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]
if QA_util_date_valid(end) == True:
__data = []
cursor = collections.find({
'code': str(code)[0:6],
"date_stamp": {
"$lte": QA_util_date_stamp(end),
"$gte": QA_util_date_stamp(start)
}
})
if format in ['json', 'dict']:
return [QA_util_dict_remove_key(data, '_id') 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_fetch_account(message={}, db=DATABASE):
"""get the account
Arguments:
query_mes {[type]} -- [description]
Keyword Arguments:
collection {[type]} -- [description] (default: {DATABASE})
Returns:
[type] -- [description]
"""
collection = DATABASE.account
return [QA_util_dict_remove_key(res, '_id') for res in collection.find(message)]
def QA_fetch_user(user_cookie, db=DATABASE):
"""
get the user
Arguments:
user_cookie : str the unique cookie_id for a user
Keyword Arguments:
db: database for query
Returns:
list --- [ACCOUNT]
"""
collection = DATABASE.account
return [QA_util_dict_remove_key(res, '_id') for res in collection.find({'user_cookie': user_cookie})]
def GQSignal_fetch_flu_cunsum_min(start,
end,
frequency='60min',
market_type=QA.MARKET_TYPE.STOCK_CN,
portfolio='myportfolio',
format='numpy',
ui_log=None,
ui_progress=None):
"""
'获取全市场股票指标日线处于上升形态的数量'
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
start = str(start)[0:10]
end = str(end)[0:10]
#code= [code] if isinstance(code,str) else code
client = QASETTING.client[AKA.SYSTEM_NAME]
# 同时写入横表和纵表，减少查询困扰
#coll_day = client.get_collection(
# 'indices_{}'.format(datetime.date.today()))
try:
if (market_type == QA.MARKET_TYPE.STOCK_CN):
#coll_indices = client.stock_cn_indices_min
coll_indices = client.get_collection('stock_cn_indices_{}'.format(portfolio))
elif (market_type == QA.MARKET_TYPE.INDEX_CN):
#coll_indices = client.index_cn_indices_min
coll_indices = client.get_collection('index_cn_indices_{}'.format(portfolio))
elif (market_type == QA.MARKET_TYPE.FUND_CN):
#coll_indices = client.future_cn_indices_min
coll_indices = client.get_collection('fund_cn_indices_{}'.format(portfolio))
elif (market_type == QA.MARKET_TYPE.FUTURE_CN):
#coll_indices = client.future_cn_indices_min
coll_indices = client.get_collection('future_cn_indices_{}'.format(portfolio))
elif (market_type == QA.MARKET_TYPE.CRYPTOCURRENCY):
#coll_indices = client.cryptocurrency_indices_min
coll_indices = client.get_collection('cryptocurrency_indices_{}'.format(portfolio))
else:
QA_util_log_info('WTF IS THIS! \n ', ui_log=ui_log)
return False
except Exception as e:
QA_util_log_info(e)
QA_util_log_info('WTF IS THIS! \n ', ui_log=ui_log)
return False
if QA_util_date_valid(end):
cursor = coll_indices.aggregate([{
'$match': {
'time_stamp': {
"$lte": QA_util_date_stamp(end),
"$gte": QA_util_date_stamp(start)
},
'type': frequency,
}
},
{"$group" : {
'_id': {
'datetime': "$datetime",
},
'datetime': {
'$first': "$datetime"},
'time_stamp': {
'$first': "$time_stamp"},
FLD.FLU_POSITIVE: { # 上涨趋势通道个股家数
'$sum': '${}'.format(FLD.FLU_POSITIVE)},
FLD.ML_FLU_TREND: { # “技术调整”个股家数
'$sum': '${}'.format(FLD.ML_FLU_TREND)},
ST.BOOTSTRAP_I: {
'$sum': {
'$cond': ['${}'.format(ST.BOOTSTRAP_I), 1, 0] }},
ST.DEADPOOL: {
'$sum': {
'$cond': ['${}'.format(ST.DEADPOOL), 1, 0] }},
FLD.FLU_POSITIVE_MASTER: {
'$sum': {
'$cond': ['${}'.format(FLD.FLU_POSITIVE_MASTER), 1, 0] }},
FLD.FLU_NEGATIVE_MASTER: {
'$sum': {
'$cond': ['${}'.format(FLD.FLU_NEGATIVE_MASTER), 1, 0] }},
ST.VOLUME_FLOW_BOOST: {
'$sum': '${}'.format(ST.VOLUME_FLOW_BOOST)},
ST.VOLUME_FLOW_BOOST_BONUS: {
'$sum': {
'$cond': ['${}'.format(ST.VOLUME_FLOW_BOOST_BONUS), 1, 0] }},
ST.CLUSTER_GROUP_TOWARDS: {
'$sum': '${}'.format(ST.CLUSTER_GROUP_TOWARDS)},
FLD.ATR_Stopline: {
'$sum': '${}'.format(FLD.ATR_Stopline)},
FLD.ATR_SuperTrend: {
'$sum': '${}'.format(FLD.ATR_SuperTrend)},
FLD.DEA: {
'$sum': '${}'.format(FLD.DEA)},
FLD.MAXFACTOR_CROSS: {
'$sum': '${}'.format(FLD.MAXFACTOR_CROSS)},
FLD.TALIB_PATTERNS: {
'$sum': '${}'.format(FLD.TALIB_PATTERNS)},
FLD.ADXm_Trend: {
'$sum': '${}'.format(FLD.ADXm_Trend)},
FLD.Volume_HMA: {
'$sum': '${}'.format(FLD.Volume_HMA)},
FLD.RENKO_TREND_S: {
'$sum': '${}'.format(FLD.RENKO_TREND_S)},
FLD.RENKO_TREND_L: {
'$sum': '${}'.format(FLD.RENKO_TREND_L)},
'total': { # 全市场个股家数
'$sum': 1}
}
},
{'$sort':{"_id.datetime":1}}])
try:
res = pd.DataFrame([QA_util_dict_remove_key(item, '_id') for item in cursor])
res = res.assign(datetime=pd.to_datetime(res.datetime)).drop_duplicates((['datetime'])).set_index('datetime',
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 GQSignal_fetch_flu_cunsum_min 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 GQSignal_fetch_flu_cunsum_min data parameter start=%s end=%s is not right' % (start,
end))