Netflix perpsNFLX

Stock perp · Binance, Hyperliquid, OKX · #268 by volume

62.080-1.46% 24h

Binance USDT · 24h 61.930 – 64.070

24h volume: $7.7M; #268 of 925
Open interest: $9.0M; −15.2% contracts · 24h
Spread: 1.60 bps; median · 24h
Size at best price: $2.2K; mean · 24h
Net taker flow: +$1.1M; every contract · 24h
Funding: +0.0000%; per period · BIN

Aperiodic metrics · NFLX · 24 h

Are takers buying or selling NFLX?

Takers bought $1.1M more than they sold on Binance, Hyperliquid and OKX over 24 h; the heaviest hour, 14:55 UTC, saw $1.5M.

+$1.5M0−$1.5M

00:55 UTCnow

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

What does it cost to cross NFLX's spread?

L1 liquidity

1.60 bps median on Binance USDT, tighter than 56% 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
#268NFLX1.60 bps

l1_liquidity.spread_bpsbusiest contract · median · 24 h

How much size waits at NFLX's best price?

L1 liquidity

$2.2K at the best bid and ask on Binance USDT, on average, deeper than 50% of the 200 most traded perps.

#1BTC$656.6K
#2ETH$268.9K
#3SOXL$87.6K
#5SOL$157.0K
#10SPCX$17.6K
#25SP500$112.0K
#50MUU$21.1K
#100INTW$1.3K
#200MON$2.3K
#268NFLX$2.2K

l1_liquidity.total_dollar_depthbusiest contract · mean · 24 h

Which way is NFLX's book leaning?

L2 imbalance

Leaned to the ask in 20 of 24 hours on Binance USDT; 46% bids over the day.

66% bid034% bid

00:55 UTCnow

l2_imbalance.imbalance_ratio_10Binance USDT · top 10 levels · hourly

Where does NFLX trade?

OHLCV

Binance USDT took 78% of $7.7M traded in 24 h; the tightest spread is OKX USDT's, 1.59 bps.

volume sharespread

Binance USDT78%1.60
Hyperliquid xyz USDC13%3.97
OKX USDT8.7%1.59

ohlcv.volume_notionaleach contract · 24 h

Does NFLX trade away from its index?

Basis

10.9 bps above its index on Binance USDT now; 0.12 bps to 11.4 bps above over 24 h.

+11.4 bps0−11.4 bps

00:55 UTCnow

basis.basis_bpsBinance USDT · hourly

Is leverage building in NFLX?

Open interest

$9.0M open across 3 contracts: −15.2% in contracts, −16.4% in dollars over 24 h.

$10.8M$9.0M

00:55 UTCnow

open_interest.open_interestbusiest contract on each venue · hourly

Who pays funding on NFLX?

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+0.0008%
OKX USDT+0.0000%

← shorts paylongs pay →

funding.funding_rateeach contract · per funding period · now

NFLX's 3 contracts · now

Named by Atlas →

Contract	Price	24h	Volume 24h	Open interest	Funding	Spread	Charts
Binance USDTperpetual-NFLX-USDT:USDT	62.080	-1.46%	$6.0M	$3.7M	+0.0000%	1.60	Charts →
Hyperliquid xyz USDCperpetual-XYZ:NFLX-USDC:USDC	61.973	-1.48%	$1.0M	$4.5M	+0.0008%	3.97	Charts →
OKX USDTperpetual-NFLX-USDT:USDT	62.060	-1.49%	$666.6K	$900.9K	+0.0000%	1.59	Charts →

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

Every chart for NFLX →

Python

import os
from datetime import date

from aperiodic import get_derivative_metrics, get_metrics

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

62.080

-1.46%

$6.0M

$3.7M

+0.0000%

1.60

Charts →

Hyperliquid xyz USDCperpetual-XYZ:NFLX-USDC:USDC

61.973

-1.48%

$1.0M

$4.5M

+0.0008%

3.97

Charts →

OKX USDTperpetual-NFLX-USDT:USDT

62.060

-1.49%

$666.6K

$900.9K

+0.0000%

1.59

Charts →

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