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.

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;
  {mid: $mid, spread_bps: (($ba-$bb)/$mid*10000),
   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)}'

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'
hl '{"type":"predictedFundings"}' | jq -r '.[] | select(.[0]=="ETH") | .[1][] | [.[0], .[1].fundingRate, .[1].nextFundingTime] | @tsv'

fundingHistory returns hourly rates (up to 500 per call; paginate by startTime). predictedFundings compares venues: the HlPerp entry is an hourly rate; the BinPerp/BybitPerp entries are 8-hour rates, so do not compare them raw. 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 converting the 8h CEX rates.
• 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.