import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import SGD
from keras.utils import np_utils
from scipy import misc
import glob
import matplotlib.pyplot as plt
from PIL import Image
import math
seed = 7
np.random.seed(seed)
width = 1
height = 1
def r_squared(y_true, y_hat):
ssr = 0
sst = 0
e = np.subtract(y_true, y_hat)
y_mean = np.mean(y_true)
for item in e:
ssr += item**2
for item in y_true:
sst += (item - y_mean)**2
r2 = 1 - ssr / sst
return r2
def compile_model(model):
lrate = 0.01
sgd = SGD(lr=lrate, momentum=0.9, decay=1e-6, nesterov=True)
model.compile(loss='sparse_categorical_crossentropy', optimizer=sgd)
return model
def create_model():
model = Sequential()
model.add(Convolution2D(32, 3, 3,
border_mode='valid',
input_shape=(100, 100, 3)))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Convolution2D(64, 3, 3,
border_mode='valid'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(2))
model.add(Activation('softmax'))
return model
def find_returns(data):
returns = []
for group in data:
count = 30
while count <= (len(group)-5):
current_data = group[count-1]
future_data = group[count+4]
p1 = np.mean(current_data)
p2 = np.mean(future_data)
returns.append(math.log(p2/p1))
count += 1
return returns
def get_pixel_values():
file_name = r'\figures_v2'
pixels = []
for filename in glob.glob(file_name + '\*.png'):
im = misc.imread(filename)
pixels.append(im)
return pixels
def convert_image():
file_name = r'\figures_v2'
for filename in glob.glob(file_name + '\*.png'):
img = Image.open(filename)
img = img.convert('RGB')
img.save(filename)
def plot_data(data):
t = np.arange(0, 29, 1)
file_name_number = 0
fig = plt.figure(frameon=False, figsize=(width, height))
for group in data:
count = 30
while count <= (len(group)-5):
high = []
low = []
for item in group[count-30:count]:
high.append(item[0])
low.append(item[1])
file_name = r'\fig_' + str(file_name_number)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax.plot(t, high[0:-1], 'b', t, low[0:-1], 'g')
fig.savefig(r'\figures_v2' + file_name, dpi=100)
fig.clf()
file_name_number += 1
count += 1
print('Created %d files!' % file_name_number)
def load_sample_data():
original_data = extract_data()
splitted_data = split_data(original_data)
useful_data = extract_useful_data(splitted_data)
return useful_data
def extract_useful_data(data):
groups = []
for group in data:
temp_buffer = []
for item in group:
temp = [item[2], item[3]]
temp = [float(i) for i in temp]
temp_buffer.append(temp)
groups.append(temp_buffer)
return groups
def split_data(data):
groups = []
for item in data:
temp_buffer = []
for string in item:
number = string.split(',')
temp_buffer.append(number)
groups.append(temp_buffer)
return groups
def extract_data():
file_name = r'\data.txt'
infile = open(file_name, 'r')
temp_buffer = []
for line in infile:
temp_buffer.append(line.strip('\n'))
temp_buffer = temp_buffer[8:]
i = 0
groups = []
temp = []
for item in temp_buffer:
if i != 390:
temp.append(item)
i += 1
else:
groups.append(temp)
temp = []
i = 0
groups.append(temp)
infile.close()
return groups
def main():
data = load_sample_data()
plot_data(data)
convert_image()
x = np.asarray(get_pixel_values())
y = np.asarray(find_returns(data))
x_train = x[0:4340]
y_train = y[0:4340]
x_test = x[0:4340]
y_test = y[0:4340]
# y_true = y_test
# y_train = np_utils.to_categorical(y_train, 2)
# y_test = np_utils.to_categorical(y_test, 2)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255.0
x_test /= 255.0
model = create_model()
model = compile_model(model)
# Fit the model
epochs = 10
model.fit(x_train, y_train, validation_data=(x_test, y_test),
nb_epoch=epochs,
shuffle=True, batch_size=100, verbose=1)
# scores = model.evaluate(x_test, y_test, verbose=0)
# print('Accuracy: %.2f%%' % (scores[1] * 100))
classes = model.predict_classes(x_test, verbose=0)
classes = list(classes)
y_test = list(y_test)
r2 = r_squared(y_test, classes)
print r2
if __name__ == '__main__':
main()
