import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import streamlit as st

# Step 1: Define a function to fetch real-time market data
def fetch_data(ticker_symbol):
    ticker = yf.Ticker(ticker_symbol)
    data = ticker.history(period="1y")  # Fetches 1 year of historical data
    return data

# Step 2: Define a function to calculate technical indicators
def calculate_indicators(df):
    # Calculate Moving Averages
    df['MA20'] = df['Close'].rolling(window=20).mean()
    df['MA50'] = df['Close'].rolling(window=50).mean()
    
    # Calculate RSI
    delta = df['Close'].diff(1)
    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
    rs = gain / loss
    df['RSI'] = 100 - (100 / (1 + rs))
    
    # Calculate MACD
    df['EMA12'] = df['Close'].ewm(span=12, adjust=False).mean()
    df['EMA26'] = df['Close'].ewm(span=26, adjust=False).mean()
    df['MACD'] = df['EMA12'] - df['EMA26']
    df['Signal'] = df['MACD'].ewm(span=9, adjust=False).mean()
    
    # Calculate OBV
    df['OBV'] = (df['Volume'] * ((df['Close'] - df['Close'].shift(1)) > 0).astype(int) - 
                 df['Volume'] * ((df['Close'] - df['Close'].shift(1)) < 0).astype(int)).cumsum()
    
    return df

# Step 3: Streamlit UI Setup for Stock Selection
st.title("Indian Share Market Analysis")

# Add a stock selector input box
st.sidebar.header("Select Stock Ticker")
ticker_symbol = st.sidebar.text_input("Enter Stock Ticker (e.g., RELIANCE.NS, ^NSEI)", "^NSEI")

# Step 4: Fetch Data and Calculate Indicators
nifty_data = fetch_data(ticker_symbol)
nifty_data = calculate_indicators(nifty_data)

# Step 5: Display Stock Data
st.subheader(f"Data Overview for {ticker_symbol}")
st.write(nifty_data.head())

# Step 6: Visualizations
st.subheader(f"Close Price for {ticker_symbol}")
fig, ax = plt.subplots(figsize=(12, 6))
sns.lineplot(x=nifty_data.index, y=nifty_data['Close'])
st.pyplot(fig)

st.subheader(f"Moving Averages and RSI for {ticker_symbol}")
fig, ax = plt.subplots(figsize=(12, 6))
sns.lineplot(x=nifty_data.index, y=nifty_data['MA20'], label='MA20')
sns.lineplot(x=nifty_data.index, y=nifty_data['MA50'], label='MA50')
sns.lineplot(x=nifty_data.index, y=nifty_data['RSI'], label='RSI')
plt.axhline(y=70, color='red', linestyle='--', label='Overbought (70)')
plt.axhline(y=30, color='green', linestyle='--', label='Oversold (30)')
plt.legend()
plt.title("Moving Averages and RSI")
st.pyplot(fig)

# Step 7: Interactive Timeframe Selection and Alerts
st.sidebar.subheader("Select Timeframe:")
timeframes = ['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max']
selected_timeframe = st.sidebar.selectbox('Timeframe', timeframes)

st.sidebar.subheader("Set Alerts:")
alert_type = st.sidebar.selectbox('Alert Type', ['Price', 'RSI'])
alert_value = st.sidebar.number_input('Enter Alert Value')

# Step 8: Run the Streamlit App (Note: Do not use st.run())
# Save as `app.py` and run it using `streamlit run app.py`