from tensorflow.keras import layers
from sklearn import neighbors
from sklearn.model_selection import GridSearchCV
from pandas.util.testing import assert_frame_equal
goog = Stocker('GOOGL')
goog.plot_stock()
# Create model
model, model_data = goog.create_prophet_model(days=90)
goog.evaluate_prediction()
# Optimize the model
goog.changepoint_prior_analysis(changepoint_priors=[0.001, 0.05, 0.1, 0.2])
goog.changepoint_prior_validation(start_date='2016-01-04',
end_date='2017-01-03',
changepoint_priors=[0.001, 0.05, 0.1, 0.2])
# Evaluate the new model
goog.evaluate_prediction()
print(goog.evaluate_prediction(nshares=1000))
# Getting the dataframe of the data
goog_data = goog.make_df('2004-08-19', '2018-03-27')
print(goog_data.head(50))
goog_data = goog_data[[
'Date', 'Open', 'High', 'Low', 'Close', 'Adj. Close', 'Volume'
]]
print(goog_data.head(50))
amazon.weekly_seasonality = True
model, model_data = amazon.create_prophet_model()
model.plot_components(model_data)
plt.show()
amazon.weekly_seasonality = False
model, model_data = amazon.create_prophet_model(days=90)
amazon.evaluate_prediction()
amazon.changepoint_prior_analysis(changepoint_priors=[0.001, 0.05, 0.1, 0.2])
amazon.changepoint_prior_validation(start_date='2016-01-04',
end_date='2017-01-03',
changepoint_priors=[0.001, 0.05, 0.1, 0.2])
amazon.changepoint_prior_validation(
start_date='2016-01-04',
end_date='2017-01-03',
changepoint_priors=[0.15, 0.2, 0.25, 0.4, 0.5, 0.6])
amazon.changepoint_prior_scale = 0.5
amazon.evaluate_prediction()
amazon.weekly_seasonality = True
amazon.evaluate_prediction()
