def QA_fetch_future_day(code,
start,
end,
format='numpy',
collections=DATABASE.future_day):
start = str(start)[0:10]
end = str(end)[0:10]
code = QA_util_code_tolist(code, auto_fill=False)
if (QA_util_dateordatetime_valid(start)) & (
QA_util_dateordatetime_valid(end)):
__data = []
cursor = collections.find(
{
'code': {
'$in': code
},
"date_stamp": {
"$lte": QA_util_date_stamp(end),
"$gte": QA_util_date_stamp(start)
}
}, {"_id": 0},
batch_size=10000)
res = pd.DataFrame([item for item in cursor])
'''数据处理（不改变格式，只进行异常排查，设置索引，选择重要的列这三个部分）'''
res = __QA_fetch_query_filter(
res, DATA_QUERY_INDEX_COLUMNS_UNIQUE.FUTURE_DAY, query=None)
'''数据格式整理'''
return QA_util_to_anyformat_from_pandas(data=res, format=format)
else:
QA_util_log_info(
'QA Error QA_fetch_future_day data parameter start=%s end=%s is not right'
% (start, end))
return None
async def QA_fetch_stock_day(code,
start,
end,
format='numpy',
frequence='day',
collections=DATABASE_ASYNC.stock_day):
'获取股票日线'
start = str(start)[0:10]
end = str(end)[0:10]
# code checking
code = QA_util_code_tolist(code)
if QA_util_date_valid(end):
__data = []
cursor = collections.find({
'code': {
'$in': code
},
"date_stamp": {
"$lte": QA_util_date_stamp(end),
"$gte": QA_util_date_stamp(start)
}
})
try:
res = pd.DataFrame([item async for item in cursor])
except SyntaxError:
print('THIS PYTHON VERSION NOT SUPPORT "async for" function')
pass
try:
res = res.drop(
'_id', axis=1).assign(volume=res.vol).query('volume>1').assign(
date=pd.to_datetime(res.date)).drop_duplicates(
(['date', 'code'])).set_index('date', drop=False)
res = res.ix[:, [
'code', 'open', 'high', 'low', 'close', 'volume', 'amount',
'date'
]]
except:
res = None
if format in ['P', 'p', 'pandas', 'pd']:
return res
elif format in ['json', 'dict']:
return QA_util_to_json_from_pandas(res)
# 多种数据格式
elif format in ['n', 'N', 'numpy']:
return numpy.asarray(res)
elif format in ['list', 'l', 'L']:
return numpy.asarray(res).tolist()
else:
print(
"QA Error QA_fetch_stock_day format parameter %s is none of \"P, p, pandas, pd , json, dict , n, N, numpy, list, l, L, !\" "
% format)
return None
else:
QA_util_log_info(
'QA Error QA_fetch_stock_day data parameter start=%s end=%s is not right'
% (start, end))
def QA_fetch_index_day(code, start, end, format='numpy', collections=DATABASE.index_day):
'获取指数日线'
start = str(start)[0:10]
end = str(end)[0:10]
code = QA_util_code_tolist(code)
if QA_util_date_valid(end) == True:
__data = []
cursor = collections.find({
'code': {'$in': code}, "date_stamp": {
"$lte": QA_util_date_stamp(end),
"$gte": QA_util_date_stamp(start)}})
if format in ['dict', 'json']:
return [data for data in cursor]
for item in cursor:
__data.append([str(item['code']), float(item['open']), float(item['high']), float(
item['low']), float(item['close']), float(item['vol']), item['date']])
# 多种数据格式
if format in ['n', 'N', 'numpy']:
__data = numpy.asarray(__data)
elif format in ['list', 'l', 'L']:
__data = __data
elif format in ['P', 'p', 'pandas', 'pd']:
__data = DataFrame(__data, columns=[
'code', 'open', 'high', 'low', 'close', 'volume', 'date'])
__data['date'] = pd.to_datetime(__data['date'])
__data = __data.set_index('date', drop=False)
return __data
else:
QA_util_log_info('something wrong with date')
def QA_count_eastmoney_stock_xjlc_record_count_one_by_one(
self, str_stock_code, collections=DATABASE.eastmoney_stock_zjlx):
codeArray = QA_util_code_tolist(str_stock_code)
cursor = collections.find({
'stock_code': {
'$in': codeArray
}
}).sort('date')
sizeRec = cursor.count()
firstRec = None
lastRec = None
if sizeRec > 0:
firstRec = cursor[0]
lastRec = cursor[sizeRec - 1]
#返回 【code 记录条数 开始日期 结束日期 】
#print(firstRec)
#print(lastRec)
firstRecDate = None
if firstRec is not None:
firstRecDate = firstRec['date']
lastRecDate = None
if lastRec is not None:
lastRecDate = lastRec['date']
return [str_stock_code, sizeRec, firstRecDate, lastRecDate]
def QA_fetch_stock_min(code,
start,
end,
format='numpy',
frequence='1min',
collections=DATABASE.stock_min):
'获取股票分钟线'
if frequence in ['1min', '1m']:
frequence = '1min'
elif frequence in ['5min', '5m']:
frequence = '5min'
elif frequence in ['15min', '15m']:
frequence = '15min'
elif frequence in ['30min', '30m']:
frequence = '30min'
elif frequence in ['60min', '60m']:
frequence = '60min'
else:
print(
"💢 Error QA_fetch_stock_min parameter frequence=%s is none of 1min 1m 5min 5m 15min 15m 30min 30m 60min 60m"
% frequence)
__data = []
# code checking
code = QA_util_code_tolist(code)
cursor = collections.find({
'code': {
'$in': code
},
"time_stamp": {
"$gte": QA_util_time_stamp(start),
"$lte": QA_util_time_stamp(end)
},
'type': frequence
})
res = pd.DataFrame([item for item in cursor])
try:
res = res.drop(
'_id', axis=1).assign(volume=res.vol).query('volume>1').assign(
datetime=pd.to_datetime(res.datetime)).drop_duplicates(
['datetime', 'code']).set_index('datetime', drop=False)
# return res
except:
res = None
if format in ['P', 'p', 'pandas', 'pd']:
return res
elif format in ['json', 'dict']:
return QA_util_to_json_from_pandas(res)
# 多种数据格式
elif format in ['n', 'N', 'numpy']:
return numpy.asarray(res)
elif format in ['list', 'l', 'L']:
return numpy.asarray(res).tolist()
else:
print(
"💢 Error QA_fetch_stock_min format parameter %s is none of \"P, p, pandas, pd , json, dict , n, N, numpy, list, l, L, !\" "
% format)
return None
def QA_fetch_index_day(code, start, end, format='numpy', collections=DATABASE.index_day):
'获取指数日线'
start = str(start)[0:10]
end = str(end)[0:10]
code = QA_util_code_tolist(code)
if QA_util_date_valid(end) == True:
cursor = collections.find({
'code': {'$in': code}, "date_stamp": {
"$lte": QA_util_date_stamp(end),
"$gte": QA_util_date_stamp(start)}}, {"_id": 0}, batch_size=10000)
res = pd.DataFrame([item for item in cursor])
try:
res = res.assign(volume=res.vol, date=pd.to_datetime(
res.date)).drop_duplicates((['date', 'code'])).set_index('date', drop=False)
except:
res = None
if format in ['P', 'p', 'pandas', 'pd']:
return res
elif format in ['json', 'dict']:
return QA_util_to_json_from_pandas(res)
# 多种数据格式
elif format in ['n', 'N', 'numpy']:
return numpy.asarray(res)
elif format in ['list', 'l', 'L']:
return numpy.asarray(res).tolist()
else:
print("QA Error QA_fetch_index_day format parameter %s is none of \"P, p, pandas, pd , n, N, numpy !\" " % format)
return None
else:
QA_util_log_info(
'QA Error QA_fetch_index_day data parameter start=%s end=%s is not right' % (start, end))
def QA_fetch_ctp_tick(code, start, end, frequence, format='pd', collections=DATABASE.ctp_tick):
"""仅供存储的ctp tick使用
Arguments:
code {[type]} -- [description]
Keyword Arguments:
format {str} -- [description] (default: {'pd'})
collections {[type]} -- [description] (default: {DATABASE.ctp_tick})
Returns:
[type] -- [description]
"""
code = QA_util_code_tolist(code, auto_fill=False)
cursor = collections.find({
'InstrumentID': {'$in': code}, "time_stamp": {
"$gte": QA_util_time_stamp(start),
"$lte": QA_util_time_stamp(end)
}, 'type': frequence
}, {"_id": 0}, batch_size=10000)
hq = pd.DataFrame([data for data in cursor]).replace(1.7976931348623157e+308,
numpy.nan).replace('', numpy.nan).dropna(axis=1)
p1 = hq.loc[:, ['ActionDay', 'AskPrice1', 'AskVolume1', 'AveragePrice', 'BidPrice1',
'BidVolume1', 'HighestPrice', 'InstrumentID', 'LastPrice',
'OpenInterest', 'TradingDay', 'UpdateMillisec',
'UpdateTime', 'Volume']]
p1 = p1.assign(datetime=p1.ActionDay.apply(QA_util_date_int2str)+' '+p1.UpdateTime + (p1.UpdateMillisec/1000000).apply(lambda x: str('%.6f' % x)[1:]),
code=p1.InstrumentID)
p1.datetime = pd.to_datetime(p1.datetime)
return p1.set_index(p1.datetime)
def QA_fetch_future_day(code, start, end, format='numpy', collections=DATABASE.future_day):
start = str(start)[0:10]
end = str(end)[0:10]
code = QA_util_code_tolist(code, auto_fill=False)
if QA_util_date_valid(end) == True:
__data = []
cursor = collections.find({
'code': {'$in': code}, "date_stamp": {
"$lte": QA_util_date_stamp(end),
"$gte": QA_util_date_stamp(start)}}, {"_id": 0}, batch_size=10000)
if format in ['dict', 'json']:
return [data for data in cursor]
for item in cursor:
__data.append([str(item['code']), float(item['open']), float(item['high']), float(
item['low']), float(item['close']), float(item['position']), float(item['price']), float(item['trade']), item['date']])
# 多种数据格式
if format in ['n', 'N', 'numpy']:
__data = numpy.asarray(__data)
elif format in ['list', 'l', 'L']:
__data = __data
elif format in ['P', 'p', 'pandas', 'pd']:
__data = DataFrame(
__data, columns=['code', 'open', 'high', 'low', 'close', 'position', 'price', 'trade', 'date']).drop_duplicates()
__data['date'] = pd.to_datetime(__data['date'])
__data = __data.set_index('date', drop=False)
else:
print("QA Error QA_fetch_future_day format parameter %s is none of \"P, p, pandas, pd , n, N, numpy !\" " % format)
return __data
else:
QA_util_log_info('QA something wrong with date')
def QA_fetch_stock_xdxr(code, format='pd', collections=DATABASE.stock_xdxr):
'获取股票除权信息/数据库'
code = QA_util_code_tolist(code)
data = pd.DataFrame([item for item in collections.find(
{'code': {'$in': code}}, batch_size=10000)]).drop(['_id'], axis=1)
data['date'] = pd.to_datetime(data['date'])
return data.set_index('date', drop=False)
def _QA_fetch_stock_adj(code,
start,
end,
format='pd',
collections=DATABASE.stock_adj):
"""获取股票复权系数 ADJ
"""
start = str(start)[0:10]
end = str(end)[0:10]
#code= [code] if isinstance(code,str) else code
# code checking
code = QA_util_code_tolist(code)
if QA_util_date_valid(end):
cursor = collections.find(
{
'code': {
'$in': code
},
"date": {
"$lte": end,
"$gte": start
}
}, {"_id": 0},
batch_size=10000)
#res=[QA_util_dict_remove_key(data, '_id') for data in cursor]
res = pd.DataFrame([item for item in cursor])
res.date = pd.to_datetime(res.date)
return res.set_index('date', drop=False)
def QA_SU_save_financial_report_day(code=None,
client=DATABASE,
ui_log=None,
ui_progress=None):
'''
save stock_day
保存财报日历
历史全部数据
:return:
'''
def __saving_work(code, stock_financial):
try:
QA_util_log_info(
'##JOB01 Now Saving WY financial_report==== {}'.format(
str(code)), ui_log)
stock_financial.insert_many(QA_util_to_json_from_pandas(
QA_fetch_get_stock_report_wy(code)),
ordered=False)
gc.collect()
except Exception as error0:
print(error0)
err.append(str(code))
if code is None:
code = QA_fetch_financial_code()
else:
code = QA_util_code_tolist(code)
if code is not None:
stock_financial = client.stock_financial_wy
stock_financial.create_index([("code", pymongo.ASCENDING),
("report_date", pymongo.ASCENDING)],
unique=True)
err = []
for item in code:
QA_util_log_info('The {} of Total {}'.format(
(code.index(item) + 1), len(code)))
strProgressToLog = 'DOWNLOAD PROGRESS {}'.format(
str(float(
(code.index(item) + 1) / len(code) * 100))[0:4] + '%',
ui_log)
intProgressToLog = int(
float((code.index(item) + 1) / len(code) * 100))
QA_util_log_info(strProgressToLog,
ui_log=ui_log,
ui_progress=ui_progress,
ui_progress_int_value=intProgressToLog)
__saving_work(item, stock_financial)
if len(err) < 1:
QA_util_log_info('SUCCESS save WY financial_report ^_^', ui_log)
else:
QA_util_log_info(' ERROR CODE \n ', ui_log)
QA_util_log_info(err, ui_log)
else:
QA_util_log_info(' No report send \n ', ui_log)
def QA_fetch_stock_technical_index(code,
start,
end=None,
type='day',
format='pd'):
'获取股票日线'
#code= [code] if isinstance(code,str) else code
# code checking
if type == 'day':
collections = DATABASE.stock_technical_index
elif type == 'week':
collections = DATABASE.stock_technical_week
elif type == 'month':
collections = DATABASE.stock_technical_month
else:
print("type should be in ['day', 'week', 'month']")
code = QA_util_code_tolist(code)
if QA_util_date_valid(end):
__data = []
cursor = collections.find(
{
'code': {
'$in': code
},
"date_stamp": {
"$lte": QA_util_date_stamp(end),
"$gte": QA_util_date_stamp(start)
}
}, {"_id": 0},
batch_size=10000)
#res=[QA_util_dict_remove_key(data, '_id') for data in cursor]
res = pd.DataFrame([item for item in cursor])
try:
res = res.drop_duplicates((['code', 'date']))
res['date'] = res['date'].apply(lambda x: str(x)[0:10])
res = res.drop(['date_stamp'], axis=1).set_index(['date', 'code'])
except:
res = None
if format in ['P', 'p', 'pandas', 'pd']:
return res
elif format in ['json', 'dict']:
return QA_util_to_json_from_pandas(res)
# 多种数据格式
elif format in ['n', 'N', 'numpy']:
return numpy.asarray(res)
elif format in ['list', 'l', 'L']:
return numpy.asarray(res).tolist()
else:
print(
"QA Error QA_fetch_stock_technical_index format parameter %s is none of \"P, p, pandas, pd , json, dict , n, N, numpy, list, l, L, !\" "
% format)
return None
else:
QA_util_log_info(
'QA Error QA_fetch_stock_technical_index data parameter start=%s end=%s is not right'
% (start, end))
def QA_fetch_index_min(code,
start,
end,
format='numpy',
frequence='1min',
collections=DATABASE.index_min):
'获取股票分钟线'
if frequence in ['1min', '1m']:
frequence = '1min'
elif frequence in ['5min', '5m']:
frequence = '5min'
elif frequence in ['15min', '15m']:
frequence = '15min'
elif frequence in ['30min', '30m']:
frequence = '30min'
elif frequence in ['60min', '60m']:
frequence = '60min'
__data = []
code = QA_util_code_tolist(code)
cursor = collections.find({
'code': {
'$in': code
},
"time_stamp": {
"$gte": QA_util_time_stamp(start),
"$lte": QA_util_time_stamp(end)
},
'type': frequence
})
if format in ['dict', 'json']:
return [data for data in cursor]
for item in cursor:
__data.append([
str(item['code']),
float(item['open']),
float(item['high']),
float(item['low']),
float(item['close']),
float(item['vol']), item['datetime'], item['time_stamp'],
item['date']
])
__data = DataFrame(__data,
columns=[
'code', 'open', 'high', 'low', 'close', 'volume',
'datetime', 'time_stamp', 'date'
])
__data['datetime'] = pd.to_datetime(__data['datetime'])
__data = __data.set_index('datetime', drop=False)
if format in ['numpy', 'np', 'n']:
return numpy.asarray(__data)
elif format in ['list', 'l', 'L']:
return numpy.asarray(__data).tolist()
elif format in ['P', 'p', 'pandas', 'pd']:
return __data
def QA_fetch_stock_divyield(code,
start,
end=None,
format='pd',
collections=DATABASE.stock_divyield):
'获取股票日线'
#code= [code] if isinstance(code,str) else code
# code checking
code = QA_util_code_tolist(code)
if QA_util_date_valid(end):
__data = []
cursor = collections.find(
{
'a_stockcode': {
'$in': code
},
"dir_dcl_date": {
"$lte": end,
"$gte": start
}
},
batch_size=10000)
#res=[QA_util_dict_remove_key(data, '_id') for data in cursor]
res = pd.DataFrame([item for item in cursor])
try:
res = res.drop('_id', axis=1).drop_duplicates(
(['dir_dcl_date', 'a_stockcode']))
res = res.ix[:, [
'a_stockcode', 'a_stocksname', 'div_info', 'div_type_code',
'bonus_shr', 'cash_bt', 'cap_shr', 'epsp', 'ps_cr', 'ps_up',
'reg_date', 'dir_dcl_date', 'a_stockcode1', 'ex_divi_date',
'prg'
]]
except:
res = None
if format in ['P', 'p', 'pandas', 'pd']:
return res
elif format in ['json', 'dict']:
return QA_util_to_json_from_pandas(res)
# 多种数据格式
elif format in ['n', 'N', 'numpy']:
return numpy.asarray(res)
elif format in ['list', 'l', 'L']:
return numpy.asarray(res).tolist()
else:
print(
"QA Error QA_fetch_stock_divyield format parameter %s is none of \"P, p, pandas, pd , json, dict , n, N, numpy, list, l, L, !\" "
% format)
return None
else:
QA_util_log_info(
'QA Error QA_fetch_stock_divyield data parameter start=%s end=%s is not right'
% (start, end))
def QA_fetch_stock_info(code, format='pd', collections=DATABASE.stock_info):
code = QA_util_code_tolist(code)
try:
data = pd.DataFrame([item for item in collections.find(
{'code': {'$in': code}}, {"_id": 0}, batch_size=10000)])
#data['date'] = pd.to_datetime(data['date'])
return data.set_index('code', drop=False)
except Exception as e:
QA_util_log_info(e)
return None
def QA_fetch_stock_block(code=None, format='pd', collections=DATABASE.stock_block):
if code is not None:
code = QA_util_code_tolist(code)
data = pd.DataFrame([item for item in collections.find(
{'code': {'$in': code}}, batch_size=10000)]).drop(['_id'], axis=1)
return data.set_index('code', drop=False)
else:
data = pd.DataFrame(
[item for item in collections.find()]).drop(['_id'], axis=1)
return data.set_index('code', drop=False)
def QA_fetch_stock_financial_calendar(code,
start,
end=None,
format='pd',
collections=DATABASE.report_calendar):
'获取股票日线'
#code= [code] if isinstance(code,str) else code
# code checking
code = QA_util_code_tolist(code)
if QA_util_date_valid(end):
__data = []
cursor = collections.find(
{
'code': {
'$in': code
},
"real_date": {
"$lte": end,
"$gte": start
}
},
batch_size=10000)
#res=[QA_util_dict_remove_key(data, '_id') for data in cursor]
res = pd.DataFrame([item for item in cursor])
try:
res = res.drop('_id', axis=1).drop_duplicates(
(['report_date', 'code']))
res = res.ix[:, [
'code', 'name', 'pre_date', 'first_date', 'second_date',
'third_date', 'real_date', 'codes', 'report_date', 'crawl_date'
]]
except:
res = None
if format in ['P', 'p', 'pandas', 'pd']:
return res
elif format in ['json', 'dict']:
return QA_util_to_json_from_pandas(res)
# 多种数据格式
elif format in ['n', 'N', 'numpy']:
return numpy.asarray(res)
elif format in ['list', 'l', 'L']:
return numpy.asarray(res).tolist()
else:
print(
"QA Error QA_fetch_stock_financial_calendar format parameter %s is none of \"P, p, pandas, pd , json, dict , n, N, numpy, list, l, L, !\" "
% format)
return None
else:
QA_util_log_info(
'QA Error QA_fetch_stock_financial_calendar data parameter start=%s end=%s is not right'
% (start, end))
def QA_fetch_stock_min(code,
start,
end,
frequence='1min',
format='numpy',
collections=DATABASE.stock_min):
'获取股票分钟线'
if (QA_util_dateordatetime_valid(start)) & (
QA_util_dateordatetime_valid(end)):
'''数据获取'''
if frequence in ['1min', '1m']:
frequence = '1min'
elif frequence in ['5min', '5m']:
frequence = '5min'
elif frequence in ['15min', '15m']:
frequence = '15min'
elif frequence in ['30min', '30m']:
frequence = '30min'
elif frequence in ['60min', '60m']:
frequence = '60min'
else:
QA_util_log_info(
"QA Error QA_fetch_stock_min parameter frequence=%s is none of 1min 1m 5min 5m 15min 15m 30min 30m 60min 60m"
% frequence)
__data = []
# code checking
code = QA_util_code_tolist(code)
cursor = collections.find(
{
'code': {
'$in': code
},
"time_stamp": {
"$gte": QA_util_time_stamp(start),
"$lte": QA_util_time_stamp(end)
},
'type': frequence
}, {"_id": 0},
batch_size=10000)
res = pd.DataFrame([item for item in cursor])
'''数据处理（不改变格式，只进行异常排查，设置索引，选择重要的列这三个部分）'''
res = __QA_fetch_query_filter(
res, DATA_QUERY_INDEX_COLUMNS_UNIQUE.STOCK_MIN, query='volume>1')
print(res)
'''数据格式整理'''
return QA_util_to_anyformat_from_pandas(data=res, format=format)
else:
QA_util_log_info(
'QA Error QA_fetch_stock_min data parameter start=%s end=%s is not right'
% (start, end))
return None
def QA_fetch_stock_xdxr(code, format='pd', collections=DATABASE.stock_xdxr):
'获取股票除权信息/数据库'
code = QA_util_code_tolist(code)
data = pd.DataFrame([
item
for item in collections.find({'code': {
'$in': code
}}, batch_size=10000)
]).drop(['_id'], axis=1)
return __QA_fetch_query_filter(data,
DATA_QUERY_INDEX_COLUMNS_UNIQUE.STOCK_XDXR,
query=None)
def QA_fetch_stock_day(code,
start,
end,
format='numpy',
frequence='day',
collections=DATABASE.stock_day):
'获取股票日线'
start = str(start)[0:10]
end = str(end)[0:10]
#code= [code] if isinstance(code,str) else code
# code checking
code = QA_util_code_tolist(code)
if QA_util_date_valid(end) == True:
__data = []
cursor = collections.find({
'code': {
'$in': code
},
"date_stamp": {
"$lte": QA_util_date_stamp(end),
"$gte": QA_util_date_stamp(start)
}
})
#res=[QA_util_dict_remove_key(data, '_id') for data in cursor]
res = pd.DataFrame([item for item in cursor])
try:
res = res.drop(
'_id', axis=1).assign(volume=res.vol).query('volume>1').assign(
date=pd.to_datetime(res.date)).drop_duplicates(
(['date', 'code'])).set_index('date', drop=False)
res = res.ix[:, [
'code', 'open', 'high', 'low', 'close', 'volume', 'amount',
'date'
]]
except:
res = None
if format in ['P', 'p', 'pandas', 'pd']:
return res
elif format in ['json', 'dict']:
return QA_util_to_json_from_pandas(res)
# 多种数据格式
elif format in ['n', 'N', 'numpy']:
return numpy.asarray(res)
elif format in ['list', 'l', 'L']:
return numpy.asarray(res).tolist()
else:
return None
else:
QA_util_log_info('something wrong with date')
def QA_fetch_index_transaction(code,
start,
end,
format='numpy',
frequence='tick',
collections=DATABASE.index_transaction):
'获取股票分钟线'
if frequence in ['tick', 'TICK', 'transaction']:
frequence = 'tick'
else:
print(
"QA Error QA_fetch_index_transaction parameter frequence=%s is none of tick Tick transaction"
% frequence)
_data = []
# code checking
code = QA_util_code_tolist(code)
cursor = collections.find(
{
'code': {
'$in': code
},
"time_stamp": {
"$gte": QA_util_time_stamp(start),
"$lte": QA_util_time_stamp(end)
},
'type': frequence
}, {"_id": 0},
batch_size=10000)
res = pd.DataFrame([item for item in cursor])
try:
res = res.assign(volume=res.vol, datetime=pd.to_datetime(
res.datetime)).query('volume>1').drop_duplicates(
['datetime', 'code']).set_index('datetime', drop=False)
# return res
except:
res = None
if format in ['P', 'p', 'pandas', 'pd']:
return res
elif format in ['json', 'dict']:
return QA_util_to_json_from_pandas(res)
# 多种数据格式
elif format in ['n', 'N', 'numpy']:
return numpy.asarray(res)
elif format in ['list', 'l', 'L']:
return numpy.asarray(res).tolist()
else:
print(
"QA Error QA_fetch_index_transaction format parameter %s is none of \"P, p, pandas, pd , json, dict , n, N, numpy, list, l, L, !\" "
% format)
return None
def QA_fetch_stock_pure_tech_indicator(code, start, end, vol='non-zero', keys='all', format='pd', collections=DATABASE.stock_tech_indicator_3):
"""
return: from start to end, doesn't exclude vol==0 dates in between, those date are continuous
and aligned with sh index already
"""
start = str(start)[0:10]
end = str(end)[0:10]
# code checking
code = QA_util_code_tolist(code)
if QA_util_date_valid(end):
if vol == 'non-zero':
cursor = collections.find({
'code': {'$in': code}, "date_stamp": {
"$lte": QA_util_date_stamp(end),
"$gte": QA_util_date_stamp(start)},
'volume': {"$gt": 0}})
else:
cursor = collections.find({
'code': {'$in': code}, "date_stamp": {
"$lte": QA_util_date_stamp(end),
"$gte": QA_util_date_stamp(start)}})
res = pd.DataFrame([item for item in cursor])
try:
# drop '_id', change 'date' from str to datetime, set 'date' to index
res = res.drop('_id', axis=1).assign(date=pd.to_datetime(
res.date)).drop_duplicates((['date', 'code'])).set_index('date', drop=False)
if keys != 'all':
if isinstance(keys, str):
res = res.ix[:, [keys]]
elif isinstance(keys, list):
res = res.ix[:, keys]
except:
res = None
if format in ['P', 'p', 'pandas', 'pd']:
return res
elif format in ['json', 'dict']:
return QA_util_to_json_from_pandas(res)
# 多种数据格式
elif format in ['n', 'N', 'numpy']:
return numpy.asarray(res)
elif format in ['list', 'l', 'L']:
return numpy.asarray(res).tolist()
else:
print("QA Error QA_fetch_tech_indicator format parameter %s is none of \"P, p, pandas, pd , json, dict , n, N, numpy, list, l, L, !\" " % format)
return None
else:
QA_util_log_info(
'QA Error QA_fetch_tech_indicator data parameter start=%s end=%s is not right' % (start, end))
def QA_fetch_stock_fianacial(code,
start,
end=None,
format='pd',
collections=DATABASE.stock_financial_analysis):
'获取quant基础数据'
#code= [code] if isinstance(code,str) else code
# code checking
code = QA_util_code_tolist(code)
if QA_util_date_valid(end):
cursor = collections.find(
{
'CODE': {
'$in': code
},
"date_stamp": {
"$lte": QA_util_date_stamp(end),
"$gte": QA_util_date_stamp(start)
}
},
batch_size=10000)
#res=[QA_util_dict_remove_key(data, '_id') for data in cursor]
res = pd.DataFrame([item for item in cursor])
try:
res.columns = [
i.lower() if i == 'CODE' else i for i in list(res.columns)
]
res = res.drop(['date_stamp', '_id'], axis=1).drop_duplicates(
(['code', 'date']))
res['RNG_RES'] = res['AVG60_RNG'] * 60 / res['RNG_60']
except:
res = None
if format in ['P', 'p', 'pandas', 'pd']:
#res['report_date'] = pd.to_datetime(res['report_date']/1000, unit='s')
return res
elif format in ['json', 'dict']:
return QA_util_to_json_from_pandas(res)
# 多种数据格式
elif format in ['n', 'N', 'numpy']:
return numpy.asarray(res)
elif format in ['list', 'l', 'L']:
return numpy.asarray(res).tolist()
else:
print(
"QA Error QA_fetch_financial_TTM format parameter %s is none of \"P, p, pandas, pd , json, dict , n, N, numpy, list, l, L, !\" "
% format)
return None
else:
QA_util_log_info(
'QA Error QA_fetch_financial_TTM data parameter start=%s end=%s is not right'
% (start, end))
def QA_fetch_financial_TTM(code,
start,
end=None,
format='pd',
collections=DATABASE.financial_TTM):
'获取财报TTM数据'
#code= [code] if isinstance(code,str) else code
# code checking
code = QA_util_code_tolist(code)
if QA_util_date_valid(end):
__data = []
cursor = collections.find(
{
'CODE': {
'$in': code
},
"date": {
"$lte": QA_util_date_stamp(end),
"$gte": QA_util_date_stamp(start)
}
},
batch_size=10000)
#res=[QA_util_dict_remove_key(data, '_id') for data in cursor]
res = pd.DataFrame([item for item in cursor])
try:
res = res.drop('_id', axis=1).drop_duplicates(
(['REPORT_DATE', 'CODE']))
except:
res = None
if format in ['P', 'p', 'pandas', 'pd']:
return res
elif format in ['json', 'dict']:
return QA_util_to_json_from_pandas(res)
# 多种数据格式
elif format in ['n', 'N', 'numpy']:
return numpy.asarray(res)
elif format in ['list', 'l', 'L']:
return numpy.asarray(res).tolist()
else:
print(
"QA Error QA_fetch_financial_TTM format parameter %s is none of \"P, p, pandas, pd , json, dict , n, N, numpy, list, l, L, !\" "
% format)
return None
else:
QA_util_log_info(
'QA Error QA_fetch_financial_TTM data parameter start=%s end=%s is not right'
% (start, end))
def QA_fetch_stock_day(code, start, end, format='numpy', frequence='day', collections=DATABASE.stock_day):
"""'获取股票日线'
Returns:
[type] -- [description]
感谢@几何大佬的提示
https://docs.mongodb.com/manual/tutorial/project-fields-from-query-results/#return-the-specified-fields-and-the-id-field-only
"""
start = str(start)[0:10]
end = str(end)[0:10]
#code= [code] if isinstance(code,str) else code
# code checking
code = QA_util_code_tolist(code)
if QA_util_date_valid(end):
cursor = collections.find({
'code': {'$in': code}, "date_stamp": {
"$lte": QA_util_date_stamp(end),
"$gte": QA_util_date_stamp(start)}}, {"_id": 0}, batch_size=10000)
#res=[QA_util_dict_remove_key(data, '_id') for data in cursor]
res = pd.DataFrame([item for item in cursor])
try:
res = res.assign(volume=res.vol, date=pd.to_datetime(
res.date)).drop_duplicates((['date', 'code'])).query('volume>1').set_index('date', drop=False)
res = res.ix[:, ['code', 'open', 'high', 'low',
'close', 'volume', 'amount', 'date']]
except:
res = None
if format in ['P', 'p', 'pandas', 'pd']:
return res
elif format in ['json', 'dict']:
return QA_util_to_json_from_pandas(res)
# 多种数据格式
elif format in ['n', 'N', 'numpy']:
return numpy.asarray(res)
elif format in ['list', 'l', 'L']:
return numpy.asarray(res).tolist()
else:
print("QA Error QA_fetch_stock_day format parameter %s is none of \"P, p, pandas, pd , json, dict , n, N, numpy, list, l, L, !\" " % format)
return None
else:
QA_util_log_info(
'QA Error QA_fetch_stock_day data parameter start=%s end=%s is not right' % (start, end))
def QA_fetch_stock_info(code, format='pd', collections=DATABASE.stock_info):
code = QA_util_code_tolist(code)
try:
data = pd.DataFrame([
item for item in collections.find({'code': {
'$in': code
}}, {"_id": 0},
batch_size=10000)
])
#data['date'] = pd.to_datetime(data['date'])
return __QA_fetch_query_filter(
data, DATA_QUERY_INDEX_COLUMNS_UNIQUE.STOCK_INFO, query=None)
except Exception as e:
QA_util_log_info(e)
return None
def QA_fetch_stock_day(code,
start,
end,
format='numpy',
frequence='day',
collections=DATABASE.stock_day):
"""'获取股票日线'
Returns:
[type] -- [description]
感谢@几何大佬的提示
https://docs.mongodb.com/manual/tutorial/project-fields-from-query-results/#return-the-specified-fields-and-the-id-field-only
"""
if (QA_util_dateordatetime_valid(start)) & (
QA_util_dateordatetime_valid(end)):
'''数据获取'''
start_date = str(start)[0:10]
end_date = str(end)[0:10]
#code= [code] if isinstance(code,str) else code
# code checking
code = QA_util_code_tolist(code)
cursor = collections.find(
{
'code': {
'$in': code
},
"date_stamp": {
"$lte": QA_util_date_stamp(end_date),
"$gte": QA_util_date_stamp(start_date)
}
}, {"_id": 0},
batch_size=10000)
#res=[QA_util_dict_remove_key(data, '_id') for data in cursor]
res = pd.DataFrame([item for item in cursor])
'''数据处理（不改变格式，只进行异常排查，设置索引，选择重要的列这三个部分）'''
res = __QA_fetch_query_filter(
res, DATA_QUERY_INDEX_COLUMNS_UNIQUE.STOCK_DAY, query='volume>1')
'''数据格式整理'''
return QA_util_to_anyformat_from_pandas(data=res, format=format)
else:
QA_util_log_info(
'QA Error QA_fetch_stock_day data parameter start=%s end=%s is not right'
% (start, end))
return None
def QA_fetch_future_min(code,
start,
end,
frequence='1min',
format='numpy',
collections=DATABASE.future_min):
'获取股票分钟线'
if frequence in ['1min', '1m']:
frequence = '1min'
elif frequence in ['5min', '5m']:
frequence = '5min'
elif frequence in ['15min', '15m']:
frequence = '15min'
elif frequence in ['30min', '30m']:
frequence = '30min'
elif frequence in ['60min', '60m']:
frequence = '60min'
start = str(start)[0:19]
end = str(end)[0:19]
code = QA_util_code_tolist(code, auto_fill=False)
if (QA_util_dateordatetime_valid(start)) & (
QA_util_dateordatetime_valid(end)):
cursor = collections.find(
{
'code': {
'$in': code
},
"time_stamp": {
"$gte": QA_util_time_stamp(start),
"$lte": QA_util_time_stamp(end)
},
'type': frequence
},
batch_size=10000)
res = pd.DataFrame([item for item in cursor])
'''数据处理（不改变格式，只进行异常排查，设置索引，选择重要的列这三个部分）'''
res = __QA_fetch_query_filter(
res, DATA_QUERY_INDEX_COLUMNS_UNIQUE.FUTURE_MIN, query=None)
'''数据格式整理'''
return QA_util_to_anyformat_from_pandas(data=res, format=format)
else:
QA_util_log_info(
'QA Error QA_fetch_future_min data parameter start=%s end=%s is not right'
% (start, end))
return None
def getMin(cls, code, start, end, if_fq='00', frequence=8):
collections = cls.collectionsMin
'获取股票分钟线'
_, type_, _ = cls.getReverseFrequence(frequence)
_data = []
# code checking
code = QA_util_code_tolist(code)
cursor = collections.find(
{
'code': {
'$in': code
},
"time_stamp": {
"$gte": QA_util_time_stamp(start),
"$lte": QA_util_time_stamp(end)
},
'type': type_
}, {"_id": 0},
batch_size=10000)
res = pd.DataFrame([item for item in cursor])
try:
res = res.assign(
volume=res.vol, datetime=pd.to_datetime(
res.datetime)).query('volume>1').drop_duplicates(
['datetime', 'code']).set_index('datetime', drop=False)
# return res
except:
res = None
if cls.format in ['P', 'p', 'pandas', 'pd']:
return res
elif cls.format in ['json', 'dict']:
return QA_util_to_json_from_pandas(res)
# 多种数据格式
elif cls.format in ['n', 'N', 'numpy']:
return numpy.asarray(res)
elif cls.format in ['list', 'l', 'L']:
return numpy.asarray(res).tolist()
else:
print(
"QA Error QA_fetch_stock_min format parameter %s is none of \"P, p, pandas, pd , json, dict , n, N, numpy, list, l, L, !\" "
% cls.format)
return None
def QA_fetch_future_min(
code,
start, end,
format='numpy',
frequence='1min',
collections=DATABASE.future_min):
'获取股票分钟线'
if frequence in ['1min', '1m']:
frequence = '1min'
elif frequence in ['5min', '5m']:
frequence = '5min'
elif frequence in ['15min', '15m']:
frequence = '15min'
elif frequence in ['30min', '30m']:
frequence = '30min'
elif frequence in ['60min', '60m']:
frequence = '60min'
_data = []
code = QA_util_code_tolist(code, auto_fill=False)
cursor = collections.find({
'code': {'$in': code}, "time_stamp": {
"$gte": QA_util_time_stamp(start),
"$lte": QA_util_time_stamp(end)
}, 'type': frequence
}, batch_size=10000)
if format in ['dict', 'json']:
return [data for data in cursor]
for item in cursor:
_data.append([str(item['code']), float(item['open']), float(item['high']), float(
item['low']), float(item['close']), float(item.get('position', 0)), float(item.get('price', 0)), float(item.get('trade', item.get('volume', 0))),
item['datetime'], item['tradetime'], item['time_stamp'], item['date'], item['type']])
_data = DataFrame(_data, columns=[
'code', 'open', 'high', 'low', 'close', 'position', 'price', 'trade', 'datetime', 'tradetime', 'time_stamp', 'date', 'type'])
_data = _data.assign(datetime=pd.to_datetime(_data['datetime'])).drop_duplicates(
(['datetime', 'code'])).set_index('datetime', drop=False)
if format in ['numpy', 'np', 'n']:
return numpy.asarray(_data)
elif format in ['list', 'l', 'L']:
return numpy.asarray(_data).tolist()
elif format in ['P', 'p', 'pandas', 'pd']:
return _data