def __init__(self, model_name):
self.model_name = model_name
self.action_names = ['A', 'D', 'M', 'L', 'R']
self.num_actions = len(self.action_names)
self.memory = deque()
self.model = Cnn(self.model_name, self.memory)
self.target_model = Cnn(self.model_name, [], target=True)
# self.state = np.zeros([1, VISION_F + VISION_B + 1, VISION_W * 2 + 1, 1])
self.previous_states = np.zeros(
[1, VISION_F + VISION_B + 1, VISION_W * 2 + 1, 4])
self.previous_actions = np.zeros([4])
self.previous_actions.fill(2)
self.q_values = np.zeros(5)
self.action = 2
self.count_states = self.model.get_count_states()
self.delay_count = 0
self.epsilon_linear = LinearControlSignal(
start_value=EPSILON_GREEDY_START_PROB,
end_value=EPSILON_GREEDY_END_PROB,
repeat=False)
self.advantage = 0
self.value = 0
self.score = 0
def findWellStruct():
allSeqs = helpers.readAllSeqs()
shutil.rmtree("bestpredictions")
os.mkdir("bestpredictions")
names = os.listdir("model")
testname = ""
for name in names:
if name[0:4] == "test":
testname = name
best, parameters = hyperparams.getHyperParams("model/" + testname)
nn_user = nnUser(parameters)
tf.reset_default_graph()
with tf.variable_scope("model" + str(int(testname[7:10])), reuse=None):
model = Cnn(parameters)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, "model/my_model_final.ckpt")
nn_user.predict(sess, model, allSeqs, "best")
def __init__(self,
learning=True,
epsilon=1,
alpha=0.1,
discount=1,
tolerance=0.01):
self.Q = dict()
self.Q1 = dict()
self.Q2 = dict()
self.s = states.States()
self.s.buildAllStates()
self.cnn = Cnn()
self.learning = learning
self.epsilon = epsilon
self.alpha = alpha
self.discount = discount
self.tolerance = tolerance
self.t = 2
self.convolutionLayersNumber = 0
self.poolingLayersNumber = 0
self.fullyConnectedLayersNumber = 0
self.convolutionLayersLimit = 3
self.poolingLayersLimit = 2
self.fullyConnectedLayersLimit = 2
self.actionsInitialState()
self.actionsConvolutionState()
self.actionsPoolingState()
self.actionsFullyConnectedState()
def Cnn_test():
# initializing the network
network = Cnn(BATCH_SIZE)
network.getTheParas(MODEL_FILE)
# load the test data
_, _, test_imgs, _, _, test_label = util.load_data(MNIST_PATH, False)
log_string('------------start test-----------')
num_batch = test_imgs.shape[0] // BATCH_SIZE
start = 0
end = start + BATCH_SIZE
loss = 0.0
total_correct = 0.0
total_seen = 0
for n in range(num_batch):
log_string('testing {}/{}(batchs) completed!'.format(n + 1, num_batch))
current_img = test_imgs[start:end, ...]
current_label = test_label[start:end, ...]
start = end
end += BATCH_SIZE
predict_val, loss_val = network.forward(current_img, current_label)
correct = np.sum(predict_val == current_label)
total_correct += correct
loss += loss_val
total_seen += BATCH_SIZE
log_string('eval mean loss: {}'.format(loss / num_batch))
log_string('eval accuracy: {}'.format(total_correct / total_seen))
def createBest(parameters):
allSeqInfo, allTurnCombs = parseData.getSeqInfo()
doneSeqs = []
testSeqs = []
nn_user = nnUser(parameters)
#Number of sequences with known structure and less than 3 alanines
validSet = helpers.validSet(allSeqInfo)
#Print to command line the current parameter information
helpers.printParamInfo(parameters, 0)
with tf.variable_scope("model0"):
model = Cnn(parameters)
#For each sequence, place it into the current batch. Then, when there are a
#"batch number" of sequences collected, train a convo network with them as
#the validation set. Then, measure the accuracy of the current network.
sess = tf.Session()
nn_user.genData([], "test")
times, costs, metTests, metTrains, metTestBest = \
nn_user.trainNet(sess,model,0,production=True,saveBest=True,outputCost=parameters.outputCost)
#Output the best metric average and corresponding hyperparameters to a file
#in the paramResults directory
helpers.writeParamInfo("model/", "testNum", parameters, metTestBest, 0)
sess.close()
def __init__(self, num_sift_features, num_classes, sift):
super(Classifier, self).__init__()
# define the cnn model:
self.cnn = Cnn()
self.sift = sift
classifier_model = []
classifier_model += [nn.BatchNorm1d(num_features=192 * 5 * 5 + 1152)]
classifier_model += [nn.Linear(192 * 5 * 5 + 1152, 2976)]
classifier_model += [nn.Dropout(0.2)]
classifier_model += [nn.ReLU()]
classifier_model += [nn.Linear(2976, 1000)]
classifier_model += [nn.Dropout(0.2)]
classifier_model += [nn.ReLU()]
classifier_model += [nn.Linear(1000, 10)]
# classifier_model += [nn.Dropout(0.2)]
# classifier_model += [nn.ReLU()]
# classifier_model += [nn.Linear(864, 432)]
# classifier_model += [nn.Dropout(0.2)]
# classifier_model += [nn.ReLU()]
# classifier_model += [nn.Linear(432, 10)]
# classifier_model += [nn.Linear(192 * 6 * 6 + 1152, 192 * 6 * 6 + 1152)]
# classifier_model += [nn.ReLU()]
# classifier_model += [nn.Dropout(0.2)]
# classifier_model += [nn.Linear(192 * 6 * 6 + 1152, 1000)]
# classifier_model += [nn.ReLU()]
# classifier_model += [nn.Dropout(0.2)]
# classifier_model += [nn.Linear(1000, 10)]
#classifier_model += [nn.Softmax(dim=1)]
self.classifier = nn.Sequential(*classifier_model)
def test(metadata_file, dataset_dir, model_file):
generator = AudioGenerator(metadata_path=metadata_file,
dataset_path=dataset_dir,
batch_size=1)
vggvox_net = Cnn(model_file)
metrics = vggvox_net.evaluate_generator(
test_generator=generator.test(), test_steps=generator.get_test_steps())
print(
f"Loss {metrics[0]}, Top 1 Accuracy {metrics[1]}, Top 5 Accuracy {metrics[2]}"
)
def train(metadata_file, dataset_dir, checkpoint_file):
generator = AudioGenerator(metadata_path=metadata_file,
dataset_path=dataset_dir,
batch_size=20)
vggvox_net = Cnn()
vggvox_net.fit_generator(model_checkpoint_path=checkpoint_file,
train_generator=generator.train(),
train_steps=generator.get_training_steps(),
validation_generator=generator.validate(),
validation_steps=generator.get_validation_steps())
def Cnn_train():
# initializing the network
network = Cnn(BATCH_SIZE)
# load the data
train_imgs, val_imgs, _, train_label, val_label, _ = util.load_data(MNIST_PATH)
for epoch in range(MAX_EPOCH):
# eval_one_epoch(network, val_imgs, val_label)
log_string('------------start train epoch {}/{}----------'.format(epoch, MAX_EPOCH))
train_one_epoch(network, train_imgs, train_label, epoch)
def reset(self, testing=False):
if testing == True:
self.epsilon = 0
self.alpha = 0
else:
self.convolutionLayersNumber = 0
self.poolingLayersNumber = 0
self.fullyConnectedLayersNumber = 0
self.epsilon = 0.9999 * self.t
del self.cnn
self.cnn = Cnn()
def test(self):
print('---------------Start Testing-----------')
convNet = ()
self.cnn = Cnn()
changeState = self.initial
convNet += (changeState, )
while changeState.name != states.TERMINATE:
changeState = self.getMaxQState(self.Q, changeState)
convNet += (changeState, )
print('TestingnConvnet', convNet)
score = self.cnn.buildModel(convNet)
print('TestScore', score)
def __init__(self):
conf_path_cnn = '/src/cnn/conf/cnn.yaml'
with open(conf_path_cnn, 'r') as fd:
self.conf = yaml.safe_load(fd)
self.batch_size = self.conf['testing']['batch_size']
self.checkpoint_path = self.conf['misc']['checkpoint_path']
self.logger = Logger
self.preProcessor = PreProcessor(Logger)
self.cnn = Cnn(Logger, conf_path_cnn, testing=True)
self.sess = tf.compat.v1.Session()
self.start()
def main():
epochs = 100
#x, y = load_data(DATA_PATH, verbose=False, num_samples=5)
x = np.load('/global/scratch/alex_vlissidis/X.npz')['x']
y = np.load('/global/scratch/alex_vlissidis/y.npz')['y']
print("training data shapes:", x.shape, y.shape)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
print("building model...")
print(x.shape)
print(y.shape[1])
model = Cnn(x.shape[1:], y.shape[1])
model.build()
history = model.train(x_train, y_train, epochs=epochs)
print("Saving model...")
model.model.save('models/model.h5')
print("Plotting...")
f, (ax1, ax2) = plt.subplots(2, 1)
ax1.plot(range(1, epochs + 1),
history.history['val_acc'],
'tab:blue',
label="validation accuracy")
ax1.plot(range(1, epochs + 1),
history.history['acc'],
'tab:red',
label="training accuracy")
ax2.plot(range(1, epochs + 1),
history.history['loss'],
'tab:orange',
label="loss")
ax2.plot(range(1, epochs + 1),
history.history['val_loss'],
'tab:green',
label="validation loss")
ax1.legend()
ax2.legend()
f.savefig('figures/training.png', dpi=300)
print("Done.")
def run(self, x, nbit, resolution, error):
nbits = str(nbit)
test_data = np.load("./data/" + nbits + "bit" + "/" + nbits + "bit" +
"_" + str(resolution) + "x" + str(resolution) +
"_test_images.npy")
train_data = np.load("./data/" + nbits + "bit" + "/" + nbits + "bit" +
"_" + str(resolution) + "x" + str(resolution) +
"_train_images.npy")
x = int(x)
if (error != 0):
test_data = np.array(
[self.pixel_bit_error(error, i, nbit) for i in test_data])
if (x == 1):
generations = input("enter the number of generations")
batchSize = input("enter the size of each batch")
generations = int(generations)
batchSize = int(batchSize)
Jesus = Cnn()
Jesus.run(train_data, test_data, resolution, error, generations,
batchSize)
if (x == 2):
if (error != 0):
train_data = np.array(
[self.pixel_bit_error(error, i, nbit) for i in train_data])
Jesus = Svm()
Jesus.run(train_data, test_data, resolution)
if (x == 3):
if (error != 0):
train_data = np.array(
[self.pixel_bit_error(error, i, nbit) for i in train_data])
k = input("k ?")
k = int(k)
Jesus = Knn(k)
Jesus.run(train_data, test_data, resolution)
if (x == 4):
Jesus = Caliente([], error)
batchSize = input("enter the size of each batch")
generations = input("enter the number of generations")
generations = int(generations)
batchSize = int(batchSize)
Jesus.run(train_data, test_data, resolution, generations,
batchSize)
def findSingleSeq(testSeq):
names = os.listdir("model")
testname = ""
for name in names:
if name[0:4] == "test":
testname = name
best, parameters = hyperparams.getHyperParams("model/" + testname)
nn_user = nnUser(parameters)
tf.reset_default_graph()
with tf.variable_scope("model" + str(int(testname[7:10])), reuse=None):
model = Cnn(parameters)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, "model/my_model_final.ckpt")
nn_user.predict(sess, model, [testSeq], "individ")
def main(args):
batch_size = 128
ds_train, ds_test, info = load_data(batch_size)
image_shape = info.features["image"].shape
image_size = tf.reduce_prod(image_shape)
num_train_samples = info.splits["train"].num_examples
num_test_samples = info.splits["test"].num_examples
cnn = Cnn()
if "init_model" in args.cnn_mode:
cnn.create_model(batch_size=batch_size,
image_shape=image_shape,
feature_outputs=int(100))
if "train" in args.cnn_mode:
cnn.train(ds_train,
ds_test,
epochs=10,
steps_per_epoch=int(num_train_samples / batch_size))
if "save" in args.cnn_mode:
cnn.save()
if "load" in args.cnn_mode:
cnn.load_combined_model()
if "test" in args.cnn_mode:
cnn.test(ds_test)
gp = DeepKernelGP(ds_train,
num_train_samples,
image_size,
10,
cnn.feature_extractor,
num_inducing_points=100)
if "train" in args.gp_mode:
gp.train(10000)
if "test" in args.gp_mode:
gp.test(ds_test, image_size, batch_size, num_test_samples)
import tensorflow as tf
from cnn import Cnn
import config
import util
x_train_orig, y_train_orig, x_test_orig, y_test_orig, classes = util.load_data_set(
)
x_train = util.pre_treat(x_train_orig)
x_test = util.pre_treat(x_test_orig)
y_train = util.pre_treat(y_train_orig, is_x=False, class_num=len(classes))
y_test = util.pre_treat(y_test_orig, is_x=False, class_num=len(classes))
cnn = Cnn(config.conv_layers, config.fc_layers, config.filters,
config.learning_rate, config.beta1, config.beta2)
(m, n_H0, n_W0, n_C0) = x_train.shape
n_y = y_train.shape[1]
# construction calculation graph
cnn.initialize(n_H0, n_W0, n_C0, n_y)
cnn.forward()
cost = cnn.cost()
optimizer = cnn.get_optimizer(cost)
predict, accuracy = cnn.predict()
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for i in range(1, config.num_epochs + 1):
def setUp(self) -> None:
self.subject = Cnn()
import signal
import matplotlib.pyplot as plt
import numpy as np
from keras.datasets import mnist
from cnn import Cnn
from plain.layers.flatten import Flatten
from plain.layers.maxPooling import MaxPooling
from plain.layers.softmax import Softmax
from vectorized.layers.conv2d import Conv2d
from vectorized.layers.fully_connected import FullyConnected
cnn = Cnn(300, 0.07)
def keyboard_interrupt_handler(signal, frame):
cnn.save()
exit(0)
def main():
plt.ion()
# cnn = Cnn.load("saved/cnn_12-28-2019_19-36")
(X_train, y_train), (X_test, y_test) = mnist.load_data()
labels = np.zeros((y_train.shape[0], 10, 1))
for index, x in enumerate(y_train):
labels[index, x] = [1]
images = X_train.reshape(
# %%
train_x = np.expand_dims(train_x, axis=1)
print(train_x[0])
plt.imshow(train_x[0][0])
per = np.random.permutation(train_x.shape[0]) # 打乱后的行号
rtrain_x = torch.from_numpy(train_x[per])
rtrain_y = torch.from_numpy(train_y[per]).squeeze()
print(per)
plt.figure()
plt.imshow(rtrain_x[0][0])
# %%
cnn = Cnn()
cnn.to(device)
print(cnn)
dummy_input = torch.randn(1, 1, 48, 48, device=device)
torch.onnx.export(cnn,
dummy_input,
"convnet.onnx",
verbose=True,
input_names=['input'],
output_names=['output'])
# %%
learn_rate = 3e-4
los = nn.CrossEntropyLoss()
loader = Loader(rtrain_x, 64, label=rtrain_y)
def train():
TIMESTAMP = "{0:%Y-%m-%d-%H-%M/}".format(datetime.now())
log.log_info('program start')
data, num_good, num_bad = util.load_train_data(num_data // 2)
log.log_debug('Data loading completed')
# resample
data, length = util.resample(data, 600)
data = util.reshape(data, length)
good_data_origin = data[:num_good, :]
bad_data_origin = data[num_good:, :]
# extract bad data for test and train
permutation = list(np.random.permutation(len(bad_data_origin)))
shuffled_bad_data = bad_data_origin[permutation, :]
test_bad_data = shuffled_bad_data[:int(num_bad * 0.3), :]
train_bad_data_origin = shuffled_bad_data[int(num_bad * 0.3):, :]
# extract corresponding good data for test and train
permutation = list(np.random.permutation(len(good_data_origin)))
shuffled_good_data = good_data_origin[permutation, :]
test_good_data = shuffled_good_data[:len(test_bad_data), :]
train_good_data = shuffled_good_data[len(test_bad_data):, :]
assert len(test_bad_data) == len(test_good_data)
# construct test data
test_y = np.array([1.] * len(test_good_data) + [0.] * len(test_bad_data), dtype=np.float).reshape(
(len(test_bad_data) + len(test_good_data), 1))
test_x = np.vstack((test_good_data, test_bad_data))
# expand the number of bad data for train
train_x = np.vstack((train_good_data, train_bad_data_origin))
train_y = np.array([1.] * len(train_good_data) + [0.] * len(train_bad_data_origin), dtype=np.float).reshape(
(len(train_bad_data_origin) + len(train_good_data), 1))
train_x, train_y, num_expand = util.expand(train_x, train_y)
# regularize
for i in range(len(train_x)):
train_x[i, :, 0] = util.regularize(train_x[i, :, 0])
train_x[i, :, 1] = util.regularize(train_x[i, :, 1])
train_x[i, :, 2] = util.regularize(train_x[i, :, 2])
for i in range(len(test_x)):
test_x[i, :, 0] = util.regularize(test_x[i, :, 0])
test_x[i, :, 1] = util.regularize(test_x[i, :, 1])
test_x[i, :, 2] = util.regularize(test_x[i, :, 2])
# random
train_x, train_y = util.shuffle_data(train_x, train_y)
log.log_debug('prepare completed')
log.log_info('convolution layers: ' + str(conv_layers))
log.log_info('filters: ' + str(filters))
log.log_info('full connected layers: ' + str(fc_layers))
log.log_info('learning rate: %f' % learning_rate)
log.log_info('keep prob: ' + str(keep_prob))
log.log_info('the number of expanding bad data: ' + str(num_expand))
log.log_info('mini batch size: ' + str(mini_batch_size))
if mini_batch_size != 0:
assert mini_batch_size <= len(train_x)
cnn = Cnn(conv_layers, fc_layers, filters, learning_rate)
(m, n_W0, n_C0) = train_x.shape
n_y = train_y.shape[1]
# construction calculation graph
cnn.initialize(n_W0, n_C0, n_y)
cost = cnn.cost()
optimizer = cnn.get_optimizer(cost)
predict, accuracy = cnn.predict()
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
# log for tensorboard
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter("resource/tsb/train/" + TIMESTAMP, sess.graph)
test_writer = tf.summary.FileWriter("resource/tsb/test/" + TIMESTAMP)
if enable_debug:
sess = tf_debug.LocalCLIDebugWrapperSession(sess)
sess.run(init)
for i in range(1, num_epochs + 1):
if mini_batch_size != 0:
num_mini_batches = int(m / mini_batch_size)
mini_batches = util.random_mini_batches(train_x, train_y, mini_batch_size)
cost_value = 0
for mini_batch in mini_batches:
(mini_batch_x, mini_batch_y) = mini_batch
_, temp_cost = sess.run([optimizer, cost], feed_dict={cnn.x: mini_batch_x, cnn.y: mini_batch_y,
cnn.keep_prob: keep_prob})
cost_value += temp_cost
cost_value /= num_mini_batches
else:
_, cost_value = sess.run([optimizer, cost],
feed_dict={cnn.x: train_x, cnn.y: train_y, cnn.keep_prob: keep_prob})
# disable dropout
summary_train, train_accuracy = sess.run([merged, accuracy],
feed_dict={cnn.x: train_x, cnn.y: train_y,
cnn.keep_prob: 1})
summary_test, test_accuracy = sess.run([merged, accuracy],
feed_dict={cnn.x: test_x, cnn.y: test_y, cnn.keep_prob: 1})
train_writer.add_summary(summary_train, i - 1)
test_writer.add_summary(summary_test, i - 1)
if print_detail and (i % 10 == 0 or i == 1):
info = '\nIteration %d\n' % i + \
'Cost: %f\n' % cost_value + \
'Train accuracy: %f\n' % train_accuracy + \
'Test accuracy: %f' % test_accuracy
log.log_info(info)
# stop when test>0.95 and train>0.99
if test_accuracy >= 0.95 and train_accuracy >= 0.99:
info = '\nIteration %d\n' % i + \
'Cost: %f\n' % cost_value + \
'Train accuracy: %f\n' % train_accuracy + \
'Test accuracy: %f' % test_accuracy
log.log_info(info)
saver.save(sess, "resource/model/" + TIMESTAMP)
break
saver.save(sess, "resource/model/" + TIMESTAMP)
train_writer.close()
test_writer.close()
log.log_info('program end')
import numpy as np
from tqdm import tqdm
from cnn import Cnn
from config import Config
from hyperparameters import Hyperparameters
import matplotlib.pyplot as plt
if __name__ == "__main__":
cfg = Config()
data = np.load(cfg.trainingDataName, allow_pickle=True)
metaData = np.load(cfg.trainingMetadataName, allow_pickle=True)
numSubjects = len(metaData[0])
print("Dataset Loaded")
hyp = Hyperparameters()
cnn = Cnn(cfg, hyp, numSubjects)
print("CNN Generated")
images = ((torch.from_numpy(np.stack(data[:, 0]))).view(
-1, cfg.imagChannels, cfg.res[0], cfg.res[1]))
oneHotVecs = torch.Tensor(list(data[:, 1]))
print("Images Converted to Tensors")
trainSize = int((1 - cfg.testPercent) * len(data))
testSize = len(data) - trainSize
trainImgs = images[:trainSize]
trainOneHotVecs = oneHotVecs[:trainSize]
testImgs = images[trainSize:]
testOneHotVecs = oneHotVecs[trainSize:]
def createTrainTestPred(parameters, testNum, save):
allSeqInfo, allTurnCombs = parseData.getSeqInfo()
doneSeqs = []
count = 0
testSeqs = []
nn_user = nnUser(parameters)
#Number of sequences with known structure and less than 3 alanines
numLessThree = len(helpers.validSet(allSeqInfo))
#Print to command line the current parameter information
helpers.printParamInfo(parameters, testNum)
with tf.variable_scope("model" + str(testNum)):
model = Cnn(parameters)
bestMetAvg = 0.0
batchNum = 0
#For each sequence, place it into the current batch. Then, when there are a
#"batch number" of sequences collected, train a convo network with them as
#the validation set. Then, measure the accuracy of the current network.
sess = tf.Session()
for seq in allSeqInfo:
if helpers.numAla(seq) < 3 and seq not in doneSeqs:
if parameters.verbose:
print seq
testSeqs.append(seq)
count += 1
doneSeqs.append(seq)
currSeq = seq
for i in range(5):
currSeq = currSeq[5] + currSeq[0:5]
doneSeqs.append(currSeq)
if count % parameters.batchSize == 0 or count == numLessThree:
batchNum += 1
nn_user.genData(testSeqs, "test")
times, costs, metTests, metTrains, metTestBest =\
nn_user.trainNet(sess,model,testNum,production=False,\
saveBest=save,outputCost=parameters.outputCost)
bestMetAvg += metTestBest * len(testSeqs) / len(
helpers.validSet(allSeqInfo))
if parameters.outputCost: #Output files of the training and testing accuracy
#over the training process
helpers.outputCostFile(times, costs, testNum, batchNum)
helpers.outputMetTest(times[0:len(metTests)], metTests,
parameters.metric, testNum, batchNum)
helpers.outputMetTrain(times[0:len(metTrains)], metTrains,
parameters.metric, testNum,
batchNum)
trainSeqs = helpers.createTrainSeqs(allSeqInfo, testSeqs)
nn_user.predict(sess, model, testSeqs, "test")
nn_user.predict(sess, model, trainSeqs, "train")
first = False
testSeqs = []
if not parameters.crossValid:
break
#Output the best metric average and corresponding hyperparameters to a file
#in the paramResults directory
helpers.writeParamInfo("paramResults/", "testNum", parameters, bestMetAvg,
testNum)
sess.close()
def __init__(self):
# Keep a reference to the "close" line in the data[0] dataseries
self.dataclose = self.datas[0].close
self.dataopen = self.datas[0].open
self.datahigh = self.datas[0].high
self.datalow = self.datas[0].low
self.datavolume = self.datas[0].volume
#initialize cnns
self.cnns = []
for i in range(1):
filename = '/home/gene/git/autoTrading/backtrader/model/' + self.getdatanames(
)[0] + str(i) + '.h5'
self.cnns.append(Cnn(filename))
# Add a MovingAverageSimple indicator
self.rsi60 = bt.indicators.RSI_Safe(self.datas[0], period=100)
self.history = []
self.holding_days = []
self.indicator_count = 15
self.indicators = []
for i in range(self.indicator_count):
self.indicators.append([])
for i in range(6, 21):
j = 0
self.indicators[j].append(
bt.indicators.RSI_Safe(self.datas[0], period=i)) # momentum
j += 1
self.indicators[j].append(
bt.indicators.WilliamsR(self.datas[0], period=i)) # momentum
j += 1
self.indicators[j].append(
bt.talib.MFI(self.datahigh,
self.datalow,
self.dataclose,
self.datavolume,
period=i)) # momentum
j += 1
self.indicators[j].append(
bt.indicators.RateOfChange(self.datas[0],
period=i)) # momentum
j += 1
self.indicators[j].append(bt.talib.CMO(self.dataclose,
period=i)) # momentum
j += 1
self.indicators[j].append(bt.talib.SMA(self.dataclose, period=i))
j += 1
self.indicators[j].append(bt.talib.SMA(self.dataopen, period=i))
j += 1
self.indicators[j].append(
bt.indicators.ExponentialMovingAverage(self.datas[0],
period=i))
j += 1
self.indicators[j].append(
bt.indicators.WeightedMovingAverage(self.datas[0], period=i))
j += 1
self.indicators[j].append(
bt.indicators.HullMovingAverage(self.datas[0], period=i))
j += 1
self.indicators[j].append(
bt.indicators.Trix(self.datas[0], period=i)) # trend
j += 1
self.indicators[j].append(
bt.indicators.CommodityChannelIndex(self.datas[0],
period=i)) # trend
j += 1
self.indicators[j].append(
bt.indicators.DetrendedPriceOscillator(self.datas[0],
period=i)) # trend
j += 1
self.indicators[j].append(
bt.indicators.DirectionalMovementIndex(self.datas[0],
period=i)) # trend
j += 1
self.indicators[j].append(
bt.indicators.BollingerBands(self.datas[0],
period=i)) # volatility
j += 1