-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathclasses.py
157 lines (103 loc) · 4.25 KB
/
classes.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
# -*- coding: utf-8 -*-
"""
Stock Prediction Classes
----------------------------------------
File containing the data and GRU model
classes for stock prediction.
----------------------------------------
Created on Tue Oct 17 03:42:18 2023
@author: Andrew Francey
"""
import yfinance as yf
import torch
import torch.nn as nn
import numpy as np
import pylab as plt
import pandas as pd
import json
from sklearn.preprocessing import StandardScaler
class Training_Data():
'''
Class for loading and converting the data to the approprate formats
'''
def __init__(self, stock_name, param):
self.name = stock_name
self.period = param['period']
data = yf.download(self.name, period=self.period, interval= param['freq'])
## Scaling the data so it is better behaved when training the ML model
x_feat = data.iloc[:,[0,4]]
self.sc = StandardScaler() # Rescaling values between -1 and 1.
x_ft = self.sc.fit_transform(x_feat.values)
self.x_ft = pd.DataFrame(columns=x_feat.columns,
data=x_ft,
index=x_feat.index)
data_raw = self.x_ft.to_numpy()
data = []
lookback = param['lookback']
self.lookback = lookback
train_split = param['train_split']
for index in range(len(data_raw)-lookback):
data.append(data_raw[index: index + lookback])
self.data = np.array(data)
train_set_size = int(np.round(train_split*self.data.shape[0]))
x_train = self.data[:train_set_size,:-1,:]
y_train = self.data[:train_set_size,-1,:]
x_test = self.data[train_set_size:,:-1,:]
y_test = self.data[train_set_size:,-1,:]
self.x_train = torch.tensor(x_train)
self.y_train = torch.tensor(y_train)
self.x_test = torch.tensor(x_test)
self.y_test = torch.tensor(y_test)
self.index = np.arange(0,len(data_raw))
def plot(self):
data = self.sc.inverse_transform(self.x_ft.values)
open_data = data[:,0]
close_data = data[:,1]
plt.plot(self.index,open_data,label="Open")
plt.plot(self.index,close_data,label="Close")
plt.title(self.name)
plt.legend()
plt.grid()
plt.show()
class Net(nn.Module):
def __init__(self, param):
super(Net, self).__init__()
input_dim = param['input_dim']
hidden_dim = param['hidden_dim']
num_layers = param['num_layers']
output_dim = param['output_dim']
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.gru = nn.GRU(input_dim, hidden_dim, num_layers, batch_first=True,
dropout=0.0)
self.fc = nn.Linear(hidden_dim, output_dim)
self.double()
def forward(self, x):
x = x.double()
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).double()
out, (hn) = self.gru(x, (h0.detach()))
out = self.fc(out[:, -1, :])
return out
def backprop(self, inputs, targets, loss, epoch, optimizer):
self.train()
inputs = inputs.double()
targets = targets.double()
outputs = self.forward(inputs)
obj_val = loss(outputs, targets)
optimizer.zero_grad()
obj_val.backward()
optimizer.step()
return obj_val.item()
def test(self, data, loss, epoch):
inputs = data.x_test
targets = data.y_test
self.eval()
with torch.no_grad():
inputs = inputs.float()
targets = targets.float()
outputs = self.forward(inputs)
cross_val = loss(outputs, targets)
return cross_val
if __name__== '__main__':
with open('param.json') as paramfile:
param = json.load(paramfile)