Update app.py

app.py

@@ -8,33 +8,28 @@ import datetime
 
 # Function to load stock data using yfinance
 @st.cache_data(ttl=86400)
-def load_yfinance_data():
-    # List of stock tickers
-    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']
-    
-    # Set the start and end dates for the past 1 year
-    start_date = (datetime.datetime.now() - datetime.timedelta(days=365)).strftime('%Y-%m-%d')
-    end_date = datetime.datetime.today().strftime('%Y-%m-%d')
-
-    # Download the stock data using yfinance
-    data = yf.download(tickers, start=start_date, end=end_date, group_by='ticker')
+def load_stock_data(tickers, start_date, end_date):
+    import yfinance as yf
+    import pandas as pd
 
-    # Process and format the data for each ticker
+    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()
-        df.reset_index(inplace=True)
+        df = data[ticker].copy().reset_index()
         df['Stock Name'] = ticker
         all_data.append(df)
-    
-    # Concatenate all the data into a single DataFrame
-    all_stock_data = pd.concat(all_data, ignore_index=True)
 
-    return all_stock_data
+    merged_data = pd.concat(all_data, ignore_index=True)
+    return merged_data
 
-# Load the data
-data = load_yfinance_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']
+start_date = (datetime.today() - pd.DateOffset(years=1)).strftime('%Y-%m-%d')
+end_date = datetime.today().strftime('%Y-%m-%d')
+
+stock_data = load_stock_data(tickers, start_date, end_date)
 
 # Perform sentiment analysis on tweets (assuming you still have your tweets data)
 tweets_data = pd.read_csv('data/stock_tweets.csv')
@@ -158,11 +153,17 @@ if st.button("Predict"):
     stock_history = data[data['Stock Name'] == selected_stock]
 
     # Date filter slider
-    min_date = stock_history['Date'].min()
-    max_date = stock_history['Date'].max()
-    date_range = st.slider("Select Date Range for Visualizations",
-                           min_value=min_date, max_value=max_date,
-                           value=(min_date, max_date))
+    min_date = pd.to_datetime(data['Date'].min()).date()
+    max_date = pd.to_datetime(data['Date'].max()).date()
+    
+    date_range = st.slider(
+        "Select Date Range for Visualizations",
+        min_value=min_date,
+        max_value=max_date,
+        value=(min_date, max_date),
+        format="YYYY-MM-DD"
+    )
+
     filtered_data = stock_history[(stock_history['Date'] >= date_range[0]) & (stock_history['Date'] <= date_range[1])]
 
     with st.expander(" Price vs Sentiment Trend"):