Update app.py

app.py

@@ -5,27 +5,25 @@ from textblob import TextBlob
 import joblib
 import matplotlib.pyplot as plt
 from datetime import datetime
+import plotly.express as px
 
 # Function to load stock data using yfinance
 @st.cache_data(ttl=86400)
 def load_stock_data(tickers, start_date, end_date):
-    import yfinance as yf
-    import pandas as pd
-
-    data = yf.download(tickers, start=start_date, end=end_date, group_by='ticker', auto_adjust=True)
-    
-    all_data = []
-    for ticker in tickers:
-        df = data[ticker].copy().reset_index()
-        df['Stock Name'] = ticker
-        all_data.append(df)
-
-    merged_data = pd.concat(all_data, ignore_index=True)
+    with st.spinner('Downloading stock data...'):
+        data = yf.download(tickers, start=start_date, end=end_date, group_by='ticker', auto_adjust=True)
+        
+        all_data = []
+        for ticker in tickers:
+            df = data[ticker].copy().reset_index()
+            df['Stock Name'] = ticker
+            all_data.append(df)
+
+        merged_data = pd.concat(all_data, ignore_index=True)
     return merged_data
 
-    
 tickers = ['TSLA', 'MSFT', 'PG', 'META', 'AMZN', 'GOOG', 'AMD', 'AAPL', 'NFLX', 'TSM',
-               'KO', 'F', 'COST', 'DIS', 'VZ', 'CRM', 'INTC', 'BA', 'BX', 'NOC', 'PYPL', 'ENPH', 'NIO', 'ZS', 'XPEV']
+           'KO', 'F', 'COST', 'DIS', 'VZ', 'CRM', 'INTC', 'BA', 'BX', 'NOC', 'PYPL', 'ENPH', 'NIO', 'ZS', 'XPEV']
 start_date = (datetime.today() - pd.DateOffset(years=1)).strftime('%Y-%m-%d')
 end_date = datetime.today().strftime('%Y-%m-%d')
 
@@ -51,7 +49,7 @@ daily_sentiment = tweets_data.groupby(['Date', 'Stock Name']).mean(numeric_only=
 daily_sentiment['Date'] = pd.to_datetime(daily_sentiment['Date'])
 
 # Merge stock data with sentiment data
-merged_data = pd.merge(data, daily_sentiment, how='left', on=['Date', 'Stock Name'])
+merged_data = pd.merge(stock_data, daily_sentiment, how='left', on=['Date', 'Stock Name'])
 
 # Fill missing sentiment values with 0 (neutral sentiment)
 merged_data['Sentiment'] = merged_data['Sentiment'].fillna(0)
@@ -110,7 +108,7 @@ st.write(latest_data_df)
 st.write("Use the inputs above to predict the next days close prices of the stock.")
 if st.button("Predict"):
     predictions = []
-    latest_date = datetime.datetime.now()
+    latest_date = datetime.now()
 
     for i in range(days_to_predict):
         X_future = pd.DataFrame({
@@ -139,22 +137,20 @@ if st.button("Predict"):
     })
 
     st.subheader("Predicted Prices")
-    # st.write(prediction_df)
     st.dataframe(prediction_df)
+    
     # Plot predictions using Plotly
-    import plotly.express as px
-    fig = px.line(prediction_df, x='Date', y='Predicted Close Price',
-                  markers=True, title=f"{selected_stock} Predicted Close Prices")
+    fig = px.line(prediction_df, x='Date', y='Predicted Close Price', markers=True, title=f"{selected_stock} Predicted Close Prices")
     st.plotly_chart(fig, use_container_width=True)
 
     # ----------------------------------------
     # Enhanced Visualizations
-    st.header(" Enhanced Stock Analysis")
-    stock_history = data[data['Stock Name'] == selected_stock]
+    st.header("Enhanced Stock Analysis")
+    stock_history = merged_data[merged_data['Stock Name'] == selected_stock]
 
     # Date filter slider
-    min_date = pd.to_datetime(data['Date'].min()).date()
-    max_date = pd.to_datetime(data['Date'].max()).date()
+    min_date = pd.to_datetime(merged_data['Date'].min()).date()
+    max_date = pd.to_datetime(merged_data['Date'].max()).date()
     
     date_range = st.slider(
         "Select Date Range for Visualizations",
@@ -166,30 +162,29 @@ if st.button("Predict"):
 
     filtered_data = stock_history[(stock_history['Date'] >= date_range[0]) & (stock_history['Date'] <= date_range[1])]
 
-    with st.expander(" Price vs Sentiment Trend"):
+    with st.expander("Price vs Sentiment Trend"):
         fig1 = px.line(filtered_data, x='Date', y=['Close', 'Sentiment'],
                        labels={'value': 'Price / Sentiment', 'variable': 'Metric'},
                        title=f"{selected_stock} - Close Price & Sentiment")
         st.plotly_chart(fig1, use_container_width=True)
 
-    with st.expander(" Volatility Over Time"):
+    with st.expander("Volatility Over Time"):
         fig2 = px.line(filtered_data, x='Date', y='Volatility',
                        title=f"{selected_stock} - 7-Day Rolling Volatility")
         st.plotly_chart(fig2, use_container_width=True)
 
-    with st.expander(" Moving Averages (MA7 vs MA14)"):
-        fig3 = px.line(filtered_data, x='Date',
-                       y=['MA7', 'MA14'],
+    with st.expander("Moving Averages (MA7 vs MA14)"):
+        fig3 = px.line(filtered_data, x='Date', y=['MA7', 'MA14'],
                        labels={'value': 'Price', 'variable': 'Moving Average'},
                        title=f"{selected_stock} - Moving Averages")
         st.plotly_chart(fig3, use_container_width=True)
 
-    with st.expander(" Daily Price Change"):
+    with st.expander("Daily Price Change"):
         fig4 = px.line(filtered_data, x='Date', y='Daily_Change',
                        title=f"{selected_stock} - Daily Price Change")
         st.plotly_chart(fig4, use_container_width=True)
 
-    with st.expander(" Sentiment Distribution"):
+    with st.expander("Sentiment Distribution"):
         fig5 = px.histogram(filtered_data, x='Sentiment', nbins=30,
                             title=f"{selected_stock} - Sentiment Score Distribution")
         st.plotly_chart(fig5, use_container_width=True)