from fastai.structured import add_datepart import tensorflow as tf 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' ]]
# -*- coding: utf-8 -*- # @Author : Ryan # @Time : 2021/9/30 22:09 # @Software : PyCharm # @Description : from stocker import Stocker import matplotlib.pyplot as plt amazon = Stocker(ticker='AMZN') 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])