def QA_quotation(code,
start,
end,
frequence,
market,
source=DATASOURCE.TDX,
output=OUTPUT_FORMAT.DATAFRAME):
"""一个统一的获取k线的方法
如果使用mongo,从本地数据库获取,失败则在线获取
Arguments:
code {str/list} -- 期货/股票的代码
start {str} -- 开始日期
end {str} -- 结束日期
frequence {enum} -- 频率 QA.FREQUENCE
market {enum} -- 市场 QA.MARKET_TYPE
source {enum} -- 来源 QA.DATASOURCE
output {enum} -- 输出类型 QA.OUTPUT_FORMAT
"""
res = None
if market == MARKET_TYPE.STOCK_CN:
if frequence == FREQUENCE.DAY:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_stock_day_adv(code, start, end)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_stock_day(code, start, end, '00')
res = QA_DataStruct_Stock_day(res.set_index(['date', 'code']))
elif source == DATASOURCE.TUSHARE:
res = QATushare.QA_fetch_get_stock_day(code, start, end, '00')
elif frequence in [
FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN, FREQUENCE.FIFTEEN_MIN,
FREQUENCE.THIRTY_MIN, FREQUENCE.SIXTY_MIN
]:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_stock_min_adv(
code, start, end, frequence=frequence)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_stock_min(code,
start,
end,
frequence=frequence)
res = QA_DataStruct_Stock_min(
res.set_index(['datetime', 'code']))
elif market == MARKET_TYPE.BOND_CN:
if frequence == FREQUENCE.DAY:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_bond_day_adv(code, start, end)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_bond_day(code, start, end, '00')
res = QA_DataStruct_Bond_day(res.set_index(['date', 'code']))
elif source == DATASOURCE.TUSHARE:
res = QATushare.QA_fetch_get_bond_day(code, start, end, '00')
elif frequence in [
FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN, FREQUENCE.FIFTEEN_MIN,
FREQUENCE.THIRTY_MIN, FREQUENCE.SIXTY_MIN
]:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_bond_min_adv(code,
start,
end,
frequence=frequence)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_bond_min(code,
start,
end,
frequence=frequence)
res = QA_DataStruct_Stock_min(
res.set_index(['datetime', 'code']))
elif market == MARKET_TYPE.FUTURE_CN:
if frequence == FREQUENCE.DAY:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_future_day_adv(code, start, end)
except:
res = None
if source == DATASOURCE.TDX or res is None:
res = QATdx.QA_fetch_get_future_day(code, start, end)
res = QA_DataStruct_Future_day(res.set_index(['date', 'code']))
elif frequence in [
FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN, FREQUENCE.FIFTEEN_MIN,
FREQUENCE.THIRTY_MIN, FREQUENCE.SIXTY_MIN
]:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_future_min_adv(
code, start, end, frequence=frequence)
except:
res = None
if source == DATASOURCE.TDX or res is None:
res = QATdx.QA_fetch_get_future_min(code,
start,
end,
frequence=frequence)
res = QA_DataStruct_Future_min(
res.set_index(['datetime', 'code']))
elif market == MARKET_TYPE.INDEX_CN:
if frequence == FREQUENCE.DAY:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_index_day_adv(code, start, end)
except:
return None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_index_day(code, start, end)
res = QA_DataStruct_Index_day(res.set_index(['date', 'code']))
elif frequence in [
FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN, FREQUENCE.FIFTEEN_MIN,
FREQUENCE.THIRTY_MIN, FREQUENCE.SIXTY_MIN
]:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_index_min_adv(
code, start, end, frequence=frequence)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_index_min(code,
start,
end,
frequence=frequence)
res = QA_DataStruct_Index_min(
res.set_index(['datetime', 'code']))
elif market == MARKET_TYPE.OPTION_CN:
if source == DATASOURCE.MONGO:
#res = QAQueryAdv.QA_fetch_option_day_adv(code, start, end)
raise NotImplementedError('CURRENT NOT FINISH THIS METHOD')
# print(type(res))
if output is OUTPUT_FORMAT.DATAFRAME:
return res.data
elif output is OUTPUT_FORMAT.DATASTRUCT:
return res
elif output is OUTPUT_FORMAT.NDARRAY:
return res.to_numpy()
elif output is OUTPUT_FORMAT.JSON:
return res.to_json()
elif output is OUTPUT_FORMAT.LIST:
return res.to_list()
def QA_quotation_adv(code,
start,
end=save_tdx.now_time(),
frequence='1min',
market=MARKET_TYPE.STOCK_CN,
source=DATASOURCE.AUTO,
output=OUTPUT_FORMAT.DATAFRAME):
"""一个统一的获取k线的方法
如果source=DATASOURCE.AUTO,优先mongo,从本地数据库获取,mongo中未下载的数据从TDX中在线补全。(仅限股票)
Arguments:
code {str/list} -- 期货/股票的代码
start {str} -- 开始日期
end {str} -- 结束日期
frequence {enum} -- 频率 QA.FREQUENCE
market {enum} -- 市场 QA.MARKET_TYPE
source {enum} -- 来源 QA.DATASOURCE
output {enum} -- 输出类型 QA.OUTPUT_FORMAT
"""
if pd.Timestamp(end) > pd.Timestamp(save_tdx.now_time()):
end = save_tdx.now_time()
res = None
if market == MARKET_TYPE.STOCK_CN:
if frequence == FREQUENCE.DAY or frequence == FREQUENCE.WEEK:
if source == DATASOURCE.AUTO:
try:
# 返回的是QA_DataStruct_Stock_day对象，为了与在线获取的数据格式保持统一，转成单索引
res = QAQueryAdv.QA_fetch_stock_day_adv(
code, start, end).data.reset_index(level='code')
# res = QAQueryAdv.QA_fetch_stock_day_adv(
# code, start, end).data.reset_index(level='code')[:14]
start_date = res.index[-1]
end_date = pd.Timestamp(end)
if end_date - start_date > datetime.timedelta(hours=17):
# 从TDX补充数据，由于仅考虑个股，在这里不做入库操作，入库还是需要save
data_tdx = QATdx.QA_fetch_get_stock_day(
code,
QA_util_get_next_period(start_date,
frequence), end_date, '00')
# data_tdx与从数据库获取的数据格式上做一些统一。
data_tdx = data_tdx.rename(columns={
"vol": "volume"
}).drop(['date', 'date_stamp'], axis=1)
data_tdx.index = pd.to_datetime(data_tdx.index,
utc=False)
res = pd.concat([res, data_tdx], sort=True)
res = QA_DataStruct_Stock_day(res.reset_index().set_index(
['date', 'code']))
except:
res = None
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_stock_day_adv(code, start, end)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_stock_day(code, start, end, '00')
res = QA_DataStruct_Stock_day(res.set_index(['date', 'code']))
elif source == DATASOURCE.TUSHARE:
res = QATushare.QA_fetch_get_stock_day(code, start, end, '00')
if frequence == FREQUENCE.WEEK:
res = QA_DataStruct_Stock_day(QA_data_day_resample(res.data))
elif frequence in [
FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN, FREQUENCE.FIFTEEN_MIN,
FREQUENCE.THIRTY_MIN, FREQUENCE.SIXTY_MIN
]:
if source == DATASOURCE.AUTO:
try:
# 返回的是QA_DataStruct_Stock_day对象，为了与在线获取的数据格式保持统一，转成单索引
res = QAQueryAdv.QA_fetch_stock_min_adv(
code, start, end,
frequence=frequence).data.reset_index(level='code')
# res = QAQueryAdv.QA_fetch_stock_min_adv(
# code, start, end, frequence=frequence).data.reset_index(level='code')[:710]
start_date = res.index[-1]
end_date = pd.Timestamp(end)
if end_date > start_date:
# 从TDX补充数据，由于仅考虑个股，在这里不做入库操作，入库还是需要save
data_tdx = QATdx.QA_fetch_get_stock_min(
code,
QA_util_get_next_period(start_date, frequence),
end_date,
frequence=frequence)
# data_tdx与从数据库获取的数据格式上做一些统一。
data_tdx = data_tdx.rename(columns={
"vol": "volume"
}).drop(
['date', 'datetime', 'date_stamp', 'time_stamp'],
axis=1)
data_tdx.index = pd.to_datetime(data_tdx.index,
utc=False)
res = pd.concat([res, data_tdx], sort=True)
res = QA_DataStruct_Stock_day(res.reset_index().set_index(
['datetime', 'code']))
except:
res = None
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_stock_min_adv(
code, start, end, frequence=frequence)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_stock_min(code,
start,
end,
frequence=frequence)
res = QA_DataStruct_Stock_min(
res.set_index(['datetime', 'code']))
elif market == MARKET_TYPE.BOND_CN:
if frequence == FREQUENCE.DAY or frequence == FREQUENCE.WEEK:
if source == DATASOURCE.AUTO:
try:
# 返回的是QA_DataStruct_Bond_day对象，为了与在线获取的数据格式保持统一，转成单索引
res = QAQueryAdv.QA_fetch_bond_day_adv(
code, start, end).data.reset_index(level='code')
# res = QAQueryAdv.QA_fetch_stock_day_adv(
# code, start, end).data.reset_index(level='code')[:14]
start_date = res.index[-1]
end_date = pd.Timestamp(end)
if end_date - start_date > datetime.timedelta(hours=17):
# 从TDX补充数据，由于仅考虑个股，在这里不做入库操作，入库还是需要save
data_tdx = QATdx.QA_fetch_get_bond_day(
code,
QA_util_get_next_period(start_date,
frequence), end_date, '00')
# data_tdx与从数据库获取的数据格式上做一些统一。
data_tdx = data_tdx.rename(columns={
"vol": "volume"
}).drop(['date', 'date_stamp'], axis=1)
data_tdx.index = pd.to_datetime(data_tdx.index)
res = pd.concat([res, data_tdx], sort=True)
res = QA_DataStruct_Bond_day(res.reset_index().set_index(
['date', 'code']))
except:
res = None
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_bond_day_adv(code, start, end)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_bond_day(code, start, end, '00')
res = QA_DataStruct_Bond_day(res.set_index(['date', 'code']))
elif source == DATASOURCE.TUSHARE:
res = QATushare.QA_fetch_get_bond_day(code, start, end, '00')
if frequence == FREQUENCE.WEEK:
res = QA_DataStruct_Bond_day(QA_data_day_resample(res.data))
elif frequence in [
FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN, FREQUENCE.FIFTEEN_MIN,
FREQUENCE.THIRTY_MIN, FREQUENCE.SIXTY_MIN
]:
if source == DATASOURCE.AUTO:
try:
# 返回的是QA_DataStruct_Stock_day对象，为了与在线获取的数据格式保持统一，转成单索引
res = QAQueryAdv.QA_fetch_bond_min_adv(
code, start, end,
frequence=frequence).data.reset_index(level='code')
# res = QAQueryAdv.QA_fetch_stock_min_adv(
# code, start, end, frequence=frequence).data.reset_index(level='code')[:710]
start_date = res.index[-1]
end_date = pd.Timestamp(end)
if end_date > start_date:
# 从TDX补充数据，由于仅考虑个股，在这里不做入库操作，入库还是需要save
data_tdx = QATdx.QA_fetch_get_bond_min(
code,
QA_util_get_next_period(start_date, frequence),
end_date,
frequence=frequence)
# data_tdx与从数据库获取的数据格式上做一些统一。
data_tdx = data_tdx.rename(columns={
"vol": "volume"
}).drop(
['date', 'datetime', 'date_stamp', 'time_stamp'],
axis=1)
data_tdx.index = pd.to_datetime(data_tdx.index)
res = pd.concat([res, data_tdx], sort=True)
res = QA_DataStruct_Bond_day(res.reset_index().set_index(
['datetime', 'code']))
except:
res = None
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_bond_min_adv(code,
start,
end,
frequence=frequence)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_bond_min(code,
start,
end,
frequence=frequence)
res = QA_DataStruct_Bond_min(
res.set_index(['datetime', 'code']))
elif market == MARKET_TYPE.FUTURE_CN:
if frequence == FREQUENCE.DAY:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_future_day_adv(code, start, end)
except:
res = None
if source == DATASOURCE.TDX or res is None:
res = QATdx.QA_fetch_get_future_day(code, start, end)
res = QA_DataStruct_Future_day(res.set_index(['date', 'code']))
elif frequence in [
FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN, FREQUENCE.FIFTEEN_MIN,
FREQUENCE.THIRTY_MIN, FREQUENCE.SIXTY_MIN
]:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_future_min_adv(
code, start, end, frequence=frequence)
except:
res = None
if source == DATASOURCE.TDX or res is None:
res = QATdx.QA_fetch_get_future_min(code,
start,
end,
frequence=frequence)
res = QA_DataStruct_Future_min(
res.set_index(['datetime', 'code']))
elif market == MARKET_TYPE.INDEX_CN:
if frequence == FREQUENCE.DAY:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_index_day_adv(code, start, end)
except:
return None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_index_day(code, start, end)
res = QA_DataStruct_Index_day(res.set_index(['date', 'code']))
elif frequence in [
FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN, FREQUENCE.FIFTEEN_MIN,
FREQUENCE.THIRTY_MIN, FREQUENCE.SIXTY_MIN
]:
if source == DATASOURCE.MONGO:
try:
res = QAQueryAdv.QA_fetch_index_min_adv(
code, start, end, frequence=frequence)
except:
res = None
if source == DATASOURCE.TDX or res == None:
res = QATdx.QA_fetch_get_index_min(code,
start,
end,
frequence=frequence)
res = QA_DataStruct_Index_min(
res.set_index(['datetime', 'code']))
elif market == MARKET_TYPE.OPTION_CN:
if source == DATASOURCE.MONGO:
#res = QAQueryAdv.QA_fetch_option_day_adv(code, start, end)
raise NotImplementedError('CURRENT NOT FINISH THIS METHOD')
# print(type(res))
if output is OUTPUT_FORMAT.DATAFRAME:
return res.data
elif output is OUTPUT_FORMAT.DATASTRUCT:
return res
elif output is OUTPUT_FORMAT.NDARRAY:
return res.to_numpy()
elif output is OUTPUT_FORMAT.JSON:
return res.to_json()
elif output is OUTPUT_FORMAT.LIST:
return res.to_list()
