":20}'

FomoQuant provides analytics and historical/statistical context, not financial advice or guaranteed predictions.