script_model.py

import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Datos de la accion que queramos
data = yf.download('MCD', start='2012-01-01', end='2022-01-01')
print(type(data))
# data.to_csv('exporting_data.csv', index=False)

# Open the file in read mode
# with open('exporting_data.csv', 'r') as f:
#     # Read the contents of the file
#     file_contents = f.read()
#     # Print the contents of the file to the console
#     print(file_contents)

# Variables predictoras
data['SMA_10'] = data['Close'].rolling(window=10).mean()
data['SMA_30'] = data['Close'].rolling(window=30).mean()
data['SMA_60'] = data['Close'].rolling(window=60).mean()
data['SMA_100'] = data['Close'].rolling(window=100).mean()
data = data.dropna()
x = data[['SMA_10', 'SMA_30', 'SMA_60', 'SMA_100']].values

# Crear la variable objetivo
y = data['Close'].values

# Datos de entrenamiento y prueba
X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size=0.2, random_state=0) 

# Modelo de regresion lineal
regressor = LinearRegression()
regressor.fit(X_train, Y_train)

# Predecir precios
prediction = regressor.predict(X_test)

# Evaluar precision del modelo
mse = mean_squared_error(Y_test, prediction)
print('Error cuadrático medio: ', mse)

# Graficar
plt.scatter(Y_test, prediction, alpha=0.5)
plt.plot([Y_test.min(), Y_test.max()], [Y_test.min(), Y_test.max()], 'k--', lw=2)
plt.xlabel('Valor Real')
plt.ylabel('Valor Predicho')
plt.show()