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Derivatives

Perpetual swap analytics for funding, OI, and price dynamics.

Pre-computed derivatives metrics — funding rates, open interest, basis, and mark/index prices — built from high-frequency exchange snapshots and aggregated into research-ready intervals.

Derivatives Datasets

Basis

Tier 1

Mark/index basis and mark-to-trade spread with mean and basis points statistics.

Key fields

BasisBasis (bps)Mark-Price Spread (bps)Mean Basis

Funding

Tier 1

Funding rate, predicted funding rate, spread metrics and update frequencies.

Key fields

Funding RatePredicted Funding RateFunding Rate Spread (bps)Mean Funding Rate Spread

Open Interest

Tier 1

Open interest values with percentage change, volatility, and OI/price change ratio.

Key fields

Open InterestOpen Interest % ChangeOpen Interest VolatilityOI/Price Change Ratio

Derivative Price

Tier 1

Mark, index, last prices with percentage changes and pairwise ratios.

Key fields

Last PriceIndex PriceMark PriceMark/Index % Change Ratio

Use Cases

Funding rate regime analysis

Detect crowded positioning and carry opportunities by monitoring funding rate levels and predicted-vs-realized spread dynamics.

Open interest divergence signals

Identify leverage build-up and potential liquidation cascades from OI/price divergences before they materialize.

Basis trading & arbitrage

Model perp-spot basis and mark-to-trade spread to evaluate and monitor basis trading strategies in real time.

Market stress indicators

Aggregate OI volatility and funding rate extremes into composite stress indicators for risk management overlays.

Directional prediction from mark/index dislocations

Use mark-to-index price change ratios as leading indicators of short-term directional pressure in perp markets.

Get started in minutes

pip install aperiodic
pypi ↗
example.py
from datetime import date
from aperiodic import get_metrics

df = get_metrics(
    api_key="your-api-key",
    metric="flow",
    timestamp="exchange",
    interval="1h",
    exchange="binance-futures",
    symbol="perpetual-BTC-USDT:USDT",
    start_date=date(2024, 1, 1),
    end_date=date(2024, 1, 31),
)

print(df.head())

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