Update app.py

app.py

@@ -176,76 +176,25 @@ def calculate_technical_indicators(df):
     high_max_14 = df['High'].rolling(window=14).max()
     df['Williams_R'] = -100 * (high_max_14 - df['Close']) / (high_max_14 - low_min_14)
 
-    return df
+    # DMI (Directional Movement Index)
+    # 計算正向運動 (+DM) 和負向運動 (-DM)
+    df['up_move'] = df['High'].diff()
+    df['down_move'] = df['Low'].diff()
+    df['+DM'] = np.where((df['up_move'] > df['down_move']) & (df['up_move'] > 0), df['up_move'], 0)
+    df['-DM'] = np.where((df['down_move'] > df['up_move']) & (df['down_move'] > 0), df['down_move'], 0)
 
-def get_business_climate_data():
-    """獲取台灣景氣燈號資料"""
-    try:
-        # 檢查檔案是否存在
-        if not os.path.exists('business_climate.csv'):
-            print("business_climate.csv 檔案不存在")
-            return pd.DataFrame()
-
-        # 讀取CSV檔案，假設列名為 Date 和 Index
-        df = pd.read_csv('business_climate.csv')
-
-        # 檢查列名並調整
-        if 'Date' not in df.columns:
-            # 如果第一列是日期，重新命名
-            df.columns = ['Date', 'Index'] if len(df.columns) == 2 else df.columns
-
-        # 轉換日期格式 (處理 YYYY-MM 格式)
-        if 'Date' in df.columns:
-            try:
-                # 如果是 YYYY-MM 格式，轉換為日期
-                df['Date'] = pd.to_datetime(df['Date'] + '-01', format='%Y-%m-%d', errors='coerce')
-            except:
-                df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
-
-        # 移除日期轉換失敗的行
-        df = df.dropna(subset=['Date'])
-
-        print(f"成功讀取景氣燈號資料：{len(df)} 筆記錄")
-        return df
+    # 計算真實範圍 (TR)
+    df['TR'] = np.max([df['High'] - df['Low'], abs(df['High'] - df['Close'].shift(1)), abs(df['Low'] - df['Close'].shift(1))], axis=0)
 
-    except Exception as e:
-        print(f"無法獲取景氣燈號資料: {str(e)}")
-        return pd.DataFrame()
+    # 計算平滑後的 +DM, -DM, TR (通常使用 14 天)
+    df['+DI'] = df['+DM'].ewm(alpha=1/14, adjust=False).mean() / df['TR'].ewm(alpha=1/14, adjust=False).mean() * 100
+    df['-DI'] = df['-DM'].ewm(alpha=1/14, adjust=False).mean() / df['TR'].ewm(alpha=1/14, adjust=False).mean() * 100
 
-def get_pmi_data():
-    """獲取台灣 PMI 資料"""
-    try:
-        # 檢查檔案是否存在
-        if not os.path.exists('taiwan_pmi.csv'):
-            print("taiwan_pmi.csv 檔案不存在")
-            return pd.DataFrame()
-
-        # 讀取CSV檔案
-        df = pd.read_csv('taiwan_pmi.csv')
-
-        # 檢查列名並調整 (處理 DATE/INDEX 或其他可能的列名)
-        if 'DATE' in df.columns:
-            df = df.rename(columns={'DATE': 'Date', 'INDEX': 'Index'})
-        elif len(df.columns) == 2:
-            df.columns = ['Date', 'Index']
-
-        # 轉換日期格式
-        if 'Date' in df.columns:
-            try:
-                # 如果是 YYYY-MM 格式，轉換為日期
-                df['Date'] = pd.to_datetime(df['Date'] + '-01', format='%Y-%m-%d', errors='coerce')
-            except:
-                df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
-
-        # 移除日期轉換失敗的行
-        df = df.dropna(subset=['Date'])
-
-        print(f"成功讀取 PMI 資料：{len(df)} 筆記錄")
-        return df
+    # 計算 ADX
+    df['DX'] = abs(df['+DI'] - df['-DI']) / (df['+DI'] + df['-DI']) * 100
+    df['ADX'] = df['DX'].ewm(alpha=1/14, adjust=False).mean()
 
-    except Exception as e:
-        print(f"無法獲取 PMI 資料: {str(e)}")
-        return pd.DataFrame()
+    return df
 
 def calculate_volume_profile(df, num_bins=50):
     """
@@ -401,7 +350,8 @@ app.layout = html.Div([
                     {'label': 'MACD 指數平滑異同移動平均線', 'value': 'MACD'},
                     {'label': '布林通道 Bollinger Bands', 'value': 'BB'},
                     {'label': 'KD 隨機指標', 'value': 'KD'},
-                    {'label': '威廉指標 %R', 'value': 'WR'}
+                    {'label': '威廉指標 %R', 'value': 'WR'},
+                    {'label': 'DMI 動向指標', 'value': 'DMI'}
                 ],
                 value='RSI',
                 style={'width': '100%'}
@@ -1034,6 +984,34 @@ def update_advanced_technical_chart(indicator, selected_stock, period):
         )
         fig.update_yaxes(range=[-100, 0], row=2, col=1)
 
+    elif indicator == 'DMI':
+        fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
+                            vertical_spacing=0.1,
+                            row_heights=[0.6, 0.4],
+                            subplot_titles=('價格走勢', 'DMI 指標'))
+
+        # 上方：價格線
+        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='收盤價',
+                                line=dict(color='black', width=1)), row=1, col=1)
+
+        # 下方：DMI 線
+        fig.add_trace(go.Scatter(x=data.index, y=data['+DI'], mode='lines', name='+DI',
+                                line=dict(color='red', width=2)), row=2, col=1)
+        fig.add_trace(go.Scatter(x=data.index, y=data['-DI'], mode='lines', name='-DI',
+                                line=dict(color='green', width=2)), row=2, col=1)
+        fig.add_trace(go.Scatter(x=data.index, y=data['ADX'], mode='lines', name='ADX',
+                                line=dict(color='blue', width=2, dash='dot')), row=2, col=1)
+
+        # DMI 參考線
+        fig.add_hline(y=20, line_dash="dash", line_color="gray", annotation_text="ADX強弱線(20)", row=2, col=1)
+
+        fig.update_layout(
+            title=f'{stock_name} - DMI 動向指標 (14日)',
+            height=500,
+            showlegend=True
+        )
+        fig.update_yaxes(range=[0, 100], row=2, col=1)
+
     return fig
 
 # 更新成交量圖表
@@ -1210,6 +1188,10 @@ def update_analysis_text(selected_stock, period):
     bb_position = data['BB_Position'].iloc[-1] if not pd.isna(data['BB_Position'].iloc[-1]) else 0.5
     k_current = data['K'].iloc[-1] if not pd.isna(data['K'].iloc[-1]) else 50
     d_current = data['D'].iloc[-1] if not pd.isna(data['D'].iloc[-1]) else 50
+    pdi_current = data['+DI'].iloc[-1] if not pd.isna(data['+DI'].iloc[-1]) else 0
+    ndi_current = data['-DI'].iloc[-1] if not pd.isna(data['-DI'].iloc[-1]) else 0
+    adx_current = data['ADX'].iloc[-1] if not pd.isna(data['ADX'].iloc[-1]) else 0
+
 
     # 技術面分析
     # 根據台股慣例修改顏色
@@ -1264,6 +1246,15 @@ def update_analysis_text(selected_stock, period):
             ),
             "。"
         ]),
+        html.P([
+            html.Strong("DMI指標："),
+            f"目前+DI ({pdi_current:.1f}) 與 -DI ({ndi_current:.1f})，",
+            html.Span(
+                "呈現多頭趨勢" if pdi_current > ndi_current else "呈現空頭趨勢",
+                style={'color': 'red' if pdi_current > ndi_current else 'green', 'font-weight': 'bold'}
+            ),
+            f"。ADX值為 {adx_current:.1f}，顯示市場趨勢{'強勁' if adx_current > 25 else '不明顯' if adx_current < 20 else '有趨勢'}"
+        ]),
         html.P([
             html.Strong("成交量分析："),
             f"近期成交量{'放大' if recent_volume > volume_avg * 1.2 else '萎縮' if recent_volume < volume_avg * 0.8 else '平穩'}，",