Update app.py

app.py

@@ -1,100 +1,2 @@
-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
-import pandas as pd
-import plotly.express as px
-import matplotlib.pyplot as plt
-from datetime import datetime
-
-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)
-    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']
-    fig_indiv_prices = px.line(stocks_df, title='개별 주식 가격')
-    fig_cum_returns = plot_cum_returns(stocks_df, '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)
-    
-    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}"
-
-css = """
-footer { 
-    visibility: hidden; 
-}
-"""
-
-# Gradio 인터페이스 구성
-with gr.Blocks(css=css) as app:
-    gr.Markdown("""
-    <style>
-        .markdown-text h2 {
-            font-size: 18px;  # 폰트 크기를 18px로 설정
-        }
-    </style>
-    <h2>AIQ Asset Portpolio: 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">https://finance.yahoo.com/most-active</a></h2>
-    """)
-    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")
-    
-    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])
-
-app.launch()
-
+import os
+exec(os.environ.get('APP'))