method2

app2.py

@@ -0,0 +1,162 @@
+import pandas as pd
+import requests
+import yfinance as yf
+from autogluon.timeseries import TimeSeriesPredictor, TimeSeriesDataFrame
+import gradio as gr
+
+# 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 Chronos-Bolt
+def prepare_data_chronos(data):
+    # Reset index and prepare data
+    df = data.reset_index()
+    
+    # Create a DataFrame in the format expected by AutoGluon TimeSeries
+    formatted_df = pd.DataFrame({
+        'item_id': ['stock'] * len(df),
+        'timestamp': pd.to_datetime(df['Date']),
+        'target': df['Close'].astype('float32').values.ravel()
+    })
+    
+    # Sort by timestamp
+    formatted_df = formatted_df.sort_values('timestamp')
+    
+    try:
+        # Create a TimeSeriesDataFrame without specifying target_column
+        ts_df = TimeSeriesDataFrame.from_data_frame(
+            formatted_df,
+            id_column='item_id',
+            timestamp_column='timestamp'
+        )
+        return ts_df
+    except Exception as e:
+        print(f"Error creating TimeSeriesDataFrame: {str(e)}")
+        raise
+
+# Functions to fetch stock indices
+def get_tw0050_stocks():
+    response = requests.get('https://answerbook.david888.com/TW0050')
+    data = response.json()
+    return [f"{code}.TW" for code in data['TW0050'].keys()]
+
+def get_sp500_stocks(limit=50):
+    response = requests.get('https://answerbook.david888.com/SP500')
+    data = response.json()
+    return list(data['SP500'].keys())[:limit]
+
+def get_nasdaq_stocks(limit=50):
+    response = requests.get('http://13.125.121.198:8090/stocks/NASDAQ100')
+    data = response.json()
+    return list(data['stocks'].keys())[:limit]
+
+def get_tw0051_stocks():
+    response = requests.get('https://answerbook.david888.com/TW0051')
+    data = response.json()
+    return [f"{code}.TW" for code in data['TW0051'].keys()]
+
+def get_sox_stocks():
+    return [
+        "NVDA", "AVGO", "GFS", "CRUS", "ON", "ASML", "QCOM", "SWKS", "MPWR", "ADI",
+        "TSM", "AMD", "TXN", "QRVO", "AMKR", "MU", "ARM", "NXPI", "TER", "ENTG",
+        "LSCC", "COHR", "ONTO", "MTSI", "KLAC", "LRCX", "MRVL", "AMAT", "INTC", "MCHP"
+    ]
+
+def get_dji_stocks():
+    response = requests.get('http://13.125.121.198:8090/stocks/DOWJONES')
+    data = response.json()
+    return list(data['stocks'].keys())
+
+# Function to get top 10 potential stocks
+def get_top_10_potential_stocks(period, selected_indices):
+    stock_list = []
+    if "\u53f0\u706350" in selected_indices:
+        stock_list += get_tw0050_stocks()
+    if "\u53f0\u7063\u4e2d\u578b100" in selected_indices:
+        stock_list += get_tw0051_stocks()
+    if "S&P\u7cbe\u7c21\u724850" in selected_indices:
+        stock_list += get_sp500_stocks()
+    if "NASDAQ\u7cbe\u7c21\u724850" in selected_indices:
+        stock_list += get_nasdaq_stocks()
+    if "\u8cfd\u57ce\u534a\u5b57\u9ad4SOX" in selected_indices:
+        stock_list += get_sox_stocks()
+    if "\u9053\u74b0DJI" in selected_indices:
+        stock_list += get_dji_stocks()
+
+    stock_predictions = []
+    prediction_length = 10
+    
+    for ticker in stock_list:
+        try:
+            data = get_stock_data(ticker, period)
+            if data.empty:
+                continue
+                
+            ts_data = prepare_data_chronos(data)
+            
+            # Create a TimeSeriesPredictor for daily data
+            predictor = TimeSeriesPredictor(
+                prediction_length=prediction_length,
+                freq="1D"
+            )
+            predictor.fit(
+                ts_data,
+                hyperparameters={
+                    "Chronos": {"model_path": "autogluon/chronos-bolt-base"}
+                }
+            )
+            
+            predictions = predictor.predict(ts_data)
+            # Calculate potential as (prediction - last_close) / last_close
+            potential = (predictions.iloc[-1] - data['Close'].iloc[-1]) / data['Close'].iloc[-1]
+            stock_predictions.append((ticker, potential, data['Close'].iloc[-1], predictions.iloc[-1]))
+            
+        except Exception as e:
+            print(f"Stock {ticker} error: {str(e)}")
+            continue
+            
+    # Sort stocks by potential in descending order, take 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, selected_indices):
+    top_10_stocks = get_top_10_potential_stocks(period, selected_indices)
+    df = pd.DataFrame(top_10_stocks, columns=[
+        "\u80a1\u7968\u4ee3\u865f",      # Ticker
+        "\u6f5b\u529b (\u767e\u5206\u6bd4)",  # Potential
+        "\u73fe\u50f9",               # Current Price
+        "\u9810\u6e2c\u50f9\u683c"       # Predicted Price
+    ])
+    return df
+
+# Define Gradio interface
+inputs = [
+    gr.Dropdown(choices=["3mo", "6mo", "9mo", "1yr"], label="\u6642\u9593\u7bc4\u570d"),
+    gr.CheckboxGroup(
+        choices=[
+            "\u53f0\u706350",              # 台灣50
+            "\u53f0\u7063\u4e2d\u578b100",    # 台灣中型100
+            "S&P\u7cbe\u7c21\u724850",     # S&P精簡版50
+            "NASDAQ\u7cbe\u7c21\u724850",  # NASDAQ精簡版50
+            "\u8cfd\u57ce\u534a\u5b57\u9ad4SOX",  # 費城半導體
+            "\u9053\u74b0DJI"             # 道瓊DJI
+        ],
+        label="\u6307\u6578\u9078\u64c7",
+        value=["\u53f0\u706350", "\u53f0\u7063\u4e2d\u578b100"]
+    )
+]
+
+outputs = gr.Dataframe(label="\u6f5b\u529b\u80a1\u63a8\u85a6\u7d50\u679c")
+
+app = gr.Interface(
+    fn=stock_prediction_app,
+    inputs=inputs,
+    outputs=outputs,
+    title="\u53f0\u80a1\u7f8e\u80a1\u6f5b\u529b\u80a1\u63a8\u85a6\u7cfb\u7d71 - Chronos-Bolt\u6a21\u578b"
+)
+
+if __name__ == "__main__":
+    app.launch()