def get_all_stock_code_info(cls, date=None, time=None, kline='kline_min5'):
sql = analyzer('date = {} and time = {}'.format(date, time))
data = pd.DataFrame(list(BaseModel(kline).query(sql)))
if len(data) == 0:
return pd.DataFrame()
data['pro'] = data.close / data.open - 1
#以上是把数据从数据库中取出
#并且求出各个时刻的收益
#注意，如果要求收益的计算方式close/pre_close-1,则close需要通过shift()调整
data2 = None
if time == 1500:
#如果time是1500，需要把明天的第一根取出来计算
x = Calendar.calc(analyzer('date = {}'.format(date))['date'],
1)['date']
sql = analyzer('date = {} and time = {}'.format(
str(x.year * 10000 + x.month * 100 + x.day), 935))
data2 = pd.DataFrame(list(BaseModel(kline).query(sql)))
if len(data2) == 0:
return pd.DataFrame()
data = pd.merge(data, data2, on='stock_code')
data['profit'] = (data.close_y / data.close_x - 1)
return data.loc[:, [
'stock_code', 'date_x', 'time_x', 'profit', 'pro'
]]
else:
time_index = KlineBase.get_time_index(time)
sql = analyzer('date = {} and time = {}'.format(date, time_index))
data2 = pd.DataFrame(list(BaseModel(kline).query(sql)))
if len(data2) == 0:
return pd.DataFrame()
data = pd.merge(data, data2, on='stock_code')
data['profit'] = (data.close_y / data.close_x - 1)
return data.loc[:, [
'stock_code', 'date_x', 'time_x', 'profit', 'pro'
]]
def read_pst(cls, stock_code=None, date=None, **kw):
"""
读取仓位
:param stock_code:
:param date:
:param kw:
:return:
"""
try:
sql = dict()
if stock_code is not None:
sql['stock_code'] = stock_code
if date is not None:
sql = dict(sql, **analyzer("date = {d}".format(d=date)))
sql = dict(sql, **kw)
cursor = MODEL_TABLE(cls.location, cls.dbname,
'position').query(sql)
pst = list(cursor)
if len(pst):
pst = pd.DataFrame(pst)
pst.drop(['_id', 'classtype'], axis=1, inplace=True)
return pst
return pd.DataFrame()
except Exception:
raise MongoIOError('query position from db raise a error')
def read_trade_menu(cls,
model_from,
start_date=None,
end_date=None,
version=None,
**kw):
"""
读取模型收益数据，读取到的数据主要包括某种策略一个或一段时间所对应的收益
:param model_from:
:param start_date:
:param end_date:
:param version:
:param kw:
:return:
"""
try:
sql = {'model_from': model_from}
if version is not None:
sql['version'] = version
if start_date is None and end_date is None:
pass
else:
if start_date is not None and end_date is None:
date = analyzer("date >= {s}".format(s=start_date))
elif end_date is not None and start_date is None:
date = analyzer("date <= {e}".format(e=end_date))
else:
date = analyzer("date >= {s} and data <= {e}".format(
s=start_date, e=end_date))
sql = dict(sql, **date)
sql = dict(sql, **kw)
cursor = MODEL_TABLE(cls.location, cls.dbname,
'trademenu').query(sql)
menus = list(cursor)
if len(menus):
menus = pd.DataFrame(menus)
menus = menus.sort_values(['date'], ascending=True)
menus = menus.reset_index(drop=True)
menus.drop(['_id', 'classtype'], axis=1, inplace=True)
return menus
return pd.DataFrame()
except Exception:
raise MongoIOError('query trade menu from db raise a error')
def upsert_batch(self, condition, *args):
"""
批量更新
condition: {field1: sign1, field2: sign2...}
例如：{'date': '>', 'stock_code': '=' ...}
args: [{data1}, {data2}...]
例如：[{'stock_code': 1, 'open': 12.50...}, ...]
"""
bulk = self.mc.initialize_ordered_bulk_op()
for item in args[0]:
qs = ''
for c, s in condition:
qs += '{} {} {}'.format(c, s, item[c])
sql = analyzer(qs)
bulk.find(sql).upsert().update({'$set': item})
def read_risk_pst(cls, stock_code=None, date=None, **kw):
"""
读取仓位数据
:return:
"""
try:
sql = dict()
if stock_code is not None:
sql['stock_code'] = stock_code
if date is not None:
sql = dict(sql, **analyzer("date = {d}".format(d=date)))
sql = dict(sql, **kw)
cursor = MODEL_TABLE(cls.location, cls.dbname,
'risk_and_position').query(sql)
rps = list(cursor)
if len(rps):
rps = pd.DataFrame(rps)
rps.drop(['_id', 'classtype'], axis=1, inplace=True)
return rps
return pd.DataFrame()
except Exception:
raise MongoIOError('query risk and position from db raise a error')
def read_risk(self, stock_code=None, date=None, **kw):
"""
读取风险参数
:param stock_code:
:param date:
:param kw:
:return:
"""
try:
sql = dict()
if stock_code is not None:
sql['stock_code'] = stock_code
if date is not None:
sql = dict(sql, **analyzer("date = {d}".format(d=date)))
sql = dict(sql, **kw)
cursor = MODEL_TABLE(self.location, self.dbname, 'risk').query(sql)
rks = list(cursor)
if len(rks):
rks = pd.DataFrame(rks)
rks.drop(['_id', 'classtype'], axis=1, inplace=True)
return rks
return pd.DataFrame()
except Exception:
raise MongoIOError('query risk from db raise a error')