> ## Documentation Index > Fetch the complete documentation index at: https://artemis.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Sector leaderboards > Rank every protocol in a sector by a metric with a single API call `summarize=true` collapses a date range into one row per asset, which is exactly the shape a leaderboard needs. * **One value per asset:** the `endDate` day's value, for every metric, whether it's `SUM`, `LAST` or `AVERAGE` * **Plus movement:** a `_PCT_CHG` series showing how far that value moved since `startDate` * **A comparison window, not a period total:** Hyperliquid's `PERP_VOLUME` over 2026-07-25 to 2026-08-01 summarizes to \$2.02B, the 2026-08-01 value; the eight daily values sum to \$59.64B See [Core concepts](/docs/artemis-api/core-concepts) for the full behaviour. ## Lending, ranked by deposits ```bash theme={null} curl -s "https://data-svc.artemisxyz.com/v2/data/LENDING_DEPOSITS,LENDING_LOANS?symbols=aave,comp,eul,fluid,hyperlend,jup,kmno,lista,lqty,morpho,qi,sky,spk,syrup,tydro,well,xvs&startDate=2026-07-25&endDate=2026-08-01&summarize=true&APIKey=$ARTEMIS_API_KEY" ``` | Protocol | Deposits | Borrows | Utilisation | | - | - | - | - | | Aave | \$24.52B | \$10.60B | 43% | | Morpho | \$10.35B | \$4.02B | 39% | | Maker | \$6.15B | \$4.80B | 78% | | Spark | \$5.46B | \$1.80B | 33% | | Maple | \$4.04B | \$1.82B | 45% | | Kamino | \$2.25B | \$0.92B | 41% | | Jupiter | \$2.03B | \$0.75B | 37% | | Compound | \$1.75B | \$0.57B | 32% | | Venus | \$1.41B | \$0.38B | 27% | | Fluid | \$1.40B | \$0.75B | 53% | | Euler | \$0.66B | \$0.51B | 77% | | HyperLend | \$0.66B | \$0.24B | 37% | | Liquity | \$0.14B | \$0.03B | 20% | | Benqi Finance | \$0.12B | \$0.03B | 28% | | Tydro | \$0.11B | \$0.05B | 45% | | Moonwell | \$0.07B | \$0.03B | 41% | | Lista | \$0.06B | \$0.04B | 69% | ## The Python version ```python theme={null} import os import requests BASE = "https://data-svc.artemisxyz.com/v2/data" KEY = os.environ["ARTEMIS_API_KEY"] def leaderboard(metrics, symbols, start="2026-07-25", end="2026-08-01"): resp = requests.get( f"{BASE}/{','.join(metrics)}", params={ "symbols": ",".join(symbols), "startDate": start, "endDate": end, "summarize": "true", "APIKey": KEY, }, timeout=120, ) resp.raise_for_status() rows = {} for s in resp.json()["series"]: if s["metric"].endswith("_PCT_CHG"): # summarize adds one per metric continue if not isinstance(s["data"], list) or not s["data"]: continue # series_columns is ["label", "value"] here, so index 1 is the value rows.setdefault(s["display_name"], {})[s["metric"]] = s["data"][0][1] return sorted( rows.items(), key=lambda kv: kv[1].get(metrics[0]) or 0, reverse=True, ) LENDING = "aave comp eul fluid hyperlend jup kmno lista lqty morpho qi sky spk syrup tydro well xvs".split() for name, vals in leaderboard(["LENDING_DEPOSITS", "LENDING_LOANS"], LENDING): dep = vals.get("LENDING_DEPOSITS") or 0 bor = vals.get("LENDING_LOANS") or 0 util = f"{100 * bor / dep:.0f}%" if dep else "n/a" print(f"{name:16} {dep/1e9:8,.2f}B {bor/1e9:8,.2f}B {util:>5}") ``` Want biggest movers rather than biggest absolute values? Sort by the `_PCT_CHG` series instead of skipping it. It's `(endDate / startDate − 1) × 100`, measured across the whole window. ## Perps, ranked by open interest ```bash theme={null} curl -s "https://data-svc.artemisxyz.com/v2/data/OPEN_INTEREST,PERP_VOLUME?symbols=hype,aster,lit,apex,edgex,extended,ostium,paradex,gns,gmtrade,hyena,paragon,tradexyz,jup&startDate=2026-07-25&endDate=2026-08-01&summarize=true&APIKey=$ARTEMIS_API_KEY" ``` | Venue | Open interest | Volume | | - | - | - | | Hyperliquid | \$10,170.4M | \$2,016.5M | | trade.xyz | \$3,534.0M | \$524.2M | | Aster | \$1,902.3M | \$834.7M | | Lighter | \$869.7M | \$509.2M | | edgeX | \$274.8M | \$74.0M | | Jupiter | \$191.9M | \$81.6M | | ApeX | \$111.4M | \$792.6M | | GMTrade | \$69.1M | \$56.3M | | Paradex | \$36.9M | \$3.4M | | HyENA | \$4.9M | \$0.9M | | Ostium | \$4.6M | \$0.6M | | Paragon | \$2.5M | \$1.5M | Twelve of the fourteen symbols rank on `OPEN_INTEREST`. Extended and Gains Network report `PERP_VOLUME` (\$176.9M and \$16.3M) with no open interest over this window, arriving as a well-formed `["Extended", null]` tuple, which is why `leaderboard()` guards against `None` as well as checking the type. ApeX stands out: \$792.6M of volume against \$111.4M of open interest, turning its book over roughly seven times in a day. `OI_BY_VOLUME_RATIO` measures that directly. In this basket, Hyperliquid (5.04), Paradex (11.00), edgeX (3.71) and Lighter (1.71) return a value. Under `summarize=true` **every** figure here is the `endDate` day's value, open interest and volume alike. `summarize` ignores the declared `aggregation_type`, so the volume column is a single day's volume, not an eight-day total. If you want a period total, sum the daily series or use `granularity`. ## Build the symbol list automatically Rather than hardcoding symbols, pull them from `/asset/`: ```python theme={null} import requests assets = requests.get("https://data-svc.artemisxyz.com/asset/", timeout=180).json()["assets"] def sector_symbols(tag): return [ a["symbol"] for a in assets if a.get("symbol") and any(g.get("value") == tag for g in (a.get("tags") or {}).get("groups") or []) ] print(leaderboard(["LENDING_DEPOSITS"], sector_symbols("lending"))[:5]) ``` This prints the same top five (Aave, Morpho, Maker, Spark, Maple). * **19 symbols, not 17:** the sector query also returns `seam` (Seamless Protocol) and `sonne` (Sonne Finance) * **Keep the `or 0` guard:** neither reports `LENDING_DEPOSITS` for this window, so a built list still needs the same handling as a hand-picked one See [Finding assets and metrics](/docs/artemis-api/discovery) for more on sector queries. 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