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')
# min60(time=1030)
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 fun(stocks):
# print(os.getpid(), stocks)
kline = 'hk_kline_day'
pixel = 64
# path = "f://zjf/image/USA_boll_kline_day_test"
interval = KlineInfo.get_interval(kline)
detal = KlineInfo.get_detal(kline)
tabel_name = "boll_" + kline
# path = "f://zjf/image/ma_kline_min30_test"
for sc in stocks:
end_time = dt.datetime(2018, 6, 25)
start_time = dt.datetime(2016, 1, 1)
data = KlineData.read_data(code=sc,
start_date=Calendar.calc(start_time,
-203)['date'],
end_date=end_time,
kline=kline,
timemerge=True)
if len(data) <= 200:
continue
data = CalBoll.cal_boll(data, interval)
data = data.dropna()
while end_time > dt.datetime(2016, 1, 1):
data0 = data.tail(pixel)
data0 = data0.reset_index(drop=True)
profit = data0['profit']
date = data0['date']
data0 = CalBoll.data_normalization(data0, pixel)
data0['date'] = date
data0['profit'] = profit
if len(data0) < pixel:
break
data0 = data0.reset_index(drop=True)
profit = data0.profit.iloc[len(data0) - 1]
date = data0.date.iloc[len(data0) - 1]
if 'min' in kline:
if abs(profit) > 0.11:
data = data[data.date < dt.datetime(
date.year, date.month, date.day)]
continue
else:
if abs(profit) > 0.1 * interval:
data = data[data.date < dt.datetime(
date.year, date.month, date.day)]
continue
try:
visualization.drow_boll_line(data=data0,
profits=profit,
date=date,
code=sc,
pixel=pixel,
table_name=tabel_name)
except Exception as e:
pass
data = data[
data.date < dt.datetime(date.year, date.month, date.day)]
end_time = date
return 1
def fun(stocks):
# print(os.getpid(), stocks)
kline = 'hk_kline_day'
table_name='MA_'+kline
pixel=64
interval = KlineInfo.get_interval(kline)
for sc in stocks:
end_time = dt.datetime(2018, 6, 25)
start_time = dt.datetime(2016, 1, 1)
data = KlineData.read_data(code=sc, start_date=Calendar.calc(start_time, -203)['date'], end_date=end_time,
kline=kline, timemerge=True)
if len(data) <= 200:
continue
data = CalMa.cal_ma(data, interval)
data = data.dropna()
print(sc)
while end_time > dt.datetime(2016, 1, 1):
data0 = data.tail(pixel)
data0 = data0.reset_index(drop=True)
profit = data0['profit']
date = data0['date']
price=data0.close.iloc[0]
data0 = CalMa.data_normalization(data0,pixel=pixel)
data0['date'] = date
data0['profit'] = profit
if len(data0) < pixel or price<2:
break
date = data0.date.iloc[len(data0) - 1]
profit = data0.profit.iloc[len(data0) - 1]
if 'min' in kline:
if abs(profit) > 0.11:
data = data[data.date < dt.datetime(date.year, date.month, date.day)]
continue
else:
if abs(profit) > 0.1*interval:
data = data[data.date < dt.datetime(date.year, date.month, date.day)]
continue
data0 = data0.reset_index(drop=True)
try:
visualization.draw_ma_line(data=data0, profits=profit,
date=date, code=sc,
table_name=table_name,pixel=pixel)
except Exception as e:
print(e)
pass
data = data[data.date<dt.datetime(date.year,date.month,date.day)]
end_time = date
return 1
def draw_rl_pic_for_day(cls, data, end_date):
code3 = KlineBase.get_all_stock_code_info_for_day(
start_date=Calendar.calc(end_date, -1)['date'], end_date=end_date)
if len(code3) == 0:
return pd.DataFrame()
data = pd.merge(data, code3, on=['stock_code'])
# data['pro'] =data.close_x/data.open_x-1
pro = data.pro.mean()
data = data[0:2500]
arr = np.zeros((51, 51))
y = 0
for i, r in data.iterrows():
if (i + 1) % 50 != 0:
# print((i + 1) % 50)
arr[y, i % 50] = r['pro']
else:
y = y + 1
# print('y', y)
arr[y, i % 50] = r['pro']
proifit = data.profit.mean()
if abs(proifit) < 0.005:
return
ax = sns.heatmap(arr, vmin=-0.03, vmax=0.03, cbar=False)
# plt.matshow(arr, cmap='hot')
ax.set_yticks([])
ax.set_xticks([])
# plt.show()
# plt.showbar(False)
if proifit >= 0:
up = '/up'
else:
up = '/down'
title = str(
data.date_x.iloc[0])[0:10] + '_' + str(pro) + '_' + str(proifit)
path = 'F:/zjf/images/hot_day/' + up + '/'
File.check_file(path=path)
file_name = path + title + '.png'
plt.savefig(fname=file_name, format='png', dpi=50)
plt.close()
gc.collect()
def fun(stocks):
# print(os.getpid(), stocks)
kline = 'index_min5'
pixel=54
n=5
count=2
interval = KlineInfo.get_interval(kline=kline)
detal=KlineInfo.get_detal(kline=kline)
table_name='A_Volume_'+kline+'_version5'
print(table_name)
for sc in stocks:
end_time = dt.datetime(2018, 8, 15)
start_time = dt.datetime(2016, 1, 1)
data = KlineData.read_data(code=sc, start_date=Calendar.calc(start_time, -203)['date'], end_date=end_time,
kline=kline, timemerge=True)
if len(data) <= 200 :
continue
data=CalMa.cal_profit(data,interval,detal)
data=data.dropna()
d = data.date.iloc[-1]
data=data[data.date<=dt.datetime(d.year,d.month,d.day,9,55)]
print(sc)
while end_time > start_time:
data0 = data.tail(pixel*4)
amax=data0.volume.max()
data0 = data0.tail(pixel)
data0 = data0.reset_index(drop=True)
profit = data0.profit.iloc[ - 1]
profitself = data0.profit_self.iloc[- 1]
date = data0['date']
if len(data0) < pixel:
break
date2 = date.iloc[ - 6]
date1 = date.iloc[ - 1]
data0=CalMa.data_normalization_volume2(data0,pixel=pixel+n*count,amax=amax)
if 'min' in kline:
if 'A' in table_name:
if date1.hour != 9 and date1.minute!=55:
data = data[data.date <= dt.datetime(date2.year, date2.month, date2.day, 9, 55)]
break
if abs(profit) > 0.11 or abs(profitself)>0.097:
data = data[data.date <= dt.datetime(date2.year, date2.month, date2.day, 9, 55)]
continue
else:
if abs(profit) > 0.1 * interval:
data = data[data.date <= dt.datetime(date2.year, date2.month, date2.day, 9, 55)]
continue
if 'A' in table_name:
if abs(profitself) > 0.097:
data = data[data.date <= dt.datetime(date2.year, date2.month, date2.day, 9, 55)]
print(sc, str(profitself) + '_' + str(date))
continue
try:
visualization.draw_volume(data=data0, profits=profit,
date=date1, code=sc,
table_name=table_name, pixel=pixel, n=n,
count=count)
pass
except Exception as e:
print(e)
pass
data = data[data.date <= dt.datetime(date2.year, date2.month, date2.day,9,55)]
end_time = date2
return 1
def fun(stocks):
# print(os.getpid(), stocks)
kline = 'index_min5'
pixel = 128
n = 0
count = 0
hour = 9
minute = 55
off = 203
interval = KlineInfo.get_interval(kline=kline)
detal = KlineInfo.get_detal(kline=kline)
table_name = 'A_MA_index_min5_time955_pixel128'
print(table_name)
for sc in stocks:
end_time = dt.datetime(2018, 8, 10)
start_time = dt.datetime(2016, 1, 1)
data = KlineData.read_data(code=sc,
start_date=Calendar.calc(start_time,
-off)['date'],
end_date=end_time,
kline=kline,
timemerge=True)
if len(data) <= 200:
continue
data = CalMa.cal_ma(data=data, interval=interval, detal=detal)
data = data.dropna()
d = data.date.iloc[-1]
data = data[
data.date <= dt.datetime(d.year, d.month, d.day, hour, minute)]
print(sc)
while end_time > start_time:
# print(sc,dt.datetime.now())
data0 = data.tail(pixel)
data0 = data0.reset_index(drop=True)
profit = data0['profit']
profit_self = data0['profit_self']
date = data0['date']
# amin, amax = data0.low.min(), data0.high.max()
if len(data0) < pixel:
break
date2 = date.iloc[pixel - interval - 1]
date1 = date.iloc[pixel - 1]
data0 = CalMa.data_normalization_ok(data0, pixel=pixel + n * count)
profit = profit.iloc[pixel - 1]
profitself = profit_self.iloc[pixel - 1]
profit_relative = 0
if 'min' in kline:
if 'A' in table_name:
if date1.hour != hour and date1.minute != minute:
data = data[data.date <= dt.datetime(
date2.year, date2.month, date2.day, hour, minute)]
break
if abs(profit) > 0.11 or abs(profitself) > 0.097:
data = data[data.date <= dt.datetime(
date2.year, date2.month, date2.day, hour, minute)]
continue
else:
if abs(profit) > 0.1 * interval:
data = data[data.date <= dt.datetime(
date2.year, date2.month, date2.day, hour, minute)]
continue
if 'A' in table_name:
if abs(profitself) > 0.097:
data = data[data.date <= dt.datetime(
date2.year, date2.month, date2.day, hour, minute)]
print(sc, str(profitself) + '_' + str(date))
continue
try:
visualization.draw_ma_line3(data=data0,
profits=profit,
date=date1,
code=sc,
table_name=table_name,
pixel=pixel,
n=n,
profit_relative=profit_relative,
count=count)
pass
except Exception as e:
print(e)
pass
data = data[data.date <= dt.datetime(date2.year, date2.month,
date2.day, hour, minute)]
end_time = date2
return 1
def fun(stocks):
# print(os.getpid(), stocks)
kline = 'usa_kline_day'
pixel = 64
length = pixel * 2
interval = KlineInfo.get_interval(kline=kline)
off = KlineInfo.get_off(kline=kline)
table_name = 'MACD_and_MA_' + kline
print(table_name)
for sc in stocks:
print(os.getpid(), sc)
end_time = dt.datetime(2018, 6, 20)
start_time = dt.datetime(2015, 1, 1)
data = KlineData.read_data(code=sc,
start_date=Calendar.calc(start_time,
-off)['date'],
end_date=end_time,
kline=kline,
timemerge=True)
if len(data) <= (off - 20):
continue
data = calculate.calculate_data(data, interval)
# data = data.sort_values(by=['date'], ascending=True)
data = data.dropna()
# data = data[0:len(data) - interval]
while end_time > dt.datetime(2016, 1, 5):
# print(sc,dt.datetime.now())
data0 = data.tail(length)
data0 = data0.reset_index(drop=True)
profit = data0['profit']
date = data0['date']
data0 = calculate.data_normalization(data=data0,
pixel=pixel,
length=length)
# print(sc, dt.datetime.now())
data0['date'] = date
data0['profit'] = profit
if len(data0) < length:
break
data0 = data0.reset_index(drop=True)
profit = data0.profit.iloc[length - 1]
date = data0.date.iloc[length - 1]
if 'min' in kline:
if 'A' in table_name:
if date.hour != 15:
data = data[data.date < dt.datetime(
date.year, date.month, date.day)]
break
if abs(profit) > 0.11:
data = data[data.date < dt.datetime(
date.year, date.month, date.day)]
continue
else:
if abs(profit) > 0.1 * interval:
data = data[data.date < dt.datetime(
date.year, date.month, date.day)]
continue
# print(sc, dt.datetime.now())
try:
visualization.draw_macd_and_ma(data=data0,
profits=profit,
date=date,
code=sc,
table_name=table_name,
pixel=pixel,
length=length)
pass
except Exception as e:
print(e)
pass
data = data[
data.date < dt.datetime(date.year, date.month, date.day)]
end_time = date
return 1
def fun(stocks):
# print(os.getpid(), stocks)
kline = 'hk_kline_min30'
pixel = 64
table_name = 'MACD_' + kline
print(table_name)
interval = KlineInfo.get_interval(kline=kline)
# detal= KlineInfo.get_detal(kline=kline)
# path = "//DESKTOP-4JKCMO0/zjf1/H_macd_kline_day"
# path = "f:/zjf/image/macd_kline_day_test"
# File.check_file(path=path)
for sc in stocks:
end_time = dt.datetime(2018, 6, 20)
start_time = dt.datetime(2015, 1, 1)
data = KlineData.read_data(code=sc,
start_date=Calendar.calc(start_time,
-203)['date'],
end_date=end_time,
kline=kline,
timemerge=True)
if len(data) <= 200:
continue
data = CalMacd.cal_macd(data, interval)
data = data.sort_values(by=['date'], ascending=True)
data = data.dropna()
# data = data[0:len(data) - interval]
print(sc)
while end_time > dt.datetime(2016, 1, 5):
# print(sc,dt.datetime.now())
data0 = data.tail(pixel)
data0 = data0.reset_index(drop=True)
profit = data0['profit']
date = data0['date']
data0 = CalMacd.data_normalization(data0, pixel=pixel)
# print(sc, dt.datetime.now())
data0['date'] = date
data0['profit'] = profit
if len(data0) < pixel:
break
data0 = data0.reset_index(drop=True)
profit = data0.profit.iloc[pixel - 1]
date = data0.date.iloc[pixel - 1]
if 'min' in kline:
if abs(profit) > 0.11:
data = data[data.date < dt.datetime(
date.year, date.month, date.day)]
end_time = date
continue
else:
if abs(profit) > 0.1 * interval:
data = data[data.date < dt.datetime(
date.year, date.month, date.day)]
end_time = date
continue
# print(sc, dt.datetime.now())
try:
visualization.draw_macd_line(data=data0,
profits=profit,
date=date,
code=sc,
table_name=table_name,
pixel=pixel)
# pass
except Exception as e:
print(e)
pass
# data = data[0:len(data) - detal]
data = data[
data.date < dt.datetime(date.year, date.month, date.day)]
end_time = date
return 1
def is_trade_day(cls, date):
curror = BaseModel('calendar').query(dict(date=Calendar.today()))
return True if curror.count() else False
def fun(stocks):
# print(os.getpid(), stocks)
kline = 'index_min5'
pixel = 64
length = 12
count = 5
interval = 48
hour = 9
minute = 55
detal = KlineInfo.get_detal(kline=kline)
# wave=0.02
# amount_data=50000000
table_name = 'A_MA_Stub_' + kline + '_version5'
print(table_name)
for sc in stocks:
print(sc)
end_time = dt.datetime(2018, 8, 15)
start_time = dt.datetime(2016, 1, 1)
data = KlineData.read_data(code=sc,
start_date=Calendar.calc(start_time,
-8)['date'],
end_date=end_time,
kline=kline,
timemerge=True)
if len(data) <= 200:
continue
# data = CalMacd.cal_macd(data=data, interval=interval, detal=detal)
# data=CalMa.cal_ma_stub(data=data,interval=interval,detal=detal)
data = CalMa.cal_profit(data=data, interval=interval, detal=detal)
data = data.dropna()
d = data.date.iloc[-1]
data = data[
data.date <= dt.datetime(d.year, d.month, d.day, hour, minute)]
while end_time > start_time:
# print(sc,dt.datetime.now())
data0 = data.tail(length)
data0 = data0.reset_index(drop=True)
profit = data0['profit']
profit_self = data0['profit_self']
date = data0['date']
# amin, amax = data0.low.min(), data0.high.max()
if len(data0) < length:
break
date1 = date.iloc[length - 1]
date2 = date.iloc[length - 10]
# data0 = CalMacd.data_normalization(data0,pixel=pixel)
data0 = CalMa.data_stub_normalization(data0, pixel=pixel)
profit = profit.iloc[length - 1]
profitself = profit_self.iloc[length - 1]
profit_relative = 0
if 'min' in kline:
if 'A' in table_name:
if date1.hour != hour and date1.minute != minute:
# data = data[data.date < dt.datetime(date2.year, date2.month, date2.day)]
break
if abs(profit) > 0.11 or abs(profitself) > 0.097:
data = data[data.date <= dt.datetime(
date2.year, date2.month, date2.day, hour, minute)]
continue
else:
if abs(profit) > 0.1 * interval:
data = data[data.date <= dt.datetime(
date2.year, date2.month, date2.day, hour, minute)]
continue
if 'A' in table_name:
if abs(profitself) > 0.097:
data = data[data.date <= dt.datetime(
date2.year, date2.month, date2.day, hour, minute)]
print(sc, str(profitself) + '_' + str(date1))
continue
try:
visualization.draw_ma_stub_line2(
data=data0,
profits=profit,
date=date1,
code=sc,
table_name=table_name,
pixel=pixel,
length=length,
profit_relative=profit_relative,
count=count)
pass
except Exception as e:
print(e)
pass
data = data[data.date <= dt.datetime(date2.year, date2.month,
date2.day, hour, minute)]
end_time = date2
return 1
def fun(stocks):
# print(os.getpid(), stocks)
kline = 'index_min5'
pixel=56
n=4
count=2
interval = KlineInfo.get_interval(kline=kline)
detal=KlineInfo.get_detal(kline=kline)
table_name='A_MACD_left_right_'+kline+'_version6'
print(table_name)
for sc in stocks:
end_time = dt.datetime(2018, 8, 10)
start_time = dt.datetime(2016, 1, 1)
data = KlineData.read_data(code=sc, start_date=Calendar.calc(start_time, -203)['date'], end_date=end_time,
kline=kline, timemerge=True)
if len(data) <= 200 :
continue
data = CalMacd.cal_macd(data=data, interval=interval, detal=detal)
data = data.dropna()
d = data.date.iloc[-1]
data = data[data.date <= dt.datetime(d.year, d.month, d.day, 9, 35)]
print(sc)
while end_time > start_time:
# print(sc,dt.datetime.now())
data0 = data.tail(pixel)
data0 = data0.reset_index(drop=True)
profit = data0['profit']
profit_self = data0['profit_self']
date = data0['date']
if len(data0) < pixel:
break
date2 = date.iloc[pixel - 6]
date1 = date.iloc[pixel - 1]
data0 = CalMacd.data_normalization(data0,pixel=pixel+n*count)
profit = profit.iloc[pixel-1]
profitself = profit_self.iloc[pixel-1]
profit_relative = 0
if 'min' in kline:
if 'A' in table_name:
if date1.hour != 9 and date1.minute!=35:
data = data[data.date <= dt.datetime(date2.year, date2.month, date2.day, 9, 35)]
break
if abs(profit) > 0.11 or abs(profitself)>0.097:
data = data[data.date <= dt.datetime(date2.year, date2.month, date2.day, 9, 35)]
continue
else:
if abs(profit) > 0.1 * interval:
data = data[data.date <= dt.datetime(date2.year, date2.month, date2.day, 9, 35)]
continue
if 'A' in table_name:
if abs(profitself) > 0.097:
data = data[data.date <= dt.datetime(date2.year, date2.month, date2.day, 9, 35)]
print(sc, str(profitself) + '_' + str(date))
continue
try:
visualization.draw_macd_line4(data=data0, profits=profit,
date=date1, code=sc,
table_name=table_name, pixel=pixel, n=n, profit_relative=profit_relative,
count=count)
pass
except Exception as e:
print(e)
pass
data = data[data.date <= dt.datetime(date2.year, date2.month, date2.day, 9, 35)]
end_time = date2
return 1
def fun(stocks):
# print(os.getpid(), stocks)
kline = 'kline_day'
table_name = 'A_new_plus_last8_MA_' + kline
print(table_name)
interval = KlineInfo.get_interval(kline)
detal = KlineInfo.get_detal(kline)
pixel = 64
n = 8
for sc in stocks:
end_time = dt.datetime(2018, 6, 25)
start_time = dt.datetime(2016, 1, 1)
data = KlineData.read_data(code=sc,
start_date=Calendar.calc(start_time,
-203)['date'],
end_date=end_time,
kline=kline,
timemerge=True)
if len(data) <= 200:
continue
data = CalMa.cal_ma(data, interval, detal)
data = data.dropna()
print(sc)
while end_time > dt.datetime(2016, 1, 1):
data0 = data.tail(pixel)
data0 = data0.reset_index(drop=True)
profit = data0['profit']
profitself = data0['profit_self']
date = data0['date']
data0 = CalMa.data_normalization2(data0, pixel)
data0['date'] = date
data0['profit'] = profit
data0['profitself'] = profitself
if len(data0) < pixel:
break
data0 = data0.reset_index(drop=True)
profit = data0.profit.iloc[len(data0) - 1]
profitself = data0.profitself.iloc[len(data0) - 1]
date = data0.date.iloc[len(data0) - 1]
if 'min' in kline:
if 'A' in table_name:
if date.hour != 15:
data = data[data.date < dt.datetime(
date.year, date.month, date.day)]
break
if abs(profit) > 0.11 or abs(profitself) > 0.097:
data = data[data.date < dt.datetime(
date.year, date.month, date.day)]
continue
else:
if abs(profit) > 0.1 * interval:
data = data[data.date < dt.datetime(
date.year, date.month, date.day)]
continue
if 'A' in table_name:
if abs(profitself) > 0.097:
data = data[data.date < dt.datetime(
date.year, date.month, date.day)]
print(sc, str(profitself) + '_' + str(date))
continue
try:
visualization.draw_ma_line2(data=data0,
profits=profit,
date=date,
code=sc,
table_name=table_name,
pixel=pixel,
n=n)
except Exception as e:
print(e)
pass
data = data[
data.date < dt.datetime(date.year, date.month, date.day)]
end_time = date
return 1
def fun(data):
stocks = data['stocks']
kline = data['kline']
pixel = 64
interval = KlineInfo.get_interval(kline=kline)
detal = KlineInfo.get_detal(kline=kline)
table_name = 'MACD_A_' + kline
day_interval = KlineInfo.get_day_interval(kline)
print(table_name)
for sc in stocks:
# end_time = dt.datetime(2018, 8, 1)
# start_time = dt.datetime(2018, 7, 16)
end_time = Calendar.today()
start_time = Calendar.calc(end_time, -day_interval)['date']
data = KlineData.read_data(code=sc,
start_date=Calendar.calc(start_time,
-203)['date'],
end_date=end_time,
kline=kline,
timemerge=True)
if len(data) <= 200:
continue
data = CalMacd.cal_macd(data=data, interval=interval, detal=detal)
data = data.dropna()
# data = data[0:len(data) - interval]
print(sc)
while end_time > start_time:
# print(sc,dt.datetime.now())
data0 = data.tail(pixel)
data0 = data0.reset_index(drop=True)
profit = data0['profit']
profit_self = data0['profit_self']
date = data0['date']
data0 = CalMacd.data_normalization(data0, pixel=pixel)
# print(sc, dt.datetime.now())
data0['date'] = date
data0['profit'] = profit
data0['profit_self'] = profit_self
# print(len(data0))
if len(data0) < pixel:
break
data0 = data0.reset_index(drop=True)
profit = data0.profit.iloc[pixel - 1]
profitself = data0.profit_self.iloc[pixel - 1]
date = data0.date.iloc[pixel - 1]
if 'min' in kline:
if 'A' in table_name:
if date.hour != 15:
data = data[data.date < dt.datetime(
date.year, date.month, date.day)]
break
if abs(profit) > 0.11 or abs(profitself) > 0.097:
data = data[data.date < dt.datetime(
date.year, date.month, date.day)]
continue
else:
if abs(profit) > 0.1 * interval:
data = data[data.date < dt.datetime(
date.year, date.month, date.day)]
continue
if 'A' in table_name:
if abs(profitself) > 0.097:
data = data[data.date < dt.datetime(
date.year, date.month, date.day)]
print(sc, str(profitself) + '_' + str(date))
continue
try:
visualization.draw_macd_line(data=data0,
profits=profit,
date=date,
code=sc,
table_name=table_name,
pixel=pixel)
pass
except Exception as e:
print(e)
pass
data = data[
data.date < dt.datetime(date.year, date.month, date.day)]
end_time = date
return 1
