Palladium perpsXPD

Commodity perp · Binance, OKX, Hyperliquid · #260 by volume

1,003.3-0.06% 24h

Binance USDT · 24h 999.800 – 1,025.0

24h volume: $8.1M; #260 of 919
Open interest: $7.0M; −2.9% contracts · 24h
Spread: 1.88 bps; median · 24h
Size at best price: $547; mean · 24h
Net taker flow: −$461.1K; every contract · 24h
Funding: +0.0000%; per period · BIN

Aperiodic metrics · XPD · 24 h

Are takers buying or selling XPD?

Takers sold $457.5K more than they bought on Binance, OKX and Hyperliquid over 24 h; the heaviest hour, 03:45 UTC, saw $182.0K.

+$182.0K0−$182.0K

00:45 UTCnow

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

What does it cost to cross XPD's spread?

L1 liquidity

1.88 bps median on Binance USDT, tighter than 50% of the 200 most traded perps.

#1BTC0.015 bps
#2ETH0.052 bps
#3SOXL1.57 bps
#5SOL1.25 bps
#10SPCX0.71 bps
#25SP5000.13 bps
#50MUU5.65 bps
#100INTW1.73 bps
#200MON6.08 bps
#260XPD1.88 bps

l1_liquidity.spread_bpsbusiest contract · median · 24 h

How much size waits at XPD's best price?

L1 liquidity

$547 at the best bid and ask on Binance USDT, on average, thinner than 79% of the 200 most traded perps.

#1BTC$658.7K
#2ETH$268.7K
#3SOXL$87.5K
#5SOL$157.1K
#10SPCX$17.5K
#25SP500$111.7K
#50MUU$21.2K
#100INTW$1.3K
#200MON$2.3K
#260XPD$547

l1_liquidity.total_dollar_depthbusiest contract · mean · 24 h

Which way is XPD's book leaning?

L2 imbalance

Leaned to the bid in 15 of 24 hours on Binance USDT; 51% bids over the day.

60% bid040% bid

00:45 UTCnow

l2_imbalance.imbalance_ratio_10Binance USDT · top 10 levels · hourly

Where does XPD trade?

OHLCV

Binance USDT took 81% of $8.1M traded in 24 h.

volume sharespread

Binance USDT81%1.88
OKX USDT12%1.98
Hyperliquid xyz USDC6.7%8.95

ohlcv.volume_notionaleach contract · 24 h

Does XPD trade away from its index?

Basis

2.70 bps above its index on Binance USDT now; 1.02 bps below to 14.9 bps above over 24 h.

+14.9 bps0−14.9 bps

00:45 UTCnow

basis.basis_bpsBinance USDT · hourly

Is leverage building in XPD?

Open interest

$7.0M open across 3 contracts: −2.9% in contracts, −3.7% in dollars over 24 h.

$7.5M$7.0M

00:45 UTCnow

open_interest.open_interestbusiest contract on each venue · hourly

Who pays funding on XPD?

Funding

Longs pay on 0 of 3 contracts; Binance USDT charges +0.0000% a period (Hyperliquid’s are hourly).

Binance USDT+0.0000%
OKX USDT+0.0000%
Hyperliquid xyz USDC−0.0027%

← shorts paylongs pay →

funding.funding_rateeach contract · per funding period · now

XPD's 3 contracts · now

Named by Atlas →

Contract	Price	24h	Volume 24h	Open interest	Funding	Spread	Charts
Binance USDTperpetual-XPD-USDT:USDT	1,003.3	-0.06%	$6.5M	$5.1M	+0.0000%	1.88	Charts →
OKX USDTperpetual-XPD-USDT:USDT	1,002.7	-0.10%	$996.0K	$500.1K	+0.0000%	1.98	Charts →
Hyperliquid xyz USDCperpetual-XYZ:PALLADIUM-USDC:USDC	1,000.5	-0.06%	$544.5K	$1.5M	−0.0027%	8.95	Charts →

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

Every chart for XPD →

Python

import os
from datetime import date

from aperiodic import get_derivative_metrics, get_metrics

# aperiodic.io/markets: XPD on Binance, OKX and Hyperliquid
# 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-XPD-USDT:USDT"),
    ("okx-perps", "perpetual-XPD-USDT:USDT"),
    ("hyperliquid-perps", "perpetual-XYZ:PALLADIUM-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-XPD-USDT:USDT

1,003.3

-0.06%

$6.5M

$5.1M

+0.0000%

1.88

Charts →

OKX USDTperpetual-XPD-USDT:USDT

1,002.7

-0.10%

$996.0K

$500.1K

+0.0000%

1.98

Charts →

Hyperliquid xyz USDCperpetual-XYZ:PALLADIUM-USDC:USDC

1,000.5

-0.06%

$544.5K

$1.5M

−0.0027%

8.95

Charts →

import os from datetime import date from aperiodic import get_derivative_metrics, get_metrics # aperiodic.io/markets: XPD on Binance, OKX and Hyperliquid # 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-XPD-USDT:USDT"), ("okx-perps", "perpetual-XPD-USDT:USDT"), ("hyperliquid-perps", "perpetual-XYZ:PALLADIUM-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, )