Prediction Markets Events

Use this endpoint to retrieve the latest snapshot of every analyzed prediction market event tracked by Octagon. Each item represents the most recent capture of an event, including model/market probabilities, confidence scores, analysis summaries, and per-market outcome breakdowns.

Pair this endpoint with Prediction Markets Events History to first discover events, then drill into the time-series for any event of interest.

Like the Chat Completions and Responses endpoints, this is a direct REST endpoint and is called relative to the Octagon API base URL:

Base URL

https://api.octagonai.co/v1

Endpoint

GET /prediction-markets/events

Query Parameters

Parameter	Type	Required	Description
limit	integer	No	Number of records to return. Default 10; minimum 1; maximum 200.
cursor	string	No	Cursor for pagination. Use the cursor returned from a previous response to continue.
has_history	boolean	No	When true, only return events that have multiple historical snapshots available for time-series use.

Response Fields

Each event in the data array includes:

Field	Type	Description
history_id	integer	Unique identifier for this snapshot.
run_id	string (UUID)	The export run that produced this snapshot.
captured_at	string (ISO 8601)	When this snapshot was captured.
event_ticker	string	Ticker identifier for the event.
name	string	Human-readable event name.
slug	string	URL-friendly slug.
image_url	string	Event image URL.
series_category	string	Category (e.g., "Politics", "Crypto", "Economics").
available_on_brokers	boolean	Whether the event is available on supported brokers.
mutually_exclusive	boolean	Whether the event's outcomes are mutually exclusive.
analysis_last_updated	string (ISO 8601)	When the analysis was last refreshed.
confidence_score	float	Octagon's confidence in the model output (0–10 scale).
model_probability	float	Octagon model probability for the event's primary/most-liquid market (0–100 percentage scale). See outcome_probabilities for the full per-market breakdown.
market_probability	float	Market-implied probability for the event's primary/most-liquid market (0–100 percentage scale). See outcome_probabilities for the full per-market breakdown.
edge_pp	float	Model edge in percentage points (model_probability - market_probability).
expected_return	float	Expected return if the model is correct.
r_score	float	Risk-adjusted score.
total_volume	float	Total trading volume.
total_open_interest	float	Total open interest.
close_time	string	When the event closes.
key_takeaway	string	One-line summary of the model's view.
current_state_summary_richtext	string	Rich-text summary of the current state.
short_answer_richtext	string	Rich-text short answer.
executive_summary_richtext	string	Rich-text executive summary.
outcome_probabilities	array	Per-market outcome breakdown (see below). null if unavailable.
has_history	boolean	Whether this event has multiple historical snapshots for time-series analysis.

outcome_probabilities items

Each item in the outcome_probabilities array represents one market/outcome within the event:

Field	Type	Description
market_ticker	string	Ticker for this specific market/outcome.
outcome_name	string	Human-readable name for the outcome.
model_probability	float	Octagon model probability for this outcome (0–100 percentage scale).
market_probability	float	Market-implied probability for this outcome (0–100 percentage scale).

Use Cases

• Discover all events currently tracked and analyzed by Octagon.
• Build a catalog or dashboard of active prediction market events.
• Filter to events with historical data (has_history=true), then drill into each event's time-series via GET /prediction-markets/events/<event_ticker>/history.
• Monitor the latest model probabilities, confidence scores, and analysis across all events.
• Use outcome_probabilities for multi-outcome backtesting — compare per-market model_probability against market_probability and settlement outcomes.

Examples

List all events

Python

import requests

url = "https://api.octagonai.co/v1/prediction-markets/events"
headers = {"Authorization": "Bearer your-octagon-api-key"}
params = {"limit": 10}

response = requests.get(url, headers=headers, params=params)
response.raise_for_status()
print(response.json())

JavaScript

const params = new URLSearchParams({
  limit: "10",
});

const response = await fetch(
  `https://api.octagonai.co/v1/prediction-markets/events?${params}`,
  {
    headers: {
      Authorization: "Bearer your-octagon-api-key",
    },
  }
);

if (!response.ok) {
  throw new Error(`Request failed: ${response.status}`);
}

const data = await response.json();
console.log(data);

cURL

curl -G "https://api.octagonai.co/v1/prediction-markets/events" \
  -H "Authorization: Bearer <your-octagon-api-key>" \
  --data-urlencode "limit=10"

Example response (truncated for brevity):

{
  "data": [
    {
      "history_id": 353,
      "run_id": "11538efb-6a39-407c-bf13-e8d6cb070fae",
      "captured_at": "2026-04-04T15:43:44.468242Z",
      "event_ticker": "FEDHIKE",
      "name": "Next Fed rate hike?",
      "slug": "next-fed-rate-hike",
      "image_url": "https://kalshi-public-docs.s3.amazonaws.com/series-images-webp/KXFEDHIKE.webp",
      "series_category": "Economics",
      "available_on_brokers": true,
      "mutually_exclusive": false,
      "analysis_last_updated": "2026-04-04T15:40:35.327497Z",
      "confidence_score": 8.0,
      "model_probability": 17.5,
      "market_probability": 35.0,
      "edge_pp": -17.5,
      "expected_return": -0.4998,
      "r_score": -2.5,
      "total_volume": 1026944.0,
      "total_open_interest": 349221.0,
      "close_time": "2028-01-01T04:59:00Z",
      "key_takeaway": "Model's 17.5% probability suggests a rate hike is overvalued at 35c, with a 2.9x payout multiple if correct.",
      "current_state_summary_richtext": "...",
      "short_answer_richtext": "...",
      "executive_summary_richtext": "...",
      "outcome_probabilities": [
        { "market_ticker": "FEDHIKE-27JUN30", "outcome_name": "Before July 2027", "model_probability": 17.5, "market_probability": 35.0 },
        { "market_ticker": "FEDHIKE-26JUN30", "outcome_name": "Before July 2026", "model_probability": 2.28, "market_probability": 5.0 },
        { "market_ticker": "FEDHIKE-26DEC31", "outcome_name": "Before 2027", "model_probability": 8.44, "market_probability": 18.0 },
        { "market_ticker": "FEDHIKE-27DEC31", "outcome_name": "Before 2028", "model_probability": 38.44, "market_probability": 63.0 }
      ],
      "has_history": true
    }
  ],
  "next_cursor": "eyJjYXB0dXJlZF9hdCI6IjIwMjYtMDQtMDRUMTU6NDM6NDQuNDY4MjQyWiIsImhpc3RvcnlfaWQiOjM1M30",
  "has_more": true
}

List only events with history

Python

import requests

url = "https://api.octagonai.co/v1/prediction-markets/events"
headers = {"Authorization": "Bearer your-octagon-api-key"}
params = {
    "limit": 10,
    "has_history": "true",
}

response = requests.get(url, headers=headers, params=params)
response.raise_for_status()

for event in response.json()["data"]:
    print(f"{event['event_ticker']}: {event['name']} (has_history={event['has_history']})")

JavaScript

const params = new URLSearchParams({
  limit: "10",
  has_history: "true",
});

const response = await fetch(
  `https://api.octagonai.co/v1/prediction-markets/events?${params}`,
  {
    headers: {
      Authorization: "Bearer your-octagon-api-key",
    },
  }
);

if (!response.ok) {
  throw new Error(`Request failed: ${response.status}`);
}

const data = await response.json();
data.data.forEach((event) => {
  console.log(`${event.event_ticker}: ${event.name} (has_history=${event.has_history})`);
});

cURL

curl -G "https://api.octagonai.co/v1/prediction-markets/events" \
  -H "Authorization: Bearer <your-octagon-api-key>" \
  --data-urlencode "limit=10" \
  --data-urlencode "has_history=true"

Paginate through all events

Python

import requests

url = "https://api.octagonai.co/v1/prediction-markets/events"
headers = {"Authorization": "Bearer your-octagon-api-key"}
cursor = None

while True:
    params = {"limit": 10}
    if cursor:
        params["cursor"] = cursor

    response = requests.get(url, headers=headers, params=params)
    response.raise_for_status()
    page = response.json()

    for event in page["data"]:
        print(event["event_ticker"], event["name"])

    if not page["has_more"]:
        break
    cursor = page["next_cursor"]

JavaScript

const headers = { Authorization: "Bearer your-octagon-api-key" };
let cursor = null;

while (true) {
  const params = new URLSearchParams({ limit: "10" });
  if (cursor) params.set("cursor", cursor);

  const response = await fetch(
    `https://api.octagonai.co/v1/prediction-markets/events?${params}`,
    { headers }
  );

  if (!response.ok) throw new Error(`Request failed: ${response.status}`);

  const page = await response.json();

  for (const event of page.data) {
    console.log(event.event_ticker, event.name);
  }

  if (!page.has_more) break;
  cursor = page.next_cursor;
}

cURL

# First page
curl -G "https://api.octagonai.co/v1/prediction-markets/events" \
  -H "Authorization: Bearer <your-octagon-api-key>" \
  --data-urlencode "limit=10"

# Next page (use next_cursor from previous response)
curl -G "https://api.octagonai.co/v1/prediction-markets/events" \
  -H "Authorization: Bearer <your-octagon-api-key>" \
  --data-urlencode "limit=10" \
  --data-urlencode "cursor=<next_cursor_value>"

Notes

• Events are ordered by most recently captured first (captured_at descending).
• Use cursor for pagination when the response includes "has_more": true.
• Each event in the response includes full analysis fields (key_takeaway, executive_summary_richtext, etc.) by default.
• Use has_history=true to filter to events with multiple snapshots, then call GET /prediction-markets/events/<event_ticker>/history to retrieve the time-series for a specific event.
• All probability values (model_probability, market_probability, and those within outcome_probabilities) are expressed as percentages on a 0–100 scale (e.g., 37.7 means 37.7%).
• outcome_probabilities is null when outcome data is not available for an event.