Mark Prices

Use client.mark_prices() when you need mark price data for perpetual contracts. This method returns a time series of mark prices used for basis analysis, mark tracking, and liquidation-related research.

Method signature

mark_prices(source, market, from_=None, to=None, allow_gaps=False, output="iterator", batch_size=65536)

Parameters

Parameter	Type	Required	Notes
source	str	Yes	Source ID
market	str	Yes	Normalized market or instrument ID
from_	str/datetime/date/int	No	Inclusive start time (ISO 8601, datetime, date, or epoch milliseconds)
to	str/datetime/date/int	No	Exclusive end time (same formats as from_)
allow_gaps	bool	No	Return covered rows and warn instead of failing on coverage gaps
output	str	No	`iterator`, `batches`, or `dataframe`
batch_size	int	No	Maximum rows per Arrow batch; must be positive

Return value

A single-pass iterator of point-series dictionaries by default, flat PyArrow batches with output="batches", or a Pandas DataFrame with output="dataframe".

Example response

[
    {
        'timestamp': 1704067200000,
        'source': 'hyperliquid',
        'market': 'BTC',
        'symbol': 'BTCUSD',
        'mark_price': 43250.75
    }
]

Fields

Mark price records include:

timestamp: observation time in UTC milliseconds; columnar output is timezone-aware UTC
source: source identifier
market: normalized market ID
symbol: venue-native, human-friendly symbol
mark_price: the mark price used for margin and liquidation calculations

Example

from datetime import datetime, timedelta, timezone
from itertools import islice
from polaris_data import PolarisClient

end = datetime.now(timezone.utc)
start = end - timedelta(days=7)

with PolarisClient() as client:
    prices = client.mark_prices(
        source="hyperliquid",
        market="BTC",
        from_=start,
        to=end,
    )
    print(list(islice(prices, 2)))

How it works

client.mark_prices() extracts prices from standardized point events with the mark_price or mark_px series name, using snapshot-first replay. See Snapshots for the full flow.

Perpetuals guide for carry, trade-flow, liquidity, and order-book workflows
Funding rates for related perpetual contract metrics
Events if you need mark prices mixed with other datapoints
Quickstart

Foundation

Trade & Price Data

Orderbook & Liquidity

Derivatives Data

Method signature

Parameters

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Example response

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How it works

​Method signature

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​Example response

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Method signature

Parameters

Return value

Example response

Fields

Example

How it works

Related documentation