Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,396 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import yfinance as yf
+import pandas as pd
+import ta
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+from datetime import datetime
+import io
+
+# 設定頁面配置
+st.set_page_config(
+    page_title="股票技術分析系統",
+    page_icon="📈",
+    layout="wide"
+)
+
+def analyze_stock(ticker_input):
+    """分析單一股票的技術指標"""
+    try:
+        ticker = yf.Ticker(ticker_input)
+        df = ticker.history(period="200d")
+        if df.empty:
+            return None, f"無法取得 {ticker_input} 的資料，請確認股票代碼是否正確。"
+    except Exception as e:
+        return None, f"取得 {ticker_input} 資料時發生錯誤: {e}"
+
+    # 處理時區和數據
+    df.index = df.index.tz_localize(None)
+    df = df.tail(200)
+
+    # 計算技術指標
+    df['MA5'] = df['Close'].rolling(window=5).mean()
+    df['MA10'] = df['Close'].rolling(window=10).mean()
+    df['MA20'] = df['Close'].rolling(window=20).mean()
+    df['RSI'] = ta.momentum.RSIIndicator(df['Close']).rsi()
+    df['OBV'] = ta.volume.OnBalanceVolumeIndicator(df['Close'], df['Volume']).on_balance_volume()
+
+    # 布林帶
+    bb = ta.volatility.BollingerBands(df['Close'])
+    df['BB_High'] = bb.bollinger_hband()
+    df['BB_Low'] = bb.bollinger_lband()
+    df['%B'] = (df['Close'] - df['BB_Low']) / (df['BB_High'] - df['BB_Low'])
+    df['BBW'] = (df['BB_High'] - df['BB_Low']) / df['MA20']
+
+    # KD指標
+    kd = ta.momentum.StochasticOscillator(df['High'], df['Low'], df['Close'])
+    df['K'] = kd.stoch()
+    df['D'] = kd.stoch_signal()
+
+    # MACD
+    macd = ta.trend.MACD(df['Close'])
+    df['MACD'] = macd.macd()
+    df['MACD_Signal'] = macd.macd_signal()
+    df['MACD_Diff'] = macd.macd_diff()
+
+    # CCI和黃金交叉
+    df['CCI'] = ta.trend.CCIIndicator(df['High'], df['Low'], df['Close']).cci()
+    df['Golden_Cross'] = df['MA5'] > df['MA20']
+
+    return df, None
+
+def create_stock_chart(df, ticker):
+    """創建股票圖表"""
+    fig = make_subplots(
+        rows=4, cols=1,
+        subplot_titles=('價格與移動平均線', 'RSI', 'MACD', 'KD指標'),
+        vertical_spacing=0.08,
+        row_heights=[0.5, 0.2, 0.2, 0.2]
+    )
+
+    # 價格與移動平均線
+    fig.add_trace(go.Candlestick(
+        x=df.index,
+        open=df['Open'],
+        high=df['High'],
+        low=df['Low'],
+        close=df['Close'],
+        name='價格'
+    ), row=1, col=1)
+
+    fig.add_trace(go.Scatter(x=df.index, y=df['MA5'], name='MA5', line=dict(color='orange')), row=1, col=1)
+    fig.add_trace(go.Scatter(x=df.index, y=df['MA10'], name='MA10', line=dict(color='blue')), row=1, col=1)
+    fig.add_trace(go.Scatter(x=df.index, y=df['MA20'], name='MA20', line=dict(color='red')), row=1, col=1)
+    fig.add_trace(go.Scatter(x=df.index, y=df['BB_High'], name='布林帶上軌', line=dict(color='gray', dash='dash')), row=1, col=1)
+    fig.add_trace(go.Scatter(x=df.index, y=df['BB_Low'], name='布林帶下軌', line=dict(color='gray', dash='dash')), row=1, col=1)
+
+    # RSI
+    fig.add_trace(go.Scatter(x=df.index, y=df['RSI'], name='RSI', line=dict(color='purple')), row=2, col=1)
+    fig.add_hline(y=70, line_dash="dash", line_color="red", row=2, col=1)
+    fig.add_hline(y=30, line_dash="dash", line_color="green", row=2, col=1)
+
+    # MACD
+    fig.add_trace(go.Scatter(x=df.index, y=df['MACD'], name='MACD', line=dict(color='blue')), row=3, col=1)
+    fig.add_trace(go.Scatter(x=df.index, y=df['MACD_Signal'], name='MACD Signal', line=dict(color='red')), row=3, col=1)
+    fig.add_trace(go.Bar(x=df.index, y=df['MACD_Diff'], name='MACD Histogram', marker_color='gray'), row=3, col=1)
+
+    # KD指標
+    fig.add_trace(go.Scatter(x=df.index, y=df['K'], name='K', line=dict(color='blue')), row=4, col=1)
+    fig.add_trace(go.Scatter(x=df.index, y=df['D'], name='D', line=dict(color='red')), row=4, col=1)
+    fig.add_hline(y=80, line_dash="dash", line_color="red", row=4, col=1)
+    fig.add_hline(y=20, line_dash="dash", line_color="green", row=4, col=1)
+
+    fig.update_layout(
+        title=f'{ticker} 技術分析圖表',
+        height=800,
+        showlegend=True,
+        xaxis_rangeslider_visible=False
+    )
+
+    return fig
+
+def get_investment_advice(df, ticker):
+    """獲取投資建議"""
+    advice_list = []
+    latest_data = df.iloc[-1]
+    
+    # RSI建議
+    if latest_data['RSI'] < 30:
+        advice_list.append({
+            'type': '買進建議',
+            'reason': f'RSI = {latest_data["RSI"]:.2f} < 30 (超賣)',
+            'color': 'success'
+        })
+    elif latest_data['RSI'] > 70:
+        advice_list.append({
+            'type': '賣出建議',
+            'reason': f'RSI = {latest_data["RSI"]:.2f} > 70 (超買)',
+            'color': 'error'
+        })
+    
+    # %B建議
+    if latest_data['%B'] < 0:
+        advice_list.append({
+            'type': '買進建議',
+            'reason': f'%B = {latest_data["%B"]:.2f} < 0 (價格低於布林帶下軌)',
+            'color': 'success'
+        })
+    elif latest_data['%B'] > 1:
+        advice_list.append({
+            'type': '賣出建議',
+            'reason': f'%B = {latest_data["%B"]:.2f} > 1 (價格高於布林帶上軌)',
+            'color': 'error'
+        })
+    
+    # 黃金交叉
+    if latest_data['Golden_Cross']:
+        advice_list.append({
+            'type': '看多信號',
+            'reason': '短期均線突破長期均線 (黃金交叉)',
+            'color': 'info'
+        })
+    
+    return advice_list
+
+def create_excel_download(df_dict):
+    """創建Excel下載檔案"""
+    from openpyxl import Workbook
+    from openpyxl.styles import PatternFill
+    from openpyxl.utils.dataframe import dataframe_to_rows
+    
+    wb = Workbook()
+    wb.remove(wb.active)
+
+    yellow_fill = PatternFill(start_color="FFFF00", end_color="FFFF00", fill_type="solid")
+    red_fill = PatternFill(start_color="FF0000", end_color="FF0000", fill_type="solid")
+    green_fill = PatternFill(start_color="00FF00", end_color="00FF00", fill_type="solid")
+    purple_fill = PatternFill(start_color="800080", end_color="800080", fill_type="solid")
+
+    advice_rows = []
+
+    for ticker_input, df in df_dict.items():
+        ws = wb.create_sheet(title=ticker_input)
+        ws.append(['股票代碼:', ticker_input])
+        ws.append(['分析日期:', datetime.now().strftime('%Y-%m-%d %H:%M:%S')])
+        ws.append([])
+
+        for r in dataframe_to_rows(df, index=True, header=True):
+            ws.append(r)
+
+        # 格式化和標色邏輯（保持原有邏輯）
+        header_row = 4
+        headers = {cell.value: cell.column for cell in ws[header_row]}
+        rsi_col = headers.get('RSI')
+        percentage_b_col = headers.get('%B')
+        
+        if rsi_col and percentage_b_col:
+            for row in range(header_row + 1, ws.max_row + 1):
+                rsi_val = ws.cell(row=row, column=rsi_col).value
+                pct_b_val = ws.cell(row=row, column=percentage_b_col).value
+
+                if rsi_val is not None:
+                    rsi_cell = ws.cell(row=row, column=rsi_col)
+                    if rsi_val < 20:
+                        rsi_cell.fill = yellow_fill
+                    elif rsi_val > 70:
+                        rsi_cell.fill = red_fill
+
+                if pct_b_val is not None:
+                    b_cell = ws.cell(row=row, column=percentage_b_col)
+                    if pct_b_val < 0:
+                        b_cell.fill = green_fill
+                    elif pct_b_val > 1:
+                        b_cell.fill = purple_fill
+
+    # 儲存到記憶體
+    output = io.BytesIO()
+    wb.save(output)
+    output.seek(0)
+    return output.getvalue()
+
+# Streamlit 應用程式主體
+def main():
+    st.title("📈 股票技術分析系統")
+    st.markdown("---")
+
+    # 側邊欄設定
+    st.sidebar.header("分析設定")
+    
+    # 選擇分析模式
+    analysis_mode = st.sidebar.radio(
+        "選擇分析模式",
+        ["單一股票分析", "批量股票分析", "從Google Sheets匯入"]
+    )
+
+    if analysis_mode == "單一股票分析":
+        st.header("🎯 單一股票分析")
+        
+        ticker_input = st.text_input("請輸入股票代碼 (例如: AAPL, 2330.TW):", value="AAPL")
+        
+        if st.button("開始分析", type="primary"):
+            if ticker_input:
+                with st.spinner(f"正在分析 {ticker_input} ..."):
+                    df, error = analyze_stock(ticker_input)
+                    
+                    if error:
+                        st.error(error)
+                    else:
+                        st.success(f"成功分析 {ticker_input}")
+                        
+                        # 顯示基本資訊
+                        col1, col2, col3, col4 = st.columns(4)
+                        latest_data = df.iloc[-1]
+                        
+                        with col1:
+                            st.metric("最新收盤價", f"${latest_data['Close']:.2f}")
+                        with col2:
+                            st.metric("RSI", f"{latest_data['RSI']:.2f}")
+                        with col3:
+                            st.metric("%B", f"{latest_data['%B']:.2f}")
+                        with col4:
+                            st.metric("成交量", f"{latest_data['Volume']:,}")
+                        
+                        # 顯示圖表
+                        fig = create_stock_chart(df, ticker_input)
+                        st.plotly_chart(fig, use_container_width=True)
+                        
+                        # 投資建議
+                        st.subheader("💡 投資建議")
+                        advice_list = get_investment_advice(df, ticker_input)
+                        
+                        if advice_list:
+                            for advice in advice_list:
+                                if advice['color'] == 'success':
+                                    st.success(f"**{advice['type']}**: {advice['reason']}")
+                                elif advice['color'] == 'error':
+                                    st.error(f"**{advice['type']}**: {advice['reason']}")
+                                else:
+                                    st.info(f"**{advice['type']}**: {advice['reason']}")
+                        else:
+                            st.info("目前沒有明確的買賣建議")
+                        
+                        # 顯示詳細數據
+                        with st.expander("查看詳細數據"):
+                            st.dataframe(df.tail(20))
+
+    elif analysis_mode == "批量股票分析":
+        st.header("📊 批量股票分析")
+        
+        ticker_text = st.text_area(
+            "請輸入股票代碼 (每行一個):",
+            value="AAPL\nMSFT\nGOOGL\n2330.TW\n2317.TW",
+            height=150
+        )
+        
+        if st.button("開始批量分析", type="primary"):
+            tickers = [ticker.strip() for ticker in ticker_text.split('\n') if ticker.strip()]
+            
+            if tickers:
+                progress_bar = st.progress(0)
+                status_text = st.empty()
+                all_data = {}
+                
+                for i, ticker in enumerate(tickers):
+                    status_text.text(f"正在分析 {ticker} ({i+1}/{len(tickers)})")
+                    progress_bar.progress((i + 1) / len(tickers))
+                    
+                    df, error = analyze_stock(ticker)
+                    if error:
+                        st.warning(f"{ticker}: {error}")
+                    else:
+                        all_data[ticker] = df
+                
+                status_text.text("分析完成!")
+                
+                if all_data:
+                    st.success(f"成功分析 {len(all_data)} 支股票")
+                    
+                    # 顯示摘要表格
+                    summary_data = []
+                    for ticker, df in all_data.items():
+                        latest = df.iloc[-1]
+                        summary_data.append({
+                            '股票代碼': ticker,
+                            '最新價格': f"${latest['Close']:.2f}",
+                            'RSI': f"{latest['RSI']:.2f}",
+                            '%B': f"{latest['%B']:.2f}",
+                            '黃金交叉': '是' if latest['Golden_Cross'] else '否'
+                        })
+                    
+                    summary_df = pd.DataFrame(summary_data)
+                    st.subheader("📋 分析摘要")
+                    st.dataframe(summary_df, use_container_width=True)
+                    
+                    # 提供Excel下載
+                    excel_data = create_excel_download(all_data)
+                    st.download_button(
+                        label="📥 下載完整分析報告 (Excel)",
+                        data=excel_data,
+                        file_name=f"技術分析總表_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx",
+                        mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+                    )
+
+    else:  # Google Sheets 匯入
+        st.header("📑 從Google Sheets匯入")
+        
+        sheet_id = st.text_input(
+            "請輸入Google Sheets ID:",
+            value="1CZlw53z9Fns3XCQmlY_PXF2qOv2Wyqyn7mRrIzDRQMg"
+        )
+        
+        if st.button("從Google Sheets匯入並分析", type="primary"):
+            try:
+                sheet_url = f"https://docs.google.com/spreadsheets/d/{sheet_id}/export?format=csv"
+                df_tickers = pd.read_csv(sheet_url)
+                tickers = df_tickers["股號"].dropna().astype(str).tolist()
+                
+                st.info(f"從Google Sheets讀取到 {len(tickers)} 個股票代碼")
+                
+                progress_bar = st.progress(0)
+                status_text = st.empty()
+                all_data = {}
+                
+                for i, ticker in enumerate(tickers):
+                    status_text.text(f"正在分析 {ticker} ({i+1}/{len(tickers)})")
+                    progress_bar.progress((i + 1) / len(tickers))
+                    
+                    df, error = analyze_stock(ticker)
+                    if error:
+                        st.warning(f"{ticker}: {error}")
+                    else:
+                        all_data[ticker] = df
+                
+                status_text.text("分析完成!")
+                
+                if all_data:
+                    st.success(f"成功分析 {len(all_data)} 支股票")
+                    
+                    # Excel下載
+                    excel_data = create_excel_download(all_data)
+                    st.download_button(
+                        label="📥 下載完整分析報告 (Excel)",
+                        data=excel_data,
+                        file_name=f"技術分析總表_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx",
+                        mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+                    )
+                    
+            except Exception as e:
+                st.error(f"讀取Google Sheets時發生錯誤: {e}")
+
+    # 說明資訊
+    with st.sidebar.expander("📖 使用說明"):
+        st.markdown("""
+        **技術指標說明:**
+        - **RSI < 30**: 超賣，考慮買進
+        - **RSI > 70**: 超買，考慮賣出
+        - **%B < 0**: 價格低於布林帶下軌
+        - **%B > 1**: 價格高於布林帶上軌
+        - **黃金交叉**: 短期均線突破長期均線
+        
+        **股票代碼格式:**
+        - 美股: AAPL, MSFT, GOOGL
+        - 台股: 2330.TW, 2317.TW
+        """)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+if __name__ == "__main__":
+    main()