app.py

import yfinance as yf
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import streamlit as st
import altair as alt
from bs4 import BeautifulSoup
import requests
from datetime import datetime
from rich import print

# Step 1: Define a function to fetch real-time market data for the selected timeframe
def fetch_data(ticker_symbol, selected_timeframe):
    try:
        ticker = yf.Ticker(ticker_symbol)
        data = ticker.history(period=selected_timeframe)  # Fetches data based on selected timeframe
        if data.empty:
            raise ValueError("No data found for the given ticker. Please check the ticker symbol.")
        return data
    except Exception as e:
        st.error(f"An error occurred while fetching data: {str(e)}")
        return pd.DataFrame()

# Step 2: Define a function to calculate indicators
def calculate_indicators(df, lengthEMA=3, lengthRSI=14, momentumLength=3, trendLength=50):
    if df.empty:
        return df

    # Calculate Moving Averages
    df['MA20'] = df['Close'].rolling(window=20).mean()
    df['MA50'] = df['Close'].rolling(window=50).mean()

    # Calculate EMA for Buy Signal
    df['SignalEMA'] = df['Close'].ewm(span=lengthEMA, adjust=False).mean()

    # Calculate RSI
    delta = df['Close'].diff(1)
    gain = (delta.where(delta > 0, 0)).rolling(window=lengthRSI).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=lengthRSI).mean()
    rs = gain / loss
    df['RSI'] = 100 - (100 / (1 + rs))

    # Calculate Momentum
    df['Momentum'] = df['Close'].diff(momentumLength)

    # Composite SIGNAL Calculation with Dynamic Adjustments
    df['SignalComposite'] = (0.5 * df['SignalEMA']) + (0.3 * (df['RSI'] - 50) / 100) + (0.2 * (df['Momentum'] / df['Close'].rolling(window=lengthRSI).mean()))

    # Smooth the Composite SIGNAL with EMA
    df['SmoothedSignal'] = df['SignalComposite'].ewm(span=lengthEMA, adjust=False).mean()

    # Trend Filter (SMA)
    df['TrendSMA'] = df['Close'].rolling(window=trendLength).mean()

    # Adjusted Buy and Sell Thresholds
    buyThreshold = 1.5  # Increased threshold for stronger signals
    sellThreshold = -1.5

    # Buy and Sell Signals with Momentum Condition
    df['BuySignal'] = (df['SmoothedSignal'] > buyThreshold) & (df['Close'] > df['TrendSMA']) & (df['Momentum'] > 0)
    df['SellSignal'] = (df['SmoothedSignal'] < sellThreshold) & (df['Close'] < df['TrendSMA']) & (df['Momentum'] < 0)

    # Add Cooldown Logic to Reduce Repeated Signals
    df['BuySignal'] = df['BuySignal'] & ~df['BuySignal'].shift(1).fillna(False)
    df['SellSignal'] = df['SellSignal'] & ~df['SellSignal'].shift(1).fillna(False)

    return df

# Step 3: Streamlit UI Setup for Stock Selection
st.title("Advanced Indian Share Market Analysis")
st.sidebar.header("Select Stock Ticker and Timeframe")

# Add a stock selector input box
ticker_symbol = st.sidebar.text_input("Enter Stock Ticker (e.g., RELIANCE.NS for NSE or TCS.BO for BSE)", "^NSEI")

# 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)


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

    # Step 5: Display Stock Data if available
    if not nifty_data.empty:
        st.subheader(f"Data Overview for {ticker_symbol}")
        st.write(nifty_data.tail())

        # Step 6: Interactive Visualization Using Plotly
        st.subheader(f"Interactive Buy and Sell Signals for {ticker_symbol}")
        fig = go.Figure()

        # Add Close Price line
        fig.add_trace(go.Scatter(
            x=nifty_data.index,
            y=nifty_data['Close'],
            mode='lines',
            name='Close Price',
            line=dict(color='blue')
        ))

        # Add Trend SMA line
        fig.add_trace(go.Scatter(
            x=nifty_data.index,
            y=nifty_data['TrendSMA'],
            mode='lines',
            name='Trend SMA',
            line=dict(color='gray')
        ))

        # Plot Buy Signals
        buy_signals = nifty_data[nifty_data['BuySignal']]
        fig.add_trace(go.Scatter(
            x=buy_signals.index,
            y=buy_signals['Close'],
            mode='markers',
            name='Buy Signal',
            marker=dict(symbol='triangle-up', color='green', size=10)
        ))

        # Plot Sell Signals
        sell_signals = nifty_data[nifty_data['SellSignal']]
        fig.add_trace(go.Scatter(
            x=sell_signals.index,
            y=sell_signals['Close'],
            mode='markers',
            name='Sell Signal',
            marker=dict(symbol='triangle-down', color='red', size=10)
        ))

        # Update layout for better readability
        fig.update_layout(
            title=f"Buy and Sell Signals with Trend Filter for {ticker_symbol}",
            xaxis_title="Date",
            yaxis_title="Close Price",
            legend_title="Legend",
            template="plotly_dark"
        )

        st.plotly_chart(fig)

        # Step 8: Altair Chart for Moving Averages
        st.subheader(f"Moving Averages for {ticker_symbol}")
        alt_chart = alt.Chart(nifty_data.reset_index()).mark_line().encode(
            x='Date:T',
            y=alt.Y('MA20:Q', title='Moving Average (20-day)'),
            color=alt.value('orange')
        ).properties(title="20-Day Moving Average")
        st.altair_chart(alt_chart, use_container_width=True)

        # Step 9: Market News Integration Using BeautifulSoup
        st.sidebar.header("Latest Market News")
        news_url = 'https://www.moneycontrol.com/news/'
        response = requests.get(news_url)
        soup = BeautifulSoup(response.content, 'html.parser')

        headlines = [headline.text for headline in soup.find_all('h2')[:5]]
        st.sidebar.subheader("Top 5 News Headlines")
        for idx, headline in enumerate(headlines, 1):
            st.sidebar.write(f"{idx}. {headline}")

        # Step 10: Alerts Using Rich Library
        st.sidebar.subheader("Set Alerts:")
        alert_type = st.sidebar.selectbox('Alert Type', ['Price', 'RSI'])
        alert_value = st.sidebar.number_input('Enter Alert Value')

        if alert_value:
            print(f"[bold cyan]Alert Set for {alert_type} at Value:[/bold cyan] {alert_value}")

except Exception as e:
    st.error(f"An error occurred: {str(e)}")