擴增到美股

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+myenv/*

.gitignore

@@ -0,0 +1,2 @@
+myenv/*
+myenv/**/*

app.py

@@ -55,58 +55,78 @@ def predict_stock(model, data, scaler, time_step=60):
     
     return predicted_prices
 
-# Function to fetch all Taiwan listed stocks (Taiwan 50 and Small 100)
-def get_all_taiwan_stocks():
+# Function to fetch Taiwan 50 and Small 100 stocks
+def get_tw0050_stocks():
     return [
-        "2345.TW", "3017.TW", "2454.TW", "2330.TW", "2395.TW", "2379.TW", "2890.TW", "2891.TW", "2882.TW", "3008.TW",
-        "5871.TW", "2303.TW", "2357.TW", "3661.TW", "2883.TW", "3711.TW", "2308.TW", "2885.TW", "2603.TW", "2881.TW",
-        "4904.TW", "2887.TW", "2880.TW", "2301.TW", "1216.TW", "2884.TW", "2327.TW", "6669.TW", "5880.TW", "1303.TW",
-        "3045.TW", "2382.TW", "2912.TW", "4938.TW", "3231.TW", "2892.TW", "2317.TW", "1590.TW", "3034.TW", "2002.TW",
-        "2412.TW", "2207.TW", "3037.TW", "1301.TW", "1326.TW", "1101.TW", "6446.TW", "2886.TW", "6505.TW", "5876.TW",
-        "3533.TW", "9904.TW", "2618.TW", "5522.TW", "2360.TW", "6005.TW", "3653.TW", "2368.TW", "2474.TW", "6285.TW",
-        "1519.TW", "9958.TW", "3044.TW", "1476.TW", "2324.TW", "2059.TW", "1402.TW", "6176.TW", "1102.TW", "2353.TW",
-        "3665.TW", "2049.TW", "2609.TW", "2478.TW", "9945.TW", "1216.TW", "8464.TW", "3702.TW", "2801.TW", "6526.TW",
-        "2845.TW", "2834.TW", "2610.TW", "4763.TW", "4958.TW", "9941.TW", "6239.TW", "2915.TW", "9917.TW", "2376.TW",
-        "1451.TW", "2313.TW", "3051.TW", "9914.TW", "1477.TW", "2377.TW", "2206.TW", "1504.TW", "2912.TW", "6409.TW",
-        "1560.TW", "1503.TW", "2615.TW", "3005.TW", "2204.TW", "3532.TW", "2888.TW", "2449.TW", "6789.TW", "3481.TW",
-        "2409.TW", "2385.TW", "3406.TW", "2352.TW", "2207.TW", "2347.TW", "6531.TW", "9910.TW", "2371.TW", "2356.TW",
-        "2492.TW", "1718.TW", "6890.TW", "8454.TW", "2633.TW", "1802.TW", "2006.TW", "2542.TW", "1513.TW", "1907.TW",
-        "1722.TW", "2809.TW", "1319.TW", "4137.TW", "2388.TW", "2812.TW", "2540.TW", "3035.TW", "2354.TW", "2027.TW",
-        "1229.TW", "2105.TW", "2408.TW", "5269.TW", "2344.TW", "3443.TW", "6415.TW", "9921.TW", "3036.TW", "6592.TW",
-        "6472.TW", "3023.TW", "6770.TW", "1795.TW", "2201.TW", "1605.TW", "8046.TW", "2312.TW", "2359.TW", "2337.TW"
+        "2330.TW", "2317.TW", "2454.TW", "2308.TW", "2881.TW", "2382.TW", "2303.TW", "2882.TW", "2891.TW", "3711.TW",
+        "2412.TW", "2886.TW", "2884.TW", "1216.TW", "2357.TW", "2885.TW", "2892.TW", "3034.TW", "2890.TW", "2327.TW",
+        "5880.TW", "2345.TW", "3231.TW", "2002.TW", "2880.TW", "3008.TW", "2883.TW", "1303.TW", "4938.TW", "2207.TW",
+        "2887.TW", "2379.TW", "1101.TW", "2603.TW", "2301.TW", "1301.TW", "5871.TW", "3037.TW", "3045.TW", "2912.TW",
+        "3017.TW", "6446.TW", "4904.TW", "3661.TW", "6669.TW", "1326.TW", "5876.TW", "2395.TW", "1590.TW", "6505.TW"
+    ]
+
+def get_tw0051_stocks():
+    return [
+        "2371.TW", "3533.TW", "2618.TW", "3443.TW", "2347.TW", "3044.TW", "2834.TW", "2385.TW", "1605.TW", "2105.TW",
+        "6239.TW", "6176.TW", "9904.TW", "1519.TW", "9910.TW", "1513.TW", "1229.TW", "9945.TW", "2313.TW", "1477.TW",
+        "3665.TW", "2354.TW", "4958.TW", "8464.TW", "9921.TW", "2812.TW", "2059.TW", "1504.TW", "2542.TW", "6770.TW",
+        "5269.TW", "2344.TW", "3023.TW", "1503.TW", "2049.TW", "2610.TW", "2633.TW", "3036.TW", "2368.TW", "3035.TW",
+        "2027.TW", "9914.TW", "2408.TW", "2809.TW", "1319.TW", "2352.TW", "2337.TW", "2006.TW", "2206.TW", "4763.TW",
+        "3005.TW", "1907.TW", "2915.TW", "1722.TW", "6285.TW", "6472.TW", "6531.TW", "3406.TW", "9958.TW", "9941.TW",
+        "1795.TW", "2201.TW", "9917.TW", "2492.TW", "6890.TW", "2845.TW", "8454.TW", "8046.TW", "6789.TW", "2388.TW",
+        "6526.TW", "1802.TW", "5522.TW", "6592.TW", "2204.TW", "2540.TW", "2539.TW", "3532.TW"
     ]
 
 # Function to get top 10 potential stocks
-def get_top_10_potential_stocks(period):
-    stock_list = get_all_taiwan_stocks()
+def get_top_10_potential_stocks(period, selected_indices):
+    stock_list = []
+    if "tw0050台灣50" in selected_indices:
+        stock_list += get_tw0050_stocks()
+    if "tw0051中型100" in selected_indices:
+        stock_list += get_tw0051_stocks()
+    if "S&P" in selected_indices:
+        stock_list.append("^GSPC")
+    if "NASDAQ" in selected_indices:
+        stock_list.append("^IXIC")
+    if "費城半導體" in selected_indices:
+        stock_list.append("^SOX")
+    if "道瓊" in selected_indices:
+        stock_list.append("^DJI")
+
     stock_predictions = []
-    
+    time_step = 60
+
     for ticker in stock_list:
         data = get_stock_data(ticker, period)
-        if data.empty:
+        if data.empty or len(data) < time_step:
+            # 如果數據為空或不足以生成訓練樣本，則跳過該股票
             continue
-        
-        # Prepare data
-        X_train, y_train, scaler = prepare_data(data)
-        
-        # Train model
-        model = train_lstm_model(X_train, y_train)
-        
-        # Predict future prices
-        predicted_prices = predict_stock(model, data, scaler)
-        
-        # Calculate the potential (e.g., last predicted price vs last actual price)
-        potential = (predicted_prices[-1] - data['Close'].values[-1]) / data['Close'].values[-1]
-        stock_predictions.append((ticker, potential, data['Close'].values[-1], predicted_prices[-1][0]))
-    
+
+        try:
+            # Prepare data
+            X_train, y_train, scaler = prepare_data(data, time_step=time_step)
+
+            # Train model
+            model = train_lstm_model(X_train, y_train)
+
+            # Predict future prices
+            predicted_prices = predict_stock(model, data, scaler, time_step=time_step)
+
+            # Calculate the potential (e.g., last predicted price vs last actual price)
+            potential = (predicted_prices[-1] - data['Close'].values[-1]) / data['Close'].values[-1]
+            stock_predictions.append((ticker, potential, data['Close'].values[-1], predicted_prices[-1][0]))
+        except Exception as e:
+            print(f"股票 {ticker} 發生錯誤: {str(e)}")
+            continue
+
     # Sort by potential and get top 10
     top_10_stocks = sorted(stock_predictions, key=lambda x: x[1], reverse=True)[:10]
     return top_10_stocks
 
 # Gradio interface function
-def stock_prediction_app(period):
+def stock_prediction_app(period, selected_indices):
     # Get top 10 potential stocks
-    top_10_stocks = get_top_10_potential_stocks(period)
+    top_10_stocks = get_top_10_potential_stocks(period, selected_indices)
     
     # Create a dataframe for display
     df = pd.DataFrame(top_10_stocks, columns=["股票代號", "潛力 (百分比)", "現價", "預測價格"])
@@ -114,9 +134,11 @@ def stock_prediction_app(period):
     return df
 
 # Define Gradio interface
-inputs = gr.Dropdown(choices=["1mo", "3mo", "6mo", "9mo", "1yr"], label="時間範圍")
+inputs = [
+    gr.Dropdown(choices=["3mo", "6mo", "9mo", "1yr"], label="時間範圍"),
+    gr.CheckboxGroup(choices=["tw0050台灣50", "tw0051中型100", "S&P", "NASDAQ", "費城半導體", "道瓊"], label="指數選擇")
+]
 outputs = gr.Dataframe(label="潛力股推薦結果")
 
-gr.Interface(fn=stock_prediction_app, inputs=inputs, outputs=outputs, title="台股潛力股推薦系統 - LSTM模型")\
+gr.Interface(fn=stock_prediction_app, inputs=inputs, outputs=outputs, title="潛力股推薦系統 - LSTM模型")\
     .launch()
-