Update app.py

app.py

@@ -1,67 +1,50 @@
 import gradio as gr
 import yfinance as yf
-from pypfopt import EfficientFrontier, risk_models, expected_returns
-import pandas as pd
-import plotly.graph_objects as go
+from prophet import Prophet
+import plotly.express as px
 from datetime import datetime
 
-def download_data(tickers, start_date, end_date):
+def download_data(tickers, start_date='2010-01-01'):
+    """ 데이터를 다운로드하는 함수, 최대 3번 시도 """
     attempts = 0
     while attempts < 3:
         try:
-            return yf.download(tickers, start=start_date, end=end_date)
+            data = yf.download(tickers, start=start_date)
+            if 'Adj Close' in data.columns:
+                return data['Adj Close'].reset_index()
+            return data.reset_index()
         except Exception as e:
             print(f"Download failed on attempt {attempts + 1}: {e}")
             attempts += 1
     raise Exception("Failed to download data after multiple attempts")
 
-def plot_efficient_frontier_custom(mu, S):
-    ef = EfficientFrontier(mu, S)
-    fig = go.Figure()
+def predict_future_prices(ticker):
+    """ Prophet을 사용한 주식 가격 예측 """
+    data = download_data(ticker)
+    data.rename(columns={'Date': 'ds', ticker: 'y'}, inplace=True)
 
-    # 효율적 투자선 생성
-    weights = ef.max_sharpe()
-    cleaned_weights = ef.clean_weights()
-    ret_sharpe, std_sharpe, _ = ef.portfolio_performance(verbose=True)
-    
-    # 투자선 및 최대 샤프 포인트 그래픽 추가
-    fig.add_scatter(x=[std_sharpe], y=[ret_sharpe], mode='markers', marker=dict(color='red', size=10), name='Max Sharpe Ratio')
-    fig.update_layout(title='Efficient Frontier', xaxis_title='Volatility (Std. Deviation)', yaxis_title='Expected Returns', legend_title='Portfolio')
-    return fig
-
-
-
-def output_results(start_date, end_date, tickers_string):
-    tickers = [ticker.strip() for ticker in tickers_string.split(',')]
-    stocks_df = yf.download(tickers, start=start_date, end=end_date)['Adj Close']
-
-    mu = expected_returns.mean_historical_return(stocks_df)
-    S = risk_models.sample_cov(stocks_df)
+    model = Prophet(daily_seasonality=False, yearly_seasonality=True)
+    model.fit(data)
 
-    # 공분산 행렬이 대칭인지 확인
-    if not (S.T == S).all().all():  # 모든 값이 대칭인지 확인
-        raise ValueError("공분산 행렬이 대칭이 아닙니다.")
-
-    ef = EfficientFrontier(mu, S)
-    weights = ef.max_sharpe()
-    cleaned_weights = ef.clean_weights()
-    fig_weights = plot_weights(cleaned_weights)
-    expected_annual_return, annual_volatility, sharpe_ratio = ef.portfolio_performance()
-
-    return fig_weights, expected_annual_return, annual_volatility, sharpe_ratio
+    future = model.make_future_dataframe(periods=5 * 365)  # 향후 5년
+    forecast = model.predict(future)
     
-# Gradio 인터페이스 초기화 및 실행
-# (여기에 Gradio 인터페이스 설정 코드 추가)
-
+    fig = px.line(forecast, x='ds', y='yhat', title=f'5-Year Future Price Forecast for {ticker}')
+    return fig, forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]
 
+# Gradio 인터페이스 설정
 with gr.Blocks() as app:
     with gr.Row():
-        start_date = gr.Textbox("2013-01-01", label="Start Date")
-        end_date = gr.Textbox(datetime.now().date(), label="End Date")
-        tickers_string = gr.Textbox("AAPL,MSFT,AMZN", label="Enter tickers separated by comma")
-        btn = gr.Button("Show Efficient Frontier")
+        ticker_input = gr.Textbox(value="AAPL", label="Enter Stock Ticker")
+        forecast_button = gr.Button("Generate 5-Year Forecast")
+    
+    forecast_chart = gr.Plot(label="Future Price Forecast")
+    forecast_data = gr.Dataframe(label="Forecast Data Table")
     
-    fig_efficient_frontier = gr.Plot(label="Efficient Frontier")
-    btn.click(fn=output_results, inputs=[start_date, end_date, tickers_string], outputs=fig_efficient_frontier)
+    forecast_button.click(
+        fn=predict_future_prices,
+        inputs=ticker_input,
+        outputs=[forecast_chart, forecast_data]
+    )
 
 app.launch()