srp.py

import streamlit as st
import yfinance as yf
import pandas as pd
from datetime import datetime, timedelta, time

# Load ticker symbols from the CSV file
@st.cache_data  # Updated from st.cache to remove deprecation warning
def load_ticker_list(file_path):
    try:
        stock_df = pd.read_csv(file_path)
        return stock_df['Symbol'].tolist()
    except Exception as e:
        st.error(f"Error loading ticker list: {str(e)}")
        return []

# Function to check if the market is open
def is_market_open():
    now = datetime.now().time()
    market_open_time = time(9, 15)    # NSE opens at 9:15 AM
    market_close_time = time(15, 30)  # NSE closes at 3:30 PM
    return market_open_time <= now <= market_close_time

# Function to calculate pivot point and support/resistance levels
def calculate_pivot_levels(stock_data):
    try:
        # Fixed the float() deprecation warnings by using .iloc[0]
        high = stock_data['High'].iloc[-1]
        low = stock_data['Low'].iloc[-1]
        close = stock_data['Close'].iloc[-1]
        
        # Calculate pivot point
        pivot = (high + low + close) / 3
        
        # Calculate support and resistance levels
        resistance1 = (2 * pivot) - low
        support1 = (2 * pivot) - high
        resistance2 = pivot + (high - low)
        support2 = pivot - (high - low)
        resistance3 = high + 2 * (pivot - low)
        support3 = low - 2 * (high - pivot)
        
        levels = {
            'Pivot Point': pivot,
            'Resistance 1': resistance1,
            'Support 1': support1,
            'Resistance 2': resistance2,
            'Support 2': support2,
            'Resistance 3': resistance3,
            'Support 3': support3
        }
        return levels
    except Exception as e:
        st.error(f"Error calculating pivot levels: {str(e)}")
        return None

# Function to fetch stock data with proper error handling
@st.cache_data(ttl=300)  # Cache for 5 minutes
def get_stock_data(symbol):
    try:
        # Add .NS suffix for NSE stocks
        nse_symbol = f"{symbol}.NS"
        
        # Try to get today's data
        today = datetime.today().date()
        stock = yf.download(
            nse_symbol,
            start=today - timedelta(days=5),  # Get more days of data
            end=today + timedelta(days=1),
            interval='1d',
            progress=False  # Disable progress bar
        )
        
        if not stock.empty:
            return stock.tail(1)  # Return only the latest day's data
        else:
            st.warning("No data available for the selected stock.")
            return pd.DataFrame()
            
    except Exception as e:
        st.error(f"Error fetching data for {symbol}: {str(e)}")
        return pd.DataFrame()

# Streamlit UI
def main():
    st.title("Support and Resistance Level Calculator for NIFTY 500 Stocks")
    
    # Load ticker symbols
    file_path = "G:/RSI/ind_nifty500list (1) (1).csv"
    ticker_list = load_ticker_list(file_path)
    
    if not ticker_list:
        st.error("Failed to load ticker list. Please check the file path.")
        return
    
    # Select a ticker from the list
    symbol = st.selectbox("Select a Stock Symbol", ticker_list)
    
    # Check market status
    if is_market_open():
        st.warning("Market is currently open. Data might not be finalized until after 3:30 PM.")
    
    if symbol:
        # Add a refresh button
        col1, col2 = st.columns([1, 4])
        with col1:
            refresh = st.button("🔄 Refresh")
        
        # Display loading spinner
        with st.spinner('Fetching stock data...'):
            if refresh:
                # Clear cache for this stock
                get_stock_data.clear()
            
            stock_data = get_stock_data(symbol)
        
        if not stock_data.empty:
            # Calculate and display levels
            levels = calculate_pivot_levels(stock_data)
            
            if levels:
                # Fixed: Get current price as a scalar value
                current_price = float(stock_data['Close'].iloc[-1])
                st.metric("Current Price", f"₹{current_price:.2f}")
                
                # Create three columns for better organization
                st.subheader(f"Support and Resistance Levels for {symbol}")
                col1, col2, col3 = st.columns(3)
                
                # Display resistance levels
                with col1:
                    st.write("### Resistance Levels")
                    for i in range(3, 0, -1):
                        value = float(levels[f'Resistance {i}'])  # Convert to float
                        st.write(f"R{i}: ₹{value:.2f}")
                
                # Display pivot point
                with col2:
                    st.write("### Pivot Point")
                    value = float(levels['Pivot Point'])  # Convert to float
                    st.write(f"PP: ₹{value:.2f}")
                
                # Display support levels
                with col3:
                    st.write("### Support Levels")
                    for i in range(1, 4):
                        value = float(levels[f'Support {i}'])  # Convert to float
                        st.write(f"S{i}: ₹{value:.2f}")
        else:
            st.error("Failed to fetch stock data. Please try again later.")

if __name__ == "__main__":
    main()