TSMC perpsTSM

Stock perp · Binance, OKX, Hyperliquid · #149 by volume

395.660-3.17% 24h

Binance USDT · 24h 392.200 – 412.960

24h volume: $29.5M; #149 of 919
Open interest: $28.5M; +2.5% contracts · 24h
Spread: 0.98 bps; median · 24h
Size at best price: $809; mean · 24h
Net taker flow: −$799.4K; every contract · 24h
Funding: +0.0251%; per period · BIN

Aperiodic metrics · TSM · 24 h

Are takers buying or selling TSM?

Takers sold $791.0K more than they bought on Binance, OKX and Hyperliquid over 24 h; the heaviest hour, 14:45 UTC, saw $579.2K.

+$579.2K0−$579.2K

00:45 UTCnow

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

What does it cost to cross TSM's spread?

L1 liquidity

0.98 bps median on Binance USDT, tighter than 73% 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
#149TSM0.98 bps
#200MON6.08 bps

l1_liquidity.spread_bpsbusiest contract · median · 24 h

How much size waits at TSM's best price?

L1 liquidity

$809 at the best bid and ask on Binance USDT, on average, thinner than 73% 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
#149TSM$809
#200MON$2.3K

l1_liquidity.total_dollar_depthbusiest contract · mean · 24 h

Which way is TSM's book leaning?

L2 imbalance

Leaned to the bid in 14 of 24 hours on Binance USDT; 50% bids over the day.

60% bid040% bid

00:45 UTCnow

l2_imbalance.imbalance_ratio_10Binance USDT · top 10 levels · hourly

Where does TSM trade?

OHLCV

Binance USDT took 70% of $29.5M traded in 24 h; the tightest spread is OKX USDT's, 0.25 bps.

volume sharespread

Binance USDT70%0.98
OKX USDT19%0.25
Hyperliquid xyz USDC11%3.28

ohlcv.volume_notionaleach contract · 24 h

Does TSM trade away from its index?

Basis

15.2 bps above its index on Binance USDT now; 2.31 bps to 15.6 bps above over 24 h.

+15.6 bps0−15.6 bps

00:45 UTCnow

basis.basis_bpsBinance USDT · hourly

Is leverage building in TSM?

Open interest

$28.5M open across 3 contracts: +2.5% in contracts, −1.1% in dollars over 24 h.

$28.9M$28.0M

00:45 UTCnow

open_interest.open_interestbusiest contract on each venue · hourly

Who pays funding on TSM?

Funding

Longs pay on 3 of 3 contracts; Binance USDT charges +0.0251% a period (Hyperliquid’s are hourly).

Binance USDT+0.0251%
OKX USDT+0.0164%
Hyperliquid xyz USDC+0.0039%

← shorts paylongs pay →

funding.funding_rateeach contract · per funding period · now

TSM's 3 contracts · now

Named by Atlas →

Contract	Price	24h	Volume 24h	Open interest	Funding	Spread	Charts
Binance USDTperpetual-TSM-USDT:USDT	395.660	-3.17%	$20.7M	$16.1M	+0.0251%	0.98	Charts →
OKX USDTperpetual-TSM-USDT:USDT	395.620	-3.16%	$5.5M	$4.3M	+0.0164%	0.25	Charts →
Hyperliquid xyz USDCperpetual-XYZ:TSM-USDC:USDC	395.150	-3.14%	$3.3M	$8.2M	+0.0039%	3.28	Charts →

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

Every chart for TSM →

Python

import os
from datetime import date

from aperiodic import get_derivative_metrics, get_metrics

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

395.660

-3.17%

$20.7M

$16.1M

+0.0251%

0.98

Charts →

OKX USDTperpetual-TSM-USDT:USDT

395.620

-3.16%

$5.5M

$4.3M

+0.0164%

0.25

Charts →

Hyperliquid xyz USDCperpetual-XYZ:TSM-USDC:USDC

395.150

-3.14%

$3.3M

$8.2M

+0.0039%

3.28

Charts →

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