def doPredict(self, data: StockData) -> float:
"""
Use the loaded trained neural network to predict the next stock value.
Args:
data: historical stock values of a company
Returns:
predicted next stock value for that company
"""
#self.model.compile(loss='mean_squared_error', optimizer='sgd')
tmp_data = data.get_from_offset(data.get_row_count() - 5)
tmp = np.array([[
tmp_data[0][1], tmp_data[1][1], tmp_data[2][1], tmp_data[3][1],
tmp_data[4][1]
]])
pred = self.model.predict(tmp)
res = data.get_last()[1]
if (pred <= 0.5):
res = res * 0.9
else:
res = res * 1.1
# TODO: extract needed data for neural network and predict result
return res
def doPredict(self, data: StockData) -> float:
"""
Use the loaded trained neural network to predict the next stock value.
Args:
data: The historical stock values of a company
Returns:
The predicted next stock value for that company
"""
# Assumptions about data: at least INPUT_SIZE pairs of type (_, float)
assert data is not None and data.get_row_count() >= INPUT_SIZE
# Extract last INPUT_SIZE floats (here: stock values) as input for neural network
# (format: numpy array of arrays)
input_values = np.array([[x[1] for x in data.get_from_offset(-INPUT_SIZE)]])
normalized_prices = []
vector_min = np.min(input_values)
vector_max = np.max(input_values)
for price in input_values:
normalized_prices.append((price - vector_min) / (vector_max - vector_min))
input_values = np.asarray(normalized_prices)
try:
# Let network predict the next stock value based on last 100 stock values
prediction = self.model.predict(input_values)[0][0]
return data.get_last()[1] + calculate_delta(prediction)
except:
logger.error("Error in predicting next stock value.")
assert False
def doPredict(self, data: StockData) -> float:
""" Use the loaded trained neural network to predict the next stock value.
Args:
data: The historical stock values of a company
Returns:
The predicted next stock value for that company
"""
# Assumptions about data: at least 100 pairs of type (_, float)
assert data is not None and data.get_row_count() >= 100
# Extract last 100 floats (here: stock values) as input for neural network (format: numpy array of arrays)
input_values = np.array([[x[1] for x in data.get_from_offset(-100)]])
try:
# Let network predict the next stock value based on last 100 stock values
return self.model.predict(input_values)
except:
logger.error("Error in predicting next stock value.")
assert False
