auto: hourly snapshot 2026-06-07 13:34

verify_april_funding.py

@@ -0,0 +1,24 @@
+import urllib.request, json, time, pandas as pd
+URL="https://api.hyperliquid.xyz/info"
+start_ms = int(pd.Timestamp("2026-04-01", tz="UTC").timestamp()*1000)
+end_ms   = int(pd.Timestamp("2026-05-01", tz="UTC").timestamp()*1000)
+rows=[]; cursor=start_ms
+while cursor < end_ms:
+    body=json.dumps({"type":"fundingHistory","coin":"BTC","startTime":cursor,"endTime":end_ms}).encode()
+    req=urllib.request.Request(URL,data=body,headers={"Content-Type":"application/json"})
+    with urllib.request.urlopen(req,timeout=30) as r:
+        chunk=json.loads(r.read().decode())
+    if not chunk: break
+    rows.extend(chunk)
+    last=int(chunk[-1].get("time",0))
+    if last<=cursor: break
+    cursor=last+1
+    time.sleep(0.15)
+df=pd.DataFrame(rows)
+df["ts"]=pd.to_datetime(df["time"].astype("int64"),unit="ms",utc=True)
+df["fbps_ann"]=df["fundingRate"].astype(float)*8760*10000
+print(f"rows: {len(df)}   expected: 720")
+print(f"span: {df.ts.min()} -> {df.ts.max()}")
+print(f"fbps_ann: mean={df.fbps_ann.mean():+.1f}  std={df.fbps_ann.std():.1f}  min={df.fbps_ann.min():+.1f}  max={df.fbps_ann.max():+.1f}")
+df.to_parquet("/tmp/april_funding_verify.parquet")
+print("OK: API returns full April funding cleanly.")