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