def check(self):
app = Dealer('127.0.0.1', 7497, 1)
# time.sleep(1)
while True:
try:
time.sleep(1)
accounting_values = app.get_accounting_values('DU1162631')
if accounting_values is not None:
BaseModel('jts_log').insert_batch([{
'date':
dt.datetime.now(),
'event':
'login-ok',
'accounting_values':
accounting_values
}])
break
else:
BaseModel('jts_log').insert_batch([{
'date':
dt.datetime.now(),
'event':
'login-ok',
'accounting_values':
accounting_values
}])
print('accounting_values None')
except Exception as e:
print('av failed')
def draw_time_trends(cls, data, profits, date, code, pixel, table_name,
detal):
# 'dif', 'dea', 'macd'
# print("x")
data = data.tail(detal)
data['volume'] = pixel / 2 - data['volume'] + 0.5 + pixel / 2 - 1
data['up'] += 0.5
volume = data['volume'].astype('int').tolist()
close = data['up'].astype('int').tolist()
matrix = np.zeros((pixel, pixel), dtype=np.bool)
val = int(pixel - 1)
for i in range(len(close)):
# matrix[volume[i], i] = True
if close[i] > pixel / 4:
matrix[int(pixel / 4):close[i], i] = True
else:
matrix[close[i]:int(pixel / 4), i] = True
matrix[volume[i]:val, i] = True
# BaseModel(table_name).insert_batch(
# {'stock_code': code, 'profit': profits, 'date': date, 'value': matrix.tolist()})
BaseModel(table_name).remove(date=date, stock_code=code)
BaseModel(table_name).insert_batch({
'stock_code': code,
'profit': profits,
'date': date,
'value': matrix.tolist()
})
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 get_stock_code_list(cls):
t = Calendar.today()
t = Calendar.calc(t, -1)['date']
obj = BaseModel('kline_day')
curror = obj.query({'date': t})
data = pd.DataFrame(list(curror))
data = data.sort_values(by=['stock_code'], ascending=True)
return data
def exe(parm):
time = parm['time']
datetime = parm['datetime']
i = parm['sc']
temp = datetime.hour * 60 + datetime.minute - 5
temp = temp // 60 * 100 + temp % 60
# print(temp)
curror = BaseModel('real_buy_sell_all_stock_code').query(
sql={
'stock_code': {
'$in': i
},
'datetime': {
'$gte':
dt.datetime(datetime.year, datetime.month, datetime.day,
temp // 100, temp % 100),
'$lt':
dt.datetime(datetime.year, datetime.month, datetime.day,
datetime.hour, datetime.minute, 0)
}
})
if curror.count() > 0:
data = pd.DataFrame(list(curror))
data['volume2'] = data.volume
data['amount2'] = data.amount
data['high'] = data.price
data['low'] = data.price
data['close'] = data.price
data['open'] = data.price
data = data.groupby(by=['stock_code'], as_index=False).agg({
'volume': 'first',
'volume2': 'last',
'amount': 'first',
'amount2': 'last',
'open': 'first',
'close': 'last',
'high': 'max',
'low': 'min'
})
data['volume'] = data.volume2 - data.volume
data['amount'] = data.amount2 - data.amount
data = data.drop(['volume2', 'amount2'], axis=1)
data['time'] = time
data['date'] = dt.datetime(datetime.year, datetime.month, datetime.day)
BaseModel('real_kline_min5').insert_batch(
data.to_dict(orient='records'))
def drow_boll_line(cls, data, profits, date, code, pixel, table_name):
# 'mid', 'top', 'bottom'
if len(data) != pixel:
return
data['mid'] += 0.5
data['top'] += 0.5
data['bottom'] += 0.5
data['low'] += 0.5
data['high'] += 0.5
mid = data['mid'].astype('int').tolist()
top = data['top'].astype('int').tolist()
bottom = data['bottom'].astype('int').tolist()
low = data['low'].astype('int').tolist()
high = data['high'].astype('int').tolist()
matrix = np.zeros((pixel, pixel), dtype=np.bool)
num = pixel - 1
for i in range(len(mid)):
matrix[(num - mid[i]), i] = True
matrix[(num - top[i]), i] = True
matrix[(num - bottom[i]), i] = True
matrix[(num - high[i]):(num - low[i]), i] = True
BaseModel(table_name).insert_batch({
'stock_code': code,
'profit': profits,
'date': date,
'value': matrix.tolist()
})
def draw_macd_line(cls, data, profits, date, code, pixel, table_name):
# 'dif', 'dea', 'macd'
if len(data) != pixel:
return
data['dif'] += 0.5
data['dea'] += 0.5
data['macd'] += 0.5
dif = data['dif'].astype('int').tolist()
dea = data['dea'].astype('int').tolist()
macd = data['macd'].astype('int').tolist()
matrix = np.zeros((pixel, pixel), dtype=np.bool)
val = int(pixel / 2 - 1)
for i in range(len(dif)):
matrix[(dif[i]), i] = True
matrix[(dea[i]), i] = True
if (macd[i]) > val:
matrix[val:(macd[i]), i] = True
else:
matrix[(macd[i]):val, i] = True
BaseModel(table_name).insert_batch({
'stock_code': code,
'profit': profits,
'date': date,
'value': matrix.tolist()
})
def get_all_info(cls, start_date, end_date, kline='kline_day'):
#不限定股票，条件为时间范围、kline
sql = {'date': {'$gte': start_date, '$lte': end_date}}
data = pd.DataFrame(list(BaseModel(kline).query(sql)))
if len(data) == 0:
return pd.DataFrame()
data.to_csv('f://hhh.csv')
def hot_min(self, date, time, kline):
data = ModelData.read_data(table_name=kline,
field=['stock_code', 'close', 'open'],
date=date,
time=time)
if len(data) <= 0 and time == 1130:
data = ModelData.read_data(table_name=kline,
field=['stock_code', 'close', 'open'],
date=date,
time=1300)
data = data.drop(['_id'], axis=1)
data['profit'] = data.close / data.open - 1
data['profit'] = data[abs(data.profit) < 0.11].profit
data['pro'] = data.profit
# data['pro'] = data.profit.map(lambda x: 1 if x > 0 else 0)
data = data.dropna()
data = pd.merge(stock_codes, data, on=['stock_code'])
data = data.fillna(0)
# print(data.stock_code.tolist())
# data=data[0:56*56]
arr = np.array(data.pro.tolist() + [0] *
(64 * 64 - len(data))).reshape(64, 64)
# ax = sns.heatmap(arr, vmin=-0.03, vmax=0.03,cbar=False)
# plt.matshow(arr, cmap='hot')
# plt.show()
BaseModel('hot_min5_version4').insert_batch(
dict(date=date, time=time, value=arr.tolist()))
def get_all_min5_info(cls, start_date, end_date, kline='index_min5'):
sql = {'date': {'$gte': start_date, '$lte': end_date}}
data = pd.DataFrame(list(BaseModel(kline).query(sql)))
if len(data) == 0:
return pd.DataFrame()
data = signaldata.merge_time(data)
# data=pd.DataFrame()
data = data.sort_values(by=['date'], ascending=True)
return data
def start(self, file):
print('strat')
status = subprocess.call('taskkill /F /IM tws.exe')
status = subprocess.call(file)
BaseModel('jts_log').insert_batch([{
'date': dt.datetime.now(),
'event': 'login-pre',
'accounting_values': status
}])
def check(self):
app = Dealer('127.0.0.1', 4002, 4002)
# x = app.get_accounting_values('DU1142167')
x = app.get_accounting_values(no)
BaseModel('jts_log').insert_batch([{
'date': dt.datetime.now(),
'event': 'login-ok',
'accounting_values': x,
'user': no
}])
app.disconnect()
def is_trade_day(cls, date):
date = dt.datetime.now()
if BaseModel('calendar').query(
sql={
'date': dt.datetime(date.year, date.month, date.day)
}).count():
return True
else:
print('no bussiness day')
return False
def draw_volume(cls, data, profits, date, code, pixel, table_name, n,
count):
if len(data) != pixel:
return
data['volume'] += 0.5
volume = data['volume'].astype('int').tolist()
# pixel=
matrix = np.zeros((pixel + n * count, pixel + n * count),
dtype=np.bool)
# val = int((pixel + n * count) / 2 - 1)
num = pixel + n * count
num2 = num // 2
mark = 0
for i in range(pixel + n):
if i < len(volume):
if (num - volume[i]) < num2:
matrix[(num - volume[i]):num2 + 1, i] = True
else:
matrix[num2:(num - volume[i]) + 1, i] = True
else:
if (num - volume[i - n]) < num2:
matrix[(num - volume[i - n]):num2 + 1, (pixel) +
mark * count:(pixel) + (mark + 1) * count] = True
else:
matrix[num2:(num - volume[i - n]) + 1, (pixel) +
mark * count:(pixel) + (mark + 1) * count] = True
mark += 1
obj = BaseModel(table_name)
# obj.remove(dict(date=date,stock_code=code))
obj.insert_batch({
'stock_code': code,
'profit': profits,
'date': date,
'value': matrix.tolist()
})
def min60(time):
min60_times = [1030, 1130, 1400, 1500]
if time in min60_times:
temp = time // 100 * 60 + time % 100 - 60
temp = temp // 60 * 100 + temp % 60
print(temp)
sql = {'time': {'$gt': temp, '$lte': time}, 'date': Calendar.today()}
curror = BaseModel('real_kline_min5').query(sql=sql)
if curror.count():
data = pd.DataFrame(list(curror))
data = data.sort_values(by=['time'], ascending=True)
data = data.groupby(by=['stock_code'], as_index=False).agg({
'volume':
'sum',
'amount':
'sum',
'open':
'first',
'close':
'last',
'high':
'max',
'low':
'min'
})
data['time'] = time
data['date'] = Calendar.today()
BaseModel('real_kline_min60').insert_batch(
data.to_dict(orient='records'))
print('min60 ok')
curror.close()
def draw_ma_line(cls, data, profits, date, pixel, code, table_name):
if len(data) != pixel:
return
data['ma5'] += 0.5
data['ma10'] += 0.5
data['ma20'] += 0.5
data['ma60'] += 0.5
data['ma120'] += 0.5
data['high'] += 0.5
data['low'] += 0.5
ma5 = data['ma5'].astype('int').tolist()
ma20 = data['ma20'].astype('int').tolist()
ma10 = data['ma10'].astype('int').tolist()
ma60 = data['ma60'].astype('int').tolist()
ma120 = data['ma120'].astype('int').tolist()
high = data.high.astype('int').tolist()
low = data.low.astype('int').tolist()
matrix = np.zeros((pixel, pixel), dtype=np.bool)
num = pixel - 1
for i in range(len(ma5)):
matrix[(num - ma5[i]), i] = True
matrix[(num - ma10[i]), i] = True
matrix[(num - ma20[i]), i] = True
matrix[(num - ma60[i]), i] = True
matrix[(num - ma120[i]), i] = True
matrix[(num - high[i]):(num - low[i]), i] = True
# BaseModel(table_name).insert_batch(
# {'stock_code': code, 'profit': profits, 'date': date, 'value': matrix.tolist()})
BaseModel(table_name).remove(date=date, stock_code=code)
BaseModel(table_name).insert_batch({
'stock_code': code,
'profit': profits,
'date': date,
'value': matrix.tolist()
})
def start_exe(kline='kline_min30'):
a=time.clock()
data = Stock.get_all_stock()
stocks = data
pool = multiprocessing.Pool(processes=4)
m = 10
li = [{'stocks':stocks[i:i + m],'kline':kline} for i in range(0, len(stocks), m)]
result = pool.map(fun, li)
spend=time.clock()-a
BaseModel('kline_data_update_mark').insert_batch({'status':200,'kline':'ma_'+kline,'other':'speed '+str(spend),'date':dt.datetime.now()})
# if __name__ == '__main__':
# start_exe(kline='kline_day')
def draw_macd_line2(cls, data, profits, profit_relative, date, code, pixel,
table_name, n):
# 'dif', 'dea', 'macd'
if len(data) != pixel:
return
data['dif'] += 0.5
data['dea'] += 0.5
data['macd'] += 0.5
dif = data['dif'].astype('int').tolist()
dea = data['dea'].astype('int').tolist()
macd = data['macd'].astype('int').tolist()
# pixel=
matrix = np.zeros((pixel, pixel), dtype=np.bool)
val = int(pixel / 2 / 2 - 1)
val2 = int(pixel * 3 / 4 - 1)
num = int(pixel / 2)
mark = 0
detal = int(pixel / n)
for i in range(len(dif)):
matrix[(dif[i]), i] = True
matrix[(dea[i]), i] = True
if (macd[i]) > val:
matrix[val:(macd[i]), i] = True
else:
matrix[(macd[i]):val, i] = True
if (i >= len(dif) - n):
# matrix[(dif[i])+num, mark*n:(mark+1)*n] = True
# matrix[(dea[i])+num, mark*n:(mark+1)*n] = True
if (macd[i] + num) > val2:
matrix[val2:(macd[i] + num),
mark * detal:(mark + 1) * detal] = True
else:
matrix[(macd[i] + num):val2,
mark * detal:(mark + 1) * detal] = True
mark += 1
BaseModel(table_name).insert_batch({
'stock_code': code,
'profit': profits,
'profit_relative': profit_relative,
'date': date,
'value': matrix.tolist()
})
def draw_ma_line2(cls, data, profits, date, pixel, code, table_name, n):
if len(data) != pixel:
return
data['ma5'] += 0.5
data['ma10'] += 0.5
data['ma20'] += 0.5
data['ma60'] += 0.5
data['ma120'] += 0.5
data['high'] += 0.5
data['low'] += 0.5
ma5 = data['ma5'].astype('int').tolist()
ma20 = data['ma20'].astype('int').tolist()
ma10 = data['ma10'].astype('int').tolist()
ma60 = data['ma60'].astype('int').tolist()
ma120 = data['ma120'].astype('int').tolist()
high = data.high.astype('int').tolist()
low = data.low.astype('int').tolist()
matrix = np.zeros((pixel, pixel), dtype=np.bool)
num = int(pixel / 2 - 1)
mark = 0
num2 = int(pixel / 2)
for i in range(len(ma5)):
matrix[(num - ma5[i]), i] = True
matrix[(num - ma10[i]), i] = True
matrix[(num - ma20[i]), i] = True
matrix[(num - ma60[i]), i] = True
matrix[(num - ma120[i]), i] = True
matrix[(num - high[i]):(num - low[i]), i] = True
if (i >= len(ma5) - n):
matrix[(num - ma5[i]) + num2, mark * n:(mark + 1) * n] = True
matrix[(num - ma10[i]) + num2, mark * n:(mark + 1) * n] = True
matrix[(num - ma20[i]) + num2, mark * n:(mark + 1) * n] = True
matrix[(num - ma60[i]) + num2, mark * n:(mark + 1) * n] = True
matrix[(num - ma120[i]) + num2, mark * n:(mark + 1) * n] = True
matrix[(num - high[i] + num2):(num - low[i]) + num2,
mark * n:(mark + 1) * n] = True
mark += 1
BaseModel(table_name).insert_batch({
'stock_code': code,
'profit': profits,
'date': date,
'value': matrix.tolist()
})
def draw_macd_line4(cls, data, profits, profit_relative, date, code, pixel,
table_name, n, count):
# 'dif', 'dea', 'macd'
if len(data) != pixel:
return
data['dif'] += 0.5
data['dea'] += 0.5
data['macd'] += 0.5
dif = data['dif'].astype('int').tolist()
dea = data['dea'].astype('int').tolist()
macd = data['macd'].astype('int').tolist()
matrix = np.zeros((pixel + n * count, pixel + n * count),
dtype=np.bool)
val = int(pixel / 2 + 2)
mark = 0
for i in range(pixel + n):
if i < len(dif):
matrix[(dif[i]), i] = True
matrix[(dea[i]), i] = True
if (macd[i]) > val:
matrix[val:(macd[i]) + 1, i] = True
else:
matrix[(macd[i]):val + 1, i] = True
else:
matrix[(dif[i - n]), (pixel) + mark * count:(pixel) +
(mark + 1) * count] = True
matrix[(dea[i - n]), (pixel) + mark * count:(pixel) +
(mark + 1) * count] = True
if (macd[i - n]) > val:
matrix[val:(macd[i - n]) + 1, (pixel) +
mark * count:(pixel) + (mark + 1) * count] = True
else:
matrix[(macd[i - n]):val + 1, (pixel) +
mark * count:(pixel) + (mark + 1) * count] = True
mark += 1
# plt.matshow(matrix,cmap='hot')
# plt.show()
BaseModel(table_name).insert_batch({
'stock_code': code,
'profit': profits,
'profit_relative': profit_relative,
'date': date,
'value': matrix.tolist()
})
def get_all_stock_code_info_for_day(cls,
start_date,
end_date,
kline='kline_day'):
#day数据不同于日内数据
sql = {'date': {'$gte': start_date, '$lte': end_date}}
data = pd.DataFrame(list(BaseModel(kline).query(sql)))
#以上操作是将数据从数据库中获取
if len(data) == 0:
return pd.DataFrame()
after_data = data[data.date == end_date]
before_data = data[data.date == start_date]
data = pd.merge(before_data, after_data, on=['stock_code'])
data['pro'] = data.close_x / data.open_x - 1
data['profit'] = data.close_y / data.close_x - 1
# print(data.head())
# print(data.head())
return data.loc[:, ['stock_code', 'date_x', 'profit', 'pro']]
def draw_ma_stub_line2(cls, data, profits, date, code, table_name, pixel,
length, profit_relative, count):
if len(data) != length:
return
data['high'] += 0.5
data['low'] += 0.5
data['open'] += 0.5
data['close'] += 0.5
high = data.high.astype('int').tolist()
low = data.low.astype('int').tolist()
close = data.close.astype('int').tolist()
open = data.open.astype('int').tolist()
matrix = np.zeros((pixel, pixel), dtype=np.bool)
# val = int((pixel + n * count) / 2 - 1)
num = pixel
mark = 0
for i in range(length):
# matrix[(num - high[i ]):(num - low[i])+1, mark * count+count//3+1: (mark + 1) * count-count//3-1] = True
if open[i] >= close[i]:
matrix[(num - open[i]):(num - close[i]) + 1,
mark * count:(mark + 1) * count] = True
matrix[(num - high[i]):(num - low[i]) + 1, mark * count +
count // 3:(mark + 1) * count - count // 3] = True
else:
matrix[(num - close[i]),
mark * count:(mark + 1) * count] = True #横收
matrix[(num - open[i]),
mark * count:(mark + 1) * count] = True #横开
matrix[(num - open[i]):(num - low[i]) + 1, mark * count +
count // 3:(mark + 1) * count - count // 3] = True
matrix[(num - high[i]):(num - close[i]) + 1, mark * count +
count // 3:(mark + 1) * count - count // 3] = True
matrix[(num - close[i]):(num - open[i]) + 1,
mark * count] = True
matrix[(num - close[i]):(num - open[i]) + 1,
(mark + 1) * count - 1] = True
mark += 1
BaseModel(table_name).insert_batch({
'stock_code': code,
'profit': profits,
'profit_relative': profit_relative,
'date': date,
'value': matrix.tolist()
})
def macd_exe(kline='kline_min30'):
a = time.clock()
stocks = Stock.get_all_stock()
pool = multiprocessing.Pool(processes=4)
m = 10
li = [{
'stocks': stocks[i:i + m],
'kline': kline
} for i in range(0, len(stocks), m)]
result = pool.map(fun, li)
BaseModel('kline_data_update_mark').insert_batch({
'status':
200,
'kline':
'macd_' + kline,
'other':
'speed ' + str(time.clock() - a),
'date':
dt.datetime.now()
})
def login(self):
BaseModel('jts_log').insert_batch([{
'date': dt.datetime.now(),
'event': 'login-start'
}])
pyautogui.PAUSE = 1
pyautogui.FAILSAFE = True # 启用自动防故障功能f
# pyautogui.click(100, 100)
pyautogui.typewrite('zjf', 0.1)
pyautogui.typewrite(['shiftleft'], 0.1)
pyautogui.typewrite('520520', 0.1)
pyautogui.typewrite(['shiftleft', '\t'], 0.1)
pyautogui.typewrite('zjf', 0.1)
pyautogui.typewrite(['shiftleft'], 0.1)
pyautogui.typewrite('804533125', 0.1)
pyautogui.typewrite(['shiftleft', '\t'], 0.1)
pyautogui.typewrite(['down', 'down', '\n'], 0.1)
pyautogui.typewrite(
['\t', '\t', '\t', '\t', '\t', '\t', '\t', '\t', '\n'], 0.1)
time.sleep(30)
self.check()
def exe():
obj = JtsStart()
app = Dealer('127.0.0.1', 4002, 4002)
# x = app.get_accounting_values('DU1142167')
x = app.get_accounting_values(no)
try:
app.disconnect()
except Exception as e:
print(e)
if x is None:
t1 = threading.Thread(target=obj.start,
args=(u'C:\Jts\ibgateway\972/ibgateway.exe', ))
t2 = threading.Thread(target=obj.login)
t1.start()
time.sleep(10)
t2.start()
else:
BaseModel('jts_log').insert_batch([{
'date': dt.datetime.now(),
'event': '不用重启',
'accounting_values': x,
'user': no
}])
print('状态不错不用重启')
'low': resell,
'high': reselh,
'close': resel,
'open': resel
})
ModelData.insert_data(table_name=kline, data=dd)
import datetime as dt
start_date = dt.datetime(2016, 1, 1)
end_date = dt.datetime(2018, 6, 20)
data = pd.DataFrame(
list(
BaseModel('calendar').query(
{'date': {
'$gte': start_date,
'$lte': end_date
}})))
sampleNo = abs(data.num.iloc[0] - data.num.iloc[len(data) - 1]) + 1
index_profit = (Stochastic.get_random(sigma=0.03, sampleNo=sampleNo))
data2 = Stock.get_stock_code_list()
stocks = data2.stock_code.tolist()
for sc in stocks:
Stochastic.get_k(stock_code=sc,
kline="kline_day",
date=data.date.tolist(),
sampleNo=sampleNo,
index_profit=index_profit)
def is_trade_day(cls, date):
curror = BaseModel('calendar').query(dict(date=Calendar.today()))
return True if curror.count() else False
class PlateName:
index_code=[]
name=[]
hangye=[]
def get_plate(self):
f=open('plate_name.txt','r')
for line in f:
line=line.split('|')
self.index_code.append(line[1])
self.name.append(line[0])
self.hangye.append(line[5].replace('\n',''))
data=pd.DataFrame(dict(index_code=self.index_code,name=self.name,hangye=self.hangye))
return data
# obj=PlateName()
# data1=obj.get_plate()
# obj2=PlateStock()
# data2=obj2.get_plate()
# data=pd.merge(data1,data2,on=['hangye'])
# BaseModel('stock_code_plate').remove({})
# BaseModel('stock_code_plate').insert_batch(data.to_dict(orient='records'))
#===========================================================================
data=pd.DataFrame(list(BaseModel('stock_code_plate').query({})))
# print(data)
data=data.loc[:,['stock_code','index_code']].sort_values(by=['stock_code'])
index_dict={}
for i,r in data.iterrows():
index_dict[r.stock_code]=r.index_code
print(index_dict)
def get_stock_code_plate(cls):
data = pd.DataFrame(list(BaseModel('stock_code_plate').query()))
data.drop_duplicates(['stock_code'], inplace=True)
return data.loc[:, ['code', 'stock_code']]
def get_all_info(cls):
data = pd.DataFrame(list(BaseModel('platestock').query()))
# print(data)
if len(data) == 0:
return pd.DataFrame()
return data.loc[:, ['hangye', 'stock_code']]
