-
Notifications
You must be signed in to change notification settings - Fork 0
/
PREDICTION NET PRGM.py
100 lines (83 loc) · 2.61 KB
/
PREDICTION NET PRGM.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
import quandl, math
import pyodbc
import pickle
import numpy as np
import pandas as pd
from sklearn import preprocessing, svm
from sklearn.model_selection import train_test_split as TTS
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from matplotlib import style
import datetime
style.use('ggplot')
df = quandl.get("WIKI/GOOGL")
print(df.head())
df = df[['Adj. Open', 'Adj. High', 'Adj. Low', 'Adj. Close', 'Adj. Volume']]
df['HL_PCT'] = (df['Adj. High'] - df['Adj. Low']) / df['Adj. Close'] * 100.0
df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open']) / df['Adj. Open'] * 100.0
df = df[['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']]
forecast_col = 'Adj. Close'
df.fillna(value=-99999, inplace=True)
forecast_out = int(math.ceil(0.01 * len(df)))
df['label'] = df[forecast_col].shift(-forecast_out)
X = np.array(df.drop(['label'], 1))
print(X)
input()
X = preprocessing.scale(X)
print(X)
input()
X_lately = X[-forecast_out:]
X = X[:-forecast_out]
print(X,X.shape)
input()
df.dropna(inplace=True)
y = np.array(df['label'])
print(y,y.shape)
input()
X_train, X_test, y_train, y_test = TTS(X, y, test_size=0.80)
for k in ['linear','poly','rbf','sigmoid']:
clf1 = svm.SVR(kernel=k)
clf1.fit(X_train, y_train)
confidence = clf1.score(X_test, y_test)
print(k,confidence)
#pickle_in=open('{}.pickle'.format(k),'rb')
#clf=pickle.load(pickle_in)
forecast_set1 = clf1.predict(X_lately)
print(forecast_set1,X_lately)
'''
df['Forecast'] = np.nan
last_date = df.iloc[-1].name
last_unix = last_date.timestamp()
one_day = 86400
next_unix = last_unix + one_day
for i in forecast_set1:
next_date = datetime.datetime.fromtimestamp(next_unix)
next_unix += 86400
df.loc[next_date] = [np.nan for _ in range(len(df.columns)-1)]+[i]
df['Adj. Close'].plot()
df['Forecast'].plot()
plt.legend(loc=4)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()
clf2 = LinearRegression()
clf2.fit(X_train, y_train)
confidence = clf2.score(X_test, y_test)
print(k,confidence)
forecast_set2 = clf2.predict(X_lately)
df['Forecast'] = np.nan
last_date = df.iloc[-1].name
last_unix = last_date.timestamp()
one_day = 86400
next_unix = last_unix + one_day
for i in forecast_set2:
next_date = datetime.datetime.fromtimestamp(next_unix)
next_unix += 86400
df.loc[next_date] = [np.nan for _ in range(len(df.columns)-1)]+[i]
df['Adj. Close'].plot()
df['Forecast'].plot()
plt.legend(loc=4)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()
'''