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Live data

Real-time state for markets — either keyed by a milestone (score, clock, period, weather, …) or by an event ticker (crypto charts, commodity timeseries, weather observations).

Public — no auth required.

Quick reference

Method Endpoint
get(milestone_id, *, include_player_stats=None) GET /live_data/milestone/{milestone_id}
get_event(event_ticker, *, range=None) GET /live_data/events/{event_ticker}
batch(milestone_ids, *, include_player_stats=None) GET /live_data/batch
game_stats(milestone_id) GET /live_data/milestone/{milestone_id}/game_stats
weather(city, *, from_ts, to, last_sec, detailed) GET /live_data/weather/{city}
weather_calibrations(city) GET /live_data/weather/{city}/calibrations
get_typed(milestone_type, milestone_id) GET /live_data/{type}/milestone/{milestone_id} (legacy)

Get one milestone's live data

live = client.live_data.get("ms_abc", include_player_stats=True)
print(live.type, live.milestone_id, live.details)

LiveData.details is a loose dict[str, Any] — the shape varies by type (football vs political race vs weather).

Get event-keyed live data

live = client.live_data.get_event("KXBTCD-25", range="1h")
print(live.type, live.details, live.default_range, live.range_options)
print(live.is_historical)  # True for matured crypto snapshots

EventLiveData has no milestone_id. Optional range is a chart-window hint (15min, 1h, 1d, …) when the underlying type supports it.

Batch (up to 100 milestones)

entries = client.live_data.batch(
    milestone_ids=["ms_a", "ms_b", "ms_c"],
    include_player_stats=False,
)
for entry in entries:
    print(entry.milestone_id, entry.type, entry.details)

milestone_ids is required and non-empty — passing [] raises ValueError. Cap: 100 ids per call.

Game stats / play-by-play

resp = client.live_data.game_stats("ms_abc")
if resp.pbp is None:
    print("no play-by-play for this milestone type")
else:
    for period in resp.pbp.periods:
        for event in period.events:
            print(event)  # free-form dict; shape varies by sport

game_stats works only for sports milestones with play-by-play coverage. Other milestone types return pbp=None. Each period's events is a list of loose dicts (no fixed play schema upstream).

Weather index

idx = client.live_data.weather("miami", last_sec=3600, detailed=True)
print(idx.city, idx.units, idx.config_version)
for point in idx.timeseries:
    print(point.t, point.status, point.v)

from_ts is the spec from query (unix milliseconds, inclusive). Named from_ts to avoid the Python keyword; the wire key is still from. last_sec is mutually exclusive with from_ts/to per spec. detailed=True attaches per-station audit readings on every point.

cals = client.live_data.weather_calibrations("miami")
for rec in cals.calibrations:
    print(rec.config_version, rec.effective_at_ms, rec.city_reference_c)

weather_calibrations returns the launch configuration plus every weekly offset calibration, ascending by effective time. Units are always Celsius on this endpoint (the published index value itself remains Fahrenheit).

Legacy get_typed

live = client.live_data.get_typed("sports_game", "ms_abc")

Prefer get() over get_typed(). The latter wraps the legacy /live_data/{type}/milestone/{id} path and is retained only for callers that still depend on it. The Python kwarg is milestone_type (not type) to avoid shadowing the built-in; the wire path still uses {type}.

Reference

::: kalshi.resources.live_data.LiveDataResource options: heading_level: 3

::: kalshi.resources.live_data.AsyncLiveDataResource options: heading_level: 3