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| import streamlit as st | |
| import yfinance as yf | |
| from statsmodels.tsa.arima.model import ARIMA | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| # Title and description | |
| st.title("S&P 500 Price Prediction in USD/Share with ARIMA") | |
| st.write("This app predicts the price of the S&P 500 index in USD/share using the ARIMA model.") | |
| # Sidebar for user inputs | |
| st.sidebar.title("Prediction Settings") | |
| start_date = st.sidebar.date_input("Start Date", value=pd.to_datetime("2015-01-01")) | |
| end_date = st.sidebar.date_input("End Date", value=pd.to_datetime("2023-12-31")) | |
| n_days = st.sidebar.slider("Days to Predict", min_value=1, max_value=30, value=7) | |
| # Fetch historical data | |
| st.write("### Historical Data in USD/Share") | |
| try: | |
| data = yf.download("^GSPC", start=start_date, end=end_date) | |
| data = data["Close"] | |
| st.line_chart(data) | |
| except Exception as e: | |
| st.error(f"Error fetching data: {e}") | |
| # Fit ARIMA model | |
| if st.button("Predict"): | |
| st.write("### Predictions in USD/Share") | |
| # Check for sufficient data | |
| if len(data) < 30: | |
| st.error("Not enough data to train the model. Please select a longer date range.") | |
| else: | |
| try: | |
| # Fit ARIMA model | |
| model = ARIMA(data, order=(5, 1, 0)) | |
| fitted_model = model.fit() | |
| # Make predictions | |
| future_index = pd.date_range(start=data.index[-1], periods=n_days + 1, freq="B")[1:] | |
| forecast = fitted_model.forecast(steps=n_days) | |
| forecast_df = pd.DataFrame({"Date": future_index, "Predicted Price (USD/Share)": forecast}).set_index("Date") | |
| # Plot results | |
| plt.figure(figsize=(10, 6)) | |
| plt.plot(data, label="Historical Data (USD/Share)") | |
| plt.plot(forecast_df, label="Predicted Data (USD/Share)", linestyle="--") | |
| plt.legend() | |
| plt.title("S&P 500 Price Prediction in USD/Share") | |
| plt.xlabel("Date") | |
| plt.ylabel("Price (USD/Share)") | |
| st.pyplot(plt) | |
| # Show prediction table | |
| st.write("### Predicted Prices in USD/Share") | |
| st.write(forecast_df) | |
| except Exception as e: | |
| st.error(f"Error in prediction: {e}") | |