Update app.py

app.py

@@ -176,10 +176,9 @@ 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)
 
-    # --- DMI (Directional Movement Index) - 已修正的計算邏輯 ---
-    # 計算正向運動 (+DM) 和負向運動 (-DM)
+    # DMI (Directional Movement Index)
     df['up_move'] = df['High'] - df['High'].shift(1)
-    df['down_move'] = df['Low'].shift(1) - df['Low'] # 修正：將當日低點與前日低點作差
+    df['down_move'] = df['Low'].shift(1) - df['Low']
     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)
 
@@ -199,21 +198,10 @@ def calculate_technical_indicators(df):
 def calculate_volume_profile(df, num_bins=50):
     """
     計算成交量分佈圖 (Volume Profile) 的數據。
-
-    Args:
-        df (pd.DataFrame): 包含 'High', 'Low', 'Volume' 欄位的 DataFrame。
-        num_bins (int): 分割價格區間的數量。
-
-    Returns:
-        tuple: 包含 (bin_edges, volume_per_bin, price_centers) 的 tuple。
-               bin_edges: 每個區間的邊界。
-               volume_per_bin: 每個區間對應的成交量。
-               price_centers: 每個區間的中心價格。
     """
     if df.empty or 'High' not in df.columns or 'Low' not in df.columns or 'Volume' not in df.columns:
         return None, None, None
 
-    # 建立一個包含所有高低點的陣列，用於確定價格範圍
     all_prices = np.concatenate([df['High'].values, df['Low'].values])
     min_price = all_prices.min()
     max_price = all_prices.max()
@@ -221,15 +209,9 @@ def calculate_volume_profile(df, num_bins=50):
     price_for_volume = (df['High'] + df['Low'] + df['Close']) / 3
     df_vol_profile = df.copy()
     df_vol_profile['Price_Indicator'] = price_for_volume
-    df_vol_profile['Volume'] = df_vol_profile['Volume'] # 確保 Volume 欄位存在
+    df_vol_profile['Volume'] = df_vol_profile['Volume']
 
-    # 創建直方圖來計算成交量分佈
-    # `density=False` 確保我們得到的是實際的成交量總和，而不是密度
-    # `bins=num_bins` 設定價格區間的數量
-    # `range` 設定價格的最小值和最大值
     hist, bin_edges = np.histogram(df_vol_profile['Price_Indicator'], bins=num_bins, range=(min_price, max_price), weights=df_vol_profile['Volume'])
-
-    # 計算每個區間的中心價格
     price_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
 
     return bin_edges, hist, price_centers
@@ -1140,7 +1122,6 @@ def update_analysis_text(selected_stock, period):
 
 
     # 技術面分析
-    # 根據台股慣例修改顏色
     technical_text = html.Div([
         html.P([
             html.Strong("價格趨勢："),
@@ -1229,7 +1210,6 @@ def update_analysis_text(selected_stock, period):
     ])
 
     # 市場展望
-    # 根據台股慣例修改顏色
     if price_change > 10:
         outlook_tone = "謹慎樂觀"
         outlook_color = "#dc3545"
@@ -1285,7 +1265,6 @@ def update_pmi_chart(selected_stock):
         return fig
 
     # 定義PMI顏色 (50以上擴張，以下緊縮)
-    # 根據台股慣例修改顏色
     def get_pmi_color(value):
         return 'red' if value >= 50 else 'green'
 
@@ -1310,7 +1289,6 @@ def update_pmi_chart(selected_stock):
     fig.add_hline(y=50, line_dash="dash", line_color="black", annotation_text="榮枯線(50)")
 
     # 添加背景色區域
-    # 根據台股慣例修改顏色
     fig.add_hrect(
         y0=50, y1=60,
         fillcolor="lightcoral", opacity=0.2,
@@ -1402,7 +1380,6 @@ def update_comparison_analysis(selected_stocks, period):
     if comparison_data:
         table_rows = []
         for item in sorted(comparison_data, key=lambda x: x['return'], reverse=True):
-            # 根據台股慣例修改顏色
             color = 'red' if item['return'] > 0 else 'green'
             table_rows.append(
                 html.Tr([
@@ -1444,7 +1421,6 @@ def update_sentiment_analysis(selected_stock):
     sentiment_score = np.random.uniform(30, 80)  # 模擬情緒分數 0-100
 
     # 建立情緒指標圓形圖
-    # 根據台股慣例修改顏色
     gauge_fig = go.Figure(go.Indicator(
         mode = "gauge+number+delta",
         value = sentiment_score,