Upload therollercoasterstrategy_195.py

therollercoasterstrategy_195.py

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+# -*- coding: utf-8 -*-
+"""therollercoasterstrategy.195
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Pn8vxMsTuQGWNoH9Bqo7XTJ1stXjNDHx
+"""
+
+!pip install neuralprophet
+
+from neuralprophet import NeuralProphet
+import yfinance as yf
+import pandas as pd
+import matplotlib.pyplot as plt
+
+stock_symbol = 'DAL'
+start_date = '2020-11-01'
+end_date = '2024-12-01'
+
+stock_data = yf.download(stock_symbol, start = start_date, end=end_date)
+
+print(stock_data.head())
+stock_data.to_csv('stock_data.csv')
+
+stocks = pd.read_csv('stock_data.csv')
+stocks['Date'] = pd.to_datetime(stocks['Date'])
+stocks = stocks[['Date', 'Close']]
+stocks.columns = ['ds', 'y']
+
+plt.plot(stocks['ds'], stocks['y'], label = 'actual', c = 'g')
+plt.show()
+
+model = NeuralProphet()
+model.fit(stocks)
+
+future = model.make_future_dataframe(stocks, periods = 300)
+
+forecast = model.predict(future)
+actual_prediction = model.predict(stocks)
+
+plt.title(".159 Est. 1367")
+plt.plot(actual_prediction['ds'], actual_prediction['yhat1'], label = "prediction_Actual", c = 'r')
+plt.plot(forecast['ds'], forecast['yhat1'], label = 'future_predictions', c = 'b')
+plt.plot(stocks['ds'], stocks['y'], label = 'actual', c = 'g')
+plt.legend()
+plt.show()
+
+"""S&P 500"""
+
+model.plot_components(forecast)