import streamlit as st
import pandas as pd
import yfinance as yf
import numpy as np
import datetime

def fetch_stock_data(symbol, start_date, end_date, interval):
    try:
        valid_intervals = {
            "1m": 8,     # 1-minute data is available for up to 8 days
            "2m": 60,    # 2-minute data is available for up to 60 days
            "5m": 60,    # 5-minute data is available for up to 60 days
            "15m": 60,   # 15-minute data is available for up to 60 days
            "30m": 60,   # 30-minute data is available for up to 60 days
            "60m": 730,  # 60-minute data is available for up to 730 days (2 years)
            "90m": 730,  # 90-minute data is available for up to 730 days (2 years)
            "1h": 730,   # 1-hour data is available for up to 730 days (2 years)
            "1d": 10000, # Daily data is available for up to maximum available
            "5d": 10000, # 5-day data is available for up to maximum available
            "1wk": 10000,# Weekly data is available for up to maximum available
            "1mo": 10000,# Monthly data is available for up to maximum available
            "3mo": 10000 # Quarterly data is available for up to maximum available
        }

        if interval not in valid_intervals:
            st.error(f"Invalid interval: {interval}. Please choose from {list(valid_intervals.keys())}")
            return

        max_days = valid_intervals[interval]
        start_date_pd = pd.to_datetime(start_date)
        end_date_pd = pd.to_datetime(end_date)
        max_start_date = end_date_pd - pd.to_timedelta(max_days, unit='d')

        if start_date_pd < max_start_date:
            st.warning(f"The selected start date exceeds the maximum allowed range for {interval} interval. Adjusting start date to {max_start_date.strftime('%Y-%m-%d')}")
            start_date_pd = max_start_date

        ticker = yf.Ticker(symbol)
        data = ticker.history(start=start_date_pd.strftime('%Y-%m-%d'), end=end_date_pd.strftime('%Y-%m-%d'), interval=interval)
        if not data.empty:
            data.reset_index(inplace=True)
            data.rename(columns={'index': 'Datetime'}, inplace=True)
            # Calculate On-Balance Volume (OBV)
            data['OBV'] = (np.sign(data['Close'].diff()) * data['Volume']).fillna(0).cumsum()
            # Calculate Relative Strength Index (RSI)
            delta = data['Close'].diff()
            gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
            loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
            rs = gain / loss
            data['RSI'] = 100 - (100 / (1 + rs))
            # Calculate Exponential Moving Average (EMA)
            data['EMA'] = data['Close'].ewm(span=20, adjust=False).mean()
            # Calculate Simple Moving Average (SMA)
            data['SMA'] = data['Close'].rolling(window=20).mean()

            st.write(f"Data for {symbol} fetched successfully with OBV, RSI, EMA, and SMA included.")
            st.write(data)
        else:
            st.error(f"No data available for {symbol}. Please check the symbol or try a different date range.")
    except Exception as e:
        st.error(f"Error fetching data for {symbol}: {e}")

# Stock List
stock_list = [
    "RELIANCE.NS", "TCS.NS", "INFY.NS", "HDFCBANK.NS", "ICICIBANK.NS",
    "KOTAKBANK.NS", "SBIN.NS", "LT.NS", "HINDUNILVR.NS", "BHARTIARTL.NS"
]

# Streamlit app layout
st.title("Share Market Data")
symbol = st.selectbox("Select Stock Symbol", options=stock_list)
interval = st.selectbox("Select Data Interval", options=["1m", "2m", "5m", "15m", "30m", "60m", "90m", "1h", "1d", "5d", "1wk", "1mo", "3mo"])

# Allow user to select a start date
start_date = st.date_input("Select Start Date", pd.to_datetime('2023-01-01'))
end_date = pd.to_datetime('today').strftime('%Y-%m-%d')

valid_intervals = {
    "1m": 8,     # 1-minute data is available for up to 8 days
    "2m": 60,    # 2-minute data is available for up to 60 days
    "5m": 60,    # 5-minute data is available for up to 60 days
    "15m": 60,   # 15-minute data is available for up to 60 days
    "30m": 60,   # 30-minute data is available for up to 60 days
    "60m": 730,  # 60-minute data is available for up to 730 days (2 years)
    "90m": 730,  # 90-minute data is available for up to 730 days (2 years)
    "1h": 730,   # 1-hour data is available for up to 730 days (2 years)
    "1d": 10000, # Daily data is available for up to maximum available
    "5d": 10000, # 5-day data is available for up to maximum available
    "1wk": 10000,# Weekly data is available for up to maximum available
    "1mo": 10000,# Monthly data is available for up to maximum available
    "3mo": 10000 # Quarterly data is available for up to maximum available
}

max_days = valid_intervals[interval]
end_date_pd = pd.to_datetime('today')
max_start_date = end_date_pd - pd.to_timedelta(max_days, unit='d')

# Adjust the start date if it exceeds the limit
if pd.to_datetime(start_date) < max_start_date:
    st.warning(f"The selected start date exceeds the maximum allowed range for {interval} interval. Adjusting start date to {max_start_date.strftime('%Y-%m-%d')}")
    start_date = max_start_date

fetch_data = st.button("Fetch and Display Data")

if fetch_data and symbol:
    fetch_stock_data(symbol, start_date.strftime('%Y-%m-%d'), end_date, interval)