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Join us on Discorddirectional_net_usd Hyperliquid: the conviction net across every account holding ≥$100k — long minus short with market-maker inventory stripped out, so it reads directional whale positioning rather than the two-sided book. See how the big book is leaning long vs short, which cryptos whales hold the most, the behavioral-class split (market makers vs directional whales), and how it has moved over time. Refreshed every ~5 minutes.
| # | Coin | Mark | Whale notional | Long / Short | Net bias | # Whales |
|---|---|---|---|---|---|---|
| 1 | ETH | $2,686.85 | $2.73B |
|
NEUTRAL | 830 |
| 2 | BTC | $83,893.50 | $2.21B |
|
SHORT | 1,159 |
| 3 | HYPE | $88.57 | $1.37B |
|
NEUTRAL | 1,214 |
| 4 | SOL | $117.19 | $528.64M |
|
SHORT | 409 |
| 5 | ZEC | $1,374.40 | $498.29M |
|
SHORT | 584 |
| 6 | XRP | $1.48 | $239.08M |
|
SHORT | 222 |
| 7 | NEAR | $4.85 | $219.36M |
|
SHORT | 394 |
| 8 | PUMP | $0.005418 | $199.03M |
|
SHORT | 310 |
gross notional includes market-maker liquidity; the API also returns directional_net_usd (net excluding market makers) for the pure conviction read. Tokenized equities are excluded; spot-wallet balances are a planned addition.
/quant/whales response object — summary, top coins and meta. The full top-coins list is Pro; the daily history series is Pro Plus.{
"scope": "whale_activity",
"timestamp": "2026-10-01T14:56:35.730000Z",
"summary": {
"accounts_tracked": 3845,
"universe_size": 15000,
"min_account_value_usd": 100000.0,
"by_class": {
"market_maker": 264,
"whale": 191,
"other": 3390
},
"by_tag_usd": {
"smart_money": {
"long_usd": 2669897520.95,
"short_usd": 2152854853.23,
"net_usd": 517042667.71,
"gross_usd": 4822752374.18,
"n_accounts": 1466
},
"high_leverage": {
"long_usd": 1241080401.99,
"short_usd": 525751553.24,
"net_usd": 715328848.75,
"gross_usd": 1766831955.23,
"n_accounts": 544
}
},
"coins_held": 178,
"total_long_usd": 4151525894.74,
"total_short_usd": 5175705633.34,
"total_net_usd": -1024179738.6,
"total_gross_usd": 9327231528.08,
"long_pct": 44.5,
"long_short_ratio": 0.802,
"net_bias": "short"
},
"top_coins": [
{
"coin": "ETH",
"mark": 2686.85,
"segment": "perp",
"positions_as_of": 1790865774922,
"gross_usd": 2725423744.92,
"long_usd": 1318138690.24,
"short_usd": 1407285054.68,
"net_usd": -89146364.43,
"directional_net_usd": 112984537.05,
"long_pct": 48.4,
"long_pct_pctile_30d": 80.3,
"net_bias": "neutral",
"n_accounts": 830,
"n_long": 428,
"n_short": 402,
"dominant_side": "short"
},
{
"coin": "BTC",
"mark": 83893.5,
"segment": "perp",
"positions_as_of": 1790865774922,
"gross_usd": 2207698514.21,
"long_usd": 1045988010.25,
"short_usd": 1161710503.96,
"net_usd": -115722493.71,
"directional_net_usd": -16457673.42,
"long_pct": 47.4,
"long_pct_pctile_30d": 10.0,
"net_bias": "short",
"n_accounts": 1159,
"n_long": 593,
"n_short": 566,
"dominant_side": "short"
},
{
"coin": "HYPE",
"mark": 88.5705,
"segment": "perp",
"positions_as_of": 1790865774922,
"gross_usd": 1368069098.03,
"long_usd": 657304599.57,
"short_usd": 710764498.46,
"net_usd": -53459898.89,
"directional_net_usd": -50806231.22,
"long_pct": 48.0,
"long_pct_pctile_30d": 2.1,
"net_bias": "neutral",
"n_accounts": 1214,
"n_long": 524,
"n_short": 690,
"dominant_side": "short"
}
],
"meta": {
"segment": "perp",
"spot_status": "not_yet_collected",
"venue": "hyperliquid",
"by_tag": {
"smart_money": 1466,
"whale": 191,
"high_leverage": 544,
"market_maker": 264
},
"classifier_version": "2026-07-02",
"note": "Hyperliquid perpetuals positioning across every account ≥ the liqmap floor. gross_usd includes market-maker liquidity; directional_net_usd excludes it for the conviction read. summary.by_tag_usd buckets (smart_money, high_leverage) are orthogonal overlays, not a breakdown of by_class."
}
}
curl -H "X-API-Key: cdk_live_yourkey" \ "https://cryptodataapi.com/api/v1/quant/whales"
Aggregated live positioning; /quant/whales/history?days=180 is the daily series behind the page chart.
Hyperliquid settles on-chain, so these are real positions, not estimates. The open question — follow the whales or fade them — is empirical, and the 180-day history is enough to answer it per market condition rather than by slogan.
Build me a whale-positioning strategy on CryptoDataAPI data.
Read live aggregate positioning from /api/v1/quant/whales. Build a signal from the net long/short skew and its CHANGE — a flip or a fast unwind, not the standing level. Test both following the signal and fading it.
Then backtest on /api/v1/quant/whales/history?days=180 joined to /api/v1/backtesting/klines at 4h/24h/7d horizons, with taker fees.
Report forward returns for follow vs fade at each horizon, split by whether the market regime was trending or ranging (use /api/v1/quant/regimes/history). Include how often the whales were simply wrong — if neither direction clears fees, say so rather than picking the better-looking loser.
Whale positioning is only a signal if you can see it change. The archive holds the classified account universe at 5-minute resolution — every Hyperliquid account over $100k, its positions and its side — so long/short flow is a time series you can lead, lag and test against forward returns rather than a snapshot you have to trust.
curl -H "X-API-Key: cdk_live_yourkey" \ "https://cryptodataapi.com/api/v1/backtesting/snapshots?data_type=hl_trader_positions&start=2026-07-01&limit=500"
This is perp positioning only, and it is a book-wide aggregate — the archive tells you what the ≥$100k cohort held, not who they were.