-
Notifications
You must be signed in to change notification settings - Fork 47
/
Copy pathtrain_lstm_v1.py
162 lines (131 loc) · 7 KB
/
train_lstm_v1.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
"""
Training lstm model v1
"""
import numpy as np
import pylab as pl
import tensorflow as tf
from sklearn.preprocessing import minmax_scale
from helpers.utils import price_to_binary_target, extract_timeseries_from_oanda_data, train_test_validation_split
from helpers.utils import remove_nan_rows, get_signal, portfolio_value, get_data_batch, get_lstm_input_output
from models import lstm_nn
from helpers.get_features import get_features, min_max_scaling
# other-params
np.set_printoptions(linewidth=75*3+5, edgeitems=6)
pl.rcParams.update({'font.size': 6})
# hyper-params
batch_size = 1024
learning_rate = 0.001
drop_keep_prob = 0.6
value_moving_average = 50
split = (0.7, 0.2, 0.1)
plotting = False
saving = False
time_steps = 5
# load data
oanda_data = np.load('data\\EUR_USD_H1.npy') # [-50000:]
output_data_raw = price_to_binary_target(oanda_data, delta=0.00027)
price_data_raw = extract_timeseries_from_oanda_data(oanda_data, ['closeMid'])
input_data_raw, input_data_dummy_raw = get_features(oanda_data)
price_data_raw = np.concatenate([[[0]],
(price_data_raw[1:] - price_data_raw[:-1]) / (price_data_raw[1:] + 1e-10)], axis=0)
# prepare data
input_data, output_data, input_data_dummy, price_data = \
remove_nan_rows([input_data_raw, output_data_raw, input_data_dummy_raw, price_data_raw])
input_data_scaled_no_dummies = (input_data - min_max_scaling[1, :]) / (min_max_scaling[0, :] - min_max_scaling[1, :])
input_data = np.concatenate([input_data_scaled_no_dummies, input_data_dummy], axis=1)
input_data, output_data = get_lstm_input_output(input_data, output_data, time_steps=time_steps)
price_data = price_data[-len(input_data):]
# split to train,test and cross validation
input_train, input_test, input_cv, output_train, output_test, output_cv, price_train, price_test, price_cv = \
train_test_validation_split([input_data, output_data, price_data], split=split)
# get dims
_, _, input_dim = np.shape(input_data)
_, output_dim = np.shape(output_data)
# forward-propagation
x, y, logits, y_, learning_r, drop_out = lstm_nn(input_dim, output_dim, time_steps=time_steps, n_hidden=[8])
# tf cost and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
train_step = tf.train.AdamOptimizer(learning_r).minimize(cost)
# init session
cost_hist_train, cost_hist_test, value_hist_train, value_hist_test, value_hist_cv, value_hist_train_ma, \
value_hist_test_ma, value_hist_cv_ma, step, step_hist, saving_score = [], [], [], [], [], [], [], [], 0, [], 0.05
saver = tf.train.Saver()
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
# train
while True:
if step == 30000:
break
# train model
x_train, y_train = get_data_batch([input_train, output_train], batch_size, sequential=False)
_, cost_train = sess.run([train_step, cost],
feed_dict={x: x_train, y: y_train, learning_r: learning_rate, drop_out: drop_keep_prob})
# keep track of stuff
step += 1
if step % 100 == 0 or step == 1:
# get y_ predictions
y_train_pred = sess.run(y_, feed_dict={x: input_train, drop_out: drop_keep_prob})
cost_test, y_test_pred = sess.run([cost, y_], feed_dict={x: input_test, y: output_test, drop_out: drop_keep_prob})
y_cv_pred = sess.run(y_, feed_dict={x: input_cv, y: output_cv, drop_out: drop_keep_prob})
# get portfolio value
signal_train, signal_test, signal_cv = get_signal(y_train_pred), get_signal(y_test_pred), get_signal(y_cv_pred)
value_train = 1 + np.cumsum(np.sum(signal_train[:-1] * price_train[1:], axis=1))
value_test = 1 + np.cumsum(np.sum(signal_test[:-1] * price_test[1:], axis=1))
value_cv = 1 + np.cumsum(np.sum(signal_cv[:-1] * price_cv[1:], axis=1))
# save history
step_hist.append(step)
cost_hist_train.append(cost_train)
cost_hist_test.append(cost_test)
value_hist_train.append(value_train[-1])
value_hist_test.append(value_test[-1])
value_hist_cv.append(value_cv[-1])
value_hist_train_ma.append(np.mean(value_hist_train[-value_moving_average:]))
value_hist_test_ma.append(np.mean(value_hist_test[-value_moving_average:]))
value_hist_cv_ma.append(np.mean(value_hist_cv[-value_moving_average:]))
print('Step {}: train {:.4f}, test {:.4f}'.format(step, cost_train, cost_test))
if plotting:
pl.figure(1, figsize=(3, 7), dpi=80, facecolor='w', edgecolor='k')
pl.subplot(211)
pl.title('cost function')
pl.plot(step_hist, cost_hist_train, color='darkorange', linewidth=0.3)
pl.plot(step_hist, cost_hist_test, color='dodgerblue', linewidth=0.3)
pl.subplot(212)
pl.title('Portfolio value')
pl.plot(step_hist, value_hist_train, color='darkorange', linewidth=0.3)
pl.plot(step_hist, value_hist_test, color='dodgerblue', linewidth=0.3)
pl.plot(step_hist, value_hist_cv, color='magenta', linewidth=1)
pl.plot(step_hist, value_hist_train_ma, color='tomato', linewidth=1.5)
pl.plot(step_hist, value_hist_test_ma, color='royalblue', linewidth=1.5)
pl.plot(step_hist, value_hist_cv_ma, color='black', linewidth=1.5)
pl.pause(1e-10)
# save if some complicated rules
if saving:
current_score = 0 if value_test[-1] < 0.01 or value_cv[-1] < 0.01 \
else np.average([value_test[-1], value_cv[-1]])
saving_score = current_score if saving_score < current_score else saving_score
if saving_score == current_score and saving_score > 0.05:
saver.save(sess, 'saved_models/lstm-v1-avg_score{:.3f}'.format(current_score), global_step=step)
print('Model saved. Average score: {:.2f}'.format(current_score))
pl.figure(2)
pl.plot(value_test, linewidth=0.2)
pl.plot(value_cv, linewidth=2)
pl.pause(1e-10)
def save_plot():
pl.figure(1, figsize=(3, 7), dpi=80, facecolor='w', edgecolor='k')
pl.subplot(211)
pl.title('cost function')
pl.plot(step_hist, cost_hist_train, color='darkorange', linewidth=0.3)
pl.plot(step_hist, cost_hist_test, color='dodgerblue', linewidth=0.3)
pl.subplot(212)
pl.title('Portfolio value')
pl.plot(step_hist, value_hist_train, color='darkorange', linewidth=0.3)
pl.plot(step_hist, value_hist_test, color='dodgerblue', linewidth=0.3)
pl.plot(step_hist, value_hist_cv, color='magenta', linewidth=1)
pl.plot(step_hist, value_hist_train_ma, color='tomato', linewidth=1.5)
pl.plot(step_hist, value_hist_test_ma, color='royalblue', linewidth=1.5)
pl.plot(step_hist, value_hist_cv_ma, color='black', linewidth=1.5)
pl.pause(1e-10)
pl.savefig('lstm_v1_{:.3f}_{:.3f}.png'.format(learning_rate, value_cv[-1]))
pl.close()
save_plot()