Applied Materials perpsAMAT

Stock perp · Binance, Hyperliquid, OKX · #223 by volume

364.060-5.49% 24h

Binance USDT · 24h 353.000 – 387.680

24h volume: $11.8M; #223 of 924
Open interest: $3.6M; −6.9% contracts · 24h
Spread: 3.39 bps; median · 24h
Size at best price: $345; mean · 24h
Net taker flow: −$904.0K; every contract · 24h
Funding: +0.0000%; per period · BIN

Aperiodic metrics · AMAT · 24 h

Are takers buying or selling AMAT?

Takers sold $901.3K more than they bought on Binance, Hyperliquid and OKX over 24 h; the heaviest hour, 17:00 UTC, saw $338.7K.

+$338.7K0−$338.7K

00:00 UTCnow

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

What does it cost to cross AMAT's spread?

L1 liquidity

3.39 bps median on Binance USDT, wider than 77% of the 200 most traded perps.

#1BTC0.015 bps
#2ETH0.052 bps
#3SOXL1.56 bps
#5SOL1.25 bps
#10AKE3.13 bps
#25XYZ:XYZ1000.35 bps
#50MUU5.65 bps
#100QNT0.62 bps
#200COPPER1.60 bps
#223AMAT3.39 bps

l1_liquidity.spread_bpsbusiest contract · median · 24 h

How much size waits at AMAT's best price?

L1 liquidity

$345 at the best bid and ask on Binance USDT, on average, thinner than 90% of the 200 most traded perps.

#1BTC$651.6K
#2ETH$268.0K
#3SOXL$87.1K
#5SOL$156.2K
#10AKE$545
#25XYZ:XYZ100$170.3K
#50MUU$22.1K
#100QNT$760
#200COPPER$10.7K
#223AMAT$345

l1_liquidity.total_dollar_depthbusiest contract · mean · 24 h

Which way is AMAT's book leaning?

L2 imbalance

Leaned to the bid in 12 of 24 hours on Binance USDT; 49% bids over the day.

65% bid035% bid

00:00 UTCnow

l2_imbalance.imbalance_ratio_10Binance USDT · top 10 levels · hourly

Where does AMAT trade?

OHLCV

Binance USDT took 78% of $11.8M traded in 24 h.

volume sharespread

Binance USDT78%3.39
Hyperliquid xyz USDC15%7.15
OKX USDT7.5%7.75

ohlcv.volume_notionaleach contract · 24 h

Does AMAT trade away from its index?

Basis

13.3 bps above its index on Binance USDT now; 6.34 bps below to 16.0 bps above over 24 h.

+16.0 bps0−16.0 bps

00:00 UTCnow

basis.basis_bpsBinance USDT · hourly

Is leverage building in AMAT?

Open interest

$3.6M open across 3 contracts: −6.9% in contracts, −10.2% in dollars over 24 h.

$4.0M$3.6M

00:00 UTCnow

open_interest.open_interestbusiest contract on each venue · hourly

Who pays funding on AMAT?

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

AMAT's 3 contracts · now

Named by Atlas →

Contract	Price	24h	Volume 24h	Open interest	Funding	Spread	Charts
Binance USDTperpetual-AMAT-USDT:USDT	364.060	-5.49%	$9.1M	$2.3M	+0.0000%	3.39	Charts →
Hyperliquid xyz USDCperpetual-XYZ:AMAT-USDC:USDC	363.340	-5.88%	$1.7M	$702.5K	+0.0006%	7.15	Charts →
OKX USDTperpetual-AMAT-USDT:USDT	364.010	-5.38%	$887.5K	$560.2K	+0.0000%	7.75	Charts →

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

Every chart for AMAT →

Python

import os
from datetime import date

from aperiodic import get_derivative_metrics, get_metrics

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

364.060

-5.49%

$9.1M

$2.3M

+0.0000%

3.39

Charts →

Hyperliquid xyz USDCperpetual-XYZ:AMAT-USDC:USDC

363.340

-5.88%

$1.7M

$702.5K

+0.0006%

7.15

Charts →

OKX USDTperpetual-AMAT-USDT:USDT

364.010

-5.38%

$887.5K

$560.2K

+0.0000%

7.75

Charts →

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