app.py

# app.py

# Import required libraries
import streamlit as st
import yfinance as yf
from datetime import date, timedelta
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import io

# Optional: Install 'ta' library for technical indicators (uncomment if needed)
# !pip install ta

# Set Streamlit page configuration
st.set_page_config(
    page_title='📈 Indian Stock Data Downloader and Volume Analyzer',
    layout='wide',
    initial_sidebar_state='expanded'
)

# Set the title of the app
st.title('📈 Indian Stock Data Downloader and Volume Analyzer')

# Sidebar for user inputs
st.sidebar.header('🔧 Configuration')

# File uploader for 'stock_list.csv'
uploaded_file = st.sidebar.file_uploader(
    "📂 Upload your stock list CSV",
    type=["csv"],
    help="The CSV file should contain 'Symbol' and 'Company Name' columns."
)

if uploaded_file is not None:
    try:
        # Read the uploaded CSV file into a DataFrame
        stock_df = pd.read_csv(uploaded_file)

        # Ensure that the required columns are present
        required_columns = {'Symbol', 'Company Name'}
        if not required_columns.issubset(stock_df.columns):
            st.error(f"The CSV file must contain the following columns: {', '.join(required_columns)}")
        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:',
                options=list(stock_dict.keys()),
                default=list(stock_dict.keys())[:5]  # Select first 5 stocks by default
            )

            # Date input widgets for date range selection
            default_start_date = date.today() - timedelta(days=365)  # Past year
            start_date = st.sidebar.date_input('📅 Start Date', default_start_date)
            end_date = st.sidebar.date_input('📅 End Date', date.today())

            # Ensure that start_date is before end_date
            if start_date > end_date:
                st.sidebar.error("❌ Start Date must be before End Date.")

            # Checkbox for selecting data options
            st.sidebar.header('📊 Data Options')
            data_options = st.sidebar.multiselect(
                'Select Data to Download:',
                ['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 and Analyze Data button
            if st.sidebar.button('📥 Download and Analyze Data'):
                if selected_stocks:
                    for company_name in selected_stocks:
                        ticker = stock_dict[company_name]
                        # Append '.NS' or '.BO' for NSE or BSE stocks if not already present
                        if not (ticker.endswith('.NS') or ticker.endswith('.BO')):
                            # Default to NSE; modify as needed
                            ticker += '.NS'
                        # Fetch data from Yahoo Finance
                        try:
                            with st.spinner(f"⏳ Fetching data for {company_name} ({ticker})..."):
                                stock_data = yf.download(ticker, start=start_date, end=end_date)
                            # Check if data is returned
                            if stock_data.empty:
                                st.warning(f"No data found for **{company_name}** ({ticker}) in the selected date range.")
                                continue
                            # Filter data based on selected options
                            available_options = [option for option in data_options if option in stock_data.columns]
                            if not available_options:
                                st.warning(f"No selected data options available for **{company_name}** ({ticker}).")
                                continue
                            stock_data = stock_data[available_options]
                            st.subheader(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) if 'Close' and 'Volume' are selected
                            if 'Close' in stock_data.columns and 'Volume' in stock_data.columns:
                                stock_data['Amount'] = stock_data['Close'] * stock_data['Volume']
                            else:
                                stock_data['Amount'] = np.nan

                            # OBV in terms of Amount
                            if 'OBV (Amount)' in indicators and 'Amount' in stock_data.columns:
                                # 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')
                                ax.set_xlabel('Date')
                                ax.set_ylabel('On-Balance Volume (Amount)')
                                ax.set_title(f"{company_name} OBV (Amount)")
                                ax.legend()
                                st.pyplot(fig_obv_amount)

                            # RSI
                            if 'RSI' in indicators and 'Close' in stock_data.columns:
                                delta = stock_data['Close'].diff()
                                up = delta.clip(lower=0)
                                down = -1 * delta.clip(upper=0)
                                roll_up = up.rolling(window=14).mean()
                                roll_down = down.rolling(window=14).mean()
                                RS = roll_up / roll_down
                                stock_data['RSI'] = 100.0 - (100.0 / (1.0 + RS))
                                # Plot RSI
                                fig_rsi, ax = plt.subplots(figsize=(12, 4))
                                ax.plot(stock_data['Date'], stock_data['RSI'], label='RSI', color='green')
                                ax.set_xlabel('Date')
                                ax.set_ylabel('RSI')
                                ax.set_title(f"{company_name} RSI")
                                ax.axhline(70, color='red', linestyle='--')
                                ax.axhline(30, color='blue', linestyle='--')
                                ax.legend()
                                st.pyplot(fig_rsi)

                            # MACD
                            if 'MACD' in indicators and 'Close' in stock_data.columns:
                                exp1 = stock_data['Close'].ewm(span=12, adjust=False).mean()
                                exp2 = stock_data['Close'].ewm(span=26, adjust=False).mean()
                                stock_data['MACD'] = exp1 - exp2
                                stock_data['MACD_Signal'] = stock_data['MACD'].ewm(span=9, adjust=False).mean()
                                stock_data['MACD_Hist'] = stock_data['MACD'] - stock_data['MACD_Signal']
                                # Plot MACD
                                fig_macd, ax = plt.subplots(figsize=(12, 4))
                                ax.plot(stock_data['Date'], stock_data['MACD'], label='MACD', color='purple')
                                ax.plot(stock_data['Date'], stock_data['MACD_Signal'], label='Signal', color='red')
                                ax.bar(stock_data['Date'], stock_data['MACD_Hist'], label='Histogram', color='grey')
                                ax.set_xlabel('Date')
                                ax.set_ylabel('MACD')
                                ax.set_title(f"{company_name} MACD")
                                ax.legend()
                                st.pyplot(fig_macd)

                            # Calculate moving averages for Volume if 'Volume' is selected
                            if 'Volume' in stock_data.columns:
                                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
                            st.download_button(
                                label=f"📥 Download {company_name} Data as CSV",
                                data=csv,
                                file_name=f"{ticker}_{start_date}_{end_date}.csv",
                                mime='text/csv'
                            )
                else:
                    st.warning("⚠️ Please select at least one stock.")
    except Exception as e:
        st.error(f"❌ Error processing the uploaded file: {e}")
else:
    st.info("🔍 Please upload your `stock_list.csv` file to get started.")
    st.markdown("""
    **Sample `stock_list.csv` Format:**

    ```csv
    Symbol,Company Name
    TCS,Tata Consultancy Services
    INFY,Infosys Limited
    RELIANCE,Reliance Industries
    HDFCBANK,HDFC Bank
    ICICIBANK,ICICI Bank
    ```

    You can create your own CSV file following the above format. Ensure that the CSV has headers `Symbol` and `Company Name`.

    **Sample CSV Download:**
    [Download Sample `stock_list.csv`](https://huggingface.co/spaces/riteshcp/Indian_Futures_OBV_Downloader/resolve/main/sample_stock_list.csv)
    """)