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Funding

Funding rate and update frequencies for perpetual futures contracts.

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funding_rate

Funding Rate

Funding Rate is the clearest running price of directional crowding in perpetual futures markets.

Positive rates usually mean longs are paying shorts to hold exposure, while negative rates imply the opposite.

funding_rate_update_frequency

Funding Rate Update Frequency

Funding Rate Update Frequency tells you how often the venue refreshed its funding observations during the interval.

Operational details like this matter because faster or noisier update cadence can affect how traders interpret prediction signals.

Perpetual futures — the dominant derivative instrument in crypto — never expire. Instead, they use a funding rate mechanism to anchor the perpetual price to spot: periodically (typically every 8 hours), longs pay shorts when the perp trades above spot, and vice versa. This seemingly mechanical arbitrage mechanism produces one of the richest sentiment signals in digital asset markets.

Endpoint

/api/v1/data/funding

Category

Derivatives

Intervals
1m5m15m30m1h4h1d
Exchanges
binance-futuresokx-perpshyperliquid-perps
Fields3
funding_rateFunding RateLast funding rate observed in the interval
funding_rate_update_frequencyFunding Rate Update FrequencyAverage number of funding-rate updates per second in the interval
funding_rate_avg_update_intervalAvg Funding Rate Update IntervalAverage seconds between funding-rate updates in the interval
Example Request
from datetime import date
from aperiodic import get_derivative_metrics

# Free preview — no API key required
df = get_derivative_metrics(
    metric="funding",
    exchange="binance-futures",
    symbol="perpetual-BTC-USDT:USDT",
    interval="5m",
    timestamp="exchange",
    start_date=date(2025, 5, 1),
    end_date=date(2025, 5, 31),
    preview=True,
)

print(df.head())

Query Parameters

timestampreqstring
string

Timestamp source. 'exchange' uses the exchange-reported timestamp, 'true' uses actual arrival time at our servers.

exchangetrue
intervalreqstring
string

Aggregation time interval for the data.

1m5m15m30m1h4h1d
exchangereqstring
string

Source exchange for the data.

binance-futuresokx-perpshyperliquid-perps
symbolreqstring
string

Trading pair symbol in the format of Atlas' universal symbology: https://github.com/aperiodic-io/atlas

start_datereqstring<date>
string<date>

Start date for the data range (YYYY-MM-DD format). Data is partitioned by year and month.

end_datereqstring<date>
string<date>

End date for the data range (YYYY-MM-DD format). Must be greater than or equal to start_date.

Successful response with download URLs for each monthly file

Schema
filesobject[]required

Array of file information for each month in the requested date range

yearintegerrequired

Year of the data file

monthintegerrequired

Month of the data file (1-12)

urlstring<uri>required

Presigned URL for direct file download (valid for 5 minutes). URLs are served from dataset-specific subdomains, e.g. ohlcv.aperiodic.io, trade-metrics.aperiodic.io, l1-metrics.aperiodic.io, l2-metrics.aperiodic.io, derivative-metrics.aperiodic.io.

Example
{
  "files": [
    {
      "year": 2024,
      "month": 1,
      "url": "https://ohlcv.aperiodic.io/binance-futures/1h/BTCUSDT/2024-01.parquet?X-Amz-Expires=300&..."
    },
    {
      "year": 2024,
      "month": 2,
      "url": "https://ohlcv.aperiodic.io/binance-futures/1h/BTCUSDT/2024-02.parquet?X-Amz-Expires=300&..."
    }
  ]
}
Try It

Prefilled with the shared DEMO-KEY and a free preview slice — send the request to see live data, no account required.

Suggestions shown — any valid value accepted
Suggestions shown — any valid value accepted
Suggestions shown — any valid value accepted
Authentication
GET/api/v1/data/preview/funding?timestamp=exchange&interval=5m&exchange=binance-futures&symbol=perpetual-BTC-USDT%3AUSDT&start_date=2025-05-01&end_date=2025-05-31
Response will appear here

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Use with AI Agents

Access Funding programmatically via our Python SDK and REST API — optimised for autonomous research workflows.

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Aperiodic

Crypto microstructure, liquidity & flow metrics — built from hundreds of terabytes of raw data, distilled into point-in-time metrics you can pull as parquet files.

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