def run(self, *args, **kwargs):
while True:
df = LoadNet().get_QVIX()
self.update_qvix.emit(df)
df_north = LoadNet().get_north()
self.update_north.emit(df_north)
df_vol, cha = Volume().update()
data, last = LoadNet().get_50_163()
ser = (data['current'] - last) / last
self.update_vol.emit(df_vol, ser)
if not self.data_model.df_op.empty:
df_month = self.data_model.iv_month_50300()
self.update_month.emit(df_month)
df_iv50, df_iv300 = self.data_model.get_iv()
self.update_iv.emit(df_iv50, df_iv300)
hp = HoldPositions()
greek = hp.update(self.data_model.df_op)
self.update_greek.emit(greek)
time.sleep(3)
def update(self):
#ax.cla()
a = LoadNet().get_sz50_price()
#a.cumsum(axis=0)
str_time = a[31]
str_time_end = "15:00:00"
str_volume = a[9]
tomorrow = float(a[2])
current = float(a[3])
#print(str_time,str_volume)
time = pd.datetime.strptime(str_time, '%H:%M:%S')
min_1 = pd.Timedelta(minutes=1)
time_end = time + min_1
#mean_data['current'][mean_data.index>time.time()] = int(str_volume)/10000
self.mean_data.loc[self.mean_data.index > time.time(),
'current'] = int(str_volume) / 10000
self.diff_data = self.mean_data.copy()
self.diff_data['current'] = pd.to_numeric(self.diff_data['current'])
self.diff_data['current'] = self.diff_data['current'].diff(1)
self.diff_data.iloc[0, 1] = float(self.mean_data.iloc[0, 1])
self.mean_data['mean'] = self.mean_data['current'] / self.mean_data[
'VOL-TDX.VOLUME']
self.cha = (current - tomorrow) / tomorrow * 100
self.res = self.mean_data['mean'][
self.mean_data.index <= time_end.time()]
return self.mean_data['mean'][
self.mean_data.index <= time_end.time()], self.cha
def __init__(self):
self.load = LoadNet()
self.months = self.load.check_month()
self.day_later = self.load.get_op_expire_day('2019')
#print(self.day_later)
self.etf_price = 2.95
self.data_price = pd.DataFrame()
self.data_iv = pd.DataFrame()
self.histroy_high = 300
def draw_qvix(self):
self.ax_QVIX.cla()
#print (LoadNet().get_QVIX())
df =LoadNet().get_QVIX()
# iv = df['QVIX'].astype(float)
# time =df.index.values
# print(iv,time)
# self.ax_QVIX.plot(time,iv)
pd.to_datetime(df['Time'])
df.index = df['Time']
#print(df.columns)
df = df.drop(['Time','Pre','max','min'],axis=1)
df.plot(ax=self.ax_QVIX)
def __init__(self):
self.ln = LoadNet()
self.stock_info_50 = self.ln.get_sz50_price()
self.stock_info_300 = self.ln.get_sz300_price()
self.stock_price_50etf = 0.0
self.stock_price_300etf = 0.0
self.df_op = pd.DataFrame()
self.df_codes = self.init_allcodes()
t = threading.Thread(target=self.loop)
t.start()
def __init__(self):
self.loadsina = LoadNet()
self.month = self.loadsina.check_month()
self.interestRate = 3.00
self.stockPrice = float(self.loadsina.get_50etf_price()[3])
self.stockPrice_future = self.stockPrice
self.iv_future = 0
self.dayLater = 0
super(Windows, self).__init__()
self.setGeometry(20, 50, 1800, 1200)
self.currentItem = None
self.create_ui()
self.fill_dayWidget()
import pandas as pd import numpy as np import matplotlib.pyplot as plt from get_day_histroy import history import matplotlib.font_manager as font_manager from load_sina import LoadNet his = history() mean_data = his.get_dayMean() ln = LoadNet() mean_data['current'] = 0 mean_data['mean'] = 0 POINTS = 100 sin_list = [0] * POINTS indx = 0 zx_index = [] fig, ax = plt.subplots() # ax.set_ylim([-2, 2]) # ax.set_xlim([0, POINTS]) # ax.set_autoscale_on(False) #ax.set_xticks(range(0, 100, 10)) # ax.set_yticks(range(-2, 3, 1)) #ax.grid(True) #line_sin, = ax.plot(mean_data, label='volume', color='cornflowerblue') ax.legend(loc='upper center', ncol=4, prop=font_manager.FontProperties(size=8)) print(mean_data) diff_data = pd.DataFrame()