def learn_nn_and_save(data: StockData, filename_to_save: str):
"""
Starts the training of the neural network and saves it to the file system
Args:
data: The data to train on
filename_to_save: The filename to save the trained NN to
"""
dates = data.get_dates()
prices = data.get_values()
# Generate training data
# Build chunks of prices from 100 consecutive days (input_prices) and 101th day (current_prices_for_plot)
current_prices_for_plot, input_prices, wanted_results = get_data(prices)
# Shape and configuration of network is optimized for binary classification problems
# see: https://keras.io/getting-started/sequential-model-guide/
network = create_model()
network.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])
# Train the neural network
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.9, patience=5, min_lr=0.000001, verbose=1)
history = network.fit(input_prices, wanted_results, epochs=500, batch_size=128, verbose=1,
validation_data=(input_prices, wanted_results), shuffle=True, callbacks=[reduce_lr])
# Evaluate the trained neural network and plot results
score = network.evaluate(input_prices, wanted_results, batch_size=128, verbose=0)
logger.debug(f"Test score: {score}")
# Draw
plt.figure()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.plot(history.history['acc'])
plt.title('training loss / testing loss by epoch')
plt.ylabel('loss/acc')
plt.xlabel('epoch')
plt.legend(['loss', 'val_loss', 'acc'], loc='best')
plt.figure()
current_price_prediction = network.predict(input_prices, batch_size=128)
logger.debug(f"current_price_prediction:")
iteration = 0
for x in current_price_prediction:
logger.debug(f"iteration {iteration} - output: {x}")
iteration = iteration + 1
plt.plot(dates[INPUT_SIZE:], current_prices_for_plot, color="black") # current prices in reality
plt.plot(dates[INPUT_SIZE:], [calculate_delta(x) for x in current_price_prediction],
color="green") # predicted prices by neural network
plt.title('current prices / predicted prices by date')
plt.ylabel('price')
plt.xlabel('date')
plt.legend(['current', 'predicted'], loc='best')
plt.show()
# Save trained model: separate network structure (stored as JSON) and trained weights (stored as HDF5)
save_keras_sequential(network, RELATIVE_PATH, filename_to_save)
def learn_nn_and_save(data: StockData, filename_to_save: str):
"""
Starts the training of the neural network and saves it to the file system
Args:
data: The data to train on
filename_to_save: The filename to save the trained NN to
"""
dates = data.get_dates()
prices = data.get_values()
# Generate training data
# Build chunks of prices from 100 consecutive days (last_prices) and 101th day (current_price)
last_prices, current_price = [], []
for i in range(0, len(prices) - 100):
last_prices.append(prices[i:100 + i])
current_price.append(float(prices[100 + i]))
network = create_model()
network.compile(loss='mean_squared_error', optimizer='adam')
# Train the neural network
history = network.fit(last_prices,
current_price,
epochs=10,
batch_size=128,
verbose=1)
# Evaluate the trained neural network and plot results
score = network.evaluate(np.array(last_prices),
current_price,
batch_size=128,
verbose=0)
logger.debug(f"Test score: {score}")
plt.figure()
plt.plot(history.history['loss'])
plt.title('training loss / testing loss by epoch')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['training', 'testing'], loc='best')
plt.figure()
current_price_prediction = network.predict(last_prices, batch_size=128)
plt.plot(dates[100:], current_price,
color="black") # current prices in reality
plt.plot(dates[100:], current_price_prediction,
color="green") # predicted prices by neural network
plt.title('current prices / predicted prices by date')
plt.ylabel('price')
plt.xlabel('date')
plt.legend(['current', 'predicted'], loc='best')
plt.show()
# Save trained model: separate network structure (stored as JSON) and trained weights (stored as HDF5)
save_keras_sequential(network, RELATIVE_PATH, filename_to_save)
