GameStop perpsGME

Stock perp · Binance, Hyperliquid, OKX · #387 by volume

25.630+0.16% 24h

Binance USDT · 24h 25.340 – 26.480

24h volume: $3.5M; #387 of 910
Open interest: $4.2M; −2.0% contracts · 24h
Spread: 3.93 bps; median · 24h
Size at best price: $2.3K; mean · 24h
Net taker flow: −$106.1K; every contract · 24h
Funding: +0.0000%; per period · BIN

Aperiodic metrics · GME · 24 h

Are takers buying or selling GME?

Takers sold $104.3K more than they bought on Binance, Hyperliquid and OKX over 24 h; the heaviest hour, 20:20 UTC, saw $55.2K.

+$69.8K0−$69.8K

23:20 UTCnow

flow.volume_delta_notionalbusiest contract on each venue · hourly · 24 h

What does it cost to cross GME's spread?

L1 liquidity

3.93 bps median on Binance USDT, wider than 80% of the 200 most traded perps.

#1BTC0.014 bps
#2ETH0.051 bps
#3SOXL1.56 bps
#5SOL1.25 bps
#10AKE3.20 bps
#25SP5000.13 bps
#50MUU5.64 bps
#100MVLL8.76 bps
#200AIN2.70 bps
#387GME3.93 bps

l1_liquidity.spread_bpsbusiest contract · median · 24 h

How much size waits at GME's best price?

L1 liquidity

$2.3K at the best bid and ask on Binance USDT, on average, deeper than 51% of the 200 most traded perps.

#1BTC$651.3K
#2ETH$269.7K
#3SOXL$87.0K
#5SOL$157.7K
#10AKE$562
#25SP500$105.4K
#50MUU$22.5K
#100MVLL$13.7K
#200AIN$181
#387GME$2.3K

l1_liquidity.total_dollar_depthbusiest contract · mean · 24 h

Which way is GME's book leaning?

L2 imbalance

Leaned to the bid in 14 of 24 hours on Binance USDT; 50% bids over the day.

64% bid036% bid

23:20 UTCnow

l2_imbalance.imbalance_ratio_10Binance USDT · top 10 levels · hourly

Where does GME trade?

OHLCV

Binance USDT took 47% of $3.5M traded in 24 h.

volume sharespread

Binance USDT47%3.93
Hyperliquid xyz USDC42%7.42
OKX USDT11%11.4

ohlcv.volume_notionaleach contract · 24 h

Does GME trade away from its index?

Basis

3.77 bps above its index on Binance USDT now; 18.2 bps below to 10.9 bps above over 24 h.

+18.2 bps0−18.2 bps

23:20 UTCnow

basis.basis_bpsBinance USDT · hourly

Is leverage building in GME?

Open interest

$4.2M open across 3 contracts: −2.0% in contracts, −2.2% in dollars over 24 h.

$4.5M$4.2M

23:20 UTCnow

open_interest.open_interestbusiest contract on each venue · hourly

Who pays funding on GME?

Funding

Longs pay on 1 of 3 contracts; Binance USDT charges +0.0000% a period (Hyperliquid’s are hourly).

Binance USDT+0.0000%
Hyperliquid xyz USDC+0.0006%
OKX USDT+0.0000%

← shorts paylongs pay →

funding.funding_rateeach contract · per funding period · now

GME's 3 contracts · now

Named by Atlas →

Contract	Price	24h	Volume 24h	Open interest	Funding	Spread	Charts
Binance USDTperpetual-GME-USDT:USDT	25.630	+0.16%	$1.6M	$1.0M	+0.0000%	3.93	Charts →
Hyperliquid xyz USDCperpetual-XYZ:GME-USDC:USDC	25.582	+0.04%	$1.4M	$2.8M	+0.0006%	7.42	Charts →
OKX USDTperpetual-GME-USDT:USDT	25.600	+0.12%	$385.0K	$343.4K	+0.0000%	11.4	Charts →

History · a year of 5m bars per series · busiest contract on each venue

Every chart for GME →

Python

import os
from datetime import date

from aperiodic import get_derivative_metrics, get_metrics

# aperiodic.io/markets: GME on Binance, Hyperliquid and OKX
# 5m bars, 2025-11-12 to 2026-11-12. Install: pip install "aperiodic[polars]"
# Needs an API key (https://aperiodic.io/home/api) on a plan with flow, basis, open_interest, funding (Pro) and l1_liquidity, l2_imbalance (Prime).
# OKX: okx-perps l1_liquidity and l2_liquidity count contracts, not coins (BTC-USDT-SWAP is 0.01 BTC each). Multiply by the contract size to match the page.
API_KEY = os.environ["APERIODIC_API_KEY"]
# timestamp="true" keys bars on capture time, as on the page; "exchange" keys them on exchange time.
PERIOD = dict(
    interval="5m",
    timestamp="true",
    start_date=date(2025, 11, 12),
    end_date=date(2026, 11, 12),
)
CONTRACTS = [
    ("binance-futures", "perpetual-GME-USDT:USDT"),
    ("hyperliquid-perps", "perpetual-XYZ:GME-USDC:USDC"),
    ("okx-perps", "perpetual-GME-USDT:USDT"),
]

frames = {}
for exchange, symbol in CONTRACTS:
    frames["flow", exchange, symbol] = get_metrics(
        api_key=API_KEY,
        metric="flow",
        exchange=exchange,
        symbol=symbol,
        **PERIOD,
    )
    frames["l1_liquidity", exchange, symbol] = get_metrics(
        api_key=API_KEY,
        metric="l1_liquidity",
        exchange=exchange,
        symbol=symbol,
        **PERIOD,
    )
    frames["l2_imbalance", exchange, symbol] = get_metrics(
        api_key=API_KEY,
        metric="l2_imbalance",
        exchange=exchange,
        symbol=symbol,
        **PERIOD,
    )
    frames["basis", exchange, symbol] = get_derivative_metrics(
        api_key=API_KEY,
        metric="basis",
        exchange=exchange,
        symbol=symbol,
        **PERIOD,
    )
    frames["open_interest", exchange, symbol] = get_derivative_metrics(
        api_key=API_KEY,
        metric="open_interest",
        exchange=exchange,
        symbol=symbol,
        **PERIOD,
    )
    frames["funding", exchange, symbol] = get_derivative_metrics(
        api_key=API_KEY,
        metric="funding",
        exchange=exchange,
        symbol=symbol,
        **PERIOD,
    )

Every number is from Aperiodic's own 5-minute bars, 15 minutes behind.

Contract

Price

24h

Volume 24h

Open interest

Funding

Spread

Binance USDTperpetual-GME-USDT:USDT

25.630

+0.16%

$1.6M

$1.0M

+0.0000%

3.93

Charts →

Hyperliquid xyz USDCperpetual-XYZ:GME-USDC:USDC

25.582

+0.04%

$1.4M

$2.8M

+0.0006%

7.42

Charts →

OKX USDTperpetual-GME-USDT:USDT

25.600

+0.12%

$385.0K

$343.4K

+0.0000%

11.4

Charts →

import os from datetime import date from aperiodic import get_derivative_metrics, get_metrics # aperiodic.io/markets: GME on Binance, Hyperliquid and OKX # 5m bars, 2025-11-12 to 2026-11-12. Install: pip install "aperiodic[polars]" # Needs an API key (https://aperiodic.io/home/api) on a plan with flow, basis, open_interest, funding (Pro) and l1_liquidity, l2_imbalance (Prime). # OKX: okx-perps l1_liquidity and l2_liquidity count contracts, not coins (BTC-USDT-SWAP is 0.01 BTC each). Multiply by the contract size to match the page. API_KEY = os.environ["APERIODIC_API_KEY"] # timestamp="true" keys bars on capture time, as on the page; "exchange" keys them on exchange time. PERIOD = dict( interval="5m", timestamp="true", start_date=date(2025, 11, 12), end_date=date(2026, 11, 12), ) CONTRACTS = [ ("binance-futures", "perpetual-GME-USDT:USDT"), ("hyperliquid-perps", "perpetual-XYZ:GME-USDC:USDC"), ("okx-perps", "perpetual-GME-USDT:USDT"), ] frames = {} for exchange, symbol in CONTRACTS: frames["flow", exchange, symbol] = get_metrics( api_key=API_KEY, metric="flow", exchange=exchange, symbol=symbol, **PERIOD, ) frames["l1_liquidity", exchange, symbol] = get_metrics( api_key=API_KEY, metric="l1_liquidity", exchange=exchange, symbol=symbol, **PERIOD, ) frames["l2_imbalance", exchange, symbol] = get_metrics( api_key=API_KEY, metric="l2_imbalance", exchange=exchange, symbol=symbol, **PERIOD, ) frames["basis", exchange, symbol] = get_derivative_metrics( api_key=API_KEY, metric="basis", exchange=exchange, symbol=symbol, **PERIOD, ) frames["open_interest", exchange, symbol] = get_derivative_metrics( api_key=API_KEY, metric="open_interest", exchange=exchange, symbol=symbol, **PERIOD, ) frames["funding", exchange, symbol] = get_derivative_metrics( api_key=API_KEY, metric="funding", exchange=exchange, symbol=symbol, **PERIOD, )