app.py

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):
    # 重設索引並準備數據
    df = data.reset_index()
    
    # 創建符合官方格式的數據框
    formatted_df = pd.DataFrame({
        'item_id': ['stock'] * len(df),
        'timestamp': pd.to_datetime(df['Date']),
        'value': df['Close'].astype('float32').values.ravel()  # 確保是 1-dimensional
    })
    
    # 按照時間戳排序
    formatted_df = formatted_df.sort_values('timestamp')
    
    try:
        # 直接創建 TimeSeriesDataFrame
        ts_df = TimeSeriesDataFrame(formatted_df)
        
        # 打印一些診斷信息
        print("TimeSeriesDataFrame shape:", ts_df.shape)
        print("Value column shape:", ts_df['value'].shape)
        
        return ts_df
    except Exception as e:
        print(f"Error creating TimeSeriesDataFrame: {str(e)}")
        print("Formatted DataFrame info:")
        print(formatted_df.info())
        raise


# def prepare_data_chronos(data):
#     # 直接使用收盤價序列
#     series = pd.Series(
#         data['Close'].values,
#         index=data.index,
#         name='value'
#     )
    
#     # 創建基本的時間序列數據框
#     df = pd.DataFrame({
#         'timestamp': series.index,
#         'value': series.values,
#         'item_id': ['stock'] * len(series)
#     })
    
#     return TimeSeriesDataFrame(df)


# Function to fetch stock indices (you already defined these)
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:
            # 獲取股票數據
            data = get_stock_data(ticker, period)
            if data.empty:
                continue
                
            # 準備數據
            ts_data = prepare_data_chronos(data)
            
            # 創建預測器
            predictor = TimeSeriesPredictor(
                prediction_length=prediction_length,
                target='value'  # 指定目標列名
            )
            
            # 訓練模型
            predictor.fit(
                ts_data,
                hyperparameters={
                    "Chronos": {"model_path": "autogluon/chronos-bolt-base"}
                }
            )
            
            # 進行預測
            predictions = predictor.predict(ts_data)
            
            # 計算潛力
            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
            
    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", "\u6f5b\u529b (\u767e\u5206\u6bd4)", "\u73fe\u50f9", "\u9810\u6e2c\u50f9\u683c"])
    return df

# Define Gradio interface
inputs = [
    gr.Dropdown(choices=["3mo", "6mo", "9mo", "1yr"], label="\u6642\u9593\u7bc4\u570d"),
    gr.CheckboxGroup(choices=["\u53f0\u706350", "\u53f0\u7063\u4e2d\u578b100", "S&P\u7cbe\u7c21\u724850", "NASDAQ\u7cbe\u7c21\u724850", "\u8cfd\u57ce\u534a\u5b57\u9ad4SOX", "\u9053\u74b0DJI"], label="\u6307\u6578\u9078\u64c7", value=["\u53f0\u706350", "\u53f0\u7063\u4e2d\u578b100"])
]
outputs = gr.Dataframe(label="\u6f5b\u529b\u80a1\u63a8\u85a6\u7d50\u679c")

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").launch()