app.py

import yfinance as yf
import pandas as pd
import numpy as np
import streamlit as st
from ta.momentum import RSIIndicator
from ta.trend import MACD
from ta.volatility import BollingerBands

# Function to fetch data from Yahoo Finance
def fetch_data(stock_ticker):
    stock_data = yf.Ticker(stock_ticker)
    df = stock_data.history(period="1y")
    return df, stock_data

# Function to calculate fundamental metrics
def calculate_fundamentals(stock_data):
    market_price = stock_data.history(period="1d")['Close'].iloc[-1]
    eps = stock_data.info.get('regularMarketEPS', stock_data.info.get('epsTrailingTwelveMonths', 0))
    if eps == 0:
        eps = 0
    shares_outstanding = stock_data.info.get('sharesOutstanding', 0)
    book_value = stock_data.info.get('bookValue', 0)
    annual_dividend = stock_data.info.get('dividendRate', 0)
    revenue = stock_data.info.get('totalRevenue', 0)
    debt = stock_data.info.get('totalDebt', 0)
    cash = stock_data.info.get('cash', 0)

    # Calculate P/E, P/B, Dividend Yield
    pe_ratio = market_price / eps if eps != 0 else None
    pb_ratio = market_price / book_value if book_value != 0 else None
    dividend_yield = (annual_dividend / market_price) * 100 if annual_dividend else 0

    # Additional ratios
    ps_ratio = stock_data.info.get('marketCap', 0) / revenue if revenue != 0 else None
    de_ratio = debt / stock_data.info.get('totalEquity', 1)

    # Calculate PEG ratio
    peg_ratio = pe_ratio / (stock_data.info.get('earningsGrowth', 0) if stock_data.info.get('earningsGrowth', 0) != 0 else 1)

    return {
        "P/E Ratio": pe_ratio,
        "EPS": eps,
        "P/B Ratio": pb_ratio,
        "Dividend Yield (%)": dividend_yield,
        "P/S Ratio": ps_ratio,
        "D/E Ratio": de_ratio,
        "PEG Ratio": peg_ratio
    }

# Function for Buy/Sell Signal based on P/E, RSI, Moving Averages
def generate_buy_sell_signal(pe_ratio, rsi, sma_50, sma_200):
    signal = "Hold"
    if pe_ratio and pe_ratio < 15:
        signal = "Buy"
    elif rsi > 70:
        signal = "Sell"
    elif sma_50 > sma_200:
        signal = "Buy"
    elif sma_50 < sma_200:
        signal = "Sell"
    return signal

# Function to calculate DCF (Discounted Cash Flow) Model
def calculate_dcf(stock_data, growth_rate=0.05, discount_rate=0.08):
    fcf = stock_data.info.get('freeCashflow', 0)  # Example: FCF (Free Cash Flow)
    if fcf == 0:
        return None

    # Project future FCF for 5 years
    future_fcf = [fcf * (1 + growth_rate) ** i for i in range(1, 6)]

    # Calculate DCF value
    dcf_value = sum([cf / (1 + discount_rate) ** i for i, cf in enumerate(future_fcf, 1)])
    return dcf_value

# Streamlit UI
def app():
    st.title("Stock Prediction and Analysis")
    
    # User input for stock ticker
    stock_ticker = st.text_input("Enter Stock Ticker (e.g., AAPL, TATAMOTORS.NS):")

    if stock_ticker:
        # Fetch stock data
        df, stock_data = fetch_data(stock_ticker)

        # Display stock data and technical indicators
        st.write(f"**Displaying last 5 rows of {stock_ticker} data:**")
        st.write(df.tail())

        # Calculate fundamental metrics
        fundamentals = calculate_fundamentals(stock_data)
        st.subheader("Fundamental Analysis")
        st.write(fundamentals)

        # Calculate technical indicators (SMA, RSI)
        sma_50 = df['Close'].rolling(window=50).mean().iloc[-1]
        sma_200 = df['Close'].rolling(window=200).mean().iloc[-1]
        rsi_indicator = RSIIndicator(df['Close'])
        rsi = rsi_indicator.rsi().iloc[-1]

        # Display technical indicators
        st.write(f"50-day SMA: {sma_50}")
        st.write(f"200-day SMA: {sma_200}")
        st.write(f"RSI: {rsi}")

        # Generate Buy/Sell Signal
        buy_sell_signal = generate_buy_sell_signal(fundamentals['P/E Ratio'], rsi, sma_50, sma_200)
        st.subheader("Buy/Sell Signal")
        st.write(f"Signal: {buy_sell_signal}")

        # DCF Value (example)
        dcf_value = calculate_dcf(stock_data)
        if dcf_value:
            st.subheader("Discounted Cash Flow (DCF) Analysis")
            st.write(f"DCF Value: {dcf_value}")
        else:
            st.write("DCF not available due to lack of Free Cash Flow data")

# Run the app
if __name__ == "__main__":
    app()