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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): | |
| """ | |
| Fetches stock data for the given ticker, period, and interval. | |
| """ | |
| return yf.download(ticker, period=period, interval=interval) | |
| # Streamlit interface setup | |
| st.title("Enhanced Breakout Trading Analysis Tool with Volume Indicator") | |
| # User inputs | |
| ticker = st.text_input("Enter Stock Ticker:", value="AAPL") | |
| # Updated to include a 1-hour time frame option | |
| timeframe_options = ["1d", "1wk", "1mo", "1h"] | |
| timeframe = st.selectbox("Select Time Frame:", options=timeframe_options, index=3) | |
| # Updated to include a 1-month period option | |
| period_options = ["1mo", "3mo", "6mo", "1y", "2y"] | |
| period = st.selectbox("Select Period:", options=period_options, index=0) | |
| analyze_button = st.button("Analyze Breakout Points") | |
| if analyze_button: | |
| try: | |
| # Fetching the stock data with the selected period and interval | |
| stock_data = fetch_stock_data(ticker, period, timeframe) | |
| if not stock_data.empty: | |
| # 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['RSI'] = ta.rsi(stock_data['Close'], length=14) | |
| macd = ta.macd(stock_data['Close']) | |
| stock_data['MACD'] = macd['MACD_12_26_9'] | |
| stock_data['MACDSignal'] = macd['MACDs_12_26_9'] | |
| # Adding volume moving average for comparison | |
| stock_data['Volume_MA20'] = ta.sma(stock_data['Volume'], length=20) | |
| # Identifying breakout points for all three logics with volume increase criterion | |
| crossover_points_logic1 = stock_data[(stock_data['SMA9'] > stock_data['SMA20']) & (stock_data['SMA9'].shift(1) < stock_data['SMA20'].shift(1)) & (stock_data['Volume'] > stock_data['Volume_MA20'])] | |
| crossover_points_logic2 = stock_data[(stock_data['SMA20'] > stock_data['SMA50']) & (stock_data['SMA20'].shift(1) < stock_data['SMA50'].shift(1)) & (stock_data['Volume'] > stock_data['Volume_MA20'])] | |
| crossover_points_original = stock_data[(stock_data['SMA50'] > stock_data['SMA200']) & (stock_data['SMA50'].shift(1) < stock_data['SMA200'].shift(1)) & (stock_data['Volume'] > stock_data['Volume_MA20'])] | |
| # Plotting | |
| fig, ax = plt.subplots(3, 1, figsize=(10, 15), 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} Price and SMA Breakout Points Analysis") | |
| ax[0].legend() | |
| # Volume and Volume MA | |
| ax[1].bar(stock_data.index, stock_data['Volume'], label='Volume', color='gray', alpha=0.3) | |
| ax[1].plot(stock_data['Volume_MA20'], label='20-Day Volume MA', color='orange') | |
| ax[1].scatter(crossover_points_logic1.index, crossover_points_logic1['Volume'], color='gold', label='Logic 1 Breakouts', zorder=5) | |
| ax[1].scatter(crossover_points_logic2.index, crossover_points_logic2['Volume'], color='violet', label='Logic 2 Breakouts', zorder=5) | |
| ax[1].scatter(crossover_points_original.index, crossover_points_original['Volume'], color='magenta', label='Original Logic Breakouts', zorder=5) | |
| ax[1].set_title(f"{ticker} Volume and Breakout Points") | |
| ax[1].legend() | |
| # RSI and MACD | |
| ax[2].plot(stock_data['RSI'], label='RSI', color='purple') | |
| ax[2].axhline(70, linestyle='--', color='grey', alpha=0.5, label='Overbought') | |
| ax[2].axhline(30, linestyle='--', color='grey', alpha=0.5, label='Oversold') | |
| ax[2].plot(stock_data['MACD'], label='MACD', color='blue') | |
| ax[2].plot(stock_data['MACDSignal'], label='MACD Signal', color='orange') | |
| ax[2].set_title(f"{ticker} RSI & MACD") | |
| ax[2].legend() | |
| # Display plot in Streamlit | |
| st.pyplot(fig) | |
| else: | |
| st.error("No data found for the specified ticker. Please try another ticker.") | |
| except Exception as e: | |
| st.error(f"An error occurred: {e}") | |