Dell perpsDELL

Stock perp · Binance, Hyperliquid, OKX · #155 by volume

339.270-10.07% 24h

Binance USDT · 24h 331.160 – 383.810

24h volume: $27.7M; #155 of 914
Open interest: $10.3M; +5.4% contracts · 24h
Spread: 2.45 bps; median · 24h
Size at best price: $408; mean · 24h
Net taker flow: −$821.7K; every contract · 24h
Funding: +0.0000%; per period · BIN

Aperiodic metrics · DELL · 24 h

Are takers buying or selling DELL?

Takers sold $819.8K more than they bought on Binance, Hyperliquid and OKX over 24 h; the heaviest hour, 14:25 UTC, saw $599.9K.

+$599.9K0−$599.9K

23:25 UTCnow

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

What does it cost to cross DELL's spread?

L1 liquidity

2.45 bps median on Binance USDT, wider than 61% of the 200 most traded perps.

#1BTC0.015 bps
#2ETH0.051 bps
#3SOXL1.56 bps
#5SOL1.25 bps
#10AKE3.18 bps
#25XYZ:XYZ1000.35 bps
#50MUU5.64 bps
#100MVLL8.76 bps
#155DELL2.45 bps
#200AIN2.70 bps

l1_liquidity.spread_bpsbusiest contract · median · 24 h

How much size waits at DELL's best price?

L1 liquidity

$408 at the best bid and ask on Binance USDT, on average, thinner than 85% of the 200 most traded perps.

#1BTC$651.7K
#2ETH$269.8K
#3SOXL$86.9K
#5SOL$157.5K
#10AKE$553
#25XYZ:XYZ100$165.1K
#50MUU$22.4K
#100MVLL$13.7K
#155DELL$408
#200AIN$180

l1_liquidity.total_dollar_depthbusiest contract · mean · 24 h

Which way is DELL's book leaning?

L2 imbalance

Leaned to the ask in 15 of 24 hours on Binance USDT; 49% bids over the day.

66% bid034% bid

23:25 UTCnow

l2_imbalance.imbalance_ratio_10Binance USDT · top 10 levels · hourly

Where does DELL trade?

OHLCV

Binance USDT took 71% of $27.7M traded in 24 h.

volume sharespread

Binance USDT71%2.45
Hyperliquid xyz USDC15%3.50
OKX USDT14%4.58

ohlcv.volume_notionaleach contract · 24 h

Does DELL trade away from its index?

Basis

12.0 bps below its index on Binance USDT now; 11.2 bps below to 8.78 bps above over 24 h.

+11.2 bps0−11.2 bps

23:25 UTCnow

basis.basis_bpsBinance USDT · hourly

Is leverage building in DELL?

Open interest

$10.3M open across 3 contracts: +5.4% in contracts, −5.6% in dollars over 24 h.

$10.9M$10.0M

23:25 UTCnow

open_interest.open_interestbusiest contract on each venue · hourly

Who pays funding on DELL?

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

DELL's 3 contracts · now

Named by Atlas →

Contract	Price	24h	Volume 24h	Open interest	Funding	Spread	Charts
Binance USDTperpetual-DELL-USDT:USDT	339.270	-10.07%	$19.7M	$3.5M	+0.0000%	2.45	Charts →
Hyperliquid xyz USDCperpetual-XYZ:DELL-USDC:USDC	339.150	-10.04%	$4.0M	$5.0M	+0.0006%	3.50	Charts →
OKX USDTperpetual-DELL-USDT:USDT	339.240	-10.05%	$4.0M	$1.8M	+0.0000%	4.58	Charts →

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

Every chart for DELL →

Python

import os
from datetime import date

from aperiodic import get_derivative_metrics, get_metrics

# aperiodic.io/markets: DELL 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-DELL-USDT:USDT"),
    ("hyperliquid-perps", "perpetual-XYZ:DELL-USDC:USDC"),
    ("okx-perps", "perpetual-DELL-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-DELL-USDT:USDT

339.270

-10.07%

$19.7M

$3.5M

+0.0000%

2.45

Charts →

Hyperliquid xyz USDCperpetual-XYZ:DELL-USDC:USDC

339.150

-10.04%

$4.0M

$5.0M

+0.0006%

3.50

Charts →

OKX USDTperpetual-DELL-USDT:USDT

339.240

-10.05%

$4.0M

$1.8M

+0.0000%

4.58

Charts →

import os from datetime import date from aperiodic import get_derivative_metrics, get_metrics # aperiodic.io/markets: DELL 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-DELL-USDT:USDT"), ("hyperliquid-perps", "perpetual-XYZ:DELL-USDC:USDC"), ("okx-perps", "perpetual-DELL-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, )