app2.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):
    # 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()