Trader Positioning by Account Type: Market Makers vs Whales API

A Long/Short Ratio Can't Tell You Who Is Long

The most-quoted derivatives stat is the long/short ratio. It's also one of the most misleading, because it treats every dollar the same. A book that's 60% long because market makers are warehousing inventory means something completely different from one that's 60% long because whales are pressing a directional bet.

The /api/v1/quant/positioning endpoint fixes that. It splits each coin's Hyperliquid book by trader type, so you can see whose money is on each side — not just the net.

Per-Coin Positioning by Account Type

For every coin it aggregates the full ≥$100k account universe (active accounts re-polled every collection cycle) into four buckets — market_maker, whale, other, and all — each with long / short / net / gross notional and a distinct account count:

meta.by_tag reports the account-class summary and meta.active_accounts the universe size behind the read.

How Accounts Are Classified

There are no labels on-chain, so accounts are sorted by behavior. A lightweight classifier scores each account on:

• Turnover — how often it flips position (dealers churn; whales hold).
• Market breadth — how many coins it makes markets in at once.
• Leverage — conservative inventory vs aggressive directional risk.
• PnL consistency — spread-capture profiles look different from directional bets.

It's a heuristic, not a registry — treat the market_maker set as a strong behavioral grouping, the same one that powers the dealer-gamma view at /quant/gex.

Pulling Positioning for One Coin

curl -H "X-API-Key: cdk_live_your_key" \
  "https://cryptodataapi.com/api/v1/quant/positioning?symbol=ETH"

{
  "scope": "positioning",
  "coins": {
    "ETH": {
      "market_maker": {"net": -12400000, "accounts": 7},
      "whale":        {"net": 41800000,  "accounts": 34},
      "other":        {"net": 9200000,   "accounts": 121},
      "all":          {"net": 38600000,  "accounts": 162}
    }
  }
}

The headline net is +$38.6M long, but the read is sharper than that: whales are decisively long (+$41.8M across 34 accounts) while market makers lean short inventory — conviction longs, not a positioning artifact.

Reading the Split: MM vs Whale vs Other

The relationship between buckets is where the signal lives:

• Whales long, MM short: directional conviction with dealers absorbing — trend can run.
• Whales and other both heavy one side: crowded — squeeze/contrarian risk.
• MM dominates the net: mostly liquidity — discount the directional read.
• Whale net flips day-over-day: a regime change in smart-money posture.

This is the structural detail an aggregate ratio averages away.

Positioning vs Whale Activity vs GEX

Three endpoints read the same ≥$100k account universe through different lenses — pick by the question you're asking:

Positioning is the per-coin microscope; whale activity is the wide-angle; GEX isolates the dealers. They share one classification pass, so the numbers reconcile.

How AI Agents Use Positioning

import httpx
p = httpx.get("https://cryptodataapi.com/api/v1/quant/positioning?symbol=ETH",
              headers={"X-API-Key": "cdk_live_your_key"}).json()["coins"]["ETH"]

whale_net = p["whale"]["net"]
mm_net = p["market_maker"]["net"]
# conviction long: whales long while dealers lean short
conviction_long = whale_net > 0 and mm_net < 0

• Signal-quality filter: require whale agreement before acting on a net.
• Crowding guard: flag coins where whale + other pile onto one side.
• Pair it up: combine with whale activity (aggregate) and GEX (dealer gamma).

Pro Plus, one call per coin or the whole universe at once.