-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtrader.py
195 lines (161 loc) · 6.56 KB
/
trader.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
import os
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from keras.models import Sequential
from keras.layers import LSTM,Dense,Dropout
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.optimizers import Adam
from sklearn.preprocessing import MinMaxScaler
from statistics import mean
from numpy import newaxis
import csv
import argparse
# Arguments
epochs = 100
batch_size = 32
past_day = 5
future_day = 1
# 以收盤價為train, 以開盤價為target label
def split_dataset(df, past_day, future_day):
X, Y = [], []
for i in range(len(df) - future_day - past_day):
X.append(np.array(df[i:i+past_day, 0]))
Y.append(np.array(df[i+past_day:i+past_day+future_day, 0]))
return np.array(X), np.array(Y)
def build_model(shape):
model = Sequential()
model.add(LSTM(64, input_shape=(shape[1], shape[2]), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(64, return_sequences=True))
model.add(Dropout(0.2))
model.add(Dense(1))
return model
def plotting(input1, input2, title, legend, x_label=None, y_label=None, grid=True, figsize=(20, 8)):
plt.figure(figsize=figsize)
plt.plot(input1)
plt.plot(input2)
plt.title(title)
plt.legend(legend)
plt.xlabel(x_label)
plt.ylabel(y_label)
plt.grid(grid)
plt.show()
def calculate_revenue(size, nu):
# 1: buy
# 0: hold
# -1: short/sell
# size = x_test.shape[0] - 1
status = 0
flag = 0
revenue = 0
with open('output.csv', mode='w') as csv_file:
writer = csv.writer(csv_file)
for i in range(size):
if (status == 1):
if (nu[i+1]<nu[i]):
writer.writerow(['-1'])
status = 0
revenue = revenue+nu[i]
else:
writer.writerow(['0'])
elif (status == 0):
if (nu[i+1]>nu[i]):
writer.writerow(['1'])
status = 1
revenue = revenue-nu[i]
elif (nu[i+1]<nu[i]):
writer.writerow(['-1'])
status = -1
revenue = revenue+nu[i]
else:
writer.writerow(['0'])
else :
if (nu[i+1]>nu[i]):
writer.writerow(['1'])
status = 0
revenue = revenue-nu[i]
else:
writer.writerow(['0'])
if (status==1) :
revenue = revenue + nu[size]
elif (status==-1) :
revenue = revenue - nu[size]
return revenue
if __name__ == "__main__":
# Main Arguments
main_path = '';
parser = argparse.ArgumentParser()
parser.add_argument("--training", default="dataset/training.csv", help="input training data file name")
parser.add_argument("--testing", default="dataset/testing.csv", help="input testing data file name")
parser.add_argument("--output", default="output.csv", help="output file name")
args = parser.parse_args()
train_df = pd.read_csv(args.training, header=None)
test_df = pd.read_csv(args.testing, header=None)
train_df.drop([1,2,3], inplace=True, axis=1)
test_df.drop([1,2,3], inplace=True, axis=1)
test_df = pd.DataFrame(np.insert(test_df.to_numpy(), 0, train_df.to_numpy()[-(past_day+1):], axis=0))
train_df = pd.DataFrame(train_df.to_numpy()[:-(past_day+1)])
# Scaling
sc = MinMaxScaler(feature_range=(-1, 1))
scaled_train_df = sc.fit_transform(train_df)
scaled_test_df = sc.transform(test_df)
# Generate training data and label
x_train, y_train = split_dataset(scaled_train_df, past_day, future_day)
x_test, y_test = split_dataset(scaled_test_df, past_day, future_day)
# Plotting Original Open and Close Price of testing.csv
plotting(x_test[:,-1], y_test, 'Price', ['close price','open price'])
# Reshape the data into (Samples, Timestep, Features)
x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], 1)
x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], 1)
# Build model
model = build_model(x_train.shape)
model.summary()
# Compile and Fit
reduce_lr = tf.keras.callbacks.LearningRateScheduler(lambda x: 1e-3 * 0.9 ** x)
early_stopping = EarlyStopping(monitor='loss', patience=10, verbose=1, mode='auto')
model.compile(optimizer=Adam(), loss='mean_squared_error')
history = model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size,validation_data=(x_test, y_test), shuffle=False, callbacks=[reduce_lr, early_stopping])
model.save('model.h5')
# Plotting Model Loss
plotting(input1=history.history['loss'], input2=history.history['val_loss'], title='Model Loss', legend=['Train','Valid'], x_label='Epochs', y_label='Loss')
# Start Predicting
predicted = model.predict(x_test)
predict = sc.inverse_transform(predicted.reshape(predicted.shape[0], predicted.shape[1]))
# 將預測data前移三天，並利用預測結果預測剩餘最後三天的股票
last = np.array([x_test[-1, 1:], x_test[-1, 2:], x_test[-1, 3:]], dtype=object)
# print(last)
# print(np.array(predicted[newaxis, -2, -1]))
last[0] = np.array(np.concatenate((np.array(last[0]), np.array(predicted[newaxis, -3, -1]))))
last[1] = np.concatenate((last[1], predicted[newaxis, -3, -1]))
last[1] = np.array(np.concatenate((last[1], predicted[newaxis, -2, -1])))
last[2] = np.concatenate((last[2], predicted[newaxis, -3, -1]))
last[2] = np.concatenate((last[2], predicted[newaxis, -2, -1]))
last[2] = np.array(np.concatenate((last[2], predicted[newaxis, -1, -1])))
last[0] = pd.DataFrame(last[0])
last[1] = pd.DataFrame(last[1])
last[2] = pd.DataFrame(last[2])
X = []
X.append(np.array(last[0]))
X.append(np.array(last[1]))
X.append(np.array(last[2]))
X = np.array(X)
# print(X)
predicted_last = model.predict(X)
predicted_last = sc.inverse_transform(predicted_last.reshape(predicted_last.shape[0], predicted_last.shape[1]))
nu = []
for i in range(20):
nu.append(predict[i, -1])
nu = nu[3:]
nu.append(predicted_last[0, -1])
nu.append(predicted_last[1, -1])
nu.append(predicted_last[2, -1])
print(nu)
ground_truth = sc.inverse_transform(y_test.reshape(-1,1))
plotting(input1=ground_truth, input2=nu, title='Open Price', legend=['y_test','predict'])
revenue = calculate_revenue(x_test.shape[0]-1, nu)
print('===========================')
print('Revenue is :', revenue)
print('===========================')