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app.py

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+import numpy as np
+import pandas as pd
+import yfinance as yf
+from sklearn.preprocessing import MinMaxScaler
+from keras.models import Sequential
+from keras.layers import LSTM, Dense, Dropout
+import gradio as gr
+import datetime
+
+# Function to fetch stock data
+def get_stock_data(ticker, period):
+    data = yf.download(ticker, period=period)
+    return data
+
+# Function to prepare the data for LSTM
+def prepare_data(data, time_step=60):
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1, 1))
+    
+    X, y = [], []
+    for i in range(time_step, len(scaled_data)):
+        X.append(scaled_data[i-time_step:i, 0])
+        y.append(scaled_data[i, 0])
+    
+    X, y = np.array(X), np.array(y)
+    X = np.reshape(X, (X.shape[0], X.shape[1], 1))
+    
+    return X, y, scaler
+
+# Function to build and train LSTM model
+def train_lstm_model(X_train, y_train):
+    model = Sequential()
+    model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
+    model.add(Dropout(0.2))
+    model.add(LSTM(units=50, return_sequences=False))
+    model.add(Dropout(0.2))
+    model.add(Dense(units=1))
+    
+    model.compile(optimizer='adam', loss='mean_squared_error')
+    model.fit(X_train, y_train, epochs=10, batch_size=32)
+    
+    return model
+
+# Function to predict stock prices
+def predict_stock(model, data, scaler, time_step=60):
+    inputs = scaler.transform(data['Close'].values.reshape(-1, 1))
+    X_test = []
+    for i in range(time_step, len(inputs)):
+        X_test.append(inputs[i-time_step:i, 0])
+    X_test = np.array(X_test)
+    X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
+    
+    predicted_prices = model.predict(X_test)
+    predicted_prices = scaler.inverse_transform(predicted_prices)
+    
+    return predicted_prices
+
+# Function to fetch all Taiwan listed stocks
+def get_all_taiwan_stocks():
+    # Here you should implement a method to get all Taiwan listed stock tickers
+    # This is a placeholder list of tickers for demonstration purposes
+    return ["2330.TW", "2317.TW", "2303.TW", "2412.TW", "2454.TW"]
+
+# Function to get top 10 potential stocks
+def get_top_10_potential_stocks(period):
+    stock_list = get_all_taiwan_stocks()
+    stock_predictions = []
+    
+    for ticker in stock_list:
+        data = get_stock_data(ticker, period)
+        if data.empty:
+            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]))
+    
+    # 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):
+    # Get top 10 potential stocks
+    top_10_stocks = get_top_10_potential_stocks(period)
+    
+    # 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"])
+    
+    return df
+
+# Define Gradio interface
+inputs = [
+    gr.inputs.Dropdown(choices=["1mo", "3mo", "6mo", "9mo", "1yr"], label="時間範圍")
+]
+outputs = gr.outputs.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")\
+    .launch()