Update app.py

app.py

@@ -1,11 +1,13 @@
+# app.py
+
 # Import required libraries
 import streamlit as st
 import yfinance as yf
 from datetime import date
 import pandas as pd
-import os
 import matplotlib.pyplot as plt
-import pandas_ta as ta  # Import pandas_ta for technical indicators
+import pandas_ta as ta  # For technical indicators
+import os
 
 # Set the title of the app
 st.title('Indian Stock Data Downloader and Volume Analyzer')
@@ -13,16 +15,16 @@ st.title('Indian Stock Data Downloader and Volume Analyzer')
 # Sidebar for user inputs
 st.sidebar.header('Select Stocks and Options')
 
-# Path to your new CSV file
-csv_file_path = 'stock_list.csv'  # Update this to the path where you saved the CSV
+# Path to your CSV file (adjusted for HF)
+csv_file_path = 'stock_list.csv'  # Ensure this file is uploaded to HF
 
 # Check if the file exists
 if not os.path.isfile(csv_file_path):
-    st.error(f"The stock list file was not found at: {csv_file_path}")
+    st.error(f"The stock list file was not found. Please upload 'stock_list.csv' to your app.")
 else:
     # Read the CSV file into a DataFrame
     stock_df = pd.read_csv(csv_file_path)
-
+    
     # Ensure that the required columns are present
     required_columns = {'Symbol', 'Company Name'}
     if not required_columns.issubset(stock_df.columns):
@@ -30,14 +32,14 @@ else:
     else:
         # Create a dictionary mapping company names to stock symbols
         stock_dict = pd.Series(stock_df['Symbol'].values, index=stock_df['Company Name']).to_dict()
-
+    
         # Multiselect widget for stock selection
         selected_stocks = st.sidebar.multiselect('Select Stocks:', list(stock_dict.keys()))
-
+    
         # Date input widgets for date range selection
         start_date = st.sidebar.date_input('Start Date', date(2021, 1, 1))
         end_date = st.sidebar.date_input('End Date', date.today())
-
+    
         # Checkbox for selecting data options
         st.sidebar.header('Data Options')
         data_options = st.sidebar.multiselect(
@@ -45,14 +47,14 @@ else:
             ['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'],
             default=['Open', 'High', 'Low', 'Close', 'Volume']
         )
-
+    
         # Technical Indicators selection
         st.sidebar.header('Technical Indicators')
         indicators = st.sidebar.multiselect(
             'Select Technical Indicators to Calculate:',
             ['OBV (Amount)', 'RSI', 'MACD']
         )
-
+    
         # Download button
         if st.sidebar.button('Download Data'):
             if selected_stocks:
@@ -72,20 +74,20 @@ else:
                         stock_data = stock_data[data_options]
                         st.write(f"**Data for {company_name} ({ticker}):**")
                         st.dataframe(stock_data)
-
+    
                         # Reset index to get 'Date' as a column
                         stock_data = stock_data.reset_index()
-
+    
                         # Calculate Amount (Close * Volume)
                         stock_data['Amount'] = stock_data['Close'] * stock_data['Volume']
-
+    
                         # OBV in terms of Amount
                         if 'OBV (Amount)' in indicators:
                             # Calculate direction
                             stock_data['Daily_Return'] = stock_data['Close'].pct_change()
                             stock_data['Direction'] = stock_data['Daily_Return'].apply(lambda x: 1 if x > 0 else (-1 if x < 0 else 0))
                             stock_data['OBV_Amount'] = (stock_data['Amount'] * stock_data['Direction']).cumsum()
-
+    
                             # Plot OBV in Amount
                             fig_obv_amount, ax = plt.subplots(figsize=(12, 4))
                             ax.plot(stock_data['Date'], stock_data['OBV_Amount'], label='OBV (Amount)', color='orange')
@@ -94,7 +96,7 @@ else:
                             ax.set_title(f"{company_name} OBV (Amount)")
                             ax.legend()
                             st.pyplot(fig_obv_amount)
-
+    
                         # RSI
                         if 'RSI' in indicators:
                             stock_data['RSI'] = ta.rsi(stock_data['Close'], length=14)
@@ -108,7 +110,7 @@ else:
                             ax.axhline(30, color='blue', linestyle='--')
                             ax.legend()
                             st.pyplot(fig_rsi)
-
+    
                         # MACD
                         if 'MACD' in indicators:
                             macd = ta.macd(stock_data['Close'])
@@ -123,47 +125,47 @@ else:
                             ax.set_title(f"{company_name} MACD")
                             ax.legend()
                             st.pyplot(fig_macd)
-
+    
                         # Calculate moving averages
                         stock_data['Volume_MA_5'] = stock_data['Volume'].rolling(window=5).mean()
                         stock_data['Volume_MA_20'] = stock_data['Volume'].rolling(window=20).mean()
-
+    
                         # Plotting price and volume
                         fig, ax1 = plt.subplots(figsize=(12, 6))
-
+    
                         # Plot the closing price
                         ax1.plot(stock_data['Date'], stock_data['Close'], color='blue', label='Close Price')
                         ax1.set_xlabel('Date')
                         ax1.set_ylabel('Close Price', color='blue')
                         ax1.tick_params(axis='y', labelcolor='blue')
-
+    
                         # Create a second y-axis for volume
                         ax2 = ax1.twinx()
                         ax2.bar(stock_data['Date'], stock_data['Volume'], color='gray', alpha=0.3, label='Volume')
-
+    
                         # Plot moving averages of volume
                         ax2.plot(stock_data['Date'], stock_data['Volume_MA_5'], color='red', label='5-Day MA')
                         ax2.plot(stock_data['Date'], stock_data['Volume_MA_20'], color='green', label='20-Day MA')
                         ax2.set_ylabel('Volume', color='gray')
                         ax2.tick_params(axis='y', labelcolor='gray')
-
+    
                         # Add legends and title
                         fig.legend(loc='upper left', bbox_to_anchor=(0.15, 0.85))
                         plt.title(f"{company_name} Price and Volume Chart with Moving Averages")
                         fig.tight_layout()
                         st.pyplot(fig)
-
+    
                         # Volume analysis
                         stock_data['Volume_Pct_Change'] = stock_data['Volume'].pct_change() * 100
                         average_volume = stock_data['Volume'].mean()
                         current_volume = stock_data['Volume'].iloc[-1]
                         volume_trend = 'increasing' if current_volume > average_volume else 'decreasing'
-
+    
                         st.subheader(f"Volume Analysis for {company_name}")
                         st.write(f"- **Average Volume:** {average_volume:,.0f}")
                         st.write(f"- **Current Volume:** {current_volume:,.0f}")
                         st.write(f"- **Volume is currently {volume_trend}.**")
-
+    
                         # Convert data to CSV
                         csv = stock_data.to_csv(index=False).encode('utf-8')
                         # Download button for CSV