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| import streamlit as st | |
| import pandas as pd | |
| import yfinance as yf | |
| import plotly.graph_objects as go | |
| from datetime import datetime, timedelta | |
| import numpy as np | |
| # Import utility functions | |
| from utils.yfinance_utils import fetch_yfinance_daily | |
| from utils.currency_utils import get_usd_sgd_rate | |
| from utils.fd_utils import calculate_fd_returns | |
| # Set page config | |
| st.set_page_config(page_title="Asset Class Comparison", layout="wide") | |
| # Title and description | |
| st.title("Asset Class Performance Comparison") | |
| st.write("Compare the performance of different asset classes over time") | |
| st.write("Note: Cryptocurrencies (BTC, ETH, SOL, DOGE) are highly volatile and should be considered high-risk investments") | |
| # Sidebar for user inputs | |
| st.sidebar.header("Investment Parameters") | |
| currency = st.sidebar.selectbox("Display Currency", ["USD", "SGD"], index=0) | |
| initial_investment = st.sidebar.number_input(f"Initial Investment Amount ({currency})", min_value=1000, value=10000, step=1000) | |
| start_date = st.sidebar.date_input("Start Date", value=datetime.now() - timedelta(days=365*10)) | |
| user_end_date = st.sidebar.date_input("End Date", value=datetime.now()) | |
| fd_rate = st.sidebar.number_input("Fixed Deposit Rate (%)", min_value=0.0, value=2.9, step=0.1) / 100 | |
| use_log_scale = st.sidebar.checkbox("Use Log Scale", value=False) | |
| # Asset selection | |
| selected_assets = st.sidebar.multiselect( | |
| "Select Assets to Compare", | |
| [ | |
| "Fixed Deposit", | |
| "Gold", | |
| "SGS Bonds", | |
| "US Treasury Bonds", | |
| "NASDAQ Composite", | |
| "NASDAQ Large Cap", | |
| "NASDAQ 100", | |
| "S&P 500", | |
| "Dow Jones", | |
| "Microsoft", | |
| "Google", | |
| "Nvidia", | |
| "Apple", | |
| "Amazon", | |
| "Tesla", | |
| "Netflix", | |
| "Meta", | |
| "Bitcoin", | |
| "Ethereum", | |
| "Solana", | |
| "Dogecoin", | |
| ], | |
| default=["Fixed Deposit", "Microsoft", "Google", "Nvidia"] | |
| ) | |
| # Today's date for reference | |
| today = datetime.now().date() | |
| usd_to_sgd = get_usd_sgd_rate() if currency == "SGD" else 1.0 | |
| currency_symbol = "$" if currency == "USD" else "S$" | |
| # Create a dictionary of tickers for yfinance | |
| tickers = { | |
| "Gold": "GC=F", | |
| "SGS Bonds": "^TNX", | |
| "US Treasury Bonds": "^TNX", | |
| "NASDAQ Composite": "^IXIC", | |
| "NASDAQ Large Cap": "^NDX", | |
| "NASDAQ 100": "^NDX", | |
| "S&P 500": "^GSPC", | |
| "Dow Jones": "^DJI", | |
| "Microsoft": "MSFT", | |
| "Google": "GOOGL", | |
| "Nvidia": "NVDA", | |
| "Apple": "AAPL", | |
| "Amazon": "AMZN", | |
| "Tesla": "TSLA", | |
| "Netflix": "NFLX", | |
| "Meta": "META", | |
| "Bitcoin": "BTC-USD", | |
| "Ethereum": "ETH-USD", | |
| "Solana": "SOL-USD", | |
| "Dogecoin": "DOGE-USD", | |
| } | |
| # Determine the effective end date for each asset | |
| asset_end_dates = {} | |
| for asset in selected_assets: | |
| if asset == "Fixed Deposit": | |
| asset_end_dates[asset] = user_end_date | |
| else: | |
| if user_end_date > today: | |
| asset_end_dates[asset] = today | |
| else: | |
| asset_end_dates[asset] = user_end_date | |
| # Warn the user if a future end date is selected for market assets | |
| if any(user_end_date > today and asset != "Fixed Deposit" for asset in selected_assets): | |
| st.warning(f"Market data is only available up to today ({today}). For market assets, the end date has been set to today.") | |
| # Calculate returns for each selected asset | |
| asset_series = {} | |
| failed_assets = [] | |
| actual_start_dates = {} | |
| for asset in selected_assets: | |
| asset_start = start_date | |
| asset_end = asset_end_dates[asset] | |
| if asset == "Fixed Deposit": | |
| fd_index = pd.date_range(start=asset_start, end=user_end_date) | |
| daily_rate = (1 + fd_rate) ** (1/365) - 1 | |
| fd_values = initial_investment * (1 + daily_rate) ** np.arange(len(fd_index)) | |
| if currency == "SGD": | |
| fd_values = fd_values * usd_to_sgd | |
| asset_series[asset] = pd.Series(fd_values, index=fd_index) | |
| actual_start_dates[asset] = asset_start | |
| else: | |
| price_data = fetch_yfinance_daily(tickers[asset], asset_start, asset_end) | |
| if price_data is not None and not price_data.empty: | |
| price_data = price_data.sort_index() | |
| actual_start = price_data.index[0] | |
| actual_start_dates[asset] = actual_start | |
| aligned_index = pd.date_range(start=actual_start, end=asset_end) | |
| price_data = price_data.reindex(aligned_index) | |
| price_data = price_data.ffill() | |
| asset_values = initial_investment * (price_data / price_data.iloc[0]) | |
| if currency == "SGD": | |
| asset_values = asset_values * usd_to_sgd | |
| asset_series[asset] = asset_values | |
| else: | |
| failed_assets.append(asset) | |
| # Combine all asset series into a single DataFrame | |
| if asset_series: | |
| returns_data = pd.DataFrame(asset_series) | |
| else: | |
| returns_data = pd.DataFrame() | |
| # Remove failed assets from selected_assets (except FD) | |
| selected_assets = [asset for asset in selected_assets if asset not in failed_assets or asset == "Fixed Deposit"] | |
| if not selected_assets: | |
| st.error("No assets could be loaded. Please try different assets.") | |
| st.stop() | |
| # Create the plot | |
| fig = go.Figure() | |
| for asset in selected_assets: | |
| fig.add_trace(go.Scatter( | |
| x=returns_data.index, | |
| y=returns_data[asset], | |
| name=asset, | |
| mode='lines' | |
| )) | |
| fig.update_layout( | |
| title="Asset Performance Comparison", | |
| xaxis_title="Date", | |
| yaxis_title=f"Investment Value ({currency_symbol})", | |
| hovermode="x unified", | |
| height=600, | |
| yaxis_type="log" if use_log_scale else "linear" | |
| ) | |
| # Display the plot | |
| st.plotly_chart(fig, use_container_width=True) | |
| # Calculate and display final returns | |
| st.subheader("Final Investment Values") | |
| for asset in selected_assets: | |
| valid_series = returns_data[asset].dropna() | |
| if not valid_series.empty: | |
| final_value = valid_series.iloc[-1] | |
| st.write(f"{asset}: {currency_symbol}{final_value:,.2f}") | |
| else: | |
| st.write(f"{asset}: Data unavailable") | |
| # Calculate and display annualized returns | |
| st.subheader("Annualized Returns") | |
| for asset in selected_assets: | |
| valid_series = returns_data[asset].dropna() | |
| if len(valid_series) > 1: | |
| actual_start = actual_start_dates[asset] | |
| days = (valid_series.index[-1] - valid_series.index[0]).days | |
| years = days / 365 | |
| final_value = valid_series.iloc[-1] | |
| annualized_return = ((final_value / initial_investment) ** (1/years) - 1) * 100 | |
| if pd.Timestamp(actual_start).date() > start_date: | |
| st.write(f"{asset}: {annualized_return:.2f}% (Data available from {actual_start.strftime('%Y-%m-%d')})") | |
| else: | |
| st.write(f"{asset}: {annualized_return:.2f}%") | |
| else: | |
| st.write(f"{asset}: N/A") | |
| # Show warnings for data availability | |
| for asset in selected_assets: | |
| if asset in actual_start_dates and pd.Timestamp(actual_start_dates[asset]).date() > start_date: | |
| st.warning(f"Data for {asset} is only available from {actual_start_dates[asset].strftime('%Y-%m-%d')}. The analysis starts from this date.") | |
| # Show warning for failed assets | |
| if failed_assets: | |
| st.warning(f"Could not load data for the following assets: {', '.join(failed_assets)}") | |