Delete app.py

app.py

@@ -1,67 +0,0 @@
-import streamlit as st
-import yfinance as yf
-import pandas as pd
-import pandas_ta as ta
-import matplotlib.pyplot as plt
-
-# Caching the stock data fetch function to improve performance
-@st.cache_data()
-def fetch_stock_data(ticker, period, interval):
-    try:
-        data = yf.download(ticker, period=period, interval=interval)
-        return data
-    except Exception as e:
-        st.error(f"Failed to fetch data: {e}")
-        return pd.DataFrame()  # Return an empty DataFrame on failure
-
-# Streamlit interface setup
-st.title("Enhanced Breakout Trading Analysis Tool with Multiple Logic")
-
-# User inputs
-ticker = st.text_input("Enter Stock Ticker:", value="AAPL")
-timeframe_options = ["1d", "1wk", "1mo"]
-timeframe = st.selectbox("Select Time Frame:", options=timeframe_options, index=0)
-period = st.selectbox("Select Period:", options=["6mo", "1y", "2y"], index=1)
-analyze_button = st.button("Analyze Breakout Points")
-
-if analyze_button:
-    stock_data = fetch_stock_data(ticker, period, timeframe)
-
-    if stock_data.empty:
-        st.error("No data found for the specified ticker. Please try another ticker.")
-    else:
-        # Calculating technical indicators
-        stock_data['SMA9'] = ta.sma(stock_data['Close'], length=9)
-        stock_data['SMA20'] = ta.sma(stock_data['Close'], length=20)
-        stock_data['SMA50'] = ta.sma(stock_data['Close'], length=50)
-        stock_data['SMA200'] = ta.sma(stock_data['Close'], length=200)
-        stock_data.dropna(subset=['SMA9', 'SMA20', 'SMA50', 'SMA200'], inplace=True)
-
-        if stock_data.empty:
-            st.error("Insufficient data for the selected period to compute all moving averages. Try a longer period.")
-        else:
-            # Identifying breakout points for all three logics after ensuring data validity
-            crossover_points_logic1 = stock_data[(stock_data['SMA9'] > stock_data['SMA20']) & (stock_data['SMA9'].shift(1) < stock_data['SMA20'].shift(1))]
-            crossover_points_logic2 = stock_data[(stock_data['SMA20'] > stock_data['SMA50']) & (stock_data['SMA20'].shift(1) < stock_data['SMA50'].shift(1))]
-            crossover_points_original = stock_data[(stock_data['SMA50'] > stock_data['SMA200']) & (stock_data['SMA50'].shift(1) < stock_data['SMA200'].shift(1))]
-
-            # Plotting
-            fig, ax = plt.subplots(2, 1, figsize=(10, 12), sharex=True)
-
-            # Price, SMAs, and breakout points for all logics
-            ax[0].plot(stock_data['Close'], label='Close Price', color='skyblue')
-            ax[0].plot(stock_data['SMA9'], label='9-Day SMA', color='orange')
-            ax[0].plot(stock_data['SMA20'], label='20-Day SMA', color='purple')
-            ax[0].plot(stock_data['SMA50'], label='50-Day SMA', color='green')
-            ax[0].plot(stock_data['SMA200'], label='200-Day SMA', color='red')
-            ax[0].scatter(crossover_points_logic1.index, crossover_points_logic1['Close'], color='gold', label='Logic 1 Breakouts', zorder=5)
-            ax[0].scatter(crossover_points_logic2.index, crossover_points_logic2['Close'], color='violet', label='Logic 2 Breakouts', zorder=5)
-            ax[0].scatter(crossover_points_original.index, crossover_points_original['Close'], color='magenta', label='Original Logic Breakouts', zorder=5)
-            ax[0].set_title(f"{ticker} Breakout Points Analysis")
-            ax[0].legend()
-
-            # Additional technical indicators (if necessary) could be plotted here
-
-            # Display plot in Streamlit
-            st.pyplot(fig)
-