import math import requests from bs4 import BeautifulSoup import FinanceDataReader as fdr import ssl import io import base64 import gradio as gr import matplotlib.pyplot as plt from datetime import datetime from concurrent.futures import ThreadPoolExecutor import pytz import yfinance as yf from datetime import datetime, timedelta # timedelta 추가 import gradio as gr from gradio.components import Dataset import matplotlib.pyplot as plt import FinanceDataReader as fdr import gradio as gr import pandas as pd from concurrent.futures import ThreadPoolExecutor, as_completed import io import base64 # 한국 표준시 (KST) 시간대 설정 kst = pytz.timezone('Asia/Seoul') # SSL 인증서 검증 비활성화 ssl._create_default_https_context = ssl._create_unverified_context def parse_input(text, cash_amount, cash_ratio): lines = text.strip().split(',') stock_inputs = [] total_target_weight = 0 for line in lines: parts = line.split() if len(parts) == 4: stock_code, currency_code, quantity_expr, target_weight_expr = parts quantity = math.floor(eval(quantity_expr.replace(' ', ''))) target_weight = eval(target_weight_expr.replace(' ', '')) target_ratio = (1 - cash_ratio / 100) * target_weight stock_inputs.append((currency_code, stock_code, quantity, target_weight, target_ratio)) total_target_weight += target_weight cash_amount = math.floor(cash_amount) if cash_amount else 0 krw_cash = {'amount': cash_amount, 'target_weight': cash_ratio / 100.0} stock_total_weight = total_target_weight for i in range(len(stock_inputs)): stock_inputs[i] = (stock_inputs[i][0], stock_inputs[i][1], stock_inputs[i][2], stock_inputs[i][3], (1 - krw_cash['target_weight']) * stock_inputs[i][3] / stock_total_weight) return stock_inputs, krw_cash def get_exchange_rate(currency_code): if currency_code.lower() == 'krw': return 1.0 ticker = f"{currency_code.upper()}KRW=X" data = yf.download(ticker, period='1d') if not data.empty: return data['Close'].iloc[0] else: raise ValueError("Failed to retrieve exchange rate data.") def get_exchange_reflected_stock_price(stock_code, currency_code): new_price = get_current_stock_price(stock_code) exchange_rate = get_exchange_rate(currency_code) return math.floor(new_price * exchange_rate) def get_current_stock_price(stock_code): df = fdr.DataReader(stock_code) return df['Close'].iloc[-1] def build_portfolio(stock_inputs, krw_cash): portfolio = {} target_weights = {} with ThreadPoolExecutor() as executor: results = executor.map(lambda x: (x[1], get_exchange_reflected_stock_price(x[1], x[0]), x[2], x[3], x[4], x[0]), stock_inputs) for stock_code, new_price, quantity, target_weight, target_ratio, currency_code in results: portfolio[stock_code] = {'quantity': quantity, 'price': new_price, 'target_weight': target_weight, 'currency': currency_code} target_weights[stock_code] = target_ratio return portfolio, target_weights, krw_cash def format_quantity(quantity): if quantity < 0: return f"({-quantity:,})" else: return f"{quantity:,}" def get_portfolio_rebalancing_info(portfolio, target_weights, krw_cash): with open('portfolio.html', 'r', encoding='utf-8') as file: css = file.read() kst = pytz.timezone('Asia/Seoul') current_time = datetime.now(kst).strftime("%I:%M %p %b-%d-%Y") total_value = sum(stock['price'] * stock['quantity'] for stock in portfolio.values()) + krw_cash['amount'] total_new_stock_value = 0 total_trade_value = 0 adjustments = [] # Calculate current weights and values current_weights = {stock_code: (stock['price'] * stock['quantity'] / total_value) * 100 for stock_code, stock in portfolio.items()} current_values = {stock_code: stock['price'] * stock['quantity'] for stock_code, stock in portfolio.items()} # Include cash in current weights and values current_weights['CASH'] = (krw_cash['amount'] / total_value) * 100 current_values['CASH'] = krw_cash['amount'] # Sort stocks by current weight in descending order sorted_stocks = sorted(current_weights.items(), key=lambda x: x[1], reverse=True) # Display current weights and values section current_info_html = "

Your Portfolio Holdings

" current_info_html += "" for stock_code, weight in sorted_stocks: current_info_html += ( f"" f"" f"" f"" f"" ) current_info_html += "
Stock CodeCurrent Weight (%)Current Value
{stock_code.upper()}{weight:.1f}%₩{current_values[stock_code]:,.0f}

" for stock_code, stock_data in portfolio.items(): current_value = stock_data['price'] * stock_data['quantity'] target_value = total_value * target_weights.get(stock_code, 0) difference = target_value - current_value trade_quantity = math.floor(difference / stock_data['price']) if difference > 0 else -math.ceil(-difference / stock_data['price']) new_quantity = trade_quantity + stock_data['quantity'] new_value = new_quantity * stock_data['price'] trade_value = trade_quantity * stock_data['price'] total_trade_value += abs(trade_value) total_new_stock_value += new_value current_value_pct = (current_value / total_value) * 100 new_value_pct = (new_value / total_value) * 100 adjustments.append((difference, current_value, target_value, current_value_pct, trade_quantity, stock_code, stock_data['price'], new_value, trade_value, stock_data['quantity'], new_quantity, target_weights[stock_code], new_value_pct, stock_data['target_weight'], stock_data['currency'])) krw_new_amount = total_value - total_new_stock_value krw_target_value = total_value * krw_cash['target_weight'] krw_difference = krw_new_amount - krw_cash['amount'] trade_quantity = krw_difference new_quantity = krw_cash['amount'] + trade_quantity new_value = new_quantity trade_value = trade_quantity current_value = krw_cash['amount'] current_value_pct = (current_value / total_value) * 100 new_value_pct = (new_value / total_value) * 100 adjustments.append((krw_difference, current_value, krw_target_value, current_value_pct, trade_quantity, 'CASH', 1, new_value, trade_value, krw_cash['amount'], new_quantity, krw_cash['target_weight'], new_value_pct, '', 'KRW')) portfolio_info = css + f"""

₩{total_value:,.0f} as of {current_time}


""" currency_totals = {stock_data['currency']: {'amount': 0, 'weight': 0} for stock_data in portfolio.values()} for stock_code, stock_data in portfolio.items(): currency = stock_data['currency'] current_value = stock_data['price'] * stock_data['quantity'] currency_totals[currency]['amount'] += current_value currency_totals[currency]['weight'] += current_value / total_value currency_totals['CASH'] = {'amount': krw_cash['amount'], 'weight': krw_cash['amount'] / total_value} sorted_currencies = sorted(currency_totals.items(), key=lambda x: x[1]['weight'], reverse=True) currency_table = "

Your Portfolio by Currency

" currency_table += "" for currency, data in sorted_currencies: currency_table += ( f"" f"" f"" f"" f"" ) currency_table += "
CurrencyTotal Weight (%)Total Value
{currency.upper()}{data['weight'] * 100:.1f}%₩{data['amount']:,}

" result_message = portfolio_info + current_info_html + currency_table + "

Re-Balancing Analysis

" result_message += "" for adj in adjustments: difference, current_value, target_value, current_value_pct, trade_quantity, stock_code, price, new_value, trade_value, old_quantity, new_quantity, target_ratio, new_value_pct, target_weight, currency = adj Buy_or_Sell = "" if trade_quantity > 0: Buy_or_Sell = f"Buy" elif trade_quantity < 0: Buy_or_Sell = f"Sell" else: Buy_or_Sell = f"" price_str = f"₩{price:,.0f}" if stock_code != 'CASH' else '' target_weight_str = f"{target_weight}" if stock_code != 'CASH' else '' target_ratio_str = f"{target_ratio * 100:.1f}%" if stock_code == 'CASH' else f"{target_ratio * 100:.1f}%" old_quantity_str = f"{old_quantity:,.0f} → {new_quantity:,.0f}" if stock_code != 'CASH' else '' trade_value_str = f"{format_quantity(trade_value)}" if trade_value != 0 else '' trade_quantity_str = ( f"{format_quantity(trade_quantity)}" if stock_code != 'CASH' and trade_value != 0 else '' ) new_value_str = f"₩{new_value:,.0f}" new_value_pct_str = f"{new_value_pct:.1f}%" result_message += ( f"" f"" f"" f"" f"" f"" f"" f"" f"" f"" f"" f"" ) result_message += "
Stock CodeTarget WeightTarget Ratio (%)Buy or Sell?Trade AmountCurrent Price per ShareEstimated # of
Shares to Buy or Sell
Quantity of UnitsMarket Value% Asset Allocation
{stock_code.upper()}{target_weight_str}{target_ratio_str}{Buy_or_Sell}{trade_value_str}{price_str}{trade_quantity_str}{old_quantity_str}{new_value_str}{new_value_pct_str}
" return result_message def rebalancing_tool(user_input, cash_amount, cash_ratio): try: stock_inputs, krw_cash = parse_input(user_input, cash_amount, cash_ratio) portfolio, target_weights, krw_cash = build_portfolio(stock_inputs, krw_cash) result = get_portfolio_rebalancing_info(portfolio, target_weights, krw_cash) return result except Exception as e: return str(e) def get_stock_prices(stock_code, days): try: df = fdr.DataReader(stock_code, end=pd.Timestamp.now().date(), data_source='yahoo') df = df[df.index >= df.index.max() - pd.DateOffset(days=days)] # 최근 days일 데이터로 제한 return df['Close'] except Exception as e: print(f"Failed to fetch data for {stock_code}: {e}") return None def plot_stock_prices(stock_codes, days): # 주식 그래프 생성을 위한 병렬 처리 stock_prices = {} with ThreadPoolExecutor() as executor: futures = {executor.submit(get_stock_prices, stock_code.strip(), int(days)): stock_code.strip() for stock_code in stock_codes.split(',')} for future in as_completed(futures): stock_code = futures[future] try: prices = future.result() if prices is not None: stock_prices[stock_code] = prices except Exception as e: print(f"Failed to fetch data for {stock_code}: {e}") # 각 주식에 대한 그래프를 그림 plt.figure(figsize=(10, 6)) for stock_code, prices in stock_prices.items(): relative_prices = prices / prices.iloc[0] # 첫 번째 데이터 포인트를 기준으로 상대적 가격 계산 plt.plot(prices.index, relative_prices, label=stock_code.upper()) # 주식 코드를 대문자로 표시 plt.xlabel('Date') plt.ylabel('Relative Price (Normalized to 1)') plt.title(f'Relative Stock Prices Over the Last {days} Days') plt.legend() # 그래프를 HTML로 변환하여 반환 html_graph = io.BytesIO() plt.savefig(html_graph, format='png', dpi=300) html_graph.seek(0) graph_encoded = base64.b64encode(html_graph.getvalue()).decode() graph_html = f'' return graph_html def cost_averaging(old_avg_price, old_quantity, new_price, new_quantity): # 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 # 현재 투자 금액 계산 current_investment = old_avg_price * old_quantity # 추가 투자 금액 계산 additional_investment = new_price * new_quantity # 총 투자 금액 total_investment = current_investment + additional_investment # 총 주식 수 total_shares = old_quantity + new_quantity # 새 평균 가격 계산 new_avg_price = total_investment / total_shares if total_shares != 0 else 0.0 # 현재 수익률 계산 current_return = (new_price - old_avg_price) / old_avg_price * 100 if old_avg_price != 0 else 0.0 # 새로운 수익률 계산 new_return = (new_price / new_avg_price - 1 ) * 100 if new_avg_price != 0 else 0.0 return new_avg_price, total_shares, total_investment, current_return, new_return, additional_investment def gradio_cost_averaging(old_avg_price, old_quantity, new_price, new_quantity): with open('portfolio.html', 'r', encoding='utf-8') as file: css = file.read() # 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 new_avg_price, total_shares, total_investment, current_return, new_return, additional_investment = cost_averaging(old_avg_price, old_quantity, new_price, new_quantity) current_return_class = "" if current_return > 0: current_return_class = f"{current_return:+,.2f}%" elif current_return < 0: current_return_class = f"{current_return:,.2f}%" else: current_return_class = f"0" new_return_class = "" if new_return > 0: new_return_class = f"{new_return:+,.2f}%" elif current_return < 0: new_return_class = f"{new_return:,.2f}%" else: new_return_class = f"0" # Construct the HTML string with the appropriate class result_html = css+ f"""
Average Price
{new_avg_price:,.0f}

Total Quantity
{total_shares:,.0f}

Total Investment
{total_investment:,.0f}

{current_return_class}

{old_avg_price:,.0f}

{new_return_class}

{new_avg_price:,.0f}

총 추가 금액: {additional_investment:,.0f}

""" return result_html # Define the interface for the Portfolio tab def portfolio_interface(input_text, cash_amount, cash_ratio): result = rebalancing_tool(input_text, cash_amount, cash_ratio) return result portfolio_inputs = [ gr.Textbox(label="🔥 Holdings", lines=2, placeholder="Format: [stock code currency quantity target weight, ...]"), gr.Number(label="🪵 Cash", value=""), gr.Slider(label="⚖️ Cash Ratio (%)", minimum=0, maximum=100, step=1) ] portfolio_interface = gr.Interface( fn=portfolio_interface, inputs=portfolio_inputs, outputs=gr.HTML(), # examples = [ # ["458730 krw 571 8,\n368590 krw 80 2", 17172, 0], # ["SCHD USD 400 8,\nQQQ USD 40 2", 1000000, 25], # ["458730 krw 571 8,\n368590 krw 80 2,\nSCHD USD 400 8,\nQQQ USD 40 2", 1000000, 25] # ], live=True ) # Define the interface for the Compare tab def compare_interface(stock_codes, period): result = plot_stock_prices(stock_codes, period) return result compare_inputs = [ gr.Textbox(label="📈 Stock Codes", lines=2, placeholder="Enter stock codes separated by comma (e.g., AAPL,GOOGL,MSFT)"), gr.Number(label="📆 Period (days)", value=90) ] compare_interface = gr.Interface( fn=compare_interface, inputs=compare_inputs, outputs=gr.HTML(), # examples = [ # ["SCHD,QQQ", 90], # ["458730,368590", 90], # ["AAPL,GOOGL,MSFT", 90] # ], live=False ) # Define the interface for the Cost Averaging tab def cost_averaging_interface(old_avg_price, old_quantity, new_price, new_quantity): result = gradio_cost_averaging(old_avg_price, old_quantity, new_price, new_quantity) return result cost_averaging_inputs = [ gr.Number(label="Old Price", value=""), gr.Number(label="Quantity", value=""), gr.Number(label="New Price", value=""), gr.Number(label="Quantity", value="") ] cost_averaging_interface = gr.Interface( fn=cost_averaging_interface, inputs=cost_averaging_inputs, outputs=gr.HTML(), # examples = [ # [78.15, 6.024272, 77.11, 1] # ], live=True ) # Combine all interfaces into a tabbed interface with gr.Blocks(css='style.css') as demo: with gr.Column(elem_id="col-container"): with gr.Tabs(): with gr.TabItem("Portfolio"): portfolio_interface.render() with gr.TabItem("Compare"): compare_interface.render() with gr.TabItem("Cost Averaging"): cost_averaging_interface.render() with gr.TabItem("📄 About"): gr.Markdown(""" # About This Application Welcome to the Portfolio Management Tool! This application provides a comprehensive suite of tools to help you manage and analyze your investment portfolio. Below is a brief overview of each feature available in this tool. ## 📊 Portfolio **Description:** This section allows you to analyze and rebalance your investment portfolio. You can input your current holdings, cash amount, and desired cash ratio, and the tool will calculate the necessary trades to achieve your target allocation. **How to Use:** 1. Enter your holdings in the format: `[stock code currency quantity target weight]`. 2. Specify your cash amount and desired cash ratio. 3. Click the "Analyze Data" button to see the rebalancing analysis. 4. View the detailed breakdown of your current portfolio and suggested trades. ## 📈 Compare **Description:** This feature enables you to compare the historical prices of multiple stocks over a specified period. It provides a visual comparison to help you understand the performance of different stocks. **How to Use:** 1. Enter the stock codes separated by commas (e.g., AAPL, GOOGL, MSFT). 2. Specify the period in days for which you want to compare the stock prices. 3. Click the "Compare Stock Prices" button to generate the comparison graph. 4. View the relative price changes of the selected stocks over the chosen period. ## 💹 Cost Averaging **Description:** This section helps you calculate the new average price of a stock when you make additional purchases. It also provides insights into the current and new return rates based on your investments. **How to Use:** 1. Enter the average price and quantity of your initial purchase in the "First Purchase" section. 2. Enter the price and quantity of your subsequent purchase in the "Second Purchase" section. 3. Click the "Calculate Cost Averaging" button to see the results. 4. View the new average price, total quantity, total investment, and return rates. ## 📄 About **Description:** This section provides an overview of the application, explaining its features and how to use them. It serves as a guide for new users to understand the functionalities available in the tool. **How to Use:** Simply read through the information provided to get acquainted with the application's capabilities. ## Disclaimer Please note that this tool is for informational purposes only and does not constitute financial advice. Always conduct your own research or consult with a financial advisor before making investment decisions. --- We hope you find this tool useful for managing your investments. If you have any feedback or suggestions, feel free to reach out! Happy Investing! """) demo.launch(share=True)