def ProcessStock(self):
#df_stockload = web.DataReader("600797.SS", "yahoo", datetime.datetime(2017,1,1), datetime.date.today())
df_stockload = GetStockDatPro(self.stockCode_Val, self.stockSdate_Val,
self.stockEdate_Val)
""" 绘制移动平均线图 """
#self.am.plot(self.numic[0:self.butNum],self.close[0:self.butNum],'#0f0ff0',linewidth=1.0)
dispCont_List = []
examp_trade = Excave_Indic_Base()
if self.IndicatInput_Val == u"金叉/死叉":
dispCont_pd, dispCont_List = examp_trade.plot_Aver_Cross(
df_stockload)
self.DispPanel.draw_avercross(df_stockload, dispCont_pd)
elif self.IndicatInput_Val == u"跳空缺口":
dispCont_pd, dispCont_List = examp_trade.plot_Jump_Thrd(
df_stockload)
self.DispPanel.draw_jumpgap(df_stockload, dispCont_pd)
elif self.IndicatInput_Val == u"N日突破":
dispCont_pd, dispCont_List = examp_trade.plot_Ndays_Break(
df_stockload)
self.DispPanel.draw_ndaysbreak(df_stockload, dispCont_pd)
else:
dispCont_List = dispCont_List
self.TextAInput.SetValue(u"指标提示信息如下:" + '\n')
for i in dispCont_List:
self.TextAInput.AppendText(i)
numic = np.arange(0, len(df_stockload.index))
butNum = len(df_stockload.index)
self.DispPanel.xylabel_tick_lim(self.stockName_Val, df_stockload.index)
self.DispPanel.draw_subgraph(df_stockload, numic)
def ProcessStock(self):
df_stockload = GetStockDatPro(self.stockCode_Val, self.stockSdate_Val, self.stockEdate_Val)
dispCont_List = []
examp_trade = Excave_Indic_Base()
if self.IndicatInput_Val == u"金叉\死叉":
dispCont_pd, dispCont_List = examp_trade.plot_Aver_Cross(df_stockload)
self.DispPanel.draw_avercross(df_stockload, dispCont_pd)
elif self.IndicatInput_Val == u'跳空缺口':
dispCont_pd, dispCont_List = examp_trade.plot_Jump_Thrd(df_stockload)
self.DispPanel.draw_jumpgap(df_stockload, dispCont_pd)
elif self.IndicatInput_Val == u'N日突破':
dispCont_pd, dispCont_List = examp_trade.plot_Ndays_Break(df_stockload)
self.DispPanel.draw_ndaysbreak(df_stockload)
else:
dispCont_List = dispCont_List
self.TextAInput.SetValue(u'指标提示信息如下:' + '\n')
for i in dispCont_List:
self.TextAInput.AppendText(i)
numic = np.arange(0, len(df_stockload.index))
butNum = len(df_stockload.index)
self.DispPanel.xylabel_tick_lim(self.stockName_Val, df_stockload.index)
self.DispPanel.draw_subgraph(df_stockload, numic)
def PstockpEvent(self, event):
dispCont_List = []
""" 选股策略执行 """
examp_trade = FactorPickStockAng()
for index, stockName in enumerate(self.StNameCodedict.keys()):
print("stockName", stockName, self.StNameCodedict[stockName])
df_stockload = GetStockDatPro(self.StNameCodedict[stockName],
self.stockSdate_Val,
self.stockEdate_Val)
df_stockload.fillna(method='bfill', inplace=True) #后一个数据填充NAN1
dispCont_List.append(stockName + '\n' +
examp_trade.fit_pick(df_stockload.Close) +
'\n')
print(dispCont_List)
""" 选股策略执行 """
""" 自定义提示框 """
myPickStock = UserDialog(self, dispCont_List)
if myPickStock.ShowModal() == wx.ID_OK:
pass
else:
pass
""" 自定义提示框 """
def ProcessLoop(self):
df_stockload = GetStockDatPro(self.stockCode_Val, self.stockSdate_Val,
self.stockEdate_Val)
dispCont_List = []
if self.pickstrate_Val == u"双趋势融合":
#多趋势融合策略执行 """
examp_trade = QuantPickTimeSys(df_stockload)
dispCont_List = examp_trade.run_factor_plot(
self.BackPanel.trade, self.BackPanel.total,
self.BackPanel.profit)
else:
#执行其他策略
pass
self.TextAInput.SetValue(u"策略提示信息如下:" + '\n')
for i in dispCont_List:
self.TextAInput.AppendText(i)
self.BackPanel.xylabel_tick_lim(self.stockName_Val)
from StockDataMod import GetStockDatPro
# from IndicatStrateMod import QuantPickTimeSys, FactorBuyNdayBreak, FactorBuyAverBreak, FactorSellNdayBreak, FactorSellAverBreak, FactorSellAtrStop
from AtrStrateMod import QuantPickTimeSys, FactorBuyNdayBreak, FactorBuyAverBreak, FactorSellNdayBreak, FactorSellAverBreak, FactorSellAtrStop
code = "000004.SZ"
start = "2018-04-17"
end = "2019-04-17"
df_stockload = GetStockDatPro(code, start, end)
buy_factors = [{
'xd': 20,
'class': FactorBuyNdayBreak
}, {
'xd': 30,
'class': FactorBuyAverBreak
}]
sell_factors = [{
'xd': 5,
'class': FactorSellNdayBreak
}, {
'xd': 30,
'class': FactorSellAverBreak
}, {
'stop_loss_n': 0.8,
'stop_win_n': 2,
'class': FactorSellAtrStop
}]
# 获取策略结果
print('data get')
