Create app.py

app.py

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+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)