/
multiplefiles_last100day_292.py
257 lines (181 loc) · 7.84 KB
/
multiplefiles_last100day_292.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
'''
@author: Glacier
'''
'''
@author: Glacier
'''
import QSTK.qstkutil.qsdateutil as du
import QSTK.qstkutil.DataAccess as da
import random
import datetime as dt
import matplotlib.pyplot as plt
import pandas
import numpy as np
import math
dataobj=da.DataAccess('ML4Trading')
symbols_toread=['ML4T-000', 'ML4T-001', 'ML4T-002', 'ML4T-003', 'ML4T-004', 'ML4T-005', 'ML4T-006', 'ML4T-007', 'ML4T-008',
'ML4T-009', 'ML4T-010', 'ML4T-011', 'ML4T-012', 'ML4T-013', 'ML4T-014', 'ML4T-015', 'ML4T-016', 'ML4T-017',
'ML4T-018', 'ML4T-019', 'ML4T-020', 'ML4T-021', 'ML4T-022', 'ML4T-023', 'ML4T-024', 'ML4T-025', 'ML4T-026',
'ML4T-027', 'ML4T-028', 'ML4T-029', 'ML4T-030', 'ML4T-031', 'ML4T-032', 'ML4T-033', 'ML4T-034', 'ML4T-035',
'ML4T-036', 'ML4T-037', 'ML4T-038', 'ML4T-039', 'ML4T-040', 'ML4T-041', 'ML4T-042', 'ML4T-043', 'ML4T-044',
'ML4T-045', 'ML4T-046', 'ML4T-047', 'ML4T-048', 'ML4T-049', 'ML4T-050', 'ML4T-051', 'ML4T-052', 'ML4T-053',
'ML4T-054', 'ML4T-055', 'ML4T-056', 'ML4T-057', 'ML4T-058', 'ML4T-059', 'ML4T-060', 'ML4T-061', 'ML4T-062',
'ML4T-063', 'ML4T-064', 'ML4T-065', 'ML4T-066', 'ML4T-067', 'ML4T-068', 'ML4T-069', 'ML4T-070', 'ML4T-071',
'ML4T-072', 'ML4T-073', 'ML4T-074', 'ML4T-075', 'ML4T-076', 'ML4T-077', 'ML4T-078', 'ML4T-079', 'ML4T-080',
'ML4T-081', 'ML4T-082', 'ML4T-083', 'ML4T-084', 'ML4T-085', 'ML4T-086', 'ML4T-087', 'ML4T-088', 'ML4T-089',
'ML4T-090', 'ML4T-091', 'ML4T-092', 'ML4T-093', 'ML4T-094', 'ML4T-095', 'ML4T-096', 'ML4T-097', 'ML4T-098',
'ML4T-099','ML4T-151','ML4T-292']
dtstart=dt.datetime(2000,2,01)
dtend=dt.datetime(2012,9,14)
ldttimestamps=du.getNYSEdays(dtstart,dtend,dt.timedelta(hours=16))
lsKeys = ['actual_close']
ldfdata=dataobj.get_data(ldttimestamps,symbols_toread,lsKeys)
ldfdata=np.array(ldfdata[0])
'''
Compute indicators and generate train dataset
'''
alltraindata=np.array([0,0,0,0,0,0])
for i in range(0,100):
rdata=ldfdata[:,i]
traindata=np.zeros((len(rdata)-21-5,6))
for j in range(21,len(rdata)-5):
m=rdata[j-21:j]
traindata[j-21,0]=np.std(m) #Standard Deviation
traindata[j-21,1]=(np.amax(m)-np.amin(m)) #Amplitude
traindata[j-21,2]=np.array(m[20]-m[19]) #Change of today and yesterday
traindata[j-21,3]=np.array((m[20]-m[19])-(m[19]-m[18])) #change of the change in three days
fr=0
mean=np.mean(m)
for l in range(0,20):
if (m[l]-mean)*(m[l+1]-mean)>0:
fr=fr+1
traindata[j-21,4]=fr
traindata[j-21,5]=rdata[j+5]
#alltraindata=alltraindata.append(traindata)
alltraindata=np.vstack((alltraindata,traindata))
alltraindata=alltraindata[1:len(alltraindata)]
#print len(alltraindata)
def buildTree(data,index=1):
# If it is a leaf node
if len(data)==1:
# MUST add[[ ]],otherwise len(leaf) will be 5!!!!!!
leaf=[[index,-1,data[0][5],-1,-1]]
index=index+1
return leaf
#if not leave
else:
#Select Feature
feature=random.randint(0,4)
redundant=True
for i in range(0,len(data)-1):
if (data[i][feature]!=data[i+1][feature]):
redundant=False
if(redundant==True):
avg=np.mean(data[:,5])
leafr=[[index,-1,avg,-1,-1]]
index=index+1
return leafr
else:
#Compute split value
randomgroup=np.random.randint(len(data),size=2)
Xrandom1=randomgroup[0]
Xrandom2=randomgroup[1]
#Very important!!! if 1,2,2,2,2 is the input data, there will still be nothing in the right dataset, here we must guarantee that this never happen!!!
while(data[Xrandom1][feature]==data[Xrandom2][feature]):
randomgroup=np.random.randint(len(data),size=2)
Xrandom1=randomgroup[0]
Xrandom2=randomgroup[1]
SplitVal=(data[Xrandom1][feature]+data[Xrandom2][feature])/2.0
#THEN DIVIDE GROUPS
#Set an initial value of leftdata and rightdata
leftdata=[0,0,0,0,0,0]
rightdata=[0,0,0,0,0,0]
for i in range (0,len(data)):
#Compute left group
if data[i][feature]<=SplitVal:
leftdata=np.vstack((leftdata,data[i]))
#leftdata=np.append([leftdata],[data[i]],axis=0)
#Delete the added first row and return the data array
#Compute right group
if data[i][feature]>SplitVal:
rightdata=np.vstack((rightdata,data[i])) # @IndentOk
#rightdata=np.append([rightdata],[data[i]],axis=0)
leftdata=leftdata[1:len(leftdata)]
rightdata=rightdata[1:len(rightdata)]
#rightdata=rightdata[1:rightdata.shape[0]]
lefttree=buildTree(leftdata,index+1)
righttree=buildTree(rightdata,index+len(lefttree)+1)
#Compute Current node
Cnode=[index,feature,SplitVal,index+1,index+len(lefttree)+1]
index=index+1
#Combine the left tree and the right tree
tree=np.vstack((Cnode,lefttree,righttree))
return tree
def QueryTree(Tree,Xtest):
#initial i, which is the (index-1) of Tree
i=0
while(Tree[i][1]!=-1):
feature=Tree[i][1]
#Go left tree
if Xtest[feature]<=Tree[i][2]:
i=Tree[i][3]-1
#Go right tree
else:
i=Tree[i][4]-1
Ytest=Tree[i][2]
return Ytest
def RandomForest(data,k,testdata):
ka=k
Ytest=np.zeros((len(testdata),k))
while (k!=0):
Tree=buildTree(data)
for i in range(0,len(testdata)):
Ytest[i,k-1]=QueryTree(Tree,testdata[i])
k=k-1
Y_randomforest=np.zeros((len(testdata)))
for i in range(0,len(testdata)):
Y_randomforest[i]=(np.sum(Ytest[i,:]))/ka
return Y_randomforest
'''Query data'''
'''Query data'''
symbols_toread=['ML4T-292']
dtstart=dt.datetime(2012,3,16)
dtend=dt.datetime(2012,9,14)
ldttimestamps=du.getNYSEdays(dtstart,dtend,dt.timedelta(hours=16))
lsKeys = ['actual_close']
ldfdata=dataobj.get_data(ldttimestamps,symbols_toread,lsKeys)
ldfdata=np.array(ldfdata[0])
rdata=ldfdata[:,0]
querydata=np.zeros((len(rdata)-21-5,6))
for i in range(21,len(rdata)-5):
m=rdata[i-21:i]
querydata[i-21,0]=np.std(m) #Standard Deviation
querydata[i-21,1]=(np.amax(m)-np.amin(m)) #Amplitude
querydata[i-21,2]=np.array(m[20]-m[19]) #Change of today and yesterday
querydata[i-21,3]=np.array((m[20]-m[19])-(m[19]-m[18])) #change of the change in three days
'''Compute Frequency'''
f=0
mean=np.mean(m)
for l in range(0,20):
if (m[l]-mean)*(m[l+1]-mean)<0:
f=f+1
querydata[i-21,4]=np.array(f)
querydata[i-21,5]=rdata[i+5] # real Y in the next fifth day
querydata=querydata[:,0:5]
predictedprice=RandomForest(alltraindata,20,querydata)
print predictedprice
d=np.arange(1,101,1)
plt.clf()
fig = plt.figure()
plt.plot(d,predictedprice,label='Ypredict',color='red')
plt.plot(d,ldfdata[:,0][26:126],label='Yactual')
plt.legend()
plt.xlabel('Days')
plt.ylabel('Prices')
plt.xlim(1,100) # set x scale
plt.ylim(0,500) # set y scale
plt.title('Ypredict vs Yatual for File 292-last100days',fontsize=12)
plt.savefig('Predicted vs actual 292_last100.pdf',format='pdf')
plt.close()
print "The RMS of 292 last 100 days is:",math.sqrt(((predictedprice-ldfdata[:,0][21:121])**2).mean(axis=0))
print "The correlation of 292 last 100 days is:",np.corrcoef(predictedprice,ldfdata[:,0][21:121])[1,0]