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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 | |
| # Caching the stock data fetch function to improve performance | |
| 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) | |