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Use client.volatility() when you need realized volatility data for risk modeling and intraperiod volatility analysis. This method calculates volatility metrics over fixed time intervals.

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Method signature

volatility(source, market, from_, to, interval, method="log_returns", limit=1000)

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Parameters

ParameterTypeRequiredNotes
sourcestrYesSource ID
marketstrYesNormalized market or instrument ID
from_str/datetime/date/intYesInclusive start time (ISO 8601, datetime, date, or epoch microseconds)
tostr/datetime/date/intYesExclusive end time (same formats as from_)
intervalstrYesDuration token such as 1m, 5m, 1h
methodstrNoCalculation method: "log_returns" (default)
limitintNoPage size (default: 1000)
Note: API key required for historical ranges. Set POLARIS_API_KEY environment variable or pass api_key to PolarisClient().

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Return value

List of bucketed realized volatility series rows.

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Example response

[
    {
        'timestamp': 1704067200000000,
        'venue': 'binance',
        'symbol': 'BTC-USDT',
        'volatility': 0.0234,
        'interval': '1h'
    }
]

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Fields

Volatility records include:
  • timestamp: bucket start time in UTC microseconds since the Unix epoch
  • venue: exchange identifier
  • symbol: normalized instrument symbol
  • volatility: realized volatility for the interval (decimal, e.g., 0.0234 = 2.34%)
  • interval: duration token such as 1m, 5m, or 1h
The default method uses log returns calculated from trade prices within each interval.

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Example

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

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

with PolarisClient() as client:
    vol_data = client.volatility(
        source="binance",
        market="BTC-USDT",
        from_=start,
        to=end,
        interval="1h",
    )

print(vol_data[:2])

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How it works

client.volatility() calculates realized volatility from standardized trade data using snapshot-first replay. The SDK queries the /snapshots endpoint for historical data, reads from local cached files when available, and derives volatility metrics from the underlying trade events. For more details on snapshot-based queries, see Snapshots.