Update app.py

app.py

@@ -15,24 +15,6 @@ def download_data(tickers, start_date, end_date):
             attempts += 1
     raise Exception("Failed to download data after multiple attempts")
 
-def output_results(start_date, end_date, tickers_string):
-    tickers = [ticker.strip() for ticker in tickers_string.split(',')]
-    stocks_df = download_data(tickers, start_date, end_date)['Adj Close']
-
-    mu = expected_returns.mean_historical_return(stocks_df)
-    S = risk_models.sample_cov(stocks_df)
-
-    # Ensure the covariance matrix is symmetric
-    if (S.T != S).any():
-        raise ValueError("Covariance matrix is not symmetric")
-
-    ef = EfficientFrontier(mu, S)
-    weights = ef.max_sharpe()
-    cleaned_weights = ef.clean_weights()
-    performance = ef.portfolio_performance(verbose=True)
-
-    return weights, performance
-
 def plot_efficient_frontier_custom(mu, S):
     ef = EfficientFrontier(mu, S)
     fig = go.Figure()
@@ -47,6 +29,27 @@ def plot_efficient_frontier_custom(mu, S):
     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)
+
+    # 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
+    
 # Gradio 인터페이스 초기화 및 실행
 # (여기에 Gradio 인터페이스 설정 코드 추가)