-
Notifications
You must be signed in to change notification settings - Fork 47
/
Copy pathtrain_logistic_regression_v3.py
154 lines (126 loc) · 6.86 KB
/
train_logistic_regression_v3.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
"""
Train logistic regression with many features, PCA, polynomials
"""
import numpy as np
import pylab as plt
from helpers.get_features import get_features_v2
from helpers.utils import portfolio_value, price_to_binary_target, get_data_batch
from helpers.utils import get_signal, remove_nan_rows, train_test_validation_split, plot_roc_curve
from helpers.utils import min_max_scale, get_pca, get_poloynomials
import tensorflow as tf
from models import logistic_regression
# hyper-params
batch_size = 1024
learning_rate = 0.002
drop_keep_prob = 0.4
value_moving_average = 50
split = (0.5, 0.3, 0.2)
plotting = False
saving = False
transaction_c = 0.000
# load data
oanda_data = np.load('data\\EUR_USD_H1.npy')[-50000:]
y_data = price_to_binary_target(oanda_data, delta=0.000275)
x_data = get_features_v2(oanda_data, time_periods=[10, 25, 50, 120, 256], return_numpy=False)
# separate, rearrange and remove nans
price = x_data['price'].as_matrix().reshape(-1, 1)
price_change = x_data['price_delta'].as_matrix().reshape(-1, 1)
x_data = x_data.drop(['price', 'price_delta'], axis=1).as_matrix()
price, price_change, x_data, y_data = remove_nan_rows([price, price_change, x_data, y_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([x_data, y_data, price_change], split=split)
# pre-process data: scale, pca, polynomial
input_train, input_test, input_cv = min_max_scale(input_train, input_test, input_cv, std_dev_threshold=2.5)
# input_train, input_test, input_cv = get_pca(input_train, input_test, input_cv, threshold=0.01)
input_train, input_test, input_cv = get_poloynomials(input_train, input_test, input_cv, degree=2)
# get dims
_, input_dim = np.shape(input_train)
_, output_dim = np.shape(output_train)
# forward-propagation
x, y, logits, y_, learning_r, drop_out = logistic_regression(input_dim, output_dim)
# 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)
# main loop
for _ in range(5000):
if step % 1000 == 0:
learning_rate *= 0.8
# 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 % 10 == 0 or step == 1:
# get y_ predictions
y_train_pred = sess.run(y_, feed_dict={x: input_train, drop_out: drop_keep_prob})
y_test_pred, cost_test = sess.run([y_, cost], feed_dict={x: input_test, y: output_test, drop_out: drop_keep_prob})
y_cv_pred = sess.run(y_, feed_dict={x: input_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 = portfolio_value(price_train, signal_train, trans_costs=transaction_c)
value_test = portfolio_value(price_test, signal_test, trans_costs=transaction_c)
value_cv = portfolio_value(price_cv, signal_cv, trans_costs=transaction_c)
# 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:
plt.figure(1, figsize=(3, 7), dpi=80, facecolor='w', edgecolor='k')
plt.subplot(211)
plt.title('cost function')
plt.plot(step_hist, cost_hist_train, color='darkorange', linewidth=0.3)
plt.plot(step_hist, cost_hist_test, color='dodgerblue', linewidth=0.3)
plt.subplot(212)
plt.title('Portfolio value')
plt.plot(step_hist, value_hist_train, color='darkorange', linewidth=0.3)
plt.plot(step_hist, value_hist_test, color='dodgerblue', linewidth=0.3)
plt.plot(step_hist, value_hist_cv, color='magenta', linewidth=1)
plt.plot(step_hist, value_hist_train_ma, color='tomato', linewidth=1.5)
plt.plot(step_hist, value_hist_test_ma, color='royalblue', linewidth=1.5)
plt.plot(step_hist, value_hist_cv_ma, color='black', linewidth=1.5)
plt.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 > 0.1:
saver.save(sess, 'saved_models/lr-v1-avg_score{:.3f}'.format(current_score), global_step=step)
print('Model saved. Average score: {:.2f}'.format(current_score))
plt.figure(2)
plt.plot(value_train, linewidth=1)
plt.plot(value_test, linewidth=1)
plt.plot(value_cv, linewidth=1)
plt.pause(1e-10)
# roc curve
roc_auc_train, fpr_train, tpr_train = plot_roc_curve(y_train_pred, output_train)
roc_auc_test, fpr_test, tpr_test = plot_roc_curve(y_test_pred, output_test)
roc_auc_cv, fpr_cv, tpr_cv = plot_roc_curve(y_cv_pred, output_cv)
plt.figure(2, figsize=(3, 3), dpi=80, facecolor='w', edgecolor='k')
plt.plot(fpr_train, tpr_train, color='darkorange', lw=2, label='Train area: {:0.2f}'.format(roc_auc_train))
plt.plot(fpr_test, tpr_test, color='dodgerblue', lw=2, label='Test area: {:0.2f}'.format(roc_auc_test))
plt.plot(fpr_cv, tpr_cv, color='magenta', lw=2, label='CV area: {:0.2f}'.format(roc_auc_cv))
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
plt.show()