app.py

# app.py

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

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

# Sidebar for user inputs
st.sidebar.header('Select Stocks and Options')

# Path to your CSV file
csv_file_path = 'ind_nifty500list.csv'  # Ensure this file is uploaded to your Hugging Face Space

# Check if the file exists
if not os.path.isfile(csv_file_path):
    st.error("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):
        st.error(f"The CSV file must contain the following columns: {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:', 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(
            '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 button
        if st.sidebar.button('Download Data'):
            if selected_stocks:
                for company_name in selected_stocks:
                    ticker = stock_dict[company_name]
                    # Append '.NS' if not already present for NSE stocks
                    if not ticker.endswith('.NS') and not ticker.endswith('.BO'):
                        ticker += '.NS'
                    # Fetch data from Yahoo Finance
                    try:
                        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
                        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')
                            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:
                            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:
                            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
                        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'
                        )
                    except Exception as e:
                        st.error(f"Error fetching data for {company_name} ({ticker}): {e}")
            else:
                st.warning("Please select at least one stock.")