Main.py

import streamlit as st
import yfinance as yf
import pandas as pd
import pandas_ta as ta
import matplotlib.pyplot as plt

# Streamlit interface setup
st.title("Breakout Trading Analysis Tool")
ticker = st.text_input("Enter Stock Ticker:", value="AAPL")
timeframe = st.selectbox("Select Time Frame:", options=["1d", "1wk", "1mo"], index=0)
analyze_button = st.button("Analyze Breakout Points")

if analyze_button:
    # Fetching the stock data
    stock_data = yf.download(ticker, period="1y", interval=timeframe)

    # Calculating technical indicators for breakout identification (e.g., moving averages)
    stock_data['SMA50'] = ta.sma(stock_data['Close'], length=50)
    stock_data['SMA200'] = ta.sma(stock_data['Close'], length=200)
    
    # Example breakout logic: SMA50 crossing above SMA200
    crossover_points = stock_data[(stock_data['SMA50'] > stock_data['SMA200']) & (stock_data['SMA50'].shift(1) < stock_data['SMA200'].shift(1))]

    # Plotting
    plt.figure(figsize=(10, 6))
    plt.plot(stock_data['Close'], label='Close Price', color='skyblue')
    plt.plot(stock_data['SMA50'], label='50-Day SMA', color='green')
    plt.plot(stock_data['SMA200'], label='200-Day SMA', color='red')
    plt.scatter(crossover_points.index, crossover_points['Close'], color='magenta', label='Breakout Points', zorder=5)
    plt.title(f"{ticker} Breakout Points Analysis")
    plt.legend()

    # Display plot in Streamlit
    st.pyplot(plt)