分钟级行情数据入库规范：统一结束时刻标签、09:30集合竞价归并、周期合成与源数据逐根一致、根数/总量/价格校验，以及存不复权加除权因子的复权方案。

 ┌──────────┐   ┌────────────┐   ┌──────────────┐   ┌────────────┐
 │ 分钟取数  │──▶│ 标签规范化  │──▶│ 集合竞价归并  │──▶│ 周期合成    │
 │ 1m/5m    │   │ 结束时刻    │   │ 09:30 并入    │   │ resample   │
 └──────────┘   └────────────┘   └──────────────┘   └─────┬──────┘
                                                          │
                                  ┌────────────┐   ┌──────▼─────┐
                                  │ 入库        │◀──│ 一致性校验  │
                                  │ 不复权存档  │   │ 量/价/根数  │
                                  └────────────┘   └────────────┘

from datetime import datetime
import pandas as pd
from quantdash import QuantDash

qd = QuantDash()  # 凭证从环境变量 QUANTDASH_API_KEY 读取

def ms(s):
    return int(datetime.strptime(s, "%Y-%m-%d %H:%M").timestamp() * 1000)

def get_min(symbol, period, day, adjust="none"):
    return qd.klines.get(symbol, period=period, adjust=adjust,
                         start_time=ms(f"{day} 09:00"), end_time=ms(f"{day} 15:10"),
                         count=10000, to_dataframe=True)

for p in ["1m", "5m", "30m"]:
    d = get_min("600519.SH", p, "2026-10-30")
    print(p, len(d), d["trade_time"].iloc[0], d["trade_time"].iloc[-1])

def resample_min(m1, rule="5min"):
    d = m1.copy()
    d["ts"] = pd.to_datetime(d["trade_time"])
    d.loc[d["ts"].dt.strftime("%H:%M") == "09:30", "ts"] += pd.Timedelta(minutes=1)
    agg = {"open": "first", "high": "max", "low": "min",
           "close": "last", "volume": "sum", "amount": "sum"}
    return (d.set_index("ts")
              .resample(rule, label="right", closed="right")
              .agg(agg).dropna(subset=["close"]))

m1 = get_min("600519.SH", "1m", "2026-10-30")
m5 = get_min("600519.SH", "5m", "2026-10-30")
mine = resample_min(m1)
print(len(mine), len(m5), int(mine["volume"].iloc[0]), int(m5["volume"].iloc[0]))

day = qd.klines.get("600519.SH", period="1d", adjust="none", count=3, to_dataframe=True)
day_vol = int(day.loc[day["trade_date"] == "2026-10-30", "volume"].iloc[0])
checks = {
    "bars_1m": len(m1) == 241,
    "bars_5m": len(m5) == 48,
    "vol_sum": int(m1["volume"].sum()) == int(m5["volume"].sum()) == day_vol,
}
print(checks, day_vol)

raw = get_min("600519.SH", "5m", "2026-06-24", adjust="none")
fwd = get_min("600519.SH", "5m", "2026-06-24", adjust="forward")
f = qd.klines.ex_factors("600519.SH", to_dataframe=True)
print(raw["close"].iloc[-1], round(fwd["close"].iloc[-1], 2),
      round(raw["close"].iloc[-1] / fwd["close"].iloc[-1], 6), f["ex_factor"].iloc[-1])