In [26]:
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import time
import pandas as pd
In [74]:
DB = pd.read_csv('005930.csv')
In [75]:
DB
Out[75]:
Date
Open
High
Low
Close
Volume
Adj Close
MA240
DIFF
0
2011-01-03
955000
966000
950000
958000
264800
924155.80
NaN
NaN
1
2011-01-04
956000
961000
949000
958000
274200
924155.80
NaN
NaN
2
2011-01-05
955000
955000
942000
942000
335700
908721.05
NaN
NaN
3
2011-01-06
942000
949000
923000
930000
387200
897144.99
NaN
NaN
4
2011-01-07
915000
929000
914000
921000
462700
888462.94
NaN
NaN
5
2011-01-10
918000
928000
909000
917000
366500
884604.25
NaN
NaN
6
2011-01-11
917000
922000
908000
913000
391000
880745.56
NaN
NaN
7
2011-01-12
914000
933000
914000
930000
409700
897144.99
NaN
NaN
8
2011-01-13
949000
949000
917000
922000
564700
889427.61
NaN
NaN
9
2011-01-14
928000
934000
915000
933000
307600
900039.00
NaN
NaN
10
2011-01-17
945000
951000
940000
949000
411600
915473.76
NaN
NaN
11
2011-01-18
950000
981000
949000
969000
592600
934767.20
NaN
NaN
12
2011-01-19
979000
1000000
966000
997000
666800
961778.01
NaN
NaN
13
2011-01-20
987000
990000
979000
982000
304800
947307.93
NaN
NaN
14
2011-01-21
976000
984000
970000
971000
333300
936696.54
NaN
NaN
15
2011-01-24
968000
983000
966000
971000
272100
936696.54
NaN
NaN
16
2011-01-25
972000
990000
971000
975000
279400
940555.23
NaN
NaN
17
2011-01-26
985000
999000
980000
998000
267000
962742.68
NaN
NaN
18
2011-01-27
995000
1002000
991000
994000
307300
958884.00
NaN
NaN
19
2011-01-28
998000
1014000
990000
1010000
382300
974318.75
NaN
NaN
20
2011-01-31
1009000
1010000
981000
981000
428200
946343.26
NaN
NaN
21
2011-02-01
982000
995000
979000
983000
269300
948272.60
NaN
NaN
22
2011-02-07
1002000
1004000
972000
972000
485000
937661.21
NaN
NaN
23
2011-02-08
970000
974000
959000
961000
609600
927049.82
NaN
NaN
24
2011-02-09
973000
973000
951000
960000
419600
926085.15
NaN
NaN
25
2011-02-10
960000
965000
935000
936000
613300
902933.02
NaN
NaN
26
2011-02-11
935000
943000
913000
915000
529700
882674.91
NaN
NaN
27
2011-02-14
928000
955000
925000
953000
391300
919332.44
NaN
NaN
28
2011-02-15
963000
970000
947000
958000
362100
924155.80
NaN
NaN
29
2011-02-16
952000
970000
945000
945000
303600
911615.07
NaN
NaN
...
...
...
...
...
...
...
...
...
...
1150
2015-06-18
1259000
1279000
1251000
1265000
164900
1265000.00
1299120.836083
-34120.836083
1151
2015-06-19
1266000
1278000
1260000
1266000
141800
1266000.00
1298880.711875
-32880.711875
1152
2015-06-22
1291000
1296000
1276000
1281000
127400
1281000.00
1298662.021958
-17662.021958
1153
2015-06-23
1309000
1328000
1291000
1321000
203100
1321000.00
1298630.531542
22369.468458
1154
2015-06-24
1300000
1311000
1291000
1302000
203900
1302000.00
1298560.940167
3439.059833
1155
2015-06-25
1290000
1303000
1269000
1269000
213900
1269000.00
1298284.037083
-29284.037083
1156
2015-06-26
1252000
1290000
1252000
1278000
206900
1278000.00
1298044.634000
-20044.634000
1157
2015-06-29
1269000
1285000
1256000
1281000
230300
1281000.00
1297805.411208
-16805.411208
1158
2015-06-30
1276000
1285000
1266000
1268000
197400
1268000.00
1297397.037792
-29397.037792
1159
2015-07-01
1268000
1302000
1259000
1295000
166400
1295000.00
1297064.205250
-2064.205250
1160
2015-07-02
1286000
1304000
1285000
1299000
164400
1299000.00
1296961.581042
2038.418958
1161
2015-07-03
1287000
1294000
1267000
1268000
142600
1268000.00
1296939.225250
-28939.225250
1162
2015-07-06
1253000
1260000
1223000
1230000
201900
1230000.00
1296655.871875
-66655.871875
1163
2015-07-07
1220000
1259000
1220000
1240000
249500
1240000.00
1296418.291708
-56418.291708
1164
2015-07-08
1240000
1251000
1232000
1239000
235600
1239000.00
1296242.250000
-57242.250000
1165
2015-07-09
1230000
1265000
1226000
1265000
280600
1265000.00
1296215.607333
-31215.607333
1166
2015-07-10
1257000
1266000
1248000
1259000
191400
1259000.00
1296328.227500
-37328.227500
1167
2015-07-13
1250000
1272000
1245000
1266000
153400
1266000.00
1296387.882917
-30387.882917
1168
2015-07-14
1265000
1270000
1221000
1225000
399700
1225000.00
1296293.131292
-71293.131292
1169
2015-07-15
1225000
1238000
1224000
1235000
167400
1235000.00
1296248.259458
-61248.259458
1170
2015-07-16
1223000
1287000
1223000
1282000
223400
1282000.00
1296411.540667
-14411.540667
1171
2015-07-17
1300000
1311000
1278000
1305000
297200
1305000.00
1296670.655208
8329.344792
1172
2015-07-20
1291000
1304000
1273000
1275000
128900
1275000.00
1296849.942042
-21849.942042
1173
2015-07-21
1275000
1277000
1247000
1263000
194000
1263000.00
1296962.802583
-33962.802583
1174
2015-07-22
1244000
1260000
1235000
1253000
268300
1253000.00
1297005.250458
-44005.250458
1175
2015-07-23
1244000
1253000
1234000
1234000
208900
1234000.00
1297075.302500
-63075.302500
1176
2015-07-24
1227000
1238000
1224000
1229000
196500
1229000.00
1297075.242375
-68075.242375
1177
2015-07-27
1229000
1247000
1228000
1230000
243000
1230000.00
1297157.373750
-67157.373750
1178
2015-07-28
1224000
1251000
1219000
1230000
267600
1230000.00
1297264.144542
-67264.144542
1179
2015-07-29
1250000
1275000
1231000
1263000
274600
1263000.00
1297475.562792
-34475.562792
1180 rows × 9 columns
In [78]:
fig = plt.figure()
chart = fig.add_subplot(1,1,1)
def animate(i):
pullData = open('sample.txt', 'r').read()
dataArray = pullData.split('\n')
xar =[]
yar =[]
for eachLine in dataArray:
if len(eachLine) > 1:
x,y = eachLine.split(',')
xar.append(int(x))
yar.append(int(y))
chart.clear()
chart.plot(xar, yar)
ani = animation.FuncAnimation(fig, animate, interval = 1000)
plt.show()
In [79]:
fig = plt.figure()
chart = fig.add_subplot(1,1,1)
DB = pd.read_csv('005930.csv')
def animate(i):
frame = 240 + i
index = DB.index[:frame]
Value = DB['Adj Close'][:frame]
#MA30 = DB['MA30'][:frame]
MA240 = DB['MA240'][:frame]
chart.clear()
chart.plot(index, Value, 'g')
#chart.plot(index, MA30, 'r')
chart.plot(index, MA240, 'b')
ani = animation.FuncAnimation(fig, animate, interval = 10)
plt.show()
In [ ]:
Content source: 3WiseMen/python
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