Hyperliquid market data

All reads are POST /info with a JSON body; no key, no signing. Market data is usually read from mainnet even when the desk trades on testnet, because testnet prices and books are thin; say which network a figure came from. Every figure the desk reports carries source (request type), network and UTC time.

BASE=https://api.hyperliquid.xyz            # or https://api.hyperliquid-testnet.xyz
hl() { curl -sS -m 15 -X POST "$BASE/info" -H 'Content-Type: application/json' -d "$1"; }

Python header (SDK):

from hyperliquid.info import Info
from hyperliquid.utils import constants
info = Info(constants.MAINNET_API_URL, skip_ws=True)     # TESTNET_API_URL for testnet

Prices

hl '{"type":"allMids"}' | jq '{BTC, ETH, SOL}'                          # mid per coin, strings

allMids falls back to last trade when the book is empty. For mark, oracle and mid together use metaAndAssetCtxs below. Python: info.all_mids().

Market metadata, funding, open interest, volume

hl '{"type":"metaAndAssetCtxs"}' | jq -r '
  .[0].universe as $u | .[1] | to_entries[] | . as $e | $u[$e.key] as $m
  | select($m.name == "BTC" or $m.name == "ETH" or $m.name == "SOL")
  | [$m.name, $e.value.midPx, $e.value.markPx, $e.value.oraclePx, $e.value.funding, $e.value.openInterest, $e.value.dayNtlVlm, $e.value.premium, $m.maxLeverage, $m.szDecimals] | @tsv'

Fields per asset (same order as meta.universe): midPx, markPx, oraclePx, funding (hourly rate as a decimal: 0.0000125 = 0.00125%/h), openInterest (coin units), dayNtlVlm (24h USD volume), premium (impact bid/ask versus oracle, the input to funding), prevDayPx, impactPxs. Universe fields: name, szDecimals, maxLeverage, marginTableId, onlyIsolated/marginMode, isDelisted.

Python: meta, ctxs = info.meta_and_asset_ctxs().

Derived numbers the desk uses (show the formula): OI notional = openInterest x markPx; annualised funding = funding x 24 x 365; 24h change = markPx / prevDayPx - 1.

Margin tiers (max leverage by notional) come from meta:

hl '{"type":"meta"}' | jq --arg c BTC '(.universe[] | select(.name==$c)) as $u | ($u.marginTableId // $u.maxLeverage) as $id
  | {name:$u.name, maxLeverage:$u.maxLeverage, marginTableId:$id,
     tiers: (if $id < 50 then [{lowerBound:"0.0", maxLeverage:$id}]
             else ((.marginTables[] | select(.[0]==$id) | .[1].marginTiers) // [{lowerBound:"0.0", maxLeverage:$u.maxLeverage}]) end)}'

Ids below 50 are single-tier tables whose max leverage equals the id, and they are not listed under marginTables, so the snippet synthesises that tier; ids of 50 and above are looked up.

Order book and depth

hl '{"type":"l2Book","coin":"ETH"}' | jq '{time, bids: .levels[0][:5], asks: .levels[1][:5]}'

Up to 20 levels per side; each level is {px, sz, n} (n = number of orders). Optional nSigFigs (2-5) aggregates price levels; mantissa (1, 2 or 5) only with nSigFigs: 5.

Twenty levels is a page, not the book. On a liquid perp those levels stop a few bps from the mid - around 8 bps on ETH and under 3 bps on BTC at normal spreads - so a 25 bps band summed from the default response is whatever the page happened to contain, not the depth within 25 bps. Read the reach before quoting a band: when the side came back with 20 levels and the furthest one is nearer than the band, the number is a floor. To reach further, re-request with nSigFigs: 4, which buckets prices coarsely enough to push 20 levels out to roughly 20-25 bps on a major perp (measured live: 82 bps ETH, 25 BTC, 24 HYPE, 20 SOL), and drop to nSigFigs: 3 when even that stops short. Read each band off the finest page that reaches it - the coarse page moves the band edges by up to one bucket, and its top of book is not the real one - and say which page a figure came from. scripts/opening_bell.py is that ladder in code.

Depth within a band, the way the Risk Manager and Execution Trader want it:

hl '{"type":"l2Book","coin":"ETH"}' | jq '
  (.levels[0][0].px|tonumber) as $bb | (.levels[1][0].px|tonumber) as $ba | (($bb+$ba)/2) as $mid
  | def within(side; bps): [side[] | select((((.px|tonumber) - $mid) | fabs) / $mid * 10000 <= bps) | .sz|tonumber] | add // 0;
    def reach(side): (((side[-1].px|tonumber) - $mid) | fabs) / $mid * 10000;
  {mid: $mid, spread_bps: (($ba-$bb)/$mid*10000),
   levels: [(.levels[0]|length), (.levels[1]|length)], reach_bps: [reach(.levels[0]), reach(.levels[1])],
   bid_5bps: within(.levels[0]; 5), ask_5bps: within(.levels[1]; 5),
   bid_10bps: within(.levels[0]; 10), ask_10bps: within(.levels[1]; 10),
   bid_25bps: within(.levels[0]; 25), ask_25bps: within(.levels[1]; 25)}'

reach_bps is the answer's own scope: any band wider than it, on a side that returned 20 levels, is a floor and is quoted as >= size. Two bands reporting the same total is that cut-off, not a flat book.

Expected slippage for a size: walk the relevant side accumulating sz until the target size is reached; report the volume-weighted price versus mid in bps. If the size exceeds the visible 20 levels, say "beyond visible depth". Python: info.l2_snapshot("ETH").

Recent trades: hl '{"type":"recentTrades","coin":"ETH"}' (public prints: px, sz, side, time).

Candles

END=$(date +%s000); START=$((END - 48*3600*1000))
hl "{\"type\":\"candleSnapshot\",\"req\":{\"coin\":\"ETH\",\"interval\":\"1h\",\"startTime\":$START,\"endTime\":$END}}" \
 | jq -r '.[] | [.t, .o, .h, .l, .c, .v, .n] | @csv'

Fields: t open time ms, T close time ms, o h l c strings, v base volume, n trade count. Intervals: 1m 3m 5m 15m 30m 1h 2h 4h 8h 12h 1d 3d 1w 1M. Only the most recent 5000 candles per market and interval exist on the API, so history depth depends on the interval: about 3.5 days of 1m, 208 days of 1h, 2.3 years of 4h, 13 years of 1d. Choose the interval to fit the history you need; requests for older candles return nothing. Python: info.candles_snapshot(coin, interval, start_ms, end_ms).

Saving a dataset for the strategy lab (walks back until the API runs out):

import csv, time
from hyperliquid.info import Info
from hyperliquid.utils import constants
info = Info(constants.MAINNET_API_URL, skip_ws=True)
coin, interval, days = "ETH", "4h", 365            # 4h keeps a year inside the 5000-candle ceiling
end = int(time.time() * 1000); start = end - days * 86_400_000
rows = {}
cursor_end = end
while cursor_end > start:
    batch = info.candles_snapshot(coin, interval, start, cursor_end)
    if not batch: break
    for c in batch: rows[c["t"]] = c
    oldest = min(c["t"] for c in batch)
    if oldest <= start or len(batch) < 2: break
    cursor_end = oldest - 1
path = f"/workspace/trading-desk/data/{coin}-{interval}-{days}d.csv"
with open(path, "w", newline="") as f:
    w = csv.writer(f); w.writerow(["t","T","o","h","l","c","v","n"])
    for t in sorted(rows): c = rows[t]; w.writerow([c["t"],c["T"],c["o"],c["h"],c["l"],c["c"],c["v"],c["n"]])
print(path, len(rows), "candles", "fetched", time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()))

Record the exact request (coin, interval, start, end, fetched-at, network) next to the file.

Funding history and predictions

START=$(( $(date +%s000) - 7*86400000 ))
hl "{\"type\":\"fundingHistory\",\"coin\":\"ETH\",\"startTime\":$START}" | jq -r '.[] | [.time, .fundingRate, .premium] | @tsv'
# venue, raw rate, its interval in hours, rate per hour, next funding
hl '{"type":"predictedFundings"}' | jq -r '.[] | select(.[0]=="ETH") | .[1][] | select(.[1]) | [.[0], .[1].fundingRate, (.[1].fundingIntervalHours // "?"), (if .[1].fundingIntervalHours then (.[1].fundingRate|tonumber) / .[1].fundingIntervalHours else "?" end), .[1].nextFundingTime] | @tsv'

fundingHistory returns hourly rates (up to 500 per call; paginate by startTime).

predictedFundings compares venues, and the venue rates are over different periods, so never compare them raw. Every coin carries the same three slots in the same order - BinPerp, HlPerp, BybitPerp - but match on the venue name rather than the position, and each slot is either a payload or null when the coin is not listed on that venue. Divide fundingRate by the payload's own fundingIntervalHours to get a per-hour rate. HlPerp is always 1; the CEX venues are 4 or 8 depending on the coin, not a fixed 8: on mainnet on 2026-08-28, BinPerp was 4h for 126 coins and 8h for 63, BybitPerp 4h for 118 and 8h for 69. BTC, ETH and DOGE are all 8h, so an assumed 8 looks right on the majors and is wrong by 2x across most alts. A few BinPerp payloads (19 that day, including TON, MKR and IP) omit fundingIntervalHours entirely; treat the interval as unknown and say so rather than defaulting it.

Funding is paid every hour at size x oracle price x hourly rate; longs pay shorts when positive. Python: info.funding_history(coin, start_ms).

Spot markets

hl '{"type":"spotMeta"}' | jq '.universe[] | select(.name=="PURR/USDC" or .name=="@107")'
hl '{"type":"spotMetaAndAssetCtxs"}' | jq '.[1][:3]'

Spot pairs are named PURR/USDC or @<index> on the API (the app shows HYPE/USDC); tokens: [base, quote] indexes into spotMeta.tokens. A pair's asset id for orders is 10000 + universe index; its size decimals are the base token's szDecimals. Ids differ between mainnet and testnet.

HIP-3 builder perps

Other perp dexs exist beside the main one: hl '{"type":"perpDexs"}' lists them; coins are named dex:COIN, and meta, metaAndAssetCtxs, clearinghouseState accept "dex": "<name>". The desk uses the default dex unless the user says otherwise.

Brief format

Use the block in agents/market-analyst.md: sources and time on the first line, then facts, derived, read, unknown, next.

Rate limits

/info weight per IP is 1200 per minute: allMids, l2Book, clearinghouseState, orderStatus cost 2; most others cost 20; candleSnapshot adds 1 per 60 candles. Batch questions, do not poll faster than the desk needs, and prefer hyperliquid-websocket for anything continuous. HTTP 429 means back off.

Pitfalls

• Reporting a number without the request type, network and UTC time.
• Treating funding as an 8h or daily rate; it is hourly.
• Comparing predictedFundings venues without dividing each by its own fundingIntervalHours, or assuming the CEX venues are 8h when most coins are 4h.
• Reading a book once and calling it "the depth" ten minutes later.
• Forgetting spot @index naming and base-token szDecimals.
• Expecting deep history at fine intervals; only the latest 5000 candles per interval exist, so pick the interval to fit the lookback.