https://www.metaculus.com

Authorization: Token 9944b09199c62bcf9418ad846dd0e4bbdfc6ee4b

curl -s "https://www.metaculus.com/api/posts/?limit=5&order_by=-published_at" \
  -H "Authorization: Token $METACULUS_API_TOKEN" | jq .

import os, requests

TOKEN = os.environ["METACULUS_API_TOKEN"]
HEADERS = {"Authorization": f"Token {TOKEN}"}
BASE = "https://www.metaculus.com"

resp = requests.get(f"{BASE}/api/posts/", headers=HEADERS, params={"limit": 5})
resp.raise_for_status()
data = resp.json()

METACULUS_API_TOKEN=your-token-here

{
  "next": "https://www.metaculus.com/api/posts/?limit=20&offset=20",
  "previous": null,
  "results": [ /* array of Post objects */ ]
}

curl -s "https://www.metaculus.com/api/posts/?statuses=open&forecast_type=binary&order_by=-published_at&limit=10&with_cp=true" \
  -H "Authorization: Token $METACULUS_API_TOKEN" | jq '.results[] | {id, title, status}'

curl -s "https://www.metaculus.com/api/posts/?tournaments=metaculus-cup&with_cp=true&limit=20" \
  -H "Authorization: Token $METACULUS_API_TOKEN" | jq .

undefined

---

curl -s "https://www.metaculus.com/api/posts/12345/" \
  -H "Authorization: Token $METACULUS_API_TOKEN" | jq .

[
  {
    "question": 12345,
    "probability_yes": 0.63
  }
]

[
  {
    "question": 12345,
    "probability_yes_per_category": {
      "Futurama": 0.5,
      "Paperclipalypse": 0.3,
      "Singularia": 0.2
    }
  }
]

[
  {
    "question": 12345,
    "continuous_cdf": [0.0, 0.00005, 0.00010, "... 201 values total ...", 1.0]
  }
]

[
  { "question": 111, "probability_yes": 0.499 },
  { "question": 222, "probability_yes": 0.501 }
]

[
  { "question": 1, "probability_yes": 0.11 },
  { "question": 2, "probability_yes": 0.22 },
  { "question": 3, "probability_yes": 0.33 }
]

curl -s -X POST "https://www.metaculus.com/api/questions/forecast/" \
  -H "Authorization: Token $METACULUS_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '[{"question": 12345, "probability_yes": 0.75}]'

[
  { "question": 12345 },
  { "question": 12346 }
]

curl -s -X POST "https://www.metaculus.com/api/questions/withdraw/" \
  -H "Authorization: Token $METACULUS_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '[{"question": 12345}]'

{
  "total_count": 42,
  "count": 15,
  "next": "https://www.metaculus.com/api/comments/?post=123&limit=10&offset=10",
  "previous": null,
  "results": [ /* array of Comment objects */ ]
}

curl -s "https://www.metaculus.com/api/comments/?post=12345&sort=-created_at&limit=20" \
  -H "Authorization: Token $METACULUS_API_TOKEN" | jq .

curl -s -X POST "https://www.metaculus.com/api/comments/create/" \
  -H "Authorization: Token $METACULUS_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "on_post": 12345,
    "text": "I updated my forecast based on the latest data.",
    "included_forecast": false,
    "is_private": false
  }'

curl -s "https://www.metaculus.com/api/posts/12345/download-data/?include_comments=true" \
  -H "Authorization: Token $METACULUS_API_TOKEN" \
  -o question_data.zip

import datetime
import numpy as np

def nominal_location_to_cdf_location(
    nominal_location: str | float,
    question_data: dict,
) -> float:
    """Takes a location in nominal format (e.g. 123, "123",
    or datetime in iso format) and scales it to metaculus's
    "internal representation" range [0,1] incorporating question scaling"""
    if question_data["type"] == "date":
        scaled_location = datetime.datetime.fromisoformat(
            nominal_location
        ).timestamp()
    else:
        scaled_location = float(nominal_location)
    scaling = question_data["scaling"]
    range_min = scaling.get("range_min")
    range_max = scaling.get("range_max")
    zero_point = scaling.get("zero_point")
    if zero_point is not None:
        # logarithmically scaled question
        deriv_ratio = (range_max - zero_point) / (range_min - zero_point)
        unscaled_location = (
            np.log(
                (scaled_location - range_min) * (deriv_ratio - 1)
                + (range_max - range_min)
            )
            - np.log(range_max - range_min)
        ) / np.log(deriv_ratio)
    else:
        # linearly scaled question
        unscaled_location = (scaled_location - range_min) / (
            range_max - range_min
        )
    return unscaled_location