def warpper(*args, **kwargs):
data = func(*args, **kwargs)
if isinstance(data.index, pd.MultiIndex):
return QA_DataStruct_Stock_day(data)
else:
return QA_DataStruct_Stock_day(data.set_index(['date', 'code'],
drop=False),
dtype='stock_day')
def warpper(*args, **kwargs):
data = func(*args, **kwargs)
if isinstance(data.index, pd.MultiIndex):
return QA_DataStruct_Stock_day(data)
else:
return QA_DataStruct_Stock_day(
data.assign(date=pd.to_datetime(data.date).dt.tz_localize(None).dt.tz_localize('Asia/Shanghai')
).set_index(['date',
'code'],
drop=False),
dtype='stock_day'
)
def get_today_all(output='pd'):
"""today all
Returns:
[type] -- [description]
"""
data = []
today = str(datetime.date.today())
codes = QA_fetch_get_stock_list('stock').code.tolist()
bestip = select_best_ip()['stock']
for code in codes:
try:
l = QA_fetch_get_stock_day(code, today, today, '00', ip=bestip)
except:
bestip = select_best_ip()['stock']
l = QA_fetch_get_stock_day(code, today, today, '00', ip=bestip)
if l is not None:
data.append(l)
res = pd.concat(data)
if output in ['pd']:
return res
elif output in ['QAD']:
return QA_DataStruct_Stock_day(
res.set_index(['date', 'code'], drop=False))
def from_tushare(dataframe, dtype='day'):
"""dataframe from tushare
Arguments:
dataframe {[type]} -- [description]
Returns:
[type] -- [description]
"""
if dtype in ['day']:
return QA_DataStruct_Stock_day(
dataframe.assign(date=pd.to_datetime(dataframe.date).dt.tz_localize(None).dt.tz_localize('Asia/Shanghai')
).set_index(['date',
'code'],
drop=False),
dtype='stock_day'
)
elif dtype in ['min']:
return QA_DataStruct_Stock_min(
dataframe.assign(datetime=pd.to_datetime(dataframe.datetime).dt.tz_localize(None).dt.tz_localize('Asia/Shanghai')
).set_index(['datetime',
'code'],
drop=False),
dtype='stock_min'
)
def from_tushare(dataframe, dtype='day'):
"""dataframe from tushare
Arguments:
dataframe {[type]} -- [description]
Returns:
[type] -- [description]
"""
if dtype in ['day']:
return QA_DataStruct_Stock_day(dataframe.set_index(['date', 'code'],
drop=False),
dtype='stock_day')
elif dtype in ['min']:
return QA_DataStruct_Stock_min(dataframe.set_index(
['datetime', 'code'], drop=False),
dtype='stock_min')
def __init__(self, frequence=9, type='stock'):
"""
Args:
frequence: 周期
type: ‘stock’：股票
‘index’：指数或etf
‘future’：期货
‘auto’：自动识别
"""
if type == 'stock':
if 5 <= frequence != 8:
# 日线以上周期
self._dataStruct = lambda df, dtype=type, if_fq='bfq': QA_DataStruct_Stock_day(
df, dtype=type, if_fq=if_fq)
else:
self._dataStruct = lambda df, dtype=type, if_fq='bfq': QA_DataStruct_Stock_min(
df, dtype=type, if_fq=if_fq)
elif type == 'index':
if 5 <= frequence != 8:
# 日线以上周期
self._dataStruct = lambda df, dtype=type, if_fq='bfq': QA_DataStruct_Index_day(
df, dtype=type, if_fq=if_fq)
else:
self._dataStruct = lambda df, dtype=type, if_fq='bfq': QA_DataStruct_Index_min(
df, dtype=type, if_fq=if_fq)
elif type == 'future':
if 5 <= frequence != 8:
# 日线以上周期
self._dataStruct = lambda df, dtype=type, if_fq='bfq': QA_DataStruct_Future_day(
df, dtype=type, if_fq=if_fq)
else:
self._dataStruct = lambda df, dtype=type, if_fq='bfq': QA_DataStruct_Future_min(
df, dtype=type, if_fq=if_fq)
else:
raise Exception("不支持的类型")
def QA_stock_day_warpper(func, *args, **kwargs):
return QA_DataStruct_Stock_day(func(*args, **kwargs))
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.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)
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)
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.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 datastruct_formater(data,
frequence=FREQUENCE.DAY,
market_type=MARKET_TYPE.STOCK_CN,
default_header=[]):
"""一个任意格式转化为DataStruct的方法
Arguments:
data {[type]} -- [description]
Keyword Arguments:
frequence {[type]} -- [description] (default: {FREQUENCE.DAY})
market_type {[type]} -- [description] (default: {MARKET_TYPE.STOCK_CN})
default_header {list} -- [description] (default: {[]})
Returns:
[type] -- [description]
"""
if isinstance(data, list):
try:
res = pd.DataFrame(data, columns=default_header)
if frequence is FREQUENCE.DAY:
if market_type is MARKET_TYPE.STOCK_CN:
return QA_DataStruct_Stock_day(
res.assign(date=pd.to_datetime(res.date)).set_index(
['date', 'code'], drop=False),
dtype='stock_day')
elif frequence in [
FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN,
FREQUENCE.FIFTEEN_MIN, FREQUENCE.THIRTY_MIN,
FREQUENCE.SIXTY_MIN
]:
if market_type is MARKET_TYPE.STOCK_CN:
return QA_DataStruct_Stock_min(res.assign(
datetime=pd.to_datetime(res.datetime)).set_index(
['datetime', 'code'], drop=False),
dtype='stock_min')
except:
pass
elif isinstance(data, np.ndarray):
try:
res = pd.DataFrame(data, columns=default_header)
if frequence is FREQUENCE.DAY:
if market_type is MARKET_TYPE.STOCK_CN:
return QA_DataStruct_Stock_day(
res.assign(date=pd.to_datetime(res.date)).set_index(
['date', 'code'], drop=False),
dtype='stock_day')
elif frequence in [
FREQUENCE.ONE_MIN, FREQUENCE.FIVE_MIN,
FREQUENCE.FIFTEEN_MIN, FREQUENCE.THIRTY_MIN,
FREQUENCE.SIXTY_MIN
]:
if market_type is MARKET_TYPE.STOCK_CN:
return QA_DataStruct_Stock_min(res.assign(
datetime=pd.to_datetime(res.datetime)).set_index(
['datetime', 'code'], drop=False),
dtype='stock_min')
except:
pass
elif isinstance(data, pd.DataFrame):
index = data.index
if isinstance(index, pd.MultiIndex):
pass
elif isinstance(index, pd.DatetimeIndex):
pass
elif isinstance(index, pd.Index):
pass
def QA_quotation(code, start, end, frequence, market, source, output):
"""一个统一的fetch
Arguments:
code {str/list} -- 证券/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 market == MARKET_TYPE.STOCK_CN:
if frequence == FREQUENCE.DAY:
if source == DATASOURCE.MONGO:
res = QAQueryAdv.QA_fetch_stock_day_adv(code, start, end)
elif source == DATASOURCE.TDX:
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:
res = QAQueryAdv.QA_fetch_stock_min_adv(code,
start,
end,
frequence=frequence)
elif source == DATASOURCE.TDX:
res = QATdx.QA_fetch_get_stock_min(code,
start,
end,
frequence=frequence)
res = QA_DataStruct_Stock_min(
res.set_index(['datetime', 'code']))
elif frequence == FREQUENCE.TICK:
if source == DATASOURCE.TDX:
res = QATdx.QA_fetch_get_stock_transaction(code, start, end)
elif frequence == FREQUENCE.REALTIME:
if source == DATASOURCE.TDX:
res = QA_DataStruct_Stock_realtime(
QATdx.QA_fetch_get_stock_realtime(code))
elif market == MARKET_TYPE.FUTURE_CN:
if frequence == FREQUENCE.DAY:
if source == DATASOURCE.MONGO:
res = QAQueryAdv.QA_fetch_future_day_adv(code, start, end)
elif source == DATASOURCE.TDX:
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:
res = QAQueryAdv.QA_fetch_future_min_adv(code,
start,
end,
frequence=frequence)
elif source == DATASOURCE.TDX:
res = QATdx.QA_fetch_get_future_min(code,
start,
end,
frequence=frequence)
res = QA_DataStruct_Future_min(
res.set_index(['datetime', 'code']))
elif frequence == FREQUENCE.TICK:
if source == DATASOURCE.TDX:
res = QATdx.QA_fetch_get_future_transaction(code, start, end)
elif frequence == FREQUENCE.REALTIME:
if source == DATASOURCE.TDX:
res = QA_DataStruct_Future_realtime(
QATdx.QA_fetch_get_future_realtime(code))
# 指数代码和股票代码是冲突重复的， sh000001 上证指数 000001 是不同的
elif market == MARKET_TYPE.INDEX_CN:
if frequence == FREQUENCE.DAY:
if source == DATASOURCE.MONGO:
res = QAQueryAdv.QA_fetch_index_day_adv(code, start, end)
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))
return res
