def predict(self, stock):
from model_handler import ModelHandler
import matplotlib.dates as mdates
import datetime as dt
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
myModelHandler = ModelHandler()
regressor = myModelHandler.load_json_model(stock)
# Importing the training set
dataset = pd.read_csv(stock.csv_name) #
dates = dataset.iloc[len(dataset) - 31:len(dataset) - 1, 0].values
dates = [dt.datetime.strptime(d, '%Y-%m-%d').date() for d in dates]
# Feature Scaling
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range=(0, 1))
dataset_test = dataset[len(dataset) - 30:]
real_stock_price = dataset_test.iloc[:, 1:2].values
# Getting the predicted stock price
dataset = dataset['Open']
inputs = dataset[len(dataset) - len(dataset_test) - 60:].values
inputs = inputs.reshape(-1, 1)
inputs = sc.fit_transform(inputs)
X_test = []
for i in range(60, 90):
X_test.append(inputs[i - 60:i, 0])
X_test = np.array(X_test)
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
predicted_stock_price = regressor.predict(X_test)
print(real_stock_price)
predicted_stock_price = sc.inverse_transform(predicted_stock_price)
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
plt.gca().xaxis.set_major_locator(mdates.DayLocator())
plt.plot(dates,
real_stock_price,
color='red',
label=f'Actual {stock.ticker} Stock Price')
plt.plot(dates,
predicted_stock_price,
color='blue',
label=f'Predicted {stock.ticker} Stock Price')
plt.gcf().autofmt_xdate()
plt.title(f'{stock.ticker} Stock Price Prediction')
plt.xlabel('Time')
plt.ylabel(f'{stock.ticker} Stock Price')
plt.legend()
plt.show()
def forecast(self, stock):
"""Loads the trained model, forecasts the values then displays a graph of the values"""
# Load the trained model
model_handler = ModelHandler()
model = model_handler.load_json_model(stock)
# Importing the training set
dataset = pd.read_csv(stock.csv_name)
dates = dataset.iloc[len(dataset) - 31:len(dataset) - 1, 0].values
dates = [dt.datetime.strptime(d, '%Y-%m-%d').date() for d in dates]
# Create the test dataset
dataset_test = dataset[len(dataset) - 30:]
real_stock_price = dataset_test.iloc[:, 1:2].values
dataset = dataset['Open']
inputs = dataset[len(dataset) - len(dataset_test) - 60:].values
inputs = inputs.reshape(-1, 1)
# Feature Scaling
sc = MinMaxScaler(feature_range=(0, 1))
inputs = sc.fit_transform(inputs)
x_test = []
x_test.append(inputs[0:60, 0])
predicted_values = []
for i in range(1, 31):
x_test_np = np.array(x_test)
x_test_np = np.reshape(x_test_np,
(x_test_np.shape[0], x_test_np.shape[1], 1))
new_data = model.predict(x_test_np)
predicted_values.append(new_data[0])
x_test[0] = np.delete(x_test[0], 0)
x_test[0] = np.concatenate([x_test[0], new_data[0]])
predicted_values = sc.inverse_transform(predicted_values)
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
plt.gca().xaxis.set_major_locator(mdates.DayLocator())
plt.plot(dates,
real_stock_price,
color='red',
label=f'Actual {stock.ticker} Stock Price')
plt.plot(dates,
predicted_values,
color='blue',
label=f'Predicted {stock.ticker} Stock Price')
plt.gcf().autofmt_xdate()
plt.title(f'{stock.ticker} Stock Price Prediction')
plt.xlabel('Time')
plt.ylabel(f'{stock.ticker} Stock Price')
plt.legend()
plt.show()