Funding Rates

Use client.funding_rates() when you need historical funding rate data for perpetual contracts. This method returns a time series of funding rates used for carry modeling and perpetual trading analysis.

Method signature

funding_rates(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',
        'funding_rate': 0.00017,
        'funding_interval': 8 * 3600  # seconds
    }
]

Fields

Funding rate 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
funding_rate: the funding rate (decimal, e.g., 0.00017 = 0.017%)
funding_interval: funding period in seconds (typically 1h, 4h, or 8h)

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:
    rates = client.funding_rates(
        source="hyperliquid",
        market="BTC",
        from_=start,
        to=end,
    )
    print(list(islice(rates, 2)))

How it works

client.funding_rates() extracts rates from standardized datapoint events labeled funding_rate using snapshot-first replay. See Snapshots for the full flow.

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

Foundation

Trade & Price Data

Orderbook & Liquidity

Derivatives Data

Method signature

Parameters

Return value

Example response

Fields

Example

How it works

​Method signature

​Parameters

​Return value

​Example response

​Fields

​Example

​How it works

​Related documentation

Method signature

Parameters

Return value

Example response

Fields

Example

How it works

Related documentation