Upload 18 files

interface/dollar_cost_averaging_interface copy.py

@@ -0,0 +1,22 @@
+import gradio as gr
+from modules.dollar_cost_averaging import dollar_cost_averaging
+
+examples = [
+    [1000, 9000, 560, 20000]
+]
+# Define the inputs
+title = gr.Markdown("<h2 style='margin: 5px'>Dollar Cost Averaging Calculator</h2>")
+first_purchase = gr.Markdown("<h3 class='h3_title'>FIRST PURCHASE</h3>")
+old_price = gr.Number(label="Old Price", value=18150)
+old_quantity = gr.Number(label="Quantity", value=95)
+second_purchase = gr.Markdown("<h3 class='h3_title'>SECOND PURCHASE</h3>")
+new_price = gr.Number(label="New Price", value=17705)
+new_quantity = gr.Number(label="Quantity", value=0)
+
+input = [old_price, old_quantity, new_price, new_quantity]
+output = gr.HTML()
+component_rows= [first_purchase, old_price, old_quantity, second_purchase, new_price, new_quantity]
+
+# Define the update function
+def update_output(*args):
+    return dollar_cost_averaging(*args)

interface/dollar_cost_averaging_interface.py

@@ -2,20 +2,21 @@ import gradio as gr
 from modules.dollar_cost_averaging import dollar_cost_averaging
 
 examples = [
-    [1000, 9000, 560, 20000]
+    ["AAPL", 200, 9000, 560, 20000]
 ]
+
 # Define the inputs
 title = gr.Markdown("<h2 style='margin: 5px'>Dollar Cost Averaging Calculator</h2>")
+stock_code = gr.Textbox(label="Stock Code", value="368590")
 first_purchase = gr.Markdown("<h3 class='h3_title'>FIRST PURCHASE</h3>")
 old_price = gr.Number(label="Old Price", value=18150)
 old_quantity = gr.Number(label="Quantity", value=95)
 second_purchase = gr.Markdown("<h3 class='h3_title'>SECOND PURCHASE</h3>")
-new_price = gr.Number(label="New Price", value=17705)
-new_quantity = gr.Number(label="Quantity", value=0)
+new_quantity = gr.Slider(label="Quantity", value=0)
 
-input = [old_price, old_quantity, new_price, new_quantity]
+input = [stock_code, old_price, old_quantity, new_quantity]
 output = gr.HTML()
-component_rows= [first_purchase, old_price, old_quantity, second_purchase, new_price, new_quantity]
+component_rows = [stock_code, first_purchase, old_price, old_quantity, second_purchase, new_quantity]
 
 # Define the update function
 def update_output(*args):

interface/retirement_planning_interface.py

@@ -9,14 +9,14 @@ examples = [
 # Define the input components
 title = gr.Markdown("<h2 style='margin: 5px'>Retirement Planning</h2>")
 title_profile = gr.Markdown("<h3 class='h3_title'>PROFILE</h3>")
-current_age = gr.Slider(label="Current Age (15-60 Years)", value=28, minimum=15, maximum=60, step=1)
+current_age = gr.Slider(label="Current Age (15-60 Years)", value=38, minimum=15, maximum=60, step=1)
 retirement_age = gr.Slider(label="Retirement Age (Upto 70 Years)", value=55, minimum=15, maximum=70, step=1)
 life_expectancy = gr.Slider(label="Life Expectancy (Upto 100 Years)", value=80, minimum=30, maximum=100, step=1)
 title_savings = gr.Markdown("<h3 class='h3_title'>SAVINGS</h3>")
 monthly_income_required = gr.Number(label="Monthly Income Required (CPP)", value=2000000)
 inflation_rate = gr.Number(label="Inflation Rate (%)", value=3)
 current_investment = gr.Number(label="Current Investment", value=10000000)
-monthly_investment = gr.Number(label="Monthly Investment", value=500000)
+monthly_investment = gr.Number(label="Monthly Investment", value=1000000)
 annual_increase_in_monthly_investment = gr.Number(label="Annual Increase in Monthly Investment", value=0)  # 추가된 입력
 reinvest_dividends = gr.Checkbox(label="Reinvest Dividends", value=True)
 title_growth = gr.Markdown("<h3 class='h3_title'>GROWTH</h3>")

interface/share_price_trend_interface.py

@@ -1,5 +1,5 @@
 import gradio as gr
-from modules.share_price_trend import share_price_trend
+from modules.share_price_trend import share_price_trend, gradio_interface
 
 examples = [
     ["AAPL,GOOGL,MSFT", 90],
@@ -17,14 +17,14 @@ stock_codes = gr.Textbox(
 period = gr.Slider(
     label="Number of Days",
     value=90,
-    maximum=3650
+    minimum=7
 )
 
 # Define output component
 input = [stock_codes, period]
-output = gr.HTML()
+output = gr.Plot()
 component_rows = [[stock_codes], [period]]
 
 # Define the update function
 def update_output(*args):
-    return share_price_trend(*args)
+    return gradio_interface(*args)

modules/dollar_cost_averaging copy.py

@@ -0,0 +1,103 @@
+from modules.utils import load_css
+
+def calculate_dollar_cost_averaging(old_avg_price, old_quantity, new_price, new_quantity):
+    # 입력값을 숫자로 변환 (각 입력값이 None일 경우 0.0으로 설정)
+    old_avg_price = float(old_avg_price) if old_avg_price else 0.0
+    old_quantity = float(old_quantity) if old_quantity else 0.0
+    new_price = float(new_price) if new_price else 0.0
+    new_quantity = float(new_quantity) if new_quantity else 0.0
+
+    # 현재 투자 금액 계산 (이전 평균 가격 * 이전 수량)
+    current_investment = old_avg_price * old_quantity
+    # 추가 투자 금액 계산 (새 가격 * 새 수량)
+    additional_investment = new_price * new_quantity
+    # 총 투자 금액 계산 (현재 투자 금액 + 추가 투자 금액)
+    total_investment = current_investment + additional_investment
+    # 총 주식 수 계산 (이전 수량 + 새 수량)
+    total_quantity = old_quantity + new_quantity
+    # 새 평균 가격 계산 (총 투자 금액 / 총 주식 수)
+    new_avg_price = total_investment / total_quantity if total_quantity != 0 else 0.0
+    # 이전 수익률 계산 (이전 평균 가격을 기준으로)
+    old_return = (new_price / old_avg_price - 1) * 100 if old_avg_price != 0 else 0.0
+    # 새로운 수익률 계산 (새 가격 / 새 평균 가격 - 1) * 100
+    new_return = (new_price / new_avg_price - 1) * 100 if new_avg_price != 0 else 0.0
+    
+    # 새 평균 가격, 총 수량, 총 투자 금액, 새로운 수익률, 추가 투자 금액, 이전 수익률 반환
+    return new_avg_price, total_quantity, total_investment, new_return, additional_investment, old_return
+
+def dollar_cost_averaging(old_avg_price, old_quantity, new_price, new_quantity):
+    css = load_css()
+
+    # 입력값을 숫자로 변환
+    old_avg_price = float(old_avg_price) if old_avg_price else 0.0
+    old_quantity = float(old_quantity) if old_quantity else 0.0
+    new_price = float(new_price) if new_price else 0.0
+    new_quantity = float(new_quantity) if new_quantity else 0.0
+
+    # 평균 가격, 총 수량, 총 투자 금액, 수익률 및 추가 투자 금액 계산
+    new_avg_price, total_quantity, total_investment, new_return, additional_investment, old_return = calculate_dollar_cost_averaging(old_avg_price, old_quantity, new_price, new_quantity)
+
+    # 수익률에 따른 클래스 설정
+    emoji_return = ""
+    new_return_class = ""
+    old_return_class = ""
+    if new_return > old_return:
+        emoji_return = "💧"
+    elif new_return < old_return:
+        emoji_return = "🔥"
+    else: 
+        emoji_return = ""
+
+    if new_return > 0:
+        new_return_class = f"<span style='color: #4caf50;'>{new_return:+,.2f}%</span>"
+    elif new_return < 0:
+        new_return_class = f"<span style='color: #f44336;'>{new_return:,.2f}%</span>"
+    else:
+        new_return_class = f"<span><strong>0</strong></span>"
+
+    if old_return > 0:
+        old_return_class = f"<span style='color: #4caf50;'>{old_return:+,.2f}%</span>"
+    elif old_return < 0:
+        old_return_class = f"<span style='color: #f44336;'>{old_return:,.2f}%</span>"
+    else:
+        old_return_class = f"<span><strong>0</strong></span>"
+
+    # HTML 결과 생성
+    result_html = css + f"""
+    <div class="wrap-text">
+        <div>
+            <div style="margin-bottom: 1.5rem;">
+                <!-- 이전 수익률 표시 -->
+                <div style="font-size: 1.5rem; margin-top: 1.5rem; margin-bottom: 1.5rem;">Old Price</div>
+                <div style="font-size: 1.5rem; color: #1c75bc;">
+                    <span style='color: #1678fb; font-weight: bold;'>{old_avg_price:,.0f}</span>
+                    <span style='font-size: 1rem;'>{old_return_class}</span>
+                </div>
+                <hr style="margin: 1.5rem 0;">
+            </div>
+        </div>    
+            <div>
+            <div style="margin-bottom: 1.5rem;">
+                <!-- 새로운 수익률 표시 -->
+                <div style="font-size: 1.5rem; margin-top: 1.5rem; margin-bottom: 1.5rem;">Average Price</div>
+                <div style="font-size: 1.5rem; color: #1c75bc;">
+                    <span style='color: #1678fb; font-weight: bold;'>{new_avg_price:,.0f}</span>
+                    <span style='font-size: 1rem;'>{new_return_class}</span>
+                </div>
+                <hr style="margin: 1.5rem 0;">
+            </div>
+        </div>    
+        <div>
+            <div style="margin-bottom: 1.5rem;">
+                <!-- 추가 투자 금액 표시 -->
+                <div style="font-size: 1.5rem; margin-top: 1.5rem; margin-bottom: 1.5rem;">Additional Investment</div>
+                <div style="font-size: 1.5rem; font-weight: bold; color: #1c75bc;">
+                    <span style='color: #1678fb'>{additional_investment:,.0f}</span>
+                </div>
+                <hr style="margin: 1.5rem 0;">
+            </div>
+        </div>
+
+    </div>
+    """
+    return result_html

modules/dollar_cost_averaging.py

@@ -1,43 +1,47 @@
+import yfinance as yf
+import FinanceDataReader as fdr
 from modules.utils import load_css
 
+def get_stock_price(stock_code):
+    try:
+        df = fdr.DataReader(stock_code)
+        return df['Close'].iloc[-1]
+    except Exception as e:
+        # 에러 발생 시 0.0으로 설정하고, 오류 메시지를 반환
+        return None
+
 def calculate_dollar_cost_averaging(old_avg_price, old_quantity, new_price, new_quantity):
-    # 입력값을 숫자로 변환 (각 입력값이 None일 경우 0.0으로 설정)
     old_avg_price = float(old_avg_price) if old_avg_price else 0.0
     old_quantity = float(old_quantity) if old_quantity else 0.0
     new_price = float(new_price) if new_price else 0.0
     new_quantity = float(new_quantity) if new_quantity else 0.0
 
-    # 현재 투자 금액 계산 (이전 평균 가격 * 이전 수량)
+    if old_quantity == 0 and new_quantity == 0:
+        return 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
+
     current_investment = old_avg_price * old_quantity
-    # 추가 투자 금액 계산 (새 가격 * 새 수량)
     additional_investment = new_price * new_quantity
-    # 총 투자 금액 계산 (현재 투자 금액 + 추가 투자 금액)
     total_investment = current_investment + additional_investment
-    # 총 주식 수 계산 (이전 수량 + 새 수량)
     total_quantity = old_quantity + new_quantity
-    # 새 평균 가격 계산 (총 투자 금액 / 총 주식 수)
     new_avg_price = total_investment / total_quantity if total_quantity != 0 else 0.0
-    # 이전 수익률 계산 (이전 평균 가격을 기준으로)
     old_return = (new_price / old_avg_price - 1) * 100 if old_avg_price != 0 else 0.0
-    # 새로운 수익률 계산 (새 가격 / 새 평균 가격 - 1) * 100
     new_return = (new_price / new_avg_price - 1) * 100 if new_avg_price != 0 else 0.0
-    
-    # 새 평균 가격, 총 수량, 총 투자 금액, 새로운 수익률, 추가 투자 금액, 이전 수익률 반환
+
     return new_avg_price, total_quantity, total_investment, new_return, additional_investment, old_return
 
-def dollar_cost_averaging(old_avg_price, old_quantity, new_price, new_quantity):
+def dollar_cost_averaging(stock_code, old_avg_price, old_quantity, new_quantity):
     css = load_css()
-
-    # 입력값을 숫자로 변환
+    
+    # 주식 가격 가져오기
+    new_price = get_stock_price(stock_code)
+    
+    new_price = float(new_price) if new_price else 0.0
     old_avg_price = float(old_avg_price) if old_avg_price else 0.0
     old_quantity = float(old_quantity) if old_quantity else 0.0
-    new_price = float(new_price) if new_price else 0.0
     new_quantity = float(new_quantity) if new_quantity else 0.0
 
-    # 평균 가격, 총 수량, 총 투자 금액, 수익률 및 추가 투자 금액 계산
     new_avg_price, total_quantity, total_investment, new_return, additional_investment, old_return = calculate_dollar_cost_averaging(old_avg_price, old_quantity, new_price, new_quantity)
 
-    # 수익률에 따른 클래스 설정
     emoji_return = ""
     new_return_class = ""
     old_return_class = ""
@@ -45,7 +49,7 @@ def dollar_cost_averaging(old_avg_price, old_quantity, new_price, new_quantity):
         emoji_return = "💧"
     elif new_return < old_return:
         emoji_return = "🔥"
-    else: 
+    else:
         emoji_return = ""
 
     if new_return > 0:
@@ -62,12 +66,19 @@ def dollar_cost_averaging(old_avg_price, old_quantity, new_price, new_quantity):
     else:
         old_return_class = f"<span><strong>0</strong></span>"
 
-    # HTML 결과 생성
     result_html = css + f"""
     <div class="wrap-text">
         <div>
             <div style="margin-bottom: 1.5rem;">
-                <!-- 이전 수익률 표시 -->
+                <div style="font-size: 1.5rem; margin-top: 1.5rem; margin-bottom: 1.5rem;">New Price</div>
+                <div style="font-size: 1.5rem; color: #1c75bc;">
+                    <span style='color: #1678fb; font-weight: bold;'>{new_price:,}</span>
+                </div>
+                <hr style="margin: 1.5rem 0;">
+            </div>
+        </div>    
+        <div>
+            <div style="margin-bottom: 1.5rem;">
                 <div style="font-size: 1.5rem; margin-top: 1.5rem; margin-bottom: 1.5rem;">Old Price</div>
                 <div style="font-size: 1.5rem; color: #1c75bc;">
                     <span style='color: #1678fb; font-weight: bold;'>{old_avg_price:,.0f}</span>
@@ -75,10 +86,9 @@ def dollar_cost_averaging(old_avg_price, old_quantity, new_price, new_quantity):
                 </div>
                 <hr style="margin: 1.5rem 0;">
             </div>
-        </div>    
-            <div>
+        </div>            
+        <div>
             <div style="margin-bottom: 1.5rem;">
-                <!-- 새로운 수익률 표시 -->
                 <div style="font-size: 1.5rem; margin-top: 1.5rem; margin-bottom: 1.5rem;">Average Price</div>
                 <div style="font-size: 1.5rem; color: #1c75bc;">
                     <span style='color: #1678fb; font-weight: bold;'>{new_avg_price:,.0f}</span>
@@ -89,7 +99,6 @@ def dollar_cost_averaging(old_avg_price, old_quantity, new_price, new_quantity):
         </div>    
         <div>
             <div style="margin-bottom: 1.5rem;">
-                <!-- 추가 투자 금액 표시 -->
                 <div style="font-size: 1.5rem; margin-top: 1.5rem; margin-bottom: 1.5rem;">Additional Investment</div>
                 <div style="font-size: 1.5rem; font-weight: bold; color: #1c75bc;">
                     <span style='color: #1678fb'>{additional_investment:,.0f}</span>
@@ -97,7 +106,6 @@ def dollar_cost_averaging(old_avg_price, old_quantity, new_price, new_quantity):
                 <hr style="margin: 1.5rem 0;">
             </div>
         </div>
-
     </div>
     """
     return result_html

modules/rebalancing.py

@@ -261,6 +261,6 @@ def rebalancing_tool(main_currency, holdings, cash_amount, allow_selling):
         portfolio_info = generate_portfolio_info(portfolio, total_value, main_currency)
         rebalancing_analysis = generate_rebalancing_analysis(portfolio, target_ratios, total_value, main_currency, cash_amount, allow_selling)
 
-        return portfolio_info + rebalancing_analysis
+        return rebalancing_analysis
     except Exception as e:
         return f"<p style='color: red;'>An error occurred: {e}</p>"

modules/share_price_trend.py

@@ -1,7 +1,8 @@
 import io
-import matplotlib.pyplot as plt
-import FinanceDataReader as fdr
 import pandas as pd
+import FinanceDataReader as fdr
+import plotly.graph_objects as go
+import gradio as gr
 from concurrent.futures import ThreadPoolExecutor, as_completed
 
 def get_stock_prices(stock_code, days):
@@ -32,97 +33,35 @@ def share_price_trend(stock_codes, days):
                 print(f"<p style='color: red;'>Failed to fetch data for {stock_code}: {e}</p>")
 
     if not stock_prices:
-        return "<p style='color: red;'>No data available for the provided stock codes.</p>"
+        return "No data available for the provided stock codes."
 
-    plt.switch_backend('agg')
-    plt.style.use('tableau-colorblind10')
+    fig = go.Figure()
 
-    fig, ax = plt.subplots(figsize=(8, 4.5))
     for stock_code, prices in stock_prices.items():
         relative_prices = prices / prices.iloc[0]
-        ax.plot(prices.index, relative_prices, label=stock_code.upper())
+        fig.add_trace(go.Scatter(
+            x=relative_prices.index,
+            y=relative_prices,
+            mode='lines',
+            name=stock_code.upper(),
+            hoverinfo='text',
+            hovertext=[f'{stock_code.upper()}: {date.strftime("%Y-%m-%d")}: {price:.2f}' for date, price in zip(prices.index, prices)]
+        ))
 
-    # Remove the axes and ticks
-    ax.spines['top'].set_visible(False)
-    ax.spines['right'].set_visible(False)
-    ax.spines['left'].set_visible(False)
-    ax.spines['bottom'].set_visible(False)
-    
-    ax.xaxis.set_visible(False)
-    ax.yaxis.set_visible(False)
+    fig.update_layout(
+        title=f'Relative Stock Prices Over the Last {days} Days',
+        xaxis_title='Date',
+        yaxis_title='Relative Price',
+        showlegend=True,
+        xaxis=dict(showline=False, showgrid=False),
+        yaxis=dict(showline=False, showgrid=False),
+        hovermode='x',
+        margin=dict(l=0, r=0, t=0, b=0),  # Adjust margins to ensure the plot fits well
+        autosize=True
+    )
     
-    # Add grid for better readability
-    ax.grid(True, which='both', linestyle='--', linewidth=0.5)
-    
-    ax.legend()
-    plt.tight_layout()
-
-    svg_graph = io.StringIO()
-    plt.savefig(svg_graph, format='svg')
-    svg_graph.seek(0)
-    svg_data = svg_graph.getvalue()
-    plt.close()
-
-    svg_data = svg_data.replace('<svg ', '<svg width="100%" height="100%" ')
-    svg_data = svg_data.replace('</svg>', '''
-    <defs>
-        <linearGradient id="grad1" x1="0%" y1="0%" x2="100%" y2="0%">
-            <stop offset="0%" style="stop-color:rgb(173,216,230);stop-opacity:1" />
-            <stop offset="100%" style="stop-color:rgb(0,191,255);stop-opacity:1" />
-        </linearGradient>
-        <filter id="dropshadow" height="130%">
-            <feGaussianBlur in="SourceAlpha" stdDeviation="3"/>
-            <feOffset dx="2" dy="2" result="offsetblur"/>
-            <feMerge> 
-                <feMergeNode/>
-                <feMergeNode in="SourceGraphic"/> 
-            </feMerge>
-        </filter>
-    </defs>
-    <style>
-        @keyframes lineAnimation {
-            from {
-                stroke-dasharray: 0, 1000;
-            }
-            to {
-                stroke-dasharray: 1000, 0;
-            }
-        }
-        path {
-            animation: lineAnimation 1s linear forwards;
-        }
-    </style>
-    </svg>''')
-
-    svg_data = svg_data.replace('stroke="#1f77b4"', 'stroke="url(#grad1)" filter="url(#dropshadow)"')
-
-    html_table = "<h3>Stock Prices Data</h3><div class='table-container'><table>"
-    html_table += "<thead><tr><th>Date</th>"
-    for stock_code in stock_prices.keys():
-        html_table += f"<th>{stock_code.upper()}</th>"
-    html_table += "</tr></thead><tbody>"
-
-    # Create a date range with only business days
-    all_dates = pd.date_range(start=min(df.index.min() for df in stock_prices.values()), end=pd.to_datetime('today'), freq='B')
-    all_dates = all_dates[::-1]  # Reverse the order of dates
-
-    for date in all_dates:
-        html_table += f"<tr><td>{date.strftime('%Y-%m-%d')}</td>"
-        for stock_code in stock_prices.keys():
-            price = stock_prices[stock_code].get(date, None)
-            if price is not None:
-                html_table += f"<td>{price:,.2f}</td>"
-            else:
-                html_table += "<td>N/A</td>"
-        html_table += "</tr>"
-
-    html_table += "</tbody></table></div>"
-
-    graph_html = f'<h3>Relative Stock Prices Over the Last {days} Days</h3>{svg_data}'
-    return graph_html + html_table
+    return fig
 
-# Example usage
-# stock_codes = "AAPL,MSFT,GOOGL"
-# days = 30
-# result = share_price_trend(stock_codes, days)
-# print(result)
+def gradio_interface(stock_codes, days):
+    fig = share_price_trend(stock_codes, days)
+    return gr.Plot(fig)