IBM perps

Stock perp · Binance, Hyperliquid, OKX · #304 by volume

245.960+0.10% 24h

Binance USDT · 24h 244.390 – 248.020

24h volume: $5.8M; #304 of 924
Open interest: $17.6M; −15.0% contracts · 24h
Spread: 0.82 bps; median · 24h
Size at best price: $434; mean · 24h
Net taker flow: −$769.4K; every contract · 24h
Funding: +0.0437%; per period · BIN

Aperiodic metrics · IBM · 24 h

Are takers buying or selling IBM?

Takers sold $762.0K more than they bought on Binance, Hyperliquid and OKX over 24 h; the heaviest hour, 16:05 UTC, saw $333.3K.

+$333.3K0−$333.3K

00:05 UTCnow

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

What does it cost to cross IBM's spread?

L1 liquidity

0.82 bps median on Binance USDT, tighter than 75% of the 200 most traded perps.

#1BTC0.015 bps
#2ETH0.052 bps
#3SOXL1.56 bps
#5SOL1.25 bps
#10AKE3.12 bps
#25XYZ:XYZ1000.35 bps
#50MUU5.65 bps
#100QNT0.62 bps
#200AIN2.69 bps
#304IBM0.82 bps

l1_liquidity.spread_bpsbusiest contract · median · 24 h

How much size waits at IBM's best price?

L1 liquidity

$434 at the best bid and ask on Binance USDT, on average, thinner than 84% of the 200 most traded perps.

#1BTC$649.6K
#2ETH$267.0K
#3SOXL$87.0K
#5SOL$156.2K
#10AKE$543
#25XYZ:XYZ100$170.7K
#50MUU$22.1K
#100QNT$757
#200AIN$180
#304IBM$434

l1_liquidity.total_dollar_depthbusiest contract · mean · 24 h

Which way is IBM's book leaning?

L2 imbalance

Leaned to the ask in 19 of 24 hours on Binance USDT; 38% bids over the day.

75% bid025% bid

00:05 UTCnow

l2_imbalance.imbalance_ratio_10Binance USDT · top 10 levels · hourly

Where does IBM trade?

OHLCV

Binance USDT took 55% of $5.8M traded in 24 h; the tightest spread is OKX USDT's, 0.41 bps.

volume sharespread

Binance USDT55%0.82
Hyperliquid xyz USDC41%3.25
OKX USDT3.5%0.41

ohlcv.volume_notionaleach contract · 24 h

Does IBM trade away from its index?

Basis

20.9 bps above its index on Binance USDT now; 11.9 bps below to 23.0 bps above over 24 h.

+23.0 bps0−23.0 bps

00:05 UTCnow

basis.basis_bpsBinance USDT · hourly

Is leverage building in IBM?

Open interest

$17.6M open across 3 contracts: −15.0% in contracts, −14.5% in dollars over 24 h.

$20.6M$17.6M

00:05 UTCnow

open_interest.open_interestbusiest contract on each venue · hourly

Who pays funding on IBM?

Funding

Longs pay on 2 of 3 contracts; Binance USDT charges +0.0437% a period (Hyperliquid’s are hourly).

Binance USDT+0.0437%
Hyperliquid xyz USDC+0.0001%
OKX USDT+0.0000%

← shorts paylongs pay →

funding.funding_rateeach contract · per funding period · now

IBM's 3 contracts · now

Named by Atlas →

Contract	Price	24h	Volume 24h	Open interest	Funding	Spread	Charts
Binance USDTperpetual-IBM-USDT:USDT	245.960	+0.10%	$3.2M	$3.3M	+0.0437%	0.82	Charts →
Hyperliquid xyz USDCperpetual-XYZ:IBM-USDC:USDC	245.740	+0.17%	$2.4M	$13.4M	+0.0001%	3.25	Charts →
OKX USDTperpetual-IBM-USDT:USDT	245.940	+0.16%	$203.3K	$954.3K	+0.0000%	0.41	Charts →

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

Every chart for IBM →

Python

import os
from datetime import date

from aperiodic import get_derivative_metrics, get_metrics

# aperiodic.io/markets: IBM on Binance, Hyperliquid and OKX
# 5m bars, 2025-11-13 to 2026-11-13. 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, 13),
    end_date=date(2026, 11, 13),
)
CONTRACTS = [
    ("binance-futures", "perpetual-IBM-USDT:USDT"),
    ("hyperliquid-perps", "perpetual-XYZ:IBM-USDC:USDC"),
    ("okx-perps", "perpetual-IBM-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-IBM-USDT:USDT

245.960

+0.10%

$3.2M

$3.3M

+0.0437%

0.82

Charts →

Hyperliquid xyz USDCperpetual-XYZ:IBM-USDC:USDC

245.740

+0.17%

$2.4M

$13.4M

+0.0001%

3.25

Charts →

OKX USDTperpetual-IBM-USDT:USDT

245.940

+0.16%

$203.3K

$954.3K

+0.0000%

0.41

Charts →

import os from datetime import date from aperiodic import get_derivative_metrics, get_metrics # aperiodic.io/markets: IBM on Binance, Hyperliquid and OKX # 5m bars, 2025-11-13 to 2026-11-13. 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, 13), end_date=date(2026, 11, 13), ) CONTRACTS = [ ("binance-futures", "perpetual-IBM-USDT:USDT"), ("hyperliquid-perps", "perpetual-XYZ:IBM-USDC:USDC"), ("okx-perps", "perpetual-IBM-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, )