Update app.py

app.py

@@ -5,8 +5,8 @@ import pandas as pd
 import matplotlib.pyplot as plt
 
 # Title and description
-st.title("S&P 500 Price Prediction with ARIMA")
-st.write("This app predicts the price of the S&P 500 index using the ARIMA model.")
+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")
@@ -15,7 +15,7 @@ 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")
+st.write("### Historical Data in USD/Share")
 try:
     data = yf.download("^GSPC", start=start_date, end=end_date)
     data = data["Close"]
@@ -25,7 +25,7 @@ except Exception as e:
 
 # Fit ARIMA model
 if st.button("Predict"):
-    st.write("### Predictions")
+    st.write("### Predictions in USD/Share")
 
     # Check for sufficient data
     if len(data) < 30:
@@ -39,20 +39,20 @@ if st.button("Predict"):
             # 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": forecast}).set_index("Date")
+            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")
-            plt.plot(forecast_df, label="Predicted Data", linestyle="--")
+            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")
+            plt.title("S&P 500 Price Prediction in USD/Share")
             plt.xlabel("Date")
-            plt.ylabel("Price")
+            plt.ylabel("Price (USD/Share)")
             st.pyplot(plt)
 
             # Show prediction table
-            st.write("### Predicted Prices")
+            st.write("### Predicted Prices in USD/Share")
             st.write(forecast_df)
         except Exception as e:
             st.error(f"Error in prediction: {e}")