Recipe 4 of 33
What are profitable Hyperliquid traders doing on a coin?
Read the coin's realized-PnL cohort matrix; the 3x smart, 2x smart and smart rows show how the top 10% of wallets by lifetime realized PnL are positioned and how much they traded in 24 hours.
GET /v1/hl-cohorts/symbols/{symbol}/cohort-metrics with axis=pnl answers it in one call. It groups a coin's
positions by each wallet's percentile rank of cumulative realized PnL: 3x smart is the top 1%, 2x smart the
next 4%, smart the next 5%, and 3x rekt the bottom 5%. include=volume,median_pnl adds each cohort's 24-hour
fill volume and its median lifetime realized PnL to the newest hour. The full parameter list is on
Coin cohort metrics.
Quick answer
labelBasis=point_in_time labels each row with the cohort the wallet was in that hour.
# HYPE by realized-PnL cohort, newest settled hour, with 24 h volume and median PnL
curl -H "X-API-Key: $VIKE_API_KEY" \
"https://data.vike.io/v1/hl-cohorts/symbols/HYPE/cohort-metrics?axis=pnl&labelBasis=point_in_time&hours=1&include=volume,median_pnl"Example response
Captured from the live API on 2026-10-24 at 17:12 UTC, trimmed to 4 of 13 cohorts; label_computed shortened.
On HYPE the top 1% held $694M, 61% of it long (bias +0.23), and filled $596M in the last 24 hours;
the bottom 5% held $514M and were 22% long (bias -0.55). Notional and volume are USD,
total_position_size is in HYPE, and bias runs from -1 (all short) to +1 (all long).
{
"axis": "pnl",
"label_basis": "point_in_time",
"metrics": [
{
"ts": "2026-10-24T16:00:00+00:00",
"cohort": "3x smart",
"position_count": 701,
"position_count_long": 490,
"total_position_value": 693637539.0,
"total_position_value_long": 425181761.7,
"total_unrealized_pnl": -38370780.99,
"total_position_size": 9194930.75,
"total_position_size_long": 5636253.78,
"bias": 0.2259479564,
"label_basis": "point_in_time",
"grading": "realized",
"volume_24h_usd": 595703299.5,
"median_realized_pnl_usd": 198602.00315099998
},
{
"ts": "2026-10-24T16:00:00+00:00",
"cohort": "2x smart",
"position_count": 1740,
"position_count_long": 1332,
"total_position_value": 116296864.2,
"total_position_value_long": 73425218.27,
"total_unrealized_pnl": -4136734.029,
"total_position_size": 1541695.93,
"total_position_size_long": 973377.21,
"bias": 0.262720518,
"label_basis": "point_in_time",
"grading": "realized",
"volume_24h_usd": 116296208.0,
"median_realized_pnl_usd": 11271.598296874996
},
{
"ts": "2026-10-24T16:00:00+00:00",
"cohort": "smart",
"position_count": 1756,
"position_count_long": 1280,
"total_position_value": 16340508.04,
"total_position_value_long": 10459936.11,
"total_unrealized_pnl": -700970.4168,
"total_position_size": 216614.78,
"total_position_size_long": 138661.06,
"bias": 0.2802461326,
"label_basis": "point_in_time",
"grading": "realized",
"volume_24h_usd": 17787786.32,
"median_realized_pnl_usd": 1628.6496990950004
},
{
"ts": "2026-10-24T16:00:00+00:00",
"cohort": "3x rekt",
"position_count": 1407,
"position_count_long": 864,
"total_position_value": 514198994.2,
"total_position_value_long": 114645853.7,
"total_unrealized_pnl": -20952027.4,
"total_position_size": 6816357.45,
"total_position_size_long": 1519841.8,
"bias": -0.5540798213,
"label_basis": "point_in_time",
"grading": "realized",
"volume_24h_usd": 128638613.8,
"median_realized_pnl_usd": -21805.273218999977
}
],
"hours": 1,
"unavailable_fields": [],
"nextCursor": "eyJtIjoiY29ob3J0X21ldHJpY3MiLCJ0IjoiMjAyNi0xMC0yNCAxNjowMDowMCJ9",
"grading": "realized",
"label_computed": {
"grading": "realized",
"label_basis": "point_in_time",
"when": "before 2026-08-06, reconstructed from fills on 2026-08-17; from 2026-08-06, frozen on the position row at its own hour",
"lookahead": false,
"resolution": "daily before 2026-08-06, hourly after",
"source": "hourly cohort snapshots"
},
"freshness": {
"newest_ts": "2026-10-24T16:00:00+00:00",
"behind_hours": 1,
"settled_lag_ceiling_hours": 3,
"stale": false
}
}Variations
Smart money in one number. latest.smart_bias is (long - short) / (long + short) over the three smart cohorts at the newest
5-minute snapshot; latest.smi is their long share minus the long share of the three rekt cohorts.
# HYPE smart-money bias and Smart Money Index now
curl -s -H "X-API-Key: $VIKE_API_KEY" \
"https://data.vike.io/v1/hl-cohorts/symbols/HYPE/indices?hours=1" \
| jq '.latest | {ts, smart_bias, smi}'The last week, labels as they were. hours=168 with labelBasis=point_in_time labels each hour with the grading that held at that
hour, so the series is safe for a backtest.
# a week of hourly smart-money positioning on HYPE, labelled point-in-time
curl -s -H "X-API-Key: $VIKE_API_KEY" \
"https://data.vike.io/v1/hl-cohorts/symbols/HYPE/cohort-metrics?axis=pnl&labelBasis=point_in_time&hours=168" \
| jq '[.metrics[] | select(.cohort == "3x smart") | {ts, bias, total_position_value}]'Dollar bands instead of percentiles. axis=pnl_abs groups by lifetime realized PnL in dollars ($1m+ down to <-$1m), graded today.
# HYPE positioning of wallets by realized PnL in dollar bands
curl -H "X-API-Key: $VIKE_API_KEY" \
"https://data.vike.io/v1/hl-cohorts/symbols/HYPE/cohort-metrics?axis=pnl_abs&hours=1"Before you rely on it
Profitable means realized in the past, and a few wallets dominate
The ladder ranks lifetime realized PnL among wallets with at least 10 closed trades; the rest sit in
unknown. It says who has made money, not who will. Rows are weighted by notional, so a handful of
large accounts decide a cohort's bias. Without labelBasis=point_in_time a historical hour carries
today's grading, which is lookahead in a backtest; volume_24h_usd and median_realized_pnl_usd
are request-time figures, not the hour's.
Ask your agent
Connected to the Vike MCP server (https://vike.io/mcp) in Claude, ChatGPT or Cursor, ask it in plain words.
The hl_cohort_metrics tool answers, at 1 credit a call.
What are the most profitable Hyperliquid traders doing on HYPE right now?