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MoFin/venv/lib/python3.12/site-packages/akshare/qhkc_web/qhkc_fund.py
T
知微 fa45d8aa5f fix: 小果地址统一node122(兼容LAN+EasyTier) 
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
  Privoxy对node122:18003返回500，直连正常
2026-06-30 02:56:35 +08:00

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#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2025/4/10 18:00
Desc: 奇货可查网站目前已经商业化运营, 特提供奇货可查-资金数据接口, 方便您程序化调用
注：期货价格为收盘价; 现货价格来自网络; 基差=现货价格-期货价格; 基差率=(现货价格-期货价格)/现货价格 * 100 %.
"""
import datetime
from typing import AnyStr
import pandas as pd
import requests
from akshare.futures.cons import (
QHKC_FUND_BS_URL,
QHKC_FUND_POSITION_URL,
QHKC_FUND_POSITION_CHANGE_URL,
QHKC_FUND_DEAL_URL,
)
def get_qhkc_fund_bs(
date: datetime.datetime.date = "20190924", url: AnyStr = QHKC_FUND_BS_URL
):
"""
奇货可查-资金-净持仓分布
可获取数据的时间段为:"2016-10-10:2019-09-30"
:param url: 网址
:param date: 中文名称
:return: 净持仓分布
:rtype: pandas.DataFrame
symbol_df
name value ratio date
IC 1552535406 0.195622 20190924
IF 536644080 0.0676182 20190924
橡胶 536439921 0.0675924 20190924
沪铜 460851099 0.0580681 20190924
豆粕 401005794 0.0505275 20190924
螺纹钢 329159263 0.0414747 20190924
焦炭 325646968 0.0410321 20190924
燃料油 313246789 0.0394697 20190924
IH 245556750 0.0309406 20190924
棉花 214538541 0.0270323 20190924
PTA 206340552 0.0259993 20190924
白糖 139901255 0.0176278 20190924
豆油 133664010 0.0168419 20190924
沪铝 109789864 0.0138337 20190924
沪锌 107440906 0.0135378 20190924
纸浆 95517374 0.0120354 20190924
苹果 81058733 0.0102136 20190924
塑料 63665245 0.00802194 20190924
菜油 61544593 0.00775474 20190924
铁矿石 60751108 0.00765475 20190924
焦煤 58327920 0.00734943 20190924
甲醇 52148752 0.00657084 20190924
沥青 49207374 0.00620022 20190924
菜粕 48266258 0.00608164 20190924
棕榈油 31615548 0.00398362 20190924
PP 29374826 0.00370128 20190924
豆一 22368376 0.00281846 20190924
玉米 13861567 0.00174658 20190924
沪锡 7485903 0.000943238 20190924
淀粉 4811234 0.000606225 20190924
棉纱 3627240 0.000457039 20190924
尿素 2290674 0.000288629 20190924
鸡蛋 2035406 0.000256465 20190924
粳米 1999282 0.000251913 20190924
油菜籽 533482 6.72197e-05 20190924
晚籼稻 0 0 20190924
强麦 0 0 20190924
沪铅 89914 1.13293e-05 20190924
豆二 379200 4.77799e-05 20190924
硅铁 5025872 0.000633269 20190924
红枣 8521668 0.00107375 20190924
锰硅 9472832 0.00119359 20190924
郑煤 9888272 0.00124594 20190924
乙二醇 18324242 0.00230889 20190924
PVC 19454830 0.00245135 20190924
玻璃 27076226 0.00341166 20190924
热卷 28832929 0.003633 20190924
沪银 375076371 0.0472603 20190924
沪镍 411622624 0.0518652 20190924
沪金 719371823 0.0906422 20190924
long_short_df
name value ratio date
空 6303252093 0.794222 20190924
多 1633136803 0.205778 20190924
"""
date = str(date)
date = date[:4] + "-" + date[4:6] + "-" + date[6:]
print(date)
payload_id = {"date": date}
r = requests.post(url, data=payload_id)
print("数据获取成功")
json_data = r.json()
symbol_name = []
for item in json_data["data"]["datas1"]:
symbol_name.append(item["name"])
symbol_value = []
for item in json_data["data"]["datas1"]:
symbol_value.append(item["value"])
long_short_name = []
for item in json_data["data"]["datas2"]:
long_short_name.append(item["name"])
long_short_value = []
for item in json_data["data"]["datas2"]:
long_short_value.append(item["value"])
symbol_df = pd.DataFrame([symbol_name, symbol_value]).T
long_short_df = pd.DataFrame([long_short_name, long_short_value]).T
symbol_df.columns = ["name", "value"]
symbol_df["ratio"] = symbol_df["value"] / symbol_df["value"].sum()
symbol_df["date"] = date
long_short_df.columns = ["name", "value"]
long_short_df["ratio"] = long_short_df["value"] / long_short_df["value"].sum()
long_short_df["date"] = date
return symbol_df, long_short_df
def get_qhkc_fund_position(
date: datetime.datetime.date = "20190924", url: AnyStr = QHKC_FUND_POSITION_URL
):
"""
奇货可查-资金-总持仓分布
可获取数据的时间段为:"2016-10-10:2019-09-30"
:param url: 网址
:param date: 中文名称
:return: 总持仓分布
:rtype: pandas.DataFrame
symbol_df
name value ratio date
IC 1552535406 0.195622 20190924
IF 536644080 0.0676182 20190924
橡胶 536439921 0.0675924 20190924
沪铜 460851099 0.0580681 20190924
豆粕 401005794 0.0505275 20190924
螺纹钢 329159263 0.0414747 20190924
焦炭 325646968 0.0410321 20190924
燃料油 313246789 0.0394697 20190924
IH 245556750 0.0309406 20190924
棉花 214538541 0.0270323 20190924
PTA 206340552 0.0259993 20190924
白糖 139901255 0.0176278 20190924
豆油 133664010 0.0168419 20190924
沪铝 109789864 0.0138337 20190924
沪锌 107440906 0.0135378 20190924
纸浆 95517374 0.0120354 20190924
苹果 81058733 0.0102136 20190924
塑料 63665245 0.00802194 20190924
菜油 61544593 0.00775474 20190924
铁矿石 60751108 0.00765475 20190924
焦煤 58327920 0.00734943 20190924
甲醇 52148752 0.00657084 20190924
沥青 49207374 0.00620022 20190924
菜粕 48266258 0.00608164 20190924
棕榈油 31615548 0.00398362 20190924
PP 29374826 0.00370128 20190924
豆一 22368376 0.00281846 20190924
玉米 13861567 0.00174658 20190924
沪锡 7485903 0.000943238 20190924
淀粉 4811234 0.000606225 20190924
棉纱 3627240 0.000457039 20190924
尿素 2290674 0.000288629 20190924
鸡蛋 2035406 0.000256465 20190924
粳米 1999282 0.000251913 20190924
油菜籽 533482 6.72197e-05 20190924
晚籼稻 0 0 20190924
强麦 0 0 20190924
沪铅 89914 1.13293e-05 20190924
豆二 379200 4.77799e-05 20190924
硅铁 5025872 0.000633269 20190924
红枣 8521668 0.00107375 20190924
锰硅 9472832 0.00119359 20190924
郑煤 9888272 0.00124594 20190924
乙二醇 18324242 0.00230889 20190924
PVC 19454830 0.00245135 20190924
玻璃 27076226 0.00341166 20190924
热卷 28832929 0.003633 20190924
沪银 375076371 0.0472603 20190924
沪镍 411622624 0.0518652 20190924
沪金 719371823 0.0906422 20190924
long_short_df
name value ratio date
空 6303252093 0.794222 20190924
多 1633136803 0.205778 20190924
"""
date = str(date)
date = date[:4] + "-" + date[4:6] + "-" + date[6:]
print(date)
payload_id = {"date": date}
r = requests.post(url, data=payload_id)
print(url)
print("数据获取成功")
json_data = r.json()
symbol_name = []
for item in json_data["data"]["datas1"]:
symbol_name.append(item["name"])
symbol_value = []
for item in json_data["data"]["datas1"]:
symbol_value.append(item["value"])
long_short_name = []
for item in json_data["data"]["datas2"]:
long_short_name.append(item["name"])
long_short_value = []
for item in json_data["data"]["datas2"]:
long_short_value.append(item["value"])
symbol_df = pd.DataFrame([symbol_name, symbol_value]).T
long_short_df = pd.DataFrame([long_short_name, long_short_value]).T
symbol_df.columns = ["name", "value"]
symbol_df["ratio"] = symbol_df["value"] / symbol_df["value"].sum()
symbol_df["date"] = date
long_short_df.columns = ["name", "value"]
long_short_df["ratio"] = long_short_df["value"] / long_short_df["value"].sum()
long_short_df["date"] = date
return symbol_df, long_short_df
def get_qhkc_fund_position_change(
date: datetime.datetime.date = "20190924",
url: AnyStr = QHKC_FUND_POSITION_CHANGE_URL,
):
"""
奇货可查-资金-净持仓变化分布
可获取数据的时间段为:"2016-10-10:2019-09-30"
:param url: 网址
:param date: 中文名称
:return: pd.DataFrame
symbol_df
name value ratio date
IC 1552535406 0.195622 20190924
IF 536644080 0.0676182 20190924
橡胶 536439921 0.0675924 20190924
沪铜 460851099 0.0580681 20190924
豆粕 401005794 0.0505275 20190924
螺纹钢 329159263 0.0414747 20190924
焦炭 325646968 0.0410321 20190924
燃料油 313246789 0.0394697 20190924
IH 245556750 0.0309406 20190924
棉花 214538541 0.0270323 20190924
PTA 206340552 0.0259993 20190924
白糖 139901255 0.0176278 20190924
豆油 133664010 0.0168419 20190924
沪铝 109789864 0.0138337 20190924
沪锌 107440906 0.0135378 20190924
纸浆 95517374 0.0120354 20190924
苹果 81058733 0.0102136 20190924
塑料 63665245 0.00802194 20190924
菜油 61544593 0.00775474 20190924
铁矿石 60751108 0.00765475 20190924
焦煤 58327920 0.00734943 20190924
甲醇 52148752 0.00657084 20190924
沥青 49207374 0.00620022 20190924
菜粕 48266258 0.00608164 20190924
棕榈油 31615548 0.00398362 20190924
PP 29374826 0.00370128 20190924
豆一 22368376 0.00281846 20190924
玉米 13861567 0.00174658 20190924
沪锡 7485903 0.000943238 20190924
淀粉 4811234 0.000606225 20190924
棉纱 3627240 0.000457039 20190924
尿素 2290674 0.000288629 20190924
鸡蛋 2035406 0.000256465 20190924
粳米 1999282 0.000251913 20190924
油菜籽 533482 6.72197e-05 20190924
晚籼稻 0 0 20190924
强麦 0 0 20190924
沪铅 89914 1.13293e-05 20190924
豆二 379200 4.77799e-05 20190924
硅铁 5025872 0.000633269 20190924
红枣 8521668 0.00107375 20190924
锰硅 9472832 0.00119359 20190924
郑煤 9888272 0.00124594 20190924
乙二醇 18324242 0.00230889 20190924
PVC 19454830 0.00245135 20190924
玻璃 27076226 0.00341166 20190924
热卷 28832929 0.003633 20190924
沪银 375076371 0.0472603 20190924
沪镍 411622624 0.0518652 20190924
沪金 719371823 0.0906422 20190924
long_short_df
name value ratio date
空 6303252093 0.794222 20190924
多 1633136803 0.205778 20190924
"""
date = str(date)
date = date[:4] + "-" + date[4:6] + "-" + date[6:]
print(date)
payload_id = {"date": date}
r = requests.post(url, data=payload_id)
print(url)
print("数据获取成功")
json_data = r.json()
symbol_name = []
for item in json_data["data"]["datas1"]:
symbol_name.append(item["name"])
symbol_value = []
for item in json_data["data"]["datas1"]:
symbol_value.append(item["value"])
long_short_name = []
for item in json_data["data"]["datas2"]:
long_short_name.append(item["name"])
long_short_value = []
for item in json_data["data"]["datas2"]:
long_short_value.append(item["value"])
symbol_df = pd.DataFrame([symbol_name, symbol_value]).T
long_short_df = pd.DataFrame([long_short_name, long_short_value]).T
symbol_df.columns = ["name", "value"]
symbol_df["ratio"] = symbol_df["value"] / symbol_df["value"].sum()
symbol_df["date"] = date
long_short_df.columns = ["name", "value"]
long_short_df["ratio"] = long_short_df["value"] / long_short_df["value"].sum()
long_short_df["date"] = date
return symbol_df, long_short_df
def get_qhkc_fund_money_change(
date: datetime.datetime.date = "20190924", url: AnyStr = QHKC_FUND_DEAL_URL
):
"""
奇货可查-资金-成交额分布
可获取数据的时间段为:"2016-10-10:2019-09-30"
:param url: 网址
:param date: 中文名称
:return: 成交额分布
:rtype: pandas.DataFrame
name value ratio date
沪镍 2.292e+10 0.145963 2019-09-25
沪银 1.22788e+10 0.0781956 2019-09-25
沪金 11196166005 0.0713011 2019-09-25
IC 1.10958e+10 0.0706619 2019-09-25
螺纹钢 1.02918e+10 0.0655416 2019-09-25
IF 9134893794 0.0581742 2019-09-25
铁矿石 7991427128 0.0508922 2019-09-25
原油 7695016910 0.0490045 2019-09-25
焦炭 5936589656 0.0378063 2019-09-25
甲醇 4.00966e+09 0.0255349 2019-09-25
沪铜 3806033147 0.0242381 2019-09-25
乙二醇 3.64376e+09 0.0232047 2019-09-25
橡胶 3286445958 0.0209292 2019-09-25
燃料油 3227355810 0.0205529 2019-09-25
豆粕 3124163112 0.0198958 2019-09-25
苹果 3.08134e+09 0.0196231 2019-09-25
沪锌 3076039116 0.0195893 2019-09-25
PTA 2.93901e+09 0.0187167 2019-09-25
IH 2578970688 0.0164238 2019-09-25
豆油 2371404714 0.0151019 2019-09-25
沥青 2.17662e+09 0.0138615 2019-09-25
白糖 1814626125 0.0115562 2019-09-25
棕榈油 1687834936 0.0107487 2019-09-25
菜粕 1.58244e+09 0.0100775 2019-09-25
焦煤 1.52553e+09 0.00971509 2019-09-25
PP 1.51981e+09 0.0096787 2019-09-25
塑料 1468988065 0.00935503 2019-09-25
沪铝 1.35968e+09 0.00865893 2019-09-25
不锈钢 1213656556 0.00772899 2019-09-25
棉花 1186243285 0.00755441 2019-09-25
鸡蛋 1175239681 0.00748433 2019-09-25
热卷 1.12293e+09 0.00715118 2019-09-25
纸浆 9.23876e+08 0.00588356 2019-09-25
沪铅 659297524 0.00419864 2019-09-25
菜油 587372274 0.00374059 2019-09-25
郑煤 5.82494e+08 0.00370953 2019-09-25
红枣 499089640 0.00317838 2019-09-25
玉米 458548474 0.0029202 2019-09-25
PVC 334434410 0.00212979 2019-09-25
玻璃 333819628 0.00212588 2019-09-25
沪锡 2.02186e+08 0.00128759 2019-09-25
豆二 185554169 0.00118167 2019-09-25
豆一 184729205 0.00117642 2019-09-25
硅铁 1.54719e+08 0.000985305 2019-09-25
淀粉 112331976 0.000715369 2019-09-25
锰硅 1.10791e+08 0.000705557 2019-09-25
尿素 78648750 0.000500862 2019-09-25
棉纱 5.17932e+07 0.000329837 2019-09-25
NR 34806750 0.000221661 2019-09-25
粳米 7375683 4.69709e-05 2019-09-25
油菜籽 2680922 1.7073e-05 2019-09-25
纤维板 2286460 1.4561e-05 2019-09-25
胶合板 831250 5.29369e-06 2019-09-25
强麦 472400 3.00841e-06 2019-09-25
晚籼稻 159318 1.01459e-06 2019-09-25
线材 90608 5.77023e-07 2019-09-25
粳稻 0 0 2019-09-25
普麦 0 0 2019-09-25
稻谷 0 0 2019-09-25
"""
date = str(date)
date = date[:4] + "-" + date[4:6] + "-" + date[6:]
print(date)
payload_id = {"date": date}
r = requests.post(url, data=payload_id)
print(url)
print("数据获取成功")
json_data = r.json()
symbol_name = []
for item in json_data["data"]["datas"]:
symbol_name.append(item["name"])
symbol_value = []
for item in json_data["data"]["datas"]:
symbol_value.append(item["value"])
symbol_df = pd.DataFrame([symbol_name, symbol_value]).T
symbol_df.columns = ["name", "value"]
symbol_df["ratio"] = symbol_df["value"] / symbol_df["value"].sum()
symbol_df["date"] = date
return symbol_df
if __name__ == "__main__":
# df1, df2 = get_qhkc_fund_bs(20190925)
# print(df1)
# print(df2)
# df1, df2 = get_qhkc_fund_position(20190925)
# print(df1)
# print(df2)
# df1, df2 = get_qhkc_fund_position_change(20190925)
# print(df1)
# print(df2)
get_qhkc_fund_money_change_df = get_qhkc_fund_money_change(20211208)
print(get_qhkc_fund_money_change_df)
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