import streamlit as st
import yfinance as yf
import pandas as pd
from datetime import datetime, timedelta, time
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry

# Define the list of ticker symbols for user selection
TICKER_SYMBOLS = [
    "AADHARHFC", "AARTIIND", "AAVAS", "ABBOTINDIA", "ACE", "ADANIENSOL", "ADANIENT", "ADANIGREEN", "ADANIPORTS", 
    "ADANIPOWER", "ATGL", "AWL", "ABCAPITAL", "ABFRL", "ABREL", "ABSLAMC", "AEGISLOG", "AFFLE", "AJANTPHARM", 
    "AKUMS", "APLLTD", "ALKEM", "ALKYLAMINE", "ALOKINDS", "AMBER", "AMBUJACEM", "ANANDRATHI", "ANANTRAJ", 
    "ANGELONE", "APARINDS", "APOLLOHOSP", "APOLLOTYRE", "APTUS", "ACI", "ASAHIINDIA", "ASHOKLEY", "ASIANPAINT", 
    "ASTERDM", "ASTRAZEN", "ASTRAL", "ATUL", "AUROPHARMA", "AVANTIFEED", "DMART", "AXISBANK", "BASF", "BEML", 
    "BLS", "BSE", "BAJAJ-AUTO", "BAJFINANCE", "BAJAJFINSV", "BAJAJHLDNG", "BALAMINES", "BALKRISIND", "BALRAMCHIN", 
    "BANDHANBNK", "BANKBARODA", "BANKINDIA", "MAHABANK", "BATAINDIA", "BAYERCROP", "BERGEPAINT", "BDL", "BEL", 
    "BHARATFORG", "BHEL", "BPCL", "BHARTIARTL", "BHARTIHEXA", "BIKAJI", "BIOCON", "BIRLACORPN", "BSOFT", 
    "BLUEDART", "BLUESTARCO", "BBTC", "BOSCHLTD", "BRIGADE", "BRITANNIA", "MAPMYINDIA", "CCL", "CESC", "CGPOWER", 
    "CIEINDIA", "CRISIL", "CAMPUS", "CANFINHOME", "CANBK", "CAPLIPOINT", "CGCL", "CARBORUNIV", "CASTROLIND", 
    "CEATLTD", "CELLO", "CENTRALBK", "CDSL", "CENTURYPLY", "CERA", "CHALET", "CHAMBLFERT", "CHEMPLASTS", 
    "CHENNPETRO", "CHOLAHLDNG", "CHOLAFIN", "CIPLA", "CUB", "CLEAN", "COALINDIA", "COCHINSHIP", "COFORGE", 
    "COLPAL", "CAMS", "CONCORDBIO", "CONCOR", "COROMANDEL", "CRAFTSMAN", "CREDITACC", "CROMPTON", "CUMMINSIND", 
    "CYIENT", "DLF", "DABUR", "DALBHARAT", "DATAPATTNS", "DEEPAKFERT", "DEEPAKNTR", "DELHIVERY", "DEVYANI", 
    "DIVISLAB", "DIXON", "LALPATHLAB", "DRREDDY", "EIDPARRY", "EIHOTEL", "EASEMYTRIP", "EICHERMOT", "ELECON", 
    "ELGIEQUIP", "EMAMILTD", "EMCURE", "ENDURANCE", "ENGINERSIN", "EQUITASBNK", "ERIS", "ESCORTS", "EXIDEIND", 
    "NYKAA", "FEDERALBNK", "FACT", "FINEORG", "FINCABLES", "FINPIPE", "FSL", "FORTIS", "GRINFRA", "GAIL", 
    "GMRINFRA", "GRSE", "GICRE", "GILLETTE", "GLAND", "GLAXO", "GLENMARK", "MEDANTA", "GODFRYPHLP", "GODREJAGRO", 
    "GODREJCP", "GODREJIND", "GODREJPROP", "GRANULES", "GRAPHITE", "GRASIM", "GESHIP", "GUJGASLTD", "GMDCLTD", 
    "GNFC", "GPPL", "GSFC", "GSPL", "HEG", "HBLPOWER", "HCLTECH", "HDFCAMC", "HDFCBANK", "HDFCLIFE", "HFCL", 
    "HAPPSTMNDS", "HAVELLS", "HEROMOTOCO", "HSCL", "HINDALCO", "HAL", "HINDCOPPER", "HINDPETRO", "HINDUNILVR", 
    "HINDZINC", "POWERINDIA", "ICICIBANK", "ICICIGI", "ICICIPRULI", "ISEC", "IDBI", "IDFCFIRSTB", "IFCI", "IIFL", 
    "INOXINDIA", "ITC", "ITI", "INDIACEM", "INDIANB", "INFY", "IRCTC", "INDIGO", "JINDALSTEL", "JSWSTEEL", "JUBLFOOD",
    "KEC", "KPITTECH", "KOTAKBANK", "LTIM", "LICHSGFIN", "LUPIN", "MRF", "MGL", "M&M", "MARICO", "MARUTI", "MFSL",
    "NESTLEIND", "ONGC", "PAYTM", "PNB", "RELIANCE", "SBIN", "SUNPHARMA", "TATACHEM", "TATAMOTORS", "TCS", "TITAN",
    "UPL", "VEDL", "WIPRO", "YESBANK", "ZEEL", "ZOMATO", "ZYDUSLIFE"
]

# Streamlit App Title
st.title("Pivot Point Calculator for Indian Share Market")

# Ticker Selection
selected_ticker = st.selectbox("Select a Ticker Symbol", TICKER_SYMBOLS)

# Date input
start_date = st.date_input("Select Start Date for Historical Data")
end_date = st.date_input("Select End Date for Historical Data")

# 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:
        high = stock_data['High'].iloc[-1]
        low = stock_data['Low'].iloc[-1]
        close = stock_data['Close'].iloc[-1]
        
        pivot = (high + low + close) / 3
        
        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 retry logic and proper error handling
@st.cache_data(ttl=300)  # Cache for 5 minutes
def get_stock_data(symbol):
    try:
        nse_symbol = f"{symbol}.NS"
        today = datetime.today().date()

        # Set up a retry session to handle intermittent connection issues
        session = requests.Session()
        retries = Retry(total=5, backoff_factor=0.5, status_forcelist=[500, 502, 503, 504])
        session.mount('https://', HTTPAdapter(max_retries=retries))

        # Use the session to download data
        stock = yf.download(
            nse_symbol,
            start=today - timedelta(days=5),
            end=today + timedelta(days=1),
            interval='1d',
            progress=False,
            session=session
        )
        
        if not stock.empty:
            return stock.tail(1)
        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()

# Load Data
if st.button("Calculate Pivot Points"):
    if selected_ticker:
        # Download historical data using yfinance
        try:
            data = get_stock_data(selected_ticker)
            if not data.empty:
                # Display historical data
                st.subheader("Historical Data")
                st.dataframe(data.tail())
                
                # Calculate pivot points and support/resistance levels
                levels = calculate_pivot_levels(data)
                if levels:
                    st.subheader(f"Pivot Point Calculations for {selected_ticker}")
                    for level_name, value in levels.items():
                        st.write(f"{level_name}: {float(value):.2f}")
            else:
                st.error("No data available for the selected ticker and date range. Please try again.")
        except Exception as e:
            st.error(f"An error occurred: {e}")
    else:
        st.error("Please select a valid ticker symbol.")