Update app.py

app.py

@@ -1,3 +1,4 @@
+# Import libraries
 import streamlit as st
 import yfinance as yf
 from statsmodels.tsa.arima.model import ARIMA
@@ -6,7 +7,10 @@ import matplotlib.pyplot as plt
 
 # Title and description
 st.title("Algorithmic Trading Bot with ARIMA")
-st.write("This app simulates an algorithmic trading bot using the ARIMA model for price prediction.")
+st.write(
+    "This app simulates an algorithmic trading bot using the ARIMA model for price prediction. "
+    "The bot predicts future stock prices and simulates trades based on the predicted trends."
+)
 
 # Sidebar for user inputs
 st.sidebar.title("Settings")
@@ -35,7 +39,7 @@ except Exception as e:
 # Trading Bot Simulation
 if st.button("Run Trading Bot"):
     st.write("### Trading Bot Simulation")
-
+    
     if len(data) < 30:
         st.error("Not enough data to train the model. Please select a longer date range.")
     else:
@@ -47,7 +51,8 @@ if st.button("Run Trading Bot"):
             # Predict future prices
             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")
+            st.write("### Debug: Forecasted Prices")
+            st.write(pd.DataFrame({"Date": future_index, "Predicted Price": forecast}))
 
             # Simulate trading
             balance = initial_balance
@@ -80,7 +85,7 @@ if st.button("Run Trading Bot"):
             # 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.plot(future_index, forecast, label="Predicted Data (USD/share)", linestyle="--")
             for trade in trades:
                 plt.scatter(trade[0], trade[2], label=trade[1], color="green" if trade[1] == "BUY" else "red")
             plt.legend()