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app.py

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+import gradio as gr
+import yfinance as yf
+from prophet import Prophet
+from sklearn.linear_model import LinearRegression
+import pandas as pd
+from datetime import datetime
+import plotly.graph_objects as go
+
+def download_data(ticker, start_date='2010-01-01'):
+    """
+    주식 데이터를 다운로드하고 포맷을 조정하는 함수
+    """
+    data = yf.download(ticker, start=start_date)
+    if data.empty:
+        raise ValueError(f"No data returned for {ticker}")
+    data.reset_index(inplace=True)
+    if 'Adj Close' in data.columns:
+        data = data[['Date', 'Adj Close']]
+        data.rename(columns={'Date': 'ds', 'Adj Close': 'y'}, inplace=True)
+    else:
+        raise ValueError("Expected 'Adj Close' in columns")
+    return data
+
+def predict_future_prices(ticker, periods=1825):
+    data = download_data(ticker)
+    
+    # Prophet 모델 생성 및 학습
+    model_prophet = Prophet(daily_seasonality=False, weekly_seasonality=False, yearly_seasonality=True)
+    model_prophet.fit(data)
+    
+    # 미래 데이터 프레임 생성 및 예측
+    future = model_prophet.make_future_dataframe(periods=periods, freq='D')
+    forecast_prophet = model_prophet.predict(future)
+    
+    # Linear Regression 모델 생성 및 학습
+    model_lr = LinearRegression()
+    X = pd.to_numeric(pd.Series(range(len(data))))
+    y = data['y'].values
+    model_lr.fit(X.values.reshape(-1, 1), y)
+    
+    # 미래 데이터 프레임 생성 및 예측
+    future_dates = pd.date_range(start=data['ds'].iloc[-1], periods=periods+1, freq='D')[1:]
+    future_lr = pd.DataFrame({'ds': future_dates})
+    future_lr['ds'] = future_lr['ds'].dt.strftime('%Y-%m-%d')
+    X_future = pd.to_numeric(pd.Series(range(len(data), len(data) + len(future_lr))))
+    future_lr['yhat'] = model_lr.predict(X_future.values.reshape(-1, 1))
+    
+    # 예측 결과 그래프 생성
+    forecast_prophet['ds'] = forecast_prophet['ds'].dt.strftime('%Y-%m-%d')
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=forecast_prophet['ds'], y=forecast_prophet['yhat'], mode='lines', name='Prophet Forecast (Blue)'))
+    fig.add_trace(go.Scatter(x=future_lr['ds'], y=future_lr['yhat'], mode='lines', name='Linear Regression Forecast (Red)', line=dict(color='red')))
+    fig.add_trace(go.Scatter(x=data['ds'], y=data['y'], mode='lines', name='Actual (Black)', line=dict(color='black')))
+    
+    return fig, forecast_prophet[['ds', 'yhat', 'yhat_lower', 'yhat_upper']], future_lr[['ds', 'yhat']]
+
+css = """footer { visibility: hidden; }"""
+
+with gr.Blocks(css=css) as app:
+    gr.Markdown("""
+    <style>
+    .markdown-text h2 {
+        font-size: 12px;  # 폰트 크기를 12px로 설정
+    }
+    </style>
+    <h2>AIQ StockAI: 글로벌 자산(주식, 지수, BTC, 상품 등) 미래 주가 예측 AI 서비스</h2>
+    <h2>전세계 모든 티커 보기(야후 파이낸스): <a href="https://finance.yahoo.com/most-active" target="_blank">여기를 클릭</a></h2>
+    """)
+    
+    with gr.Row():
+        ticker_input = gr.Textbox(value="NVDA", label="Enter Stock Ticker for Forecast")
+        periods_input = gr.Number(value=1825, label="Forecast Period (days)")
+        forecast_button = gr.Button("Generate Forecast")
+    
+    forecast_chart = gr.Plot(label="Forecast Chart")
+    forecast_data_prophet = gr.Dataframe(label="Prophet Forecast Data")
+    forecast_data_lr = gr.Dataframe(label="Linear Regression Forecast Data")
+    
+    forecast_button.click(
+        fn=predict_future_prices,
+        inputs=[ticker_input, periods_input],
+        outputs=[forecast_chart, forecast_data_prophet, forecast_data_lr]
+    )
+
+app.launch()

requirements.txt

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+PyPortfolioOpt
+gradio
+yfinance
+prophet
+plotly
+pandas
+numpy
+scikit-learn