Update app.py

app.py

@@ -1,136 +1,49 @@
 import gradio as gr
 import yfinance as yf
-from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices
-from pypfopt import EfficientFrontier
-from pypfopt import risk_models
-from pypfopt import expected_returns
-from pypfopt import plotting
-import copy
-import numpy as np
+from pypfopt import EfficientFrontier, risk_models, expected_returns
 import pandas as pd
-import plotly.express as px
-import matplotlib.pyplot as plt
+import plotly.graph_objects as go
 from datetime import datetime
-from prophet import Prophet
 
-def predict_future_prices(ticker, periods=1825):  # 5년간의 데이터 예측
-    data = yf.download(ticker, start="2010-01-01")['Adj Close'].reset_index()
-    data.rename(columns={'Date': 'ds', 'Adj Close': 'y'}, inplace=True)
-    
-    model = Prophet(daily_seasonality=False, weekly_seasonality=False, yearly_seasonality=True)
-    model.fit(data)
+def plot_efficient_frontier_custom(mu, S):
+    ef = EfficientFrontier(mu, S)
+    fig = go.Figure()
     
-    future = model.make_future_dataframe(periods=periods, freq='D')
-    forecast = model.predict(future)
+    # 효율적 투자선 그리기
+    weights = ef.efficient_frontier()
+    rets, stds = [], []
+    for weight in weights:
+        ef.set_weights(weight)
+        performance = ef.portfolio_performance()
+        rets.append(performance[0])
+        stds.append(performance[1])
+    fig.add_trace(go.Scatter(x=stds, y=rets, mode='lines', name='Efficient Frontier'))
+
+    # 최대 샤프 비율 포인트
+    ef.max_sharpe()
+    ret_sharpe, std_sharpe, _ = ef.portfolio_performance()
+    fig.add_trace(go.Scatter(x=[std_sharpe], y=[ret_sharpe], mode='markers', marker=dict(color='red', size=14), name='Max Sharpe Ratio'))
     
-    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']]
-
-
-
-def plot_cum_returns(data, title, initial_capital=1000):
-    # 일일 누적 수익률 계산 및 시각화
-    daily_cum_returns = (1 + data.dropna().pct_change()).cumprod() * initial_capital
-    fig = px.line(daily_cum_returns, title=title)
+    fig.update_layout(title='Efficient Frontier Plot', xaxis_title='Standard Deviation', yaxis_title='Return', legend_title='Legend')
     return fig
 
-def plot_efficient_frontier_and_max_sharpe(mu, S):
-    # 최대 샤프 비율로 포트폴리오 최적화 및 효율적 투자선 그리기
-    ef = EfficientFrontier(mu, S)
-    fig, ax = plt.subplots(figsize=(6, 4))
-    ef_max_sharpe = copy.deepcopy(ef)
-    plotting.plot_efficient_frontier(ef, ax=ax, show_assets=False)
-    ef_max_sharpe.max_sharpe(risk_free_rate=0.02)
-    ret_tangent, std_tangent, _ = ef_max_sharpe.portfolio_performance()
-    ax.scatter(std_tangent, ret_tangent, marker="*", s=100, c="r", label="Max Sharpe")
-    ax.legend()
-    return fig
-
-def plot_weights(weights):
-    # 포트폴리오 최적 투자 비율 그래프 출력
-    labels = weights.keys()
-    sizes = weights.values()
-    fig, ax = plt.subplots()
-    ax.pie(sizes, labels=labels, autopct='%1.1f%%')
-    ax.axis('equal')
-    return fig
 def output_results(start_date, end_date, tickers_string):
     tickers = tickers_string.split(',')
     stocks_df = yf.download(tickers, start=start_date, end=end_date)['Adj Close']
-
-    tickers_info = {}
-    news_data = {}
-    for ticker in tickers:
-        ticker_obj = yf.Ticker(ticker)
-        tickers_info[ticker] = ticker_obj.info
-        news_data[ticker] = ticker_obj.news
-
-    # 결과 데이터 생성
-    fig_indiv_prices = px.line(stocks_df, title='개별 주식 가격')
-    daily_returns = stocks_df.pct_change(fill_method=None).dropna()
-    daily_cum_returns = (1 + daily_returns).cumprod() * 1000
-    fig_cum_returns = px.line(daily_cum_returns, title='개별 주식의 누적 수익률 ($1,000 시작)')
-    corr_df = stocks_df.corr().round(2)
-    fig_corr = px.imshow(corr_df, text_auto=True, title='주식 간 상관 관계')
-
     mu = expected_returns.mean_historical_return(stocks_df)
     S = risk_models.sample_cov(stocks_df)
-    ef = EfficientFrontier(mu, S)
-    weights = ef.max_sharpe(risk_free_rate=0.02)
-    cleaned_weights = ef.clean_weights()
-    fig_weights = plot_weights(cleaned_weights)
-    expected_annual_return, annual_volatility, sharpe_ratio = ef.portfolio_performance()
-    fig_efficient_frontier = plot_efficient_frontier_and_max_sharpe(mu, S)
-
-    # 티커 정보와 뉴스를 하나의 문자열로 조합
-    ticker_info_output = "\n\n".join([
-        f"{ticker} ({tickers_info[ticker].get('longName', 'N/A')}, {tickers_info[ticker].get('sector', '정보 없음')})\n" +
-        "\n".join([f"뉴스: {news['title']} - {news['link']}" for news in news_data[ticker][:3]])
-        for ticker in tickers
-    ])
 
-    return fig_cum_returns, fig_efficient_frontier, fig_corr, fig_indiv_prices, fig_weights, \
-           f"{expected_annual_return*100:.2f}%", f"{annual_volatility*100:.2f}%", f"{sharpe_ratio:.2f}", ticker_info_output
+    fig_efficient_frontier = plot_efficient_frontier_custom(mu, S)
+    return fig_efficient_frontier
 
+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")
+    
+    fig_efficient_frontier = gr.Plot(label="Efficient Frontier")
+    btn.click(fn=output_results, inputs=[start_date, end_date, tickers_string], outputs=fig_efficient_frontier)
 
-css = """footer { visibility: hidden; }"""
-
-with gr.Blocks(css=css) as app:
-    gr.Markdown("""
-    <style>
-    .markdown-text h2 {
-        font-size: 18px;  # 폰트 크기를 18px로 설정
-    }
-    </style>
-    <h2>AIQ 자산 포트폴리오: 글로벌 자산(주식, 지수, BTC, 상품 등) AI 포트폴리오 최적화 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.Tabs():
-        with gr.TabItem("Portfolio Analysis"):
-            with gr.Row():
-                start_date = gr.Textbox("2013-01-01", label="시작 일자")
-                end_date = gr.Textbox(datetime.now().date(), label="종료 일자")
-                tickers_string = gr.Textbox("NVDA,^GSPC,GC=F,MSFT,BTC-USD", label="주식 티커를 쉼표로 구분하여 입력하세요")
-                btn = gr.Button("포트폴리오 최적화 결과 보기")
-            with gr.Row():
-                expected_annual_return = gr.Text(label="예상 연간 수익률")
-                annual_volatility = gr.Text(label="연간 변동성")
-                sharpe_ratio = gr.Text(label="샤프 비율")
-            with gr.Column():
-                fig_cum_returns = gr.Plot(label="최적화된 포트폴리오의 누적 수익률 (시작 가격 $1,000)")
-                fig_efficient_frontier = gr.Plot(label="효율적 투자선")
-                fig_corr = gr.Plot(label="주식 간 상관 관계")
-                fig_indiv_prices = gr.Plot(label="개별 주식 가격")
-                fig_weights = gr.Plot(label="포트폴리오 최적 투자 비율")
-                ticker_info_output = gr.Textbox(label="티커 정보 및 뉴스")
-                btn.click(fn=output_results, inputs=[start_date, end_date, tickers_string], outputs=[fig_cum_returns, fig_efficient_frontier, fig_corr, fig_indiv_prices, fig_weights, expected_annual_return, annual_volatility, sharpe_ratio, ticker_info_output])
-
-        with gr.TabItem("Future Price Forecast"):
-            with gr.Column():
-                ticker_input = gr.Textbox(value="AAPL", label="Enter Stock Ticker for Forecast")
-                forecast_button = gr.Button("Generate Forecast")
-                forecast_chart = gr.Plot(label="Forecast Chart")
-                forecast_data = gr.Dataframe()
-                forecast_button.click(fn=predict_future_prices, inputs=[ticker_input], outputs=[forecast_chart, forecast_data])
-
-app.launch()
+app.launch()