def stock_em_gpzy_profile() -> pd.DataFrame:
"""
东方财富网-数据中心-特色数据-股权质押-股权质押市场概况
http://data.eastmoney.com/gpzy/marketProfile.aspx
:return: 股权质押市场概况
:rtype: pandas.DataFrame
"""
url = "http://dcfm.eastmoney.com/EM_MutiSvcExpandInterface/api/js/get"
params = {
"type": "ZD_SUM",
"token": "70f12f2f4f091e459a279469fe49eca5",
"cmd": "",
"st": "tdate",
"sr": "-1",
"p": "1",
"ps": "5000",
"js": "var zvxnZOnT={pages:(tp),data:(x),font:(font)}",
"rt": "52583914",
}
temp_df = pd.DataFrame()
res = requests.get(url, params=params)
data_text = res.text
data_json = demjson.decode(data_text[data_text.find("={") + 1 :])
map_dict = dict(
zip(
pd.DataFrame(data_json["font"]["FontMapping"])["code"],
pd.DataFrame(data_json["font"]["FontMapping"])["value"],
)
)
for key, value in map_dict.items():
data_text = data_text.replace(key, str(value))
data_json = demjson.decode(data_text[data_text.find("={") + 1 :])
temp_df = temp_df.append(pd.DataFrame(data_json["data"]), ignore_index=True)
temp_df.columns = [
"交易日期",
"sc_zsz",
"平均质押比例(%)",
"涨跌幅",
"A股质押总比例(%)",
"质押公司数量",
"质押笔数",
"质押总股数(股)",
"质押总市值(元)",
"沪深300指数",
]
temp_df = temp_df[
[
"交易日期",
"平均质押比例(%)",
"涨跌幅",
"A股质押总比例(%)",
"质押公司数量",
"质押笔数",
"质押总股数(股)",
"质押总市值(元)",
"沪深300指数",
]
]
temp_df["交易日期"] = pd.to_datetime(temp_df["交易日期"])
return temp_df
def _get_tx_start_year(symbol: str = "sh000919") -> pd.DataFrame:
"""
腾讯证券-获取所有股票数据的第一天, 注意这个数据是腾讯证券的历史数据第一天
http://gu.qq.com/sh000919/zs
:param symbol: 带市场标识的股票代码
:type symbol: str
:return: 开始日期
:rtype: pandas.DataFrame
"""
url = "http://web.ifzq.gtimg.cn/other/klineweb/klineWeb/weekTrends"
params = {
"code": symbol,
"type": "qfq",
"_var": "trend_qfq",
"r": "0.3506048543943414",
}
r = requests.get(url, params=params)
data_text = r.text
if not demjson.decode(data_text[data_text.find("={") + 1 :])["data"]:
url = "https://proxy.finance.qq.com/ifzqgtimg/appstock/app/newfqkline/get"
params = {
"_var": "kline_dayqfq",
"param": f"{symbol},day,,,320,qfq",
"r": "0.751892490072597",
}
r = requests.get(url, params=params)
data_text = r.text
start_date = demjson.decode(data_text[data_text.find("={") + 1 :])["data"][
symbol
]["day"][0][0]
return start_date
start_date = demjson.decode(data_text[data_text.find("={") + 1 :])["data"][0][0]
return start_date
def stock_em_gpzy_industry_data() -> pd.DataFrame:
"""
东方财富网-数据中心-特色数据-股权质押-上市公司质押比例-行业数据
http://data.eastmoney.com/gpzy/industryData.aspx
:return: pandas.DataFrame
"""
url = "http://dcfm.eastmoney.com/EM_MutiSvcExpandInterface/api/js/get"
page_num = _get_page_num_gpzy_industry_data()
temp_df = pd.DataFrame()
for page in range(1, page_num + 1):
print(f"一共{page_num}页, 正在下载第{page}页")
params = {
"type": "ZD_HY_SUM",
"token": "70f12f2f4f091e459a279469fe49eca5",
"cmd": "",
"st": "amtshareratio_pj",
"sr": "-1",
"p": str(page),
"ps": "5000",
"js": "var SIqThurI={pages:(tp),data:(x),font:(font)}",
"rt": "52584617",
}
res = requests.get(url, params=params)
data_text = res.text
data_json = demjson.decode(data_text[data_text.find("={") + 1:])
map_dict = dict(
zip(
pd.DataFrame(data_json["font"]["FontMapping"])["code"],
pd.DataFrame(data_json["font"]["FontMapping"])["value"],
))
for key, value in map_dict.items():
data_text = data_text.replace(key, str(value))
data_json = demjson.decode(data_text[data_text.find("={") + 1:])
temp_df = temp_df.append(pd.DataFrame(data_json["data"]),
ignore_index=True)
temp_df.columns = [
"统计时间",
"-",
"行业",
"平均质押比例(%)",
"公司家数",
"质押总笔数",
"质押总股本",
"最新质押市值",
]
temp_df = temp_df[[
"统计时间", "行业", "平均质押比例(%)", "公司家数", "质押总笔数", "质押总股本", "最新质押市值"
]]
temp_df["统计时间"] = pd.to_datetime(temp_df["统计时间"])
return temp_df
def macro_australia_retail_rate_monthly() -> pd.DataFrame:
"""
东方财富-经济数据-澳大利亚-零售销售月率
http://data.eastmoney.com/cjsj/foreign_5_0.html
:return: 零售销售月率
:rtype: pandas.DataFrame
"""
url = "http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx"
params = {
"type": "GJZB",
"sty": "HKZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "2000",
"mkt": "5",
"stat": "0",
"pageNo": "1",
"pageNum": "1",
"_": "1625474966006",
}
r = requests.get(url, params=params)
data_text = r.text
data_json = demjson.decode(data_text[1:-1])
temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]])
temp_df.columns = [
"时间",
"前值",
"现值",
"发布日期",
]
temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date
temp_df["前值"] = pd.to_numeric(temp_df["前值"])
temp_df["现值"] = pd.to_numeric(temp_df["现值"])
temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date
return temp_df
def macro_china_hk_rate_of_unemployment() -> pd.DataFrame:
"""
东方财富-经济数据一览-中国香港-失业率
https://data.eastmoney.com/cjsj/foreign_8_2.html
:return: 失业率
:rtype: pandas.DataFrame
"""
url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx"
params = {
"type": "GJZB",
"sty": "HKZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "2000",
"mkt": "8",
"stat": "2",
"pageNo": "1",
"pageNum": "1",
"_": "1621332091873",
}
r = requests.get(url, params=params)
data_text = r.text
data_json = demjson.decode(data_text[1:-1])
temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]])
temp_df.columns = [
"时间",
"前值",
"现值",
"发布日期",
]
temp_df['前值'] = pd.to_numeric(temp_df['前值'])
temp_df['现值'] = pd.to_numeric(temp_df['现值'])
temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date
temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date
return temp_df
def match_main_contract(symbol: str = "shfe") -> pd.DataFrame:
"""
指定交易所的所有可以提供数据的合约
https://finance.sina.com.cn/futuremarket/index.shtml
:param symbol: choice of {"dce", "czce", "shfe", "cffex"}
:type symbol: str
:return: 指定交易所的所有可以提供数据的合约
:rtype: pandas.DataFrame
"""
subscribe_list = []
exchange_symbol_list = zh_subscribe_exchange_symbol(
symbol).iloc[:, 1].tolist()
for item in exchange_symbol_list:
zh_match_main_contract_payload.update({"node": item})
res = requests.get(zh_match_main_contract_url,
params=zh_match_main_contract_payload)
data_json = demjson.decode(res.text)
data_df = pd.DataFrame(data_json)
try:
main_contract = data_df[
data_df['name'].str.contains("连续")
& data_df['symbol'].str.extract(
r'([\w])(\d)').iloc[:, 1].str.contains("0")].iloc[0, :3]
subscribe_list.append(main_contract)
except:
# print(item, "无主力连续合约")
continue
# print("主力连续合约获取成功")
temp_df = pd.DataFrame(subscribe_list)
return temp_df
def _get_page_num_dxsyl(market: str = "上海主板") -> int:
"""
东方财富网-数据中心-新股数据-打新收益率-总页数
http://data.eastmoney.com/xg/xg/dxsyl.html
:param market: choice of {"上海主板", "创业板", "深圳主板"}
:type market: str
:return: 总页数
:rtype: int
"""
market_map = {"上海主板": "2", "创业板": "3", "深圳主板": "4"}
url = "http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx"
params = {
"type": "NS",
"sty": "NSDXSYL",
"st": "16",
"sr": "-1",
"p": "1",
"ps": "50",
"js": "var oyfyNYmO={pages:(pc),data:[(x)]}",
"stat": market_map[market],
"rt": "52898446",
}
res = requests.get(url, params=params)
data_json = demjson.decode(res.text[res.text.find("={") + 1:])
return data_json["pages"]
def sw_index_representation_spot() -> pd.DataFrame:
"""
申万-市场表征实时行情数据
http://www.swsindex.com/idx0120.aspx?columnid=8831
:return: 市场表征实时行情数据
:rtype: pandas.DataFrame
"""
url = "http://www.swsindex.com/handler.aspx"
params = {
"tablename": "swzs",
"key": "L1",
"p": "1",
"where": "L1 in('801001','801002','801003','801005','801300','801901','801903','801905','801250','801260','801270','801280','802613')",
"orderby": "",
"fieldlist": "L1,L2,L3,L4,L5,L6,L7,L8,L11",
"pagecount": "9",
"timed": "1632300641756",
}
r = requests.get(url, params=params)
data_json = demjson.decode(r.text)
temp_df = pd.DataFrame(data_json["root"])
temp_df.columns = ["指数代码", "指数名称", "昨收盘", "今开盘", "成交额", "最高价", "最低价", "最新价", "成交量"]
temp_df["昨收盘"] = pd.to_numeric(temp_df["昨收盘"])
temp_df["今开盘"] = pd.to_numeric(temp_df["今开盘"])
temp_df["成交额"] = pd.to_numeric(temp_df["成交额"])
temp_df["最高价"] = pd.to_numeric(temp_df["最高价"])
temp_df["最低价"] = pd.to_numeric(temp_df["最低价"])
temp_df["最新价"] = pd.to_numeric(temp_df["最新价"])
temp_df["成交量"] = pd.to_numeric(temp_df["成交量"])
return temp_df
def macro_germany_cpi_yearly() -> pd.DataFrame:
"""
消费者物价指数年率终值
http://data.eastmoney.com/cjsj/foreign_1_2.html
:return: 消费者物价指数年率终值
:rtype: pandas.DataFrame
"""
url = "http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx"
params = {
"type": "GJZB",
"sty": "HKZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "2000",
"mkt": "1",
"stat": "2",
"pageNo": "1",
"pageNum": "1",
"_": "1625474966006",
}
r = requests.get(url, params=params)
data_text = r.text
data_json = demjson.decode(data_text[1:-1])
temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]])
temp_df.columns = [
"时间",
"前值",
"现值",
"发布日期",
]
temp_df["前值"] = pd.to_numeric(temp_df["前值"])
temp_df["现值"] = pd.to_numeric(temp_df["现值"])
return temp_df
def macro_swiss_gbd_bank_rate():
"""
东方财富-经济数据-瑞士-央行公布利率决议
http://data.eastmoney.com/cjsj/foreign_2_5.html
:return: 央行公布利率决议
:rtype: pandas.DataFrame
"""
url = "http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx"
params = {
"type": "GJZB",
"sty": "HKZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "2000",
"mkt": "2",
"stat": "5",
'pageNo': '1',
'pageNum': '1',
"_": "1625474966006",
}
r = requests.get(url, params=params)
data_text = r.text
data_json = demjson.decode(data_text[1:-1])
temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]])
temp_df.columns = [
"时间",
"前值",
"现值",
"发布日期",
]
temp_df["前值"] = pd.to_numeric(temp_df["前值"])
temp_df["现值"] = pd.to_numeric(temp_df["现值"])
return temp_df
def zh_subscribe_exchange_symbol(symbol: str = "dce") -> dict:
"""
交易所具体的可交易品种
http://vip.stock.finance.sina.com.cn/quotes_service/view/qihuohangqing.html#titlePos_1
:param symbol: choice of {'czce', 'dce', 'shfe', 'cffex'}
:type symbol: str
:return: 交易所具体的可交易品种
:rtype: dict
"""
r = requests.get(zh_subscribe_exchange_symbol_url)
r.encoding = "gbk"
data_text = r.text
data_json = demjson.decode(
data_text[data_text.find("{"):data_text.find("};") + 1])
if symbol == "czce":
data_json["czce"].remove("郑州商品交易所")
return pd.DataFrame(data_json["czce"])
if symbol == "dce":
data_json["dce"].remove("大连商品交易所")
return pd.DataFrame(data_json["dce"])
if symbol == "shfe":
data_json["shfe"].remove("上海期货交易所")
return pd.DataFrame(data_json["shfe"])
if symbol == "cffex":
data_json["cffex"].remove("中国金融期货交易所")
return pd.DataFrame(data_json["cffex"])
def fund_em_new_found() -> pd.DataFrame:
"""
基金数据-新发基金-新成立基金
http://fund.eastmoney.com/data/xinfound.html
:return: 新成立基金
:rtype: pandas.DataFrame
"""
url = "http://fund.eastmoney.com/data/FundNewIssue.aspx"
params = {
"t": "xcln",
"sort": "jzrgq,desc",
"y": "",
"page": "1,50000",
"isbuy": "1",
"v": "0.4069919776543214",
}
r = requests.get(url, params=params)
data_text = r.text
data_json = demjson.decode(data_text.strip("var newfunddata="))
temp_df = pd.DataFrame(data_json["datas"])
temp_df.columns = [
"基金代码",
"基金简称",
"发行公司",
"_",
"基金类型",
"募集份额",
"成立日期",
"成立来涨幅",
"基金经理",
"申购状态",
"集中认购期",
"_",
"_",
"_",
"_",
"_",
"_",
"_",
"优惠费率",
]
temp_df = temp_df[[
"基金代码",
"基金简称",
"发行公司",
"基金类型",
"集中认购期",
"募集份额",
"成立日期",
"成立来涨幅",
"基金经理",
"申购状态",
"优惠费率",
]]
temp_df['募集份额'] = pd.to_numeric(temp_df['募集份额'])
temp_df['成立日期'] = pd.to_datetime(temp_df['成立日期']).dt.date
temp_df['成立来涨幅'] = pd.to_numeric(temp_df['成立来涨幅'].str.replace(',', ''))
temp_df['优惠费率'] = temp_df['优惠费率'].str.strip("%")
temp_df['优惠费率'] = pd.to_numeric(temp_df['优惠费率'])
return temp_df
def _get_page_num_sy_yq_list(symbol: str = "沪深两市",
trade_date: str = "2019-12-31") -> int:
"""
东方财富网-数据中心-特色数据-商誉-商誉减值预期明细
http://data.eastmoney.com/sy/yqlist.html
:return: int 获取 商誉减值预期明细 的总页数
"""
symbol_dict = {
"沪市主板": f"(MKT='shzb' and ENDDATE=^{trade_date}^)",
"深市主板": f"(MKT='szzb' and ENDDATE=^{trade_date}^)",
"中小板": f"(MKT='zxb' and ENDDATE=^{trade_date}^)",
"创业板": f"(MKT='cyb' and ENDDATE=^{trade_date}^)",
"沪深两市": f"(ENDDATE=^{trade_date}^)",
}
url = "http://dcfm.eastmoney.com/EM_MutiSvcExpandInterface/api/js/get"
params = {
"type": "SY_YG",
"token": "894050c76af8597a853f5b408b759f5d",
"st": "NOTICEDATE",
"sr": "-1",
"p": "1",
"ps": "50",
"js": "var {name}=".format(name=ctx.call("getCode", 8)) +
"{pages:(tp),data:(x),font:(font)}",
"filter": symbol_dict[symbol],
"rt": "52589731",
}
res = requests.get(url, params=params)
data_json = demjson.decode(res.text[res.text.find("={") + 1:])
return data_json["pages"]
def stock_hk_spot() -> pd.DataFrame:
"""
新浪财经-港股的所有港股的实时行情数据
http://vip.stock.finance.sina.com.cn/mkt/#qbgg_hk
:return: 实时行情数据
:rtype: pandas.DataFrame
"""
res = requests.get(hk_sina_stock_list_url,
params=hk_sina_stock_dict_payload)
data_json = [
demjson.decode(tt) for tt in [
item + "}" for item in res.text[1:-1].split("},")
if not item.endswith("}")
]
]
data_df = pd.DataFrame(data_json)
data_df = data_df[[
"symbol",
"name",
"engname",
"tradetype",
"lasttrade",
"prevclose",
"open",
"high",
"low",
"volume",
"amount",
"ticktime",
"buy",
"sell",
"pricechange",
"changepercent",
]]
return data_df
def _get_page_num_sy_list(symbol: str = "沪市主板",
trade_date: str = "2019-12-31") -> int:
"""
东方财富网-数据中心-特色数据-商誉-个股商誉明细
http://data.eastmoney.com/sy/list.html
:param symbol: choice of {"沪市主板", "深市主板", "中小板", "创业板", "沪深两市"}
:type symbol: str
:param trade_date: 参考网站指定的数据日期
:type trade_date: str
:return: 个股商誉明细 的总页数
:rtype: int
"""
symbol_dict = {
"沪市主板": f"""(TRADE_BOARD="shzb")(REPORT_DATE='{trade_date}')""",
"深市主板": f"""(TRADE_BOARD="szzb")(REPORT_DATE='{trade_date}')""",
"中小板": f"""(TRADE_BOARD="zxb")(REPORT_DATE='{trade_date}')""",
"创业板": f"""(TRADE_BOARD="cyb")(REPORT_DATE='{trade_date}')""",
"沪深两市": f"(REPORT_DATE='{trade_date}')",
}
url = "http://datacenter.eastmoney.com/api/data/get"
params = {
"type": "RPT_GOODWILL_STOCKDETAILS",
"sty": "ALL",
"p": "1",
"ps": "50",
"sr": "-1,-1",
"st": "NOTICE_DATE,SECURITY_CODE",
"var": "QvxsKBaH",
"filter": symbol_dict[symbol],
"rt": "53324381",
}
res = requests.get(url, params=params)
data_json = demjson.decode(res.text[res.text.find("{"):-1])
return data_json["result"]["pages"]
def stock_em_qbzf() -> pd.DataFrame:
"""
东方财富网-数据中心-新股数据-增发-全部增发
http://data.eastmoney.com/other/gkzf.html
:return: 全部增发
:rtype: pandas.DataFrame
"""
url = "http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx"
params = {
"st": "5",
"sr": "-1",
"ps": "5000",
"p": "1",
"type": "SR",
"sty": "ZF",
"js": '({"pages":(pc),"data":[(x)]})',
"stat": "0",
}
r = requests.get(url, params=params)
data_text = r.text
data_json = demjson.decode(data_text[1:-1])
temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]])
temp_df.columns = [
"股票代码",
"股票简称",
"发行方式",
"发行总数",
"发行价格",
"最新价",
"发行日期",
"增发上市日期",
"_",
"增发代码",
"网上发行",
"_",
"_",
"_",
"_",
"_",
"_",
]
temp_df = temp_df[[
"股票代码",
"股票简称",
"增发代码",
"发行方式",
"发行总数",
"网上发行",
"发行价格",
"最新价",
"发行日期",
"增发上市日期",
]]
temp_df["锁定期"] = "1-3年"
temp_df['发行总数'] = pd.to_numeric(temp_df['发行总数'])
temp_df['发行价格'] = pd.to_numeric(temp_df['发行价格'])
temp_df['最新价'] = pd.to_numeric(temp_df['最新价'])
temp_df['发行日期'] = pd.to_datetime(temp_df['发行日期']).dt.date
temp_df['增发上市日期'] = pd.to_datetime(temp_df['增发上市日期']).dt.date
return temp_df
def match_main_contract(symbol: str = "cffex") -> str:
"""
新浪财经-期货-主力合约
http://vip.stock.finance.sina.com.cn/quotes_service/view/qihuohangqing.html#titlePos_1
:param symbol: choice of {'czce', 'dce', 'shfe', 'cffex'}
:type symbol: str
:return: 主力合约的字符串
:rtype: str
"""
subscribe_exchange_list = []
exchange_symbol_list = zh_subscribe_exchange_symbol(
symbol).iloc[:, 1].tolist()
for item in exchange_symbol_list:
# item = 'sngz_qh'
zh_match_main_contract_payload.update({"node": item})
res = requests.get(zh_match_main_contract_url,
params=zh_match_main_contract_payload)
data_json = demjson.decode(res.text)
data_df = pd.DataFrame(data_json)
try:
main_contract = data_df[data_df.iloc[:, 3:].duplicated()]
print(main_contract["symbol"].values[0])
subscribe_exchange_list.append(main_contract["symbol"].values[0])
except:
if len(data_df) == 1:
subscribe_exchange_list.append(data_df["symbol"].values[0])
print(data_df["symbol"].values[0])
else:
print(item, "无主力合约")
continue
print(f"{symbol}主力合约获取成功")
return ",".join([item for item in subscribe_exchange_list])
def macro_canada_trade() -> pd.DataFrame:
"""
东方财富-经济数据-加拿大-贸易帐
http://data.eastmoney.com/cjsj/foreign_7_2.html
:return: 贸易帐
:rtype: pandas.DataFrame
"""
url = "http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx"
params = {
"type": "GJZB",
"sty": "HKZB",
"js": "({data:[(x)],pages:(pc)})",
"p": "1",
"ps": "2000",
"mkt": "7",
"stat": "2",
'pageNo': '1',
'pageNum': '1',
"_": "1625474966006",
}
r = requests.get(url, params=params)
data_text = r.text
data_json = demjson.decode(data_text[1:-1])
temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]])
temp_df.columns = [
"时间",
"前值",
"现值",
"发布日期",
]
temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date
temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date
temp_df["前值"] = pd.to_numeric(temp_df["前值"]) / 100
temp_df["现值"] = pd.to_numeric(temp_df["现值"]) / 100
return temp_df
def stock_em_yysj(date: str = "20200331") -> pd.DataFrame:
"""
东方财富-数据中心-年报季报-预约披露时间
http://data.eastmoney.com/bbsj/202003/yysj.html
:param date: "20190331", "20190630", "20190930", "20191231"; 从 20081231 开始
:type date: str
:return: 指定时间的上市公司预约披露时间数据
:rtype: pandas.DataFrame
"""
url = "http://dcfm.eastmoney.com/em_mutisvcexpandinterface/api/js/get"
params = {
"type": "YJBB21_YYPL",
"token": "70f12f2f4f091e459a279469fe49eca5",
"st": "frdate",
"sr": "1",
"p": "1",
"ps": "5000",
"js": "var HXutCoUP={pages:(tp),data: (x),font:(font)}",
"filter": f"(securitytypecode='058001001')(reportdate=^{'-'.join([date[:4], date[4:6], date[6:]])}^)",
"rt": "52907209",
}
r = requests.get(url, params=params)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{") :])
temp_df = pd.DataFrame(data_json["data"])
return temp_df
def stock_zh_index_spot() -> pd.DataFrame:
"""
新浪财经-行情中心首页-A股-分类-所有指数
大量采集会被目标网站服务器封禁 IP, 如果被封禁 IP, 请 10 分钟后再试
http://vip.stock.finance.sina.com.cn/mkt/#hs_s
:return: 所有指数的实时行情数据
:rtype: pandas.DataFrame
"""
big_df = pd.DataFrame()
page_count = get_zh_index_page_count()
zh_sina_stock_payload_copy = zh_sina_index_stock_payload.copy()
for page in tqdm(range(1, page_count + 1), leave=False):
zh_sina_stock_payload_copy.update({"page": page})
res = requests.get(zh_sina_index_stock_url, params=zh_sina_stock_payload_copy)
data_json = demjson.decode(res.text)
big_df = big_df.append(pd.DataFrame(data_json), ignore_index=True)
big_df = big_df.applymap(_replace_comma)
big_df["trade"] = big_df["trade"].astype(float)
big_df["pricechange"] = big_df["pricechange"].astype(float)
big_df["changepercent"] = big_df["changepercent"].astype(float)
big_df["buy"] = big_df["buy"].astype(float)
big_df["sell"] = big_df["sell"].astype(float)
big_df["settlement"] = big_df["settlement"].astype(float)
big_df["open"] = big_df["open"].astype(float)
big_df["high"] = big_df["high"].astype(float)
big_df["low"] = big_df["low"].astype(float)
big_df.columns = [
"代码",
"名称",
"最新价",
"涨跌额",
"涨跌幅",
"_",
"_",
"昨收",
"今开",
"最高",
"最低",
"成交量",
"成交额",
"_",
"_",
]
big_df = big_df[
[
"代码",
"名称",
"最新价",
"涨跌额",
"涨跌幅",
"昨收",
"今开",
"最高",
"最低",
"成交量",
"成交额",
]
]
return big_df
def futures_inventory_em(exchange: str = "上海期货交易所",
symbol: str = "沪铝") -> pd.DataFrame:
"""
东方财富网-数据中心-期货库存数据
http://data.eastmoney.com/ifdata/kcsj.html
:param exchange: choice of {"上海期货交易所", "郑州商品交易所", "大连商品交易所"}
:type exchange: str
:param symbol: http://data.eastmoney.com/ifdata/kcsj.html 对应的中文名称, 如: 沪铝
:type symbol: str
:return: 指定交易所和指定品种的库存数据
:rtype: pandas.DataFrame
"""
url = "http://data.eastmoney.com/ifdata/kcsj.html"
r = requests.get(url)
soup = BeautifulSoup(r.text, "lxml")
temp_soup = soup.find(attrs={"id": "select_jys"}).find_all("option")
temp_key = [item.text for item in temp_soup]
temp_value = [item.get("value") for item in temp_soup]
exchange_dict = dict(zip(temp_key, temp_value))
url = 'http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx'
params = {
'type': 'QHKC',
'sty': 'QHKCSX',
'_': '1618311930407',
}
r = requests.get(url, params=params)
data_text = r.text
data_json = demjson.decode(data_text[1:-1])
temp_df = pd.DataFrame(data_json)
temp_df = temp_df.iloc[:, 0].str.split(',', expand=True)
symbol_dict = dict(zip(temp_df.iloc[:, 3], temp_df.iloc[:, 2]))
url = "http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx"
params = {
"type": "QHKC",
"sty": "QHKCMX",
"mkt": exchange_dict[exchange],
"code": symbol_dict[symbol],
"stat": "1",
"_": "1587887394138",
}
r = requests.get(url, params=params)
data_text = r.text
data_json = demjson.decode(data_text[1:-1])
temp_df = pd.DataFrame(data_json).iloc[:, 0].str.split(",", expand=True)
temp_df.columns = ["日期", "库存", "增减"]
return temp_df
def stock_zh_index_daily_tx(symbol: str = "sz980017") -> pd.DataFrame:
"""
腾讯证券-日频-股票或者指数历史数据
作为 stock_zh_index_daily 的补充, 因为在新浪中有部分指数数据缺失
注意都是: 前复权, 不同网站复权方式不同, 不可混用数据
http://gu.qq.com/sh000919/zs
:param symbol: 带市场标识的股票或者指数代码
:type symbol: str
:return: 后复权的股票和指数数据
:rtype: pandas.DataFrame
"""
start_date = _get_tx_start_year(symbol=symbol)
url = "https://proxy.finance.qq.com/ifzqgtimg/appstock/app/newfqkline/get"
range_start = int(start_date.split("-")[0])
range_end = datetime.date.today().year + 1
temp_df = pd.DataFrame()
for year in tqdm(range(range_start, range_end)):
params = {
"_var": "kline_dayqfq",
"param": f"{symbol},day,{year}-01-01,{year + 1}-12-31,640,qfq",
"r": "0.8205512681390605",
}
res = requests.get(url, params=params)
text = res.text
try:
inner_temp_df = pd.DataFrame(
demjson.decode(text[text.find("={") + 1 :])["data"][symbol]["day"]
)
except:
inner_temp_df = pd.DataFrame(
demjson.decode(text[text.find("={") + 1 :])["data"][symbol]["qfqday"]
)
temp_df = temp_df.append(inner_temp_df, ignore_index=True)
if temp_df.shape[1] == 6:
temp_df.columns = ["date", "open", "close", "high", "low", "amount"]
else:
temp_df = temp_df.iloc[:, :6]
temp_df.columns = ["date", "open", "close", "high", "low", "amount"]
temp_df["date"] = pd.to_datetime(temp_df["date"]).dt.date
temp_df["open"] = pd.to_numeric(temp_df["open"])
temp_df["close"] = pd.to_numeric(temp_df["close"])
temp_df["high"] = pd.to_numeric(temp_df["high"])
temp_df["low"] = pd.to_numeric(temp_df["low"])
temp_df["amount"] = pd.to_numeric(temp_df["amount"])
temp_df.drop_duplicates(inplace=True)
return temp_df
def fund_hold_structure_em() -> pd.DataFrame:
"""
天天基金网-基金数据-规模份额-持有人结构
http://fund.eastmoney.com/data/cyrjglist.html
:return: 持有人结构
:rtype: pandas.DataFrame
"""
url = "http://fund.eastmoney.com/data/FundDataPortfolio_Interface.aspx"
params = {
"dt": "11",
"pi": "1",
"pn": "50",
"mc": "hypzDetail",
"st": "desc",
"sc": "reportdate",
}
r = requests.get(url, params=params)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{"):-1])
total_page = data_json["pages"]
big_df = pd.DataFrame()
for page in range(1, int(total_page) + 1):
params.update({"pi": page})
r = requests.get(url, params=params)
data_text = r.text
data_json = demjson.decode(data_text[data_text.find("{"):-1])
temp_df = pd.DataFrame(data_json["data"])
big_df = big_df.append(temp_df, ignore_index=True)
big_df.reset_index(inplace=True)
big_df["index"] = big_df["index"] + 1
big_df.columns = [
"序号",
"截止日期",
"基金家数",
'机构持有比列',
'个人持有比列',
'内部持有比列',
'总份额',
]
big_df["截止日期"] = pd.to_datetime(big_df["截止日期"]).dt.date
big_df["基金家数"] = pd.to_numeric(big_df["基金家数"])
big_df["机构持有比列"] = pd.to_numeric(big_df["机构持有比列"])
big_df["个人持有比列"] = pd.to_numeric(big_df["个人持有比列"])
big_df["内部持有比列"] = pd.to_numeric(big_df["内部持有比列"])
big_df["总份额"] = pd.to_numeric(big_df["总份额"].str.replace(",", ""))
return big_df
def index_stock_cons_sina(symbol: str = "000300") -> pd.DataFrame:
"""
新浪新版股票指数成份页面, 目前该接口可获取指数数量较少
http://vip.stock.finance.sina.com.cn/mkt/#zhishu_000040
:param symbol: 指数代码
:type symbol: str
:return: 指数的成份股
:rtype: pandas.DataFrame
"""
if symbol == "000300":
symbol = "hs300"
url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeStockCountSimple"
params = {"node": f"{symbol}"}
r = requests.get(url, params=params)
page_num = math.ceil(int(r.json()) / 80) + 1
temp_df = pd.DataFrame()
for page in range(1, page_num):
url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeData"
params = {
"page": str(page),
"num": "80",
"sort": "symbol",
"asc": "1",
"node": "hs300",
"symbol": "",
"_s_r_a": "init",
}
r = requests.get(url, params=params)
temp_df = temp_df.append(
pd.DataFrame(demjson.decode(r.text)), ignore_index=True
)
return temp_df
url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeDataSimple"
params = {
"page": 1,
"num": "3000",
"sort": "symbol",
"asc": "1",
"node": f"zhishu_{symbol}",
"_s_r_a": "setlen",
}
r = requests.get(url, params=params)
return pd.DataFrame(demjson.decode(r.text))
def fund_etf_fund_info_em(fund: str = "511280",
start_date: str = "20000101",
end_date: str = "20500101") -> pd.DataFrame:
"""
东方财富网站-天天基金网-基金数据-场内交易基金-历史净值明细
http://fundf10.eastmoney.com/jjjz_511280.html
:param fund: 场内交易基金代码, 可以通过 fund_etf_fund_daily_em 来获取
:type fund: str
:param start_date: 开始统计时间
:type start_date: str
:param end_date: 结束统计时间
:type end_date: str
:return: 东方财富网站-天天基金网-基金数据-场内交易基金-历史净值明细
:rtype: pandas.DataFrame
"""
url = "http://api.fund.eastmoney.com/f10/lsjz"
headers = {
"User-Agent":
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36",
"Referer": f"http://fundf10.eastmoney.com/jjjz_{fund}.html",
}
params = {
"callback": "jQuery183023608994033331676_1588250653363",
"fundCode": fund,
"pageIndex": "1",
"pageSize": "10000",
"startDate":
"-".join([start_date[:4], start_date[4:6], start_date[6:]]),
"endDate": "-".join([end_date[:4], end_date[4:6], end_date[6:]]),
"_": round(time.time() * 1000),
}
r = requests.get(url, params=params, headers=headers)
text_data = r.text
data_json = demjson.decode(text_data[text_data.find("{"):-1])
temp_df = pd.DataFrame(data_json["Data"]["LSJZList"])
temp_df.columns = [
"净值日期",
"单位净值",
"累计净值",
"_",
"_",
"_",
"日增长率",
"申购状态",
"赎回状态",
"_",
"_",
"_",
"_",
]
temp_df = temp_df[["净值日期", "单位净值", "累计净值", "日增长率", "申购状态", "赎回状态"]]
temp_df["净值日期"] = pd.to_datetime(temp_df["净值日期"]).dt.date
temp_df["单位净值"] = pd.to_numeric(temp_df["单位净值"])
temp_df["累计净值"] = pd.to_numeric(temp_df["累计净值"])
temp_df["日增长率"] = pd.to_numeric(temp_df["日增长率"])
return temp_df
def stock_ipo_declare() -> pd.DataFrame:
"""
东方财富网-数据中心-新股申购-首发申报信息-首发申报企业信息
https://data.eastmoney.com/xg/xg/sbqy.html
:return: 首发申报企业信息
:rtype: pandas.DataFrame
"""
url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx"
params = {
"st": "1",
"sr": "-1",
"ps": "500",
"p": "1",
"type": "NS",
"sty": "NSFR",
"js": "({data:[(x)],pages:(pc)})",
"mkt": "1",
"fd": "2021-04-02",
}
r = requests.get(url, params=params)
data_text = r.text
data_json = demjson.decode(data_text[1:-1])
temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]])
temp_df.reset_index(inplace=True)
temp_df["index"] = temp_df.index + 1
temp_df.columns = [
"序号",
"会计师事务所",
"_",
"保荐机构",
"_",
"律师事务所",
"_",
"_",
"拟上市地",
"_",
"_",
"备注",
"申报企业",
"_",
"_",
"_",
"_",
]
temp_df = temp_df[[
"序号",
"申报企业",
"拟上市地",
"保荐机构",
"会计师事务所",
"律师事务所",
"备注",
]]
return temp_df
def air_quality_watch_point(city: str = "杭州",
start_date: str = "20220408",
end_date: str = "20220409") -> pd.DataFrame:
"""
真气网-监测点空气质量-细化到具体城市的每个监测点
指定之间段之间的空气质量数据
https://www.zq12369.com/
:param city: 调用 ak.air_city_table() 接口获取
:type city: str
:param start_date: e.g., "20190327"
:type start_date: str
:param end_date: e.g., ""20200327""
:type end_date: str
:return: 指定城市指定日期区间的观测点空气质量
:rtype: pandas.DataFrame
"""
start_date = "-".join([start_date[:4], start_date[4:6], start_date[6:]])
end_date = "-".join([end_date[:4], end_date[4:6], end_date[6:]])
url = "https://www.zq12369.com/api/zhenqiapi.php"
file_data = _get_file_content(file_name="crypto.js")
ctx = py_mini_racer.MiniRacer()
ctx.eval(file_data)
method = "GETCITYPOINTAVG"
ctx.call("encode_param", method)
ctx.call("encode_param", start_date)
ctx.call("encode_param", end_date)
city_param = ctx.call("encode_param", city)
ctx.call("encode_secret", method, city_param, start_date, end_date)
payload = {
"appId":
"a01901d3caba1f362d69474674ce477f",
"method":
ctx.call("encode_param", method),
"city":
city_param,
"startTime":
ctx.call("encode_param", start_date),
"endTime":
ctx.call("encode_param", end_date),
"secret":
ctx.call("encode_secret", method, city_param, start_date, end_date),
}
headers = {
"User-Agent":
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.122 Safari/537.36"
}
r = requests.post(url, data=payload, headers=headers)
data_text = r.text
data_json = demjson.decode(ctx.call("decode_result", data_text))
temp_df = pd.DataFrame(data_json["rows"])
return temp_df
def _get_zh_stock_ah_page_count() -> int:
"""
腾讯财经-港股-AH-总页数
https://stockapp.finance.qq.com/mstats/#mod=list&id=hk_ah&module=HK&type=AH&sort=3&page=3&max=20
:return: 总页数
:rtype: int
"""
hk_payload_copy = hk_payload.copy()
hk_payload_copy.update({"reqPage": 1})
res = requests.get(hk_url, params=hk_payload_copy, headers=hk_headers)
data_json = demjson.decode(
res.text[res.text.find("{"):res.text.rfind("}") + 1])
page_count = data_json["data"]["page_count"]
return page_count
def stock_sector_detail(sector: str = "gn_gfgn") -> pd.DataFrame:
"""
新浪行业-板块行情-成份详情
http://finance.sina.com.cn/stock/sl/#area_1
:param sector: stock_sector_spot 返回的 label 值, choice of {"新浪行业", "概念", "地域", "行业"}; "启明星行业" 无详情
:type sector: str
:return: 指定 sector 的板块详情
:rtype: pandas.DataFrame
"""
url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeStockCount"
params = {
"node": sector
}
r = requests.get(url, params=params)
total_num = int(r.json())
total_page_num = math.ceil(int(total_num) / 80)
big_df = pd.DataFrame()
url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeData"
for page in tqdm(range(1, total_page_num+1), leave=True):
params = {
"page": str(page),
"num": "80",
"sort": "symbol",
"asc": "1",
"node": sector,
"symbol": "",
"_s_r_a": "page",
}
r = requests.get(url, params=params)
data_text = r.text
data_json = demjson.decode(data_text)
temp_df = pd.DataFrame(data_json)
big_df = big_df.append(temp_df, ignore_index=True)
big_df['trade'] = pd.to_numeric(big_df['trade'])
big_df['pricechange'] = pd.to_numeric(big_df['pricechange'])
big_df['changepercent'] = pd.to_numeric(big_df['changepercent'])
big_df['buy'] = pd.to_numeric(big_df['buy'])
big_df['sell'] = pd.to_numeric(big_df['sell'])
big_df['settlement'] = pd.to_numeric(big_df['settlement'])
big_df['open'] = pd.to_numeric(big_df['open'])
big_df['high'] = pd.to_numeric(big_df['high'])
big_df['low'] = pd.to_numeric(big_df['low'])
big_df['volume'] = pd.to_numeric(big_df['volume'])
big_df['amount'] = pd.to_numeric(big_df['amount'])
big_df['per'] = pd.to_numeric(big_df['per'])
big_df['pb'] = pd.to_numeric(big_df['pb'])
big_df['mktcap'] = pd.to_numeric(big_df['mktcap'])
big_df['nmc'] = pd.to_numeric(big_df['nmc'])
big_df['turnoverratio'] = pd.to_numeric(big_df['turnoverratio'])
return big_df
def stock_institute_hold_detail(stock: str = "600433",
quarter: str = "20201") -> pd.DataFrame:
"""
新浪财经-股票-机构持股详情
http://vip.stock.finance.sina.com.cn/q/go.php/vComStockHold/kind/jgcg/index.phtml
:param stock: 股票代码
:type stock: str
:param quarter: 从 2005 年开始, {"一季报":1, "中报":2 "三季报":3 "年报":4}, e.g., "20191", 其中的 1 表示一季报; "20193", 其中的 3 表示三季报;
:type quarter: str
:return: 指定股票和财报时间的机构持股数据
:rtype: pandas.DataFrame
"""
url = "http://vip.stock.finance.sina.com.cn/q/api/jsonp.php/var%20details=/ComStockHoldService.getJGCGDetail"
params = {
"symbol": stock,
"quarter": quarter,
}
r = requests.get(url, params=params)
text_data = r.text
json_data = demjson.decode(text_data[text_data.find("{"):-2])
big_df = pd.DataFrame()
for item in json_data["data"].keys():
inner_temp_df = pd.DataFrame(json_data["data"][item]).T.iloc[:-1, :]
inner_temp_df.reset_index(inplace=True)
big_df = big_df.append(inner_temp_df, ignore_index=True)
if not big_df.empty:
big_df["index"] = big_df["index"].str.split("_", expand=True)[0]
big_df.rename(columns={"index": "institute"}, inplace=True)
big_df = big_df.iloc[:, :12]
big_df.columns = [
"持股机构类型",
"持股机构代码",
"持股机构简称",
"持股机构全称",
"持股数",
"最新持股数",
"持股比例",
"最新持股比例",
"占流通股比例",
"最新占流通股比例",
"持股比例增幅",
"占流通股比例增幅",
]
big_df["持股机构类型"] = big_df["持股机构类型"].str.replace("fund", "基金")
big_df["持股机构类型"] = big_df["持股机构类型"].str.replace(
"socialSecurity", "全国社保")
big_df["持股机构类型"] = big_df["持股机构类型"].str.replace("qfii", "QFII")
return big_df
else:
return None