MANTA perps

Perp · Binance, Hyperliquid · #628 by volume

0.04620-2.08% 24h

Binance USDT · 24h 0.04487 – 0.04792

24h volume: $1.2M; #628 of 914
Open interest: $3.6M; −0.2% contracts · 24h
Spread: 4.21 bps; median · 24h
Size at best price: $140; mean · 24h
Net taker flow: +$6.1K; every contract · 24h
Funding: +0.0050%; per period · BIN

Aperiodic metrics · MANTA · 24 h

Every metric on MANTA, bar by bar →

Are takers buying or selling MANTA?

Flow

Takers bought $6.2K more than they sold on Binance and Hyperliquid over 24 h; the heaviest hour, 20:25 UTC, saw $12.0K.

+$14.5K0−$14.5K

23:25 UTCnow

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

What does it cost to cross MANTA's spread?

L1 liquidity

4.21 bps median on Binance USDT, wider than 80% 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
#200AIN2.70 bps
#628MANTA4.21 bps

l1_liquidity.spread_bpsbusiest contract · median · 24 h

How much size waits at MANTA's best price?

L1 liquidity

$140 at the best bid and ask on Binance USDT, on average, thinner than 98% 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
#200AIN$180
#628MANTA$140

l1_liquidity.total_dollar_depthbusiest contract · mean · 24 h

Which way is MANTA's book leaning?

L2 imbalance

Leaned to the ask in 18 of 24 hours on Binance USDT; 47% bids over the day.

65% bid035% bid

23:25 UTCnow

l2_imbalance.imbalance_ratio_10Binance USDT · top 10 levels · hourly

Where does MANTA trade?

OHLCV

Binance USDT took 96% of $1.2M traded in 24 h.

volume sharespread

Binance USDT96%4.21
Hyperliquid USDC4.3%10.7

ohlcv.volume_notionaleach contract · 24 h

Does MANTA trade away from its index?

Basis

3.76 bps below its index on Binance USDT now; 13.6 bps below to 5.73 bps above over 24 h.

+13.6 bps0−13.6 bps

23:25 UTCnow

basis.basis_bpsBinance USDT · hourly

Is leverage building in MANTA?

Open interest

$3.6M open across 2 contracts: −0.2% in contracts, −1.8% in dollars over 24 h.

$3.7M$3.6M

23:25 UTCnow

open_interest.open_interestbusiest contract on each venue · hourly

Who pays funding on MANTA?

Funding

Longs pay on 2 of 2 contracts; Binance USDT charges +0.0050% a period (Hyperliquid’s are hourly).

Binance USDT+0.0050%
Hyperliquid USDC+0.0013%

← shorts paylongs pay →

funding.funding_rateeach contract · per funding period · now

MANTA's 2 contracts · now

Named by Atlas →

Contract	Price	24h	Volume 24h	Open interest	Funding	Spread	Charts
Binance USDTperpetual-MANTA-USDT:USDT	0.04620	-2.08%	$1.2M	$3.3M	+0.0050%	4.21	Charts →
Hyperliquid USDCperpetual-MANTA-USDC:USDC	0.04626	-1.87%	$52.6K	$252.1K	+0.0013%	10.7	Charts →

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

Every chart for MANTA →

Python

import os
from datetime import date

from aperiodic import get_derivative_metrics, get_metrics

# aperiodic.io/markets: MANTA on Binance and Hyperliquid
# 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).
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-MANTA-USDT:USDT"),
    ("hyperliquid-perps", "perpetual-MANTA-USDC:USDC"),
]

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-MANTA-USDT:USDT

0.04620

-2.08%

$1.2M

$3.3M

+0.0050%

4.21

Charts →

Hyperliquid USDCperpetual-MANTA-USDC:USDC

0.04626

-1.87%

$52.6K

$252.1K

+0.0013%

10.7

Charts →

import os from datetime import date from aperiodic import get_derivative_metrics, get_metrics # aperiodic.io/markets: MANTA on Binance and Hyperliquid # 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). 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-MANTA-USDT:USDT"), ("hyperliquid-perps", "perpetual-MANTA-USDC:USDC"), ] 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, )