API reference · Live events

Get fair value

GET/v1/models/fair-value

A pre-event statistic per open market, from how often the speaker said the market's words in past recorded appearances.

On this page

Auth. An API token: Authorization: Bearer lb_… (create one). Signed-in web sessions work too.

Limits. One model call per call (100 a day on Free; unlimited on Builder and Standard).

Parameters

Query parameters

Name	Type	Required	Description
`event_id`	string	no	An event (`ev_…`); every open market of it. Send this or `ticker`.
`ticker`	string	no	One market. Send this or `event_id`.
`window_days`	integer	no	History window in days. 1 to 3650.

Request

Terminal

curl "https://api.langbot.ai/v1/models/fair-value?event_id=ev_nvda_q3_2026" \
  -H "Authorization: Bearer $LANGBOT_API_TOKEN"

Python

from langbot import LangbotClient

async with LangbotClient() as lb:  # reads LANGBOT_API_TOKEN
    fv = await lb.fair_value("ev_nvda_q3_2026")
    for m in fv["markets"]:
        print(m["ticker"], m["model_p"], m["n_events"], m["market_mid"])

TypeScript

import { LangbotClient } from "@langbotai/sdk";

const lb = new LangbotClient(); // reads LANGBOT_API_TOKEN
const fv = await lb.fairValue({ eventId: "ev_nvda_q3_2026" });
console.log(fv.markets);

Response

200 OK

JSON

{
  "ts": 1760040000000,
  "as_of": 1760040000000,
  "window_days": 365,
  "event": { "id": "ev_nvda_q3_2026", "title": "NVIDIA Q3 FY2027 earnings call", "kind": "earnings", "starts_at": 1760050800000 },
  "model": { "name": "fair_value", "version": 1, "prior_strength_events": 5 },
  "count": 1,
  "markets": [
    {
      "ticker": "KXEARNINGSMENTIONNVDA-26OCT-BLACKWELL",
      "phrase": "blackwell",
      "min_count": 1,
      "model_p": 0.94,
      "history_p": 0.97,
      "n_events": 6,
      "per_minute_rate": 0.21,
      "expected_minutes": 60,
      "base_rate": { "scope": "speaker_phrase", "yes_rate": 0.86, "n": 7 },
      "market_mid": 0.92,
      "difference": 0.02,
      "history": [],
      "excluded": { "count": 0, "by_reason": {}, "events": [] },
      "caveats": ["6 past appearances"]
    }
  ],
  "caveats": []
}

model_p is a Poisson rate scaled to the event's expected length and shrunk toward the venue base rate. difference is model_p − market_mid. Statistics under a stated model, not a signal: read n_events and caveats.

Errors

Status	`code`	When
400	`bad_request`	Neither `event_id` nor `ticker` was sent, or the ticker is not on the event.
404	`not_found`	Unknown event or ticker.
401	`auth_required`	No token was sent.
401	`token_invalid`	The token was not recognised (mistyped, revoked or expired).
422	`validation_error`	A parameter failed validation; `detail[].loc` names it.
429	`quota_exceeded`	Today's model calls are spent; `quota.resets_at` says when they reset.
429	`rate_limited`	Over your request budget; wait `Retry-After` seconds.

Every error body is {"detail": …, "code": "…"}; branch on code. All codes: Errors.