{"slug":"galleonlabs-hyperliquid-market-data","name":"hyperliquid-market-data","description":"Read live Hyperliquid market data from the desk computer with curl or the Python SDK - mid, mark and oracle prices, order book depth, funding (current, predicted, historical), open interest, volume, candles, perp and spot metadata, margin tiers, and how to save datasets for the strategy lab. Read-only, no key. Use for any market brief, depth read, funding question or data pull.","long_description":"---\nname: hyperliquid-market-data\ndescription: Read live Hyperliquid market data from the desk computer with curl or the Python SDK - mid, mark and oracle prices, order book depth, funding (current, predicted, historical), open interest, volume, candles, perp and spot metadata, margin tiers, and how to save datasets for the strategy lab. Read-only, no key. Use for any market brief, depth read, funding question or data pull.\nlicense: MIT\nmetadata:\n  version: \"1.1.0\"\n  author: Galleon Labs\n  category: hyperliquid\n  network-default: mainnet-for-reads\n---\n\n# Hyperliquid market data\n\nAll 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.\n\n```bash\nBASE=https://api.hyperliquid.xyz            # or https://api.hyperliquid-testnet.xyz\nhl() { curl -sS -m 15 -X POST \"$BASE/info\" -H 'Content-Type: application/json' -d \"$1\"; }\n```\n\nPython header (SDK):\n\n```python\nfrom hyperliquid.info import Info\nfrom hyperliquid.utils import constants\ninfo = Info(constants.MAINNET_API_URL, skip_ws=True)     # TESTNET_API_URL for testnet\n```\n\n## Prices\n\n```bash\nhl '{\"type\":\"allMids\"}' | jq '{BTC, ETH, SOL}'                          # mid per coin, strings\n```\n\n`allMids` falls back to last trade when the book is empty. For mark, oracle and mid together use `metaAndAssetCtxs` below. Python: `info.all_mids()`.\n\n## Market metadata, funding, open interest, volume\n\n```bash\nhl '{\"type\":\"metaAndAssetCtxs\"}' | jq -r '\n  .[0].universe as $u | .[1] | to_entries[] | . as $e | $u[$e.key] as $m\n  | select($m.name == \"BTC\" or $m.name == \"ETH\" or $m.name == \"SOL\")\n  | [$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, $m.szDecimals] | @tsv'\n```\n\nFields per asset (same order as `meta.universe`): `midPx`, `markPx`, `oraclePx`, `funding` (**hourly** rate as a decimal: `0.0000125` = 0.00125%/h), `openInterest` (coin units), `dayNtlVlm` (24h USD volume), `premium` (impact bid/ask versus oracle, the input to funding), `prevDayPx`, `impactPxs`. Universe fields: `name`, `szDecimals`, `maxLeverage`, `marginTableId`, `onlyIsolated`/`marginMode`, `isDelisted`.\n\nPython: `meta, ctxs = info.meta_and_asset_ctxs()`.\n\nDerived numbers the desk uses (show the formula): OI notional = `openInterest x markPx`; annualised funding = `funding x 24 x 365`; 24h change = `markPx / prevDayPx - 1`.\n\nMargin tiers (max leverage by notional) come from `meta`:\n\n```bash\nhl '{\"type\":\"meta\"}' | jq --arg c BTC '(.universe[] | select(.name==$c)) as $u | ($u.marginTableId // $u.maxLeverage) as $id\n  | {name:$u.name, maxLeverage:$u.maxLeverage, marginTableId:$id,\n     tiers: (if $id < 50 then [{lowerBound:\"0.0\", maxLeverage:$id}]\n             else ((.marginTables[] | select(.[0]==$id) | .[1].marginTiers) // [{lowerBound:\"0.0\", maxLeverage:$u.maxLeverage}]) end)}'\n```\n\nIds 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.\n\n## Order book and depth\n\n```bash\nhl '{\"type\":\"l2Book\",\"coin\":\"ETH\"}' | jq '{time, bids: .levels[0][:5], asks: .levels[1][:5]}'\n```\n\nUp 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`.\n\n**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.\n\nDepth within a band, the way the Risk Manager and Execution Trader want it:\n\n```bash\nhl '{\"type\":\"l2Book\",\"coin\":\"ETH\"}' | jq '\n  (.levels[0][0].px|tonumber) as $bb | (.levels[1][0].px|tonumber) as $ba | (($bb+$ba)/2) as $mid\n  | def within(side; bps): [side[] | select((((.px|tonumber) - $mid) | fabs) / $mid * 10000 <= bps) | .sz|tonumber] | add // 0;\n    def reach(side): (((side[-1].px|tonumber) - $mid) | fabs) / $mid * 10000;\n  {mid: $mid, spread_bps: (($ba-$bb)/$mid*10000),\n   levels: [(.levels[0]|length), (.levels[1]|length)], reach_bps: [reach(.levels[0]), reach(.levels[1])],\n   bid_5bps: within(.levels[0]; 5), ask_5bps: within(.levels[1]; 5),\n   bid_10bps: within(.levels[0]; 10), ask_10bps: within(.levels[1]; 10),\n   bid_25bps: within(.levels[0]; 25), ask_25bps: within(.levels[1]; 25)}'\n```\n\n`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.\n\nExpected 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\")`.\n\nRecent trades: `hl '{\"type\":\"recentTrades\",\"coin\":\"ETH\"}'` (public prints: `px, sz, side, time`).\n\n## Candles\n\n```bash\nEND=$(date +%s000); START=$((END - 48*3600*1000))\nhl \"{\\\"type\\\":\\\"candleSnapshot\\\",\\\"req\\\":{\\\"coin\\\":\\\"ETH\\\",\\\"interval\\\":\\\"1h\\\",\\\"startTime\\\":$START,\\\"endTime\\\":$END}}\" \\\n | jq -r '.[] | [.t, .o, .h, .l, .c, .v, .n] | @csv'\n```\n\nFields: `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)`.\n\nSaving a dataset for the strategy lab (walks back until the API runs out):\n\n```python\nimport csv, time\nfrom hyperliquid.info import Info\nfrom hyperliquid.utils import constants\ninfo = Info(constants.MAINNET_API_URL, skip_ws=True)\ncoin, interval, days = \"ETH\", \"4h\", 365            # 4h keeps a year inside the 5000-candle ceiling\nend = int(time.time() * 1000); start = end - days * 86_400_000\nrows = {}\ncursor_end = end\nwhile cursor_end > start:\n    batch = info.candles_snapshot(coin, interval, start, cursor_end)\n    if not batch: break\n    for c in batch: rows[c[\"t\"]] = c\n    oldest = min(c[\"t\"] for c in batch)\n    if oldest <= start or len(batch) < 2: break\n    cursor_end = oldest - 1\npath = f\"/workspace/trading-desk/data/{coin}-{interval}-{days}d.csv\"\nwith open(path, \"w\", newline=\"\") as f:\n    w = csv.writer(f); w.writerow([\"t\",\"T\",\"o\",\"h\",\"l\",\"c\",\"v\",\"n\"])\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\"]])\nprint(path, len(rows), \"candles\", \"fetched\", time.strftime(\"%Y-%m-%dT%H:%M:%SZ\", time.gmtime()))\n```\n\nRecord the exact request (coin, interval, start, end, fetched-at, network) next to the file.\n\n## Funding history and predictions\n\n```bash\nSTART=$(( $(date +%s000) - 7*86400000 ))\nhl \"{\\\"type\\\":\\\"fundingHistory\\\",\\\"coin\\\":\\\"ETH\\\",\\\"startTime\\\":$START}\" | jq -r '.[] | [.time, .fundingRate, .premium] | @tsv'\n# venue, raw rate, its interval in hours, rate per hour, next funding\nhl '{\"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'\n```\n\n`fundingHistory` returns hourly rates (up to 500 per call; paginate by `startTime`).\n\n`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.\n\nFunding is paid every hour at `size x oracle price x hourly rate`; longs pay shorts when positive. Python: `info.funding_history(coin, start_ms)`.\n\n## Spot markets\n\n```bash\nhl '{\"type\":\"spotMeta\"}' | jq '.universe[] | select(.name==\"PURR/USDC\" or .name==\"@107\")'\nhl '{\"type\":\"spotMetaAndAssetCtxs\"}' | jq '.[1][:3]'\n```\n\nSpot 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.\n\n## HIP-3 builder perps\n\nOther 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.\n\n## Brief format\n\nUse the block in `agents/market-analyst.md`: sources and time on the first line, then facts, derived, read, unknown, next.\n\n## Rate limits\n\n`/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.\n\n## Pitfalls\n\n- Reporting a number without the request type, network and UTC time.\n- Treating `funding` as an 8h or daily rate; it is hourly.\n- Comparing `predictedFundings` venues without dividing each by its own `fundingIntervalHours`, or assuming the CEX venues are 8h when most coins are 4h.\n- Reading a book once and calling it \"the depth\" ten minutes later.\n- Forgetting spot `@index` naming and base-token `szDecimals`.\n- Expecting deep history at fine intervals; only the latest 5000 candles per interval exist, so pick the interval to fit the lookback.\n","tagline":"Read live Hyperliquid market data from the desk computer with curl or the Python SDK - mid, mark and oracle prices, order book depth, funding (current, predicted, historical), open interest, volume, candles, perp and spot metadata, margin tiers, and how to save datasets for the s","category":"design-creative","tags":["agent-skill"],"author":"Galleon Labs","verified":false,"attribution":{"status":"registry_indexed","statusLabel":"Registry indexed","shortLabel":"REGISTRY INDEXED","sourceLabel":"github candidate review","sourceDetail":"galleonlabs/hypergrok-trading-desk","creatorName":"Galleon Labs","creatorUrl":"https://github.com/galleonlabs","sourceUrl":"https://github.com/galleonlabs/hypergrok-trading-desk/tree/main/skills/hyperliquid-market-data","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/galleonlabs-hyperliquid-market-data#claim-this-skill","claimCta":"Claim this skill","trustNote":"This listing was indexed from public sources and is not marked official until a maintainer claim is approved.","publicNote":"Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals."},"stats":{"stars":59,"forks":9,"verified_installs":0,"successful_runs":0,"total_outcomes":0,"rating":0,"review_count":0,"quality_score":36.45},"quality":{"score":65,"tier":"promising","label":"Promising","summary":"Useful candidate, but compare it with alternatives before adopting.","signals":[{"label":"GitHub stars","value":"59","tone":"neutral"},{"label":"Freshness","value":"5d ago","tone":"positive"},{"label":"Install ready","value":"Yes","tone":"positive"},{"label":"License","value":"MIT","tone":"neutral"}],"warnings":["The skill references `scripts/opening_bell.py` and `agents/market-analyst.md`, but these files are not included in the skill directory. This may cause confusion for agents trying to locate them."]},"trust":{"version":"trust-score-v5","score":57,"base_score":65,"outcome_confidence":0,"tier":"risk","label":"Do not auto-install","summary":"Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.","recommendedAction":"Choose a stronger alternative or inspect the source manually before any install attempt.","decision":{"install_policy":"human_review_before_install","auto_install_allowed":false,"human_review_required":true,"sandbox_first":true,"agent_action":"Compare alternatives before installing.","reasoning":["57/100 Trust Score v5","65/100 Trust Score v4 baseline","Needs more real agent outcomes before unattended install","Install path is missing","Review before production"],"review_required_when":["The workspace contains production secrets, payments, private customer data, or irreversible actions.","The install command requests shell, network, credential, database, or broad filesystem access.","Outcome evidence is missing, recently failed, or required human review.","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"]},"dimensions":[{"id":"github_adoption","label":"GitHub adoption","score":48,"weight":0.13,"status":"warn","detail":"59 GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":43,"weight":0.08,"status":"warn","detail":"59 stars, 9 forks; issue activity unavailable in current metadata"},{"id":"maintenance","label":"Recent maintenance","score":100,"weight":0.14,"status":"pass","detail":"5d since push"},{"id":"license","label":"License clarity","score":86,"weight":0.09,"status":"pass","detail":"MIT"},{"id":"documentation","label":"README/SKILL.md completeness","score":76,"weight":0.14,"status":"info","detail":"Public metadata needs stronger README/SKILL.md context"},{"id":"dependency_risk","label":"Dependency/runtime risk","score":46,"weight":0.12,"status":"warn","detail":"command execution surface, credential or environment access"},{"id":"installability","label":"Install availability","score":92,"weight":0.1,"status":"pass","detail":"npx skills add galleonlabs/hypergrok-trading-desk --skill hyperliquid-market-data"},{"id":"install_safety","label":"Install command safety","score":92,"weight":0.1,"status":"pass","detail":"standard package or runtime install path"},{"id":"permission_surface","label":"Permission surface","score":22,"weight":0.07,"status":"fail","detail":"secrets or environment access, shell or command execution"},{"id":"repository","label":"Repository evidence","score":86,"weight":0.04,"status":"pass","detail":"https://github.com/galleonlabs/hypergrok-trading-desk/tree/main/skills/hyperliquid-market-data"},{"id":"review_status","label":"Review status","score":66,"weight":0.05,"status":"info","detail":"AI review data available"},{"id":"agent_outcomes","label":"Agent Proven outcomes","score":54,"weight":0.13,"status":"info","detail":"No agent outcome data yet"}],"checks":[{"status":"warn","label":"GitHub adoption","detail":"59 GitHub stars"},{"status":"warn","label":"Stars/forks activity","detail":"59 stars, 9 forks; issue activity unavailable in current metadata"},{"status":"pass","label":"Recent maintenance","detail":"5d since push"},{"status":"pass","label":"License clarity","detail":"MIT"},{"status":"info","label":"README/SKILL.md completeness","detail":"Public metadata needs stronger README/SKILL.md context"},{"status":"warn","label":"Dependency/runtime risk","detail":"command execution surface, credential or environment access"},{"status":"pass","label":"Install availability","detail":"npx skills add galleonlabs/hypergrok-trading-desk --skill hyperliquid-market-data"},{"status":"pass","label":"Install command safety","detail":"standard package or runtime install path"},{"status":"fail","label":"Permission surface","detail":"secrets or environment access, shell or command execution"},{"status":"pass","label":"Repository evidence","detail":"https://github.com/galleonlabs/hypergrok-trading-desk/tree/main/skills/hyperliquid-market-data"},{"status":"info","label":"Review status","detail":"AI review data available"},{"status":"info","label":"Agent Proven outcomes","detail":"No agent outcome data yet"},{"status":"warn","label":"Ownership","detail":"No approved owner claim yet"},{"status":"info","label":"OpenAgentSkill usage","detail":"No local usage activity yet"},{"status":"info","label":"Agent outcomes","detail":"No agent outcome data yet"}],"strengths":["AI review approved","Install path is available","Repository evidence is available","Recently maintained repository","Install command has no obvious high-risk pattern","Outcome loop is ready but needs first real agent run"],"warnings":["The skill references `scripts/opening_bell.py` and `agents/market-analyst.md`, but these files are not included in the skill directory. 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Review the current source before installing.","installSafety":"standard package or runtime install path","permissionSurface":"secrets or environment access, shell or command execution","documentation":"Usable metadata, review docs","agentOutcomes":"No agent outcome data yet","agentProvenScore":0,"outcomeConfidence":"0%","installPolicy":"human_review_before_install"},"installReadiness":{"ready":false,"command":null,"policy":"human_review_before_install","label":"Human review before install","notes":["The tracked source changed or could not be synchronized. Review the current source before installing.","Repository evidence is available","License is declared","No Agent Proven outcome evidence yet","5d since push","Financial domain: human review is required before use in a live investment workflow.","Trust Score v5 requires review or sandbox-only use before install."]},"agentCompatibility":["Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"riskSummary":{"level":"medium","label":"Review before production","notes":["The skill references `scripts/opening_bell.py` and `agents/market-analyst.md`, but these files are not included in the skill directory. 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None guarantees runtime safety."},"skill":{"slug":"galleonlabs-hyperliquid-market-data","name":"hyperliquid-market-data","description":"Read live Hyperliquid market data from the desk computer with curl or the Python SDK - mid, mark and oracle prices, order book depth, funding (current, predicted, historical), open interest, volume, candles, perp and spot metadata, margin tiers, and how to save datasets for the strategy lab. Read-only, no key. Use for any market brief, depth read, funding question or data pull.","category":"design-creative","url":"https://www.openagentskill.com/skills/galleonlabs-hyperliquid-market-data","repository":"https://github.com/galleonlabs/hypergrok-trading-desk/tree/main/skills/hyperliquid-market-data","github_repo":"galleonlabs/hypergrok-trading-desk"},"suited_tasks":["Finance and quant workflows","Claude Code teams","builders willing to evaluate younger projects","Retrieve market data","Compare financial signals","Generate investor-ready analysis","Inspect visual requirements","Generate reusable assets"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"install":{"source_evidence":{"status":"source-needs-review","sourceRecorded":true,"canOfferInstall":false,"path":"skills/hyperliquid-market-data/SKILL.md","revision":"15733578b5358ab838f9d9f245af1c4e03ef3b2a","notice":"The tracked source changed or could not be synchronized. 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