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backend/.venv/lib/python3.9/site-packages/akshare/energy/__init__.py
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#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2019/12/17 16:54
Desc:
"""
backend/.venv/lib/python3.9/site-packages/akshare/energy/energy_carbon.py
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#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2024/6/25 15:00
Desc: 碳排放交易
北京市碳排放权电子交易平台-北京市碳排放权公开交易行情
https://www.bjets.com.cn/article/jyxx/
深圳碳排放交易所-国内碳情
http://www.cerx.cn/dailynewsCN/index.htm
深圳碳排放交易所-国际碳情
http://www.cerx.cn/dailynewsOuter/index.htm
湖北碳排放权交易中心-现货交易数据-配额-每日概况
http://www.cerx.cn/dailynewsOuter/index.htm
广州碳排放权交易中心-行情信息
http://www.cnemission.com/article/hqxx/
"""
from io import StringIO
import pandas as pd
import requests
from bs4 import BeautifulSoup
from tqdm import tqdm
from akshare.utils import demjson
from akshare.utils.cons import headers
def energy_carbon_domestic(symbol: str = "湖北") -> pd.DataFrame:
"""
碳交易网-行情信息
http://www.tanjiaoyi.com/
:param symbol: choice of {'湖北', '上海', '北京', '重庆', '广东', '天津', '深圳', '福建'}
:type symbol: str
:return: 行情信息
:rtype: pandas.DataFrame
"""
url = "http://k.tanjiaoyi.com:8080/KDataController/getHouseDatasInAverage.do"
params = {
"lcnK": "53f75bfcefff58e4046ccfa42171636c",
"brand": "TAN",
}
r = requests.get(url, params=params)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("(") + 1 : -1])
temp_df = pd.DataFrame(data_json[symbol])
temp_df.columns = [
"成交价",
"_",
"成交量",
"地点",
"成交额",
"日期",
"_",
]
temp_df = temp_df[
[
"日期",
"成交价",
"成交量",
"成交额",
"地点",
]
]
temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
temp_df["成交价"] = pd.to_numeric(temp_df["成交价"], errors="coerce")
temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
return temp_df
def energy_carbon_bj() -> pd.DataFrame:
"""
北京市碳排放权电子交易平台-北京市碳排放权公开交易行情
https://www.bjets.com.cn/article/jyxx/
:return: 北京市碳排放权公开交易行情
:rtype: pandas.DataFrame
"""
url = "https://www.bjets.com.cn/article/jyxx/"
r = requests.get(url, verify=False, headers=headers)
soup = BeautifulSoup(r.text, features="lxml")
total_page = (
soup.find("table")
.find("script")
.string.split("=")[-1]
.strip()
.strip(";")
.strip('"')
)
temp_df = pd.DataFrame()
for i in tqdm(
range(1, int(total_page) + 1),
desc="Please wait for a moment",
leave=False,
):
if i == 1:
i = ""
url = f"https://www.bjets.com.cn/article/jyxx/?{i}"
r = requests.get(url, verify=False, headers=headers)
r.encoding = "utf-8"
df = pd.read_html(StringIO(r.text))[0]
temp_df = pd.concat(objs=[temp_df, df], ignore_index=True)
temp_df.columns = ["日期", "成交量", "成交均价", "成交额"]
temp_df["成交单位"] = (
temp_df["成交额"]
.str.split("(", expand=True)
.iloc[:, 1]
.str.split("）", expand=True)
.iloc[:, 0]
.str.split(")", expand=True)
.iloc[:, 0]
)
temp_df["成交额"] = (
temp_df["成交额"]
.str.split("(", expand=True)
.iloc[:, 0]
.str.split("（", expand=True)
.iloc[:, 0]
)
temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
temp_df["成交均价"] = pd.to_numeric(temp_df["成交均价"], errors="coerce")
temp_df["成交额"] = temp_df["成交额"].str.replace(",", "")
temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
temp_df.sort_values(by="日期", inplace=True)
temp_df.reset_index(inplace=True, drop=True)
return temp_df
def energy_carbon_sz() -> pd.DataFrame:
"""
深圳碳排放交易所-国内碳情
http://www.cerx.cn/dailynewsCN/index.htm
:return: 国内碳情每日行情数据
:rtype: pandas.DataFrame
"""
url = "http://www.cerx.cn/dailynewsCN/index.htm"
r = requests.get(url, headers=headers)
soup = BeautifulSoup(r.text, features="lxml")
page_num = int(soup.find(attrs={"class": "pagebar"}).find_all("option")[-1].text)
big_df = pd.read_html(StringIO(r.text), header=0)[0]
for page in tqdm(
range(2, page_num + 1), desc="Please wait for a moment", leave=False
):
url = f"http://www.cerx.cn/dailynewsCN/index_{page}.htm"
r = requests.get(url, headers=headers)
temp_df = pd.read_html(StringIO(r.text), header=0)[0]
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
big_df["交易日期"] = pd.to_datetime(big_df["交易日期"], errors="coerce").dt.date
big_df["开盘价"] = pd.to_numeric(big_df["开盘价"], errors="coerce")
big_df["最高价"] = pd.to_numeric(big_df["最高价"], errors="coerce")
big_df["最低价"] = pd.to_numeric(big_df["最低价"], errors="coerce")
big_df["成交均价"] = pd.to_numeric(big_df["成交均价"], errors="coerce")
big_df["收盘价"] = pd.to_numeric(big_df["收盘价"], errors="coerce")
big_df["成交量"] = pd.to_numeric(big_df["成交量"], errors="coerce")
big_df["成交额"] = pd.to_numeric(big_df["成交额"], errors="coerce")
big_df.sort_values(by="交易日期", inplace=True)
big_df.reset_index(inplace=True, drop=True)
return big_df
def energy_carbon_eu() -> pd.DataFrame:
"""
深圳碳排放交易所-国际碳情
http://www.cerx.cn/dailynewsOuter/index.htm
:return: 国际碳情每日行情数据
:rtype: pandas.DataFrame
"""
url = "http://www.cerx.cn/dailynewsOuter/index.htm"
r = requests.get(url, headers=headers)
soup = BeautifulSoup(r.text, features="lxml")
page_num = int(soup.find(attrs={"class": "pagebar"}).find_all("option")[-1].text)
big_df = pd.read_html(StringIO(r.text), header=0)[0]
for page in tqdm(
range(2, page_num + 1), desc="Please wait for a moment", leave=False
):
url = f"http://www.cerx.cn/dailynewsOuter/index_{page}.htm"
r = requests.get(url)
temp_df = pd.read_html(StringIO(r.text), header=0)[0]
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
big_df["交易日期"] = pd.to_datetime(big_df["交易日期"], errors="coerce").dt.date
big_df["开盘价"] = pd.to_numeric(big_df["开盘价"], errors="coerce")
big_df["最高价"] = pd.to_numeric(big_df["最高价"], errors="coerce")
big_df["最低价"] = pd.to_numeric(big_df["最低价"], errors="coerce")
big_df["成交均价"] = pd.to_numeric(big_df["成交均价"], errors="coerce")
big_df["收盘价"] = pd.to_numeric(big_df["收盘价"], errors="coerce")
big_df["成交量"] = pd.to_numeric(big_df["成交量"], errors="coerce")
big_df["成交额"] = pd.to_numeric(big_df["成交额"], errors="coerce")
big_df.sort_values(by="交易日期", inplace=True)
big_df.reset_index(inplace=True, drop=True)
return big_df
def energy_carbon_hb() -> pd.DataFrame:
"""
湖北碳排放权交易中心-现货交易数据-配额-每日概况
http://www.hbets.cn/list/13.html?page=42
:return: 现货交易数据-配额-每日概况行情数据
:rtype: pandas.DataFrame
"""
url = "https://www.hbets.cn/"
r = requests.get(url, headers=headers)
soup = BeautifulSoup(r.text, features="lxml")
data_text = (
soup.find(name="div", attrs={"class": "threeLeft"}).find_all("script")[1].text
)
start_pos = data_text.find("cjj = '[") + 7 # 找到 JSON 数组开始的位置
end_pos = data_text.rfind("cjj =") - 31 # 找到 JSON 数组结束的位置
data_json = demjson.decode(data_text[start_pos:end_pos])
temp_df = pd.DataFrame.from_dict(data_json)
temp_df.rename(
columns={
"riqi": "日期",
"cjj": "成交价",
"cjl": "成交量",
"zx": "最新",
"zd": "涨跌",
},
inplace=True,
)
temp_df = temp_df[
[
"日期",
"成交价",
"成交量",
"最新",
"涨跌",
]
]
temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
temp_df["成交价"] = pd.to_numeric(temp_df["成交价"], errors="coerce")
temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
temp_df["最新"] = pd.to_numeric(temp_df["最新"], errors="coerce")
temp_df["涨跌"] = pd.to_numeric(temp_df["涨跌"], errors="coerce")
return temp_df
def energy_carbon_gz() -> pd.DataFrame:
"""
广州碳排放权交易中心-行情信息
http://www.cnemission.com/article/hqxx/
:return: 行情信息数据
:rtype: pandas.DataFrame
"""
url = "http://ets.cnemission.com/carbon/portalIndex/markethistory"
params = {
"Top": "1",
"beginTime": "2010-01-01",
"endTime": "2030-09-12",
}
r = requests.get(url, params=params)
temp_df = pd.read_html(StringIO(r.text), header=0)[1]
temp_df.columns = [
"日期",
"品种",
"开盘价",
"收盘价",
"最高价",
"最低价",
"涨跌",
"涨跌幅",
"成交数量",
"成交金额",
]
temp_df["日期"] = pd.to_datetime(
temp_df["日期"], format="%Y%m%d", errors="coerce"
).dt.date
temp_df["开盘价"] = pd.to_numeric(temp_df["开盘价"], errors="coerce")
temp_df["收盘价"] = pd.to_numeric(temp_df["收盘价"], errors="coerce")
temp_df["最高价"] = pd.to_numeric(temp_df["最高价"], errors="coerce")
temp_df["最低价"] = pd.to_numeric(temp_df["最低价"], errors="coerce")
temp_df["涨跌"] = pd.to_numeric(temp_df["涨跌"], errors="coerce")
temp_df["涨跌幅"] = temp_df["涨跌幅"].str.strip("%")
temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
temp_df["成交数量"] = pd.to_numeric(temp_df["成交数量"], errors="coerce")
temp_df["成交金额"] = pd.to_numeric(temp_df["成交金额"], errors="coerce")
temp_df.sort_values(by="日期", inplace=True)
temp_df.reset_index(inplace=True, drop=True)
return temp_df
if __name__ == "__main__":
energy_carbon_domestic_df = energy_carbon_domestic(symbol="湖北")
print(energy_carbon_domestic_df)
energy_carbon_domestic_df = energy_carbon_domestic(symbol="深圳")
print(energy_carbon_domestic_df)
energy_carbon_bj_df = energy_carbon_bj()
print(energy_carbon_bj_df)
energy_carbon_sz_df = energy_carbon_sz()
print(energy_carbon_sz_df)
energy_carbon_eu_df = energy_carbon_eu()
print(energy_carbon_eu_df)
energy_carbon_hb_df = energy_carbon_hb()
print(energy_carbon_hb_df)
energy_carbon_gz_df = energy_carbon_gz()
print(energy_carbon_gz_df)
backend/.venv/lib/python3.9/site-packages/akshare/energy/energy_oil_em.py
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#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2024/1/20 23:00
Desc: 东方财富-数据中心-中国油价
https://data.eastmoney.com/cjsj/oil_default.html
"""
import pandas as pd
import requests
def energy_oil_hist() -> pd.DataFrame:
"""
汽柴油历史调价信息
https://data.eastmoney.com/cjsj/oil_default.html
:return: 汽柴油历史调价信息
:rtype: pandas.DataFrame
"""
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
params = {
"reportName": "RPTA_WEB_YJ_BD",
"columns": "ALL",
"sortColumns": "dim_date",
"sortTypes": "-1",
"token": "894050c76af8597a853f5b408b759f5d",
"pageNumber": "1",
"pageSize": "1000",
"source": "WEB",
"p": "1",
"pageNo": "1",
"pageNum": "1",
}
r = requests.get(url, params=params)
data_json = r.json()
temp_df = pd.DataFrame(data_json["result"]["data"])
temp_df.columns = ["调整日期", "汽油价格", "柴油价格", "汽油涨跌", "柴油涨跌"]
temp_df["调整日期"] = pd.to_datetime(temp_df["调整日期"], errors="coerce").dt.date
temp_df["汽油价格"] = pd.to_numeric(temp_df["汽油价格"], errors="coerce")
temp_df["柴油价格"] = pd.to_numeric(temp_df["柴油价格"], errors="coerce")
temp_df["汽油涨跌"] = pd.to_numeric(temp_df["汽油涨跌"], errors="coerce")
temp_df["柴油涨跌"] = pd.to_numeric(temp_df["柴油涨跌"], errors="coerce")
temp_df.sort_values(by=["调整日期"], inplace=True)
temp_df.reset_index(inplace=True, drop=True)
return temp_df
def energy_oil_detail(date: str = "20220517") -> pd.DataFrame:
"""
全国各地区的汽油和柴油油价
https://data.eastmoney.com/cjsj/oil_default.html
:param date: 可以调用 ak.energy_oil_hist() 得到可以获取油价的调整时间
:type date: str
:return: oil price at specific date
:rtype: pandas.DataFrame
"""
date = "-".join([date[:4], date[4:6], date[6:]])
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
params = {
"reportName": "RPTA_WEB_YJ_JH",
"columns": "ALL",
"filter": f"(dim_date='{date}')",
"sortColumns": "cityname",
"sortTypes": "1",
"token": "894050c76af8597a853f5b408b759f5d",
"pageNumber": "1",
"pageSize": "1000",
"source": "WEB",
}
r = requests.get(url, params=params)
data_json = r.json()
temp_df = pd.DataFrame(data_json["result"]["data"]).iloc[:, 1:]
temp_df.columns = [
"日期",
"地区",
"V_0",
"V_92",
"V_95",
"V_89",
"ZDE_0",
"ZDE_92",
"ZDE_95",
"ZDE_89",
"QE_0",
"QE_92",
"QE_95",
"QE_89",
"首字母",
]
del temp_df["首字母"]
temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
temp_df["V_0"] = pd.to_numeric(temp_df["V_0"], errors="coerce")
temp_df["V_92"] = pd.to_numeric(temp_df["V_92"], errors="coerce")
temp_df["V_95"] = pd.to_numeric(temp_df["V_95"], errors="coerce")
temp_df["V_89"] = pd.to_numeric(temp_df["V_89"], errors="coerce")
temp_df["ZDE_0"] = pd.to_numeric(temp_df["ZDE_0"], errors="coerce")
temp_df["ZDE_92"] = pd.to_numeric(temp_df["ZDE_92"], errors="coerce")
temp_df["ZDE_95"] = pd.to_numeric(temp_df["ZDE_95"], errors="coerce")
temp_df["ZDE_89"] = pd.to_numeric(temp_df["ZDE_89"], errors="coerce")
temp_df["QE_0"] = pd.to_numeric(temp_df["QE_0"], errors="coerce")
temp_df["QE_92"] = pd.to_numeric(temp_df["QE_92"], errors="coerce")
temp_df["QE_95"] = pd.to_numeric(temp_df["QE_95"], errors="coerce")
temp_df["QE_89"] = pd.to_numeric(temp_df["QE_89"], errors="coerce")
return temp_df
if __name__ == "__main__":
energy_oil_hist_df = energy_oil_hist()
print(energy_oil_hist_df)
energy_oil_detail_df = energy_oil_detail(date="20240118")
print(energy_oil_detail_df)