def kdj_select():
ResultSymbols = list()
ResultDf = pd.DataFrame()
iNum = 0
for iSymbol in da.symbol_list():
print(iSymbol[:-4], ':', iNum)
iNum += 1
df = da.data_read(iSymbol)
startDate = '20180501'
df1 = df[df['trade_date'] > startDate]
df2 = df1[df1['kdX'] == 1] #满足金叉的数据
df2_success = df2[(df2['ROC+1'] > 0) | (df2['ROC+2'] > 0) |
(df2['ROC+3'] > 0)] #把预测出金叉中后三天内上涨的画出来
if len(df2) != 0:
success_rate = len(df2_success) / len(df2)
else:
continue
if success_rate > 0.6:
ResultSymbols.append(iSymbol)
if df.iloc[-1, :]['kdX'] == 1:
ResultDf = ResultDf.append(df.iloc[-1, :])
print(iSymbol, ':',
'--------------------OK----------------------')
#ResultDf.to_pickle('result_data/Select_macd.pkl')
return ResultDf
da.data_cal_index()
# 数据获取及分析，分析今日出现MACD金叉，金叉Pre的股票
import pandas as pd
import symbol_select as ss
select_Df_macd = ss.macd_select()
#select_Df_kdj=ss.kdj_select()
# 绘图
import kplot as kp
import dataAnalysis as da
import matplotlib.pyplot as plt
#ResultDf=pd.read_pickle('result_data/Select_macd.pkl')
#select_Df2=select_Df[select_Df['kdX']==1]#
select_Df = select_Df_macd
if len(select_Df) != 0:
Symbollist = select_Df.loc[:, 'ts_code']
else:
Symbollist = []
Symbollist.to_pickle('Symbollist.pkl')
for iSymbol in Symbollist:
df = da.data_read(iSymbol)
startDate = '20180501'
df1 = df[df['trade_date'] > startDate]
#fig=kp.kplot(df1,'MACD')#绘图
#fig=kp.kplot(df1,'KDJ')#绘图
fig = kp.kplot(df1, 'MACD_KDJ')
fig.savefig('MACD_%s.jpg' % df1['ts_code'][0], dpi=600)
ax2.grid(True)
plt.setp(ax2.get_xticklabels(), visible=True)
elif index == 'vol':
fig = plt.figure(figsize=[12, 8])
ax1 = plt.axes([0.1, 0.3, 0.8, 0.6])
ax_kline(ax1, df)
ax2 = plt.axes([0.1, 0.1, 0.8, 0.2], sharex=ax1)
ax2.bar(df['iNum'], df['vol'], color='b')
ax2.plot(df['iNum'], df[['mavol5']])
ax2.plot(df['iNum'], df[['mavol10']])
# make these tick labels invisible
ax2.set_ylabel('vol')
ax2.grid(True)
plt.setp(ax2.get_xticklabels(), visible=True)
elif index == 'incomeSum':
fig = plt.figure(figsize=[12, 8])
ax1 = plt.axes([0.1, 0.3, 0.8, 0.6])
ax_kline(ax1, df)
ax2 = plt.axes([0.1, 0.1, 0.8, 0.2], sharex=ax1)
ax2.plot(df['iNum'], df[['incomeSum']])
# make these tick labels invisible
ax2.set_ylabel('incomeSum')
ax2.grid(True)
plt.setp(ax2.get_xticklabels(), visible=True)
return fig
if __name__ == '__main__':
df = da.data_read('600000') #读取某个文件数据
kplot(df, 'MACD')
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 5 17:04:26 2019
@author: mac
"""
# 把所有数据获取一遍，并更新数据信息，每天运行一次即可
import dataAnalysis as da
import pandas as pd
df = da.data_read('000002.SZ')
# 绘图
import kplot as kp
import dataAnalysis as da
import matplotlib.pyplot as plt
startDate = '20181201'
df1 = df[df['trade_date'] > startDate]
df1['mavol5'] = df1['vol'].rolling(5).mean()
df1['mavol10'] = df1['vol'].rolling(10).mean()
df1.to_csv('a1.csv')
fig = kp.kplot(df1, 'vol')