def rnn_predict(stock, start, end):
# get stock data
try:
df = get_stock_data(stock, start, end, json=False)
except:
# error info
e = sys.exc_info()
print(e)
print("rnn predict fail")
return e
# normalize
scaler = Normalize(df, max=True)
normalized = scaler.normalize_data(df)
# get training and testing inputs and outputs
train_inputs, train_targets, test_inputs, test_targets = train_test_split(
normalized)
train_inputs = np.array(train_inputs)
train_targets = np.array(train_targets)
test_inputs = np.array(test_inputs)
test_targets = np.array(test_targets)
# returns 3d array in format [inputs, timesteps, features]
train_inputs = to_3d(train_inputs)
test_inputs = to_3d(test_inputs)
NN = RNN_V2()
train_outputs = NN.train(train_inputs, train_targets, epochs=100)
test_outputs = NN.test(test_inputs)
# de-normalize
train_outputs = scaler.denormalize_data(train_outputs)
train_targets = scaler.denormalize_data(train_targets)
test_outputs = scaler.denormalize_data(test_outputs)
test_targets = scaler.denormalize_data(test_targets).T
# accuracy
accuracy = 100 - mape(test_targets, test_outputs)
return df[4:], pd.DataFrame(train_outputs), pd.DataFrame(
test_outputs), str(round(accuracy, 2))
def test_normalize(self):
test = np.arange(1000)
# normalize
scaler = Normalize(test)
normalized = scaler.normalize_data(test)
min_val = min(normalized)
max_val = max(normalized)
# ensure values scaled to range (0, 1)
self.assertGreaterEqual(min_val, 0.0)
self.assertLessEqual(max_val, 1.0)
# denormalize
denormalized = scaler.denormalize_data(normalized)
# ensure denormalized values are the same as the original
for x, y in zip(test, denormalized):
try:
self.assertEqual(x, y)
except AssertionError:
self.assertAlmostEqual(x, y, 12)
def lstm_predict(stock, start, end):
# shift start date -4 days for correct test/train i/o
# get stock data
try:
df = get_stock_data(stock, start, end, json=False)
except:
# error info
e = sys.exc_info()
print(e)
print("lstm predict fail")
return e
stock = df
scaler = Normalize(df)
df = scaler.normalize_data(df)
train_max_index = round((len(df) - 1) * 0.75)
training_input_1 = [[df[i - 6], df[i - 5]]
for i in range(6, train_max_index)]
training_input_2 = [[df[i - 4], df[i - 3]]
for i in range(6, train_max_index)]
training_input_3 = [[df[i - 2], df[i - 1]]
for i in range(6, train_max_index)]
target = [[i] for i in df[6:train_max_index]]
training_input_1 = np.array(training_input_1, dtype=float)
training_input_2 = np.array(training_input_2, dtype=float)
training_input_3 = np.array(training_input_3, dtype=float)
target = np.array(target, dtype=float)
assert len(training_input_1) == len(training_input_2) == len(
training_input_3) == len(target)
# create neural network
NN = LSTM()
# number of training cycles
training_cycles = 100
# train the neural network
for cycle in range(training_cycles):
for n in training_input_1:
output = NN.train(training_input_1, training_input_2,
training_input_3, target)
# de-Normalize
output = scaler.denormalize_data(output)
target = scaler.denormalize_data(target)
# transpose
output = output.T
# change data type so it can be plotted
prices = pd.DataFrame(output)
# [price 2 days ago, price yesterday] for each day in range
testing_input_1 = [[df[i - 6], df[i - 5]]
for i in range(train_max_index, len(df))]
testing_input_2 = [[df[i - 4], df[i - 3]]
for i in range(train_max_index, len(df))]
testing_input_3 = [[df[i - 2], df[i - 1]]
for i in range(train_max_index, len(df))]
test_target = [[i] for i in df[train_max_index:len(df)]]
assert len(testing_input_1) == len(testing_input_2) == len(
testing_input_3) == len(test_target)
testing_input_1 = np.array(testing_input_1, dtype=float)
testing_input_2 = np.array(testing_input_2, dtype=float)
testing_input_3 = np.array(testing_input_3, dtype=float)
test_target = np.array(test_target, dtype=float)
# test the network with unseen data
test = NN.test(testing_input_1, testing_input_2, testing_input_3)
# de-Normalize data
test = scaler.denormalize_data(test)
test_target = scaler.denormalize_data(test_target)
# transplose test results
test = test.T
# accuracy
accuracy = 100 - mape(test_target, test)
return stock[6:], prices, pd.DataFrame(test), str(round(accuracy, 2))
