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