Bitcoin Perpetual Futures Data

• Bitcoin perpetual futures contract data comprises live and historical records, including open interest, funding rates, traded volume, and liquidation metrics that capture market dynamics.
• Datasets aggregate multi-dimensional metrics from exchanges, enabling reconciliation of trade volume with open interest and identifying discrepancies such as excess open-interest variations.
• Statistical models like GARCH and pricing frameworks calibrate funding rates and risk measures, informing liquidity assessments and regulatory oversight in digital asset markets.

A Bitcoin perpetual futures contract (“perpetual swap”) is a derivative on the BTC/USD rate with no expiry, designed to track the underlying primarily via cashflow transfers (“funding”) between long and short positions. Perpetual futures contract data encompasses live and historical records of notional position, funding rates, traded volume, open interest, liquidation metrics, margin settings, and reconciliation anomalies. This data is central to pricing, risk management, regulatory evaluation, and empirical research on digital asset markets.

1. Structure of Bitcoin Perpetual Futures Contract Data

Bitcoin perpetual swap datasets consist of multi-dimensional streams sourced from cryptocurrency derivatives exchanges (e.g., ByBit, Binance, BitMEX, OKX, Deribit, Kraken, HTX). Core observable variables include:

• Spot trades: Every matched order, including block trades
• Liquidations: Forced closes due to margin insufficiency
• Open interest (OI): Total size (in contracts or notional) outstanding at each timestamp
• Traded volume: Aggregated value of all executed contracts over a time interval
• Funding rates: Periodic cashflow rates, typically every 8 hours, that align the perpetual swap price with the underlying spot
• Index/spot price: Reference BTC/USD price, typically as an exchange composite

Time resolution varies, spanning tick-level (millisecond) to coarser aggregations. Data is normalized for volume comparison (e.g., BTC to USD) using period-mean prices (Giagkiozis et al., 2023).

2. Formal Metrics and Definitions

The formal definition of key dataset components is as follows:

• Open Interest at time t:

$OI_t = \sum_{j=1}^M |P_j(t)|$

where $P_j(t)$ is trader $j$'s net position.

• Trading Volume in $(t_i, t_{i+1}]$:

$$V_T^{(t_i, t_{i+1}]} = \sum_{k:t_i < t_k \leq t_{i+1}} V_k$$

with $V_k$ the k-th trade size.

• Funding Rate:

Determined as a function of futures price $F_t$ and index price $S_t$, loosely:

$$r_f(t) \approx \text{premium}(F_t, S_t) + \text{interest component}$$

with periodic settlement (typically every 8 hours) (Nimmagadda et al., 2019).

• Minimal Trading Volume to Support Observed OI Changes:

$mTV_{t_{i+1}} = |OI_{t_{i+1}} - OI_{t_i}|$

• Consistency/Feasibility Condition:

$P_j(t)$0

If not satisfied, detected OI updates cannot be justified by reported trading—a signal of data integrity failures or misreporting (Giagkiozis et al., 2023).

3. Empirical Characteristics and Reconciliation

The reconciliation of open interest with traded volume exposes significant disparities across exchanges. For well-monitored periods (e.g., January and July–September 2023), systematic misquotations are detectable via the “excess open-interest total variation”:

$P_j(t)$1

where $P_j(t)$2 and $P_j(t)$3 is total reported volume (Giagkiozis et al., 2023).

Exchanges consistently exhibiting $P_j(t)$4 (e.g., ByBit, OKX) report OI changes not commensurate with trade records, as summarized:

Exchange-market	Period	$P_j(t)$5 (BTC/Notional)	$P_j(t)$6 (BTC/Notional)	$P_j(t)$7 (BTC/Notional)
ByBit USDT (Jan 2023)	$P_j(t)$8M BTC ($P_j(t)$9B)	$j$0M BTC ($j$1B)	$j$2M BTC ($j$3B)
OKX USDT (Jul-Sep 2023)	$j$4M BTC ($j$5B)	$j$6M BTC ($j$7B)	$j$8M BTC ($j$9B)

Discrepancies are detected at all time resolutions: daily, hourly, even minute-by-minute. Exchanges such as Kraken, BitMEX, and HTX maintain near-perfect consistency ($(t_i, t_{i+1}]$0), while ByBit and OKX display $(t_i, t_{i+1}]$1 for daily unreconciled intervals (Giagkiozis et al., 2023).

4. Funding Rate Processes and Statistical Properties

Funding rates are essential in anchoring perpetual prices to spot. They are nonstationary, heteroskedastic, and exhibit volatility clustering, as shown by significant ARCH effects (ARCH-LM $(t_i, t_{i+1}]$2) and GARCH model selection. For BitMEX, EGARCH(1,1) best fits the funding rate process (Nimmagadda et al., 2019). The funding rate calculation consists of an “interest-rate component” and a “premium-index component,” with dampening thresholds (e.g., $(t_i, t_{i+1}]$3 per period):

• $(t_i, t_{i+1}]$4

Bivariate Granger causality (with VAR methods) reveals bidirectional predictive feedback between funding rate changes and price increments—implying both margin economics and spot moves inform each other (Nimmagadda et al., 2019).

5. Leverage, Liquidation, and Margin Data

High-frequency datasets enable estimation of liquidation rates, leverage at liquidation, and margin adequacy:

• Average daily forced-liquidation rates for BTC perpetuals on BitMEX: $(t_i, t_{i+1}]$5 (longs), $(t_i, t_{i+1}]$6 (shorts).
• Mean leverage at liquidation: $(t_i, t_{i+1}]$760× (both long and short).
• Speculation index: Mean trading volume to open interest $(t_i, t_{i+1}]$8, with high right skew.
• GEV-model-based optimal daily margin (for 1% margin-call probability): $(t_i, t_{i+1}]$9 (long, 3× max leverage), $$V_T^{(t_i, t_{i+1}]} = \sum_{k:t_i < t_k \leq t_{i+1}} V_k$$0 (short, 5× max leverage), whereas naïve normality assumptions drastically understate these needs (Cheng et al., 2021).

Margin calls and liquidations cluster around large negative spot returns. Existing low-margin (high-leverage) exchange settings systematically result in higher rates of forced liquidations.

6. Pricing Models and Calibration to Observed Data

No-arbitrage perpetual pricing—captured by risk-neutral expectations—shows that the perpetual price $$V_T^{(t_i, t_{i+1}]} = \sum_{k:t_i < t_k \leq t_{i+1}} V_k$$1 can be written as the risk-neutral expectation of spot at a random horizon, with the anchoring time stochastic according to funding intensity. In continuous time:

$$V_T^{(t_i, t_{i+1}]} = \sum_{k:t_i < t_k \leq t_{i+1}} V_k$$2

with $$V_T^{(t_i, t_{i+1}]} = \sum_{k:t_i < t_k \leq t_{i+1}} V_k$$3 the mean-reversion/funding intensity; in discrete time, the analog is a geometric waiting time (Ackerer et al., 2023).

Observed “basis” ($$V_T^{(t_i, t_{i+1}]} = \sum_{k:t_i < t_k \leq t_{i+1}} V_k$$4) and realized funding rates calibrate $$V_T^{(t_i, t_{i+1}]} = \sum_{k:t_i < t_k \leq t_{i+1}} V_k$$5 and theoretical price curves. Setting the “interest” component of funding equal to the USD-BTC rate differential (the “zero-basis” rule) forces $$V_T^{(t_i, t_{i+1}]} = \sum_{k:t_i < t_k \leq t_{i+1}} V_k$$6 identically. Replication by dynamic spot/loan strategies is possible when this holds, further linking perpetual data to primitive instruments (Ackerer et al., 2023).

7. Market Integrity, Risk, and Reporting Implications

Bitcoin perpetual futures contract data are foundational for multiple layers of the market microstructure and prudential regulation:

• Exchange liability bounds: OI sets the lower bound on counterparty liability, informing minimum collateral/margin requirements.
• Solvency checks: $$V_T^{(t_i, t_{i+1}]} = \sum_{k:t_i < t_k \leq t_{i+1}} V_k$$7 proof-of-reserves bounds possible exposure; overstated OI may mask leverage risks or even insolvency.
• Risk management: Traders calibrate aggressor-side flows and stress test positions based on credible OI and liquidation metrics.
• Data integrity risks: Systematic misreporting (e.g., OI swings not substantiated by trade volume, delayed liquidations, or omitted block trades) undermines pricing, can distort market sentiment indicators, and may shield exchange malfeasance (Giagkiozis et al., 2023).

The reconciliation framework highlights the need for standardized timestamping, unified reporting, and continuous auditability. High-integrity, granular, and reconciled Bitcoin perpetual futures contract data remain prerequisites for both sound empirical research and robust market operation.