def QA_fetch_industry_adv(code: Union[str, List, Tuple] = None,
cursor_date: Union[str, datetime.datetime] = None,
start: Union[str, datetime.datetime] = None,
end: Union[str, datetime.datetime] = None,
levels: Union[str, List, Tuple] = None,
src: str = "sw") -> pd.DataFrame:
"""本地获取指定股票或股票列表的行业
Args:
code (Union[str, List, Tuple], optional): 股票代码或列表，默认为 None, 查询所有股票代码.
cursor_date (Union[str, datetime.datetime], optional): 一般指调仓日，此时不需要再设置 start 与 end
start(Union[str, datetime.datetime], optional): 起始时间，默认为 None.
end(Union[str, datetime.datetime], optional): 截止时间, 默认为 None.
levels (Union[str, List, Tuple], optional): [description]. 对应行业分级级别，默认为 None，查询所有行业分级数据
src (str, optional): 分级来源，默认为 "sw"(目前仅支持申万行业分类).
Returns:
pd.DataFrame: 行业信息
"""
coll = DATABASE.industry
if not code:
code = QA_fetch_stock_list().index.tolist()
if isinstance(code, str):
code = [code]
if isinstance(levels, str):
levels = [
levels,
]
if not levels:
levels = ["l1", "l2", "l3"]
levels = list(map(lambda x: x.lower(), levels))
df_tmp = pd.DataFrame()
if not cursor_date:
if not start:
qry = {
"code": {
"$in": code
},
"level": {
"$in": levels
},
"src": src.lower()
}
else:
qry = {
"code": {
"$in": code
},
"level": {
"$in": levels
},
"src": src.lower(),
"in_date_stamp": {
"$lte":
QA_util_date_stamp(
pd.Timestamp(start).strftime("%Y-%m-%d"))
}
}
if coll.count_documents(filter=qry) < 1:
print("找不到对应行业数据")
return pd.DataFrame()
cursor = coll.find(qry)
df_tmp = pd.DataFrame(cursor).drop(columns="_id")
if end:
df_tmp = df_tmp.loc[df_tmp.out_date_stamp > QA_util_date_stamp(
pd.Timestamp(end).strftime("%Y-%m-%d"))]
else:
qry = {
"code": {
"$in": code
},
"level": {
"$in": levels
},
"src": src.lower(),
"in_date_stamp": {
"$lte":
QA_util_date_stamp(
pd.Timestamp(cursor_date).strftime("%Y-%m-%d"))
}
}
if coll.count_documents(filter=qry) < 1:
print("找不到对应行业数据")
return pd.DataFrame()
cursor = coll.find(qry)
df_tmp = pd.DataFrame(cursor).drop(columns="_id")
df_tmp.loc[df_tmp.out_date_stamp > QA_util_date_stamp(
pd.Timestamp(cursor_date).strftime("%Y-%m-%d"))]
df_tmp.in_date = pd.to_datetime(df_tmp.in_date).dt.tz_localize(
None).dt.tz_localize('Asia/Shanghai')
df_tmp.out_date = pd.to_datetime(df_tmp.out_date).dt.tz_localize(
None).dt.tz_localize('Asia/Shanghai')
return df_tmp.drop(columns=["in_date_stamp", "out_date_stamp"])
# print(QA_fetch_get_crosssection_financial('2020-03-31'))
# print(QA_fetch_crosssection_financial("2020-03-31", fields="basic_eps"))
# df = QA_fetch_financial_adv(start="2018-06-30", end="2018-09-30")
# print(df.loc['000528', ["report_date", "f_ann_date",
# "ann_date", "basic_eps", "report_type", "update_flag", "report_label"]])
# print(df)
# print(QA_fetch_stock_basic(status="D"))
# 最近财务数据获取测试
# print(QA_fetch_last_financial(
# code="000596", cursor_date="2020-10-08"))
# print(QA_fetch_last_financial(
# code=QA_fetch_stock_list().index.tolist(), cursor_date="2020-10-08"))
# print(QA_fetch_last_financial(
# code = '000001', cursor_date = '2020-10-08'
# ))
code = QA_fetch_stock_list().index.tolist()
cursor_date = '2020-10-08'
df_origin = QA_fetch_last_financial(code=code,
cursor_date=cursor_date,
sheet_type="balancesheet")
# print(QA_fetch_last_financial(
# cursor_date="2018-08-31"))
# print(QA_fetch_last_financial(
# cursor_date="2018-08-31", code=["000528"], fields=["report_date", "ann_date", "f_ann_date", "update_flag"]))
# print(QA_fetch_financial_adv(
# cursor_date="2018-08-31"))
# 股票基本信息获取测试
# print(QA_fetch_stock_basic("000001"))
# print(QA_fetch_stock_basic(status=["P", "D"]))
# 行业获取测试
# print(QA_fetch_industry_adv(start="1998-01-01", end="2020-12-02").head())