First commit of the app

.gitignore

@@ -0,0 +1,87 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+env/
+ENV/
+.env
+.venv
+env.bak/
+venv.bak/
+
+# IDE specific files
+.idea/
+.vscode/
+*.swp
+*.swo
+.DS_Store
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Streamlit
+.streamlit/
+
+# Logs
+*.log
+logs/
+
+# Local development settings
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+# Misc
+.DS_Store
+Thumbs.db

README.md

@@ -12,3 +12,72 @@ short_description: An asset class comparator for my personal usecase
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# Asset Class Performance Comparison
+
+This Streamlit app allows you to compare the performance of different asset classes over time, including stocks, bonds, gold, and fixed deposits.
+
+## Features
+
+- Compare multiple asset classes simultaneously
+- Choose between USD and SGD currencies
+- Customize investment parameters (initial amount, time period)
+- View performance graphs and annualized returns
+- Support for major indices and individual stocks
+
+## How to Use
+
+1. Select your preferred currency (USD or SGD)
+2. Enter your initial investment amount
+3. Choose the time period for comparison
+4. Select the assets you want to compare
+5. View the performance graph and returns
+
+## Assets Available
+
+- Fixed Deposit
+- Gold
+- SGS Bonds
+- US Treasury Bonds
+- Major Indices (NASDAQ, S&P 500, Dow Jones)
+- Individual Stocks (Microsoft, Google, Nvidia, Apple, etc.)
+
+## Technical Details
+
+The app uses:
+- Streamlit for the web interface
+- yfinance for market data
+- Plotly for interactive graphs
+- Pandas for data manipulation
+
+## Hosting
+
+This app is hosted on Hugging Face Spaces. You can access it at [link to be added after deployment].
+
+## Local Development
+
+To run locally:
+1. Clone this repository
+2. Install dependencies: `pip install -r requirements.txt`
+3. Run the app: `streamlit run app.py`
+
+## Project Structure
+
+```
+asset-class-comparison/
+├── app.py                 # Main Streamlit application
+├── requirements.txt       # Python dependencies
+├── README.md             # This file
+└── utils/                # Utility functions
+    ├── yfinance_utils.py # yfinance data fetching utilities
+    ├── currency_utils.py # Currency conversion utilities
+    └── fd_utils.py       # Fixed deposit calculation utilities
+```
+
+## Contributing
+
+Contributions are welcome! Please feel free to submit a Pull Request.
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details. 

app.py

@@ -0,0 +1,195 @@
+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")
+
+# 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
+
+# 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",
+    ],
+    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",
+}
+
+# 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
+)
+
+# 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)}")

notebooks/scratchpad.ipynb

@@ -0,0 +1,237 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import yfinance as yf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[*********************100%***********************]  1 of 1 completed\n"
+     ]
+    }
+   ],
+   "source": [
+    "ticker = \"META\"\n",
+    "start_date = \"2010-01-01\"\n",
+    "end_date = \"2020-01-10\"\n",
+    "\n",
+    "data = yf.download(ticker, start=start_date, end=end_date)  # [\"Close\"][ticker]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>Price</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ticker</th>\n",
+       "      <th>META</th>\n",
+       "      <th>META</th>\n",
+       "      <th>META</th>\n",
+       "      <th>META</th>\n",
+       "      <th>META</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-05-18</th>\n",
+       "      <td>38.050671</td>\n",
+       "      <td>44.788914</td>\n",
+       "      <td>37.821750</td>\n",
+       "      <td>41.852752</td>\n",
+       "      <td>573576400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-05-21</th>\n",
+       "      <td>33.870365</td>\n",
+       "      <td>36.488029</td>\n",
+       "      <td>32.845198</td>\n",
+       "      <td>36.358638</td>\n",
+       "      <td>168192700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-05-22</th>\n",
+       "      <td>30.854584</td>\n",
+       "      <td>33.432435</td>\n",
+       "      <td>30.794866</td>\n",
+       "      <td>32.457032</td>\n",
+       "      <td>101786600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-05-23</th>\n",
+       "      <td>31.849892</td>\n",
+       "      <td>32.347546</td>\n",
+       "      <td>31.212894</td>\n",
+       "      <td>31.222848</td>\n",
+       "      <td>73600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-05-24</th>\n",
+       "      <td>32.875057</td>\n",
+       "      <td>33.054213</td>\n",
+       "      <td>31.620969</td>\n",
+       "      <td>32.795434</td>\n",
+       "      <td>50237200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-01-03</th>\n",
+       "      <td>207.691162</td>\n",
+       "      <td>209.413043</td>\n",
+       "      <td>205.979229</td>\n",
+       "      <td>206.238019</td>\n",
+       "      <td>11188400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-01-06</th>\n",
+       "      <td>211.602707</td>\n",
+       "      <td>211.781855</td>\n",
+       "      <td>205.551226</td>\n",
+       "      <td>205.730374</td>\n",
+       "      <td>17058900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-01-07</th>\n",
+       "      <td>212.060547</td>\n",
+       "      <td>213.573421</td>\n",
+       "      <td>210.756694</td>\n",
+       "      <td>211.821682</td>\n",
+       "      <td>14912400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-01-08</th>\n",
+       "      <td>214.210419</td>\n",
+       "      <td>215.225638</td>\n",
+       "      <td>211.612661</td>\n",
+       "      <td>212.000831</td>\n",
+       "      <td>13475000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-01-09</th>\n",
+       "      <td>217.275970</td>\n",
+       "      <td>217.355597</td>\n",
+       "      <td>215.265442</td>\n",
+       "      <td>216.519526</td>\n",
+       "      <td>12642800</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1923 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Price            Close        High         Low        Open     Volume\n",
+       "Ticker            META        META        META        META       META\n",
+       "Date                                                                 \n",
+       "2012-05-18   38.050671   44.788914   37.821750   41.852752  573576400\n",
+       "2012-05-21   33.870365   36.488029   32.845198   36.358638  168192700\n",
+       "2012-05-22   30.854584   33.432435   30.794866   32.457032  101786600\n",
+       "2012-05-23   31.849892   32.347546   31.212894   31.222848   73600000\n",
+       "2012-05-24   32.875057   33.054213   31.620969   32.795434   50237200\n",
+       "...                ...         ...         ...         ...        ...\n",
+       "2020-01-03  207.691162  209.413043  205.979229  206.238019   11188400\n",
+       "2020-01-06  211.602707  211.781855  205.551226  205.730374   17058900\n",
+       "2020-01-07  212.060547  213.573421  210.756694  211.821682   14912400\n",
+       "2020-01-08  214.210419  215.225638  211.612661  212.000831   13475000\n",
+       "2020-01-09  217.275970  217.355597  215.265442  216.519526   12642800\n",
+       "\n",
+       "[1923 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "return-on-investment",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

@@ -0,0 +1,6 @@
+streamlit==1.32.0
+pandas==2.2.1
+yfinance==0.2.36
+plotly==5.19.0
+python-dotenv==1.0.1
+numpy==1.26.4

tests/integration/test_yfinance_utils_integration.py

@@ -0,0 +1,12 @@
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')))
+
+from utils import yfinance_utils
+import pandas as pd
+
+def test_fetch_yfinance_daily_real():
+    result = yfinance_utils.fetch_yfinance_daily('MSFT', '2023-01-01', '2023-01-10')
+    assert result is not None
+    assert not result.empty
+    assert isinstance(result, pd.Series) 

tests/unit/test_currency_utils.py

@@ -0,0 +1,21 @@
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')))
+
+import pytest
+from unittest.mock import patch
+from utils import currency_utils
+
+def test_get_usd_sgd_rate_success():
+    with patch('yfinance.download') as mock_download:
+        import pandas as pd
+        df = pd.DataFrame({'Close': [1.3, 1.35]}, index=pd.date_range('2020-01-01', periods=2))
+        mock_download.return_value = df
+        rate = currency_utils.get_usd_sgd_rate()
+        assert rate == 1.35
+
+def test_get_usd_sgd_rate_fail():
+    with patch('yfinance.download') as mock_download:
+        mock_download.side_effect = Exception('fail')
+        rate = currency_utils.get_usd_sgd_rate()
+        assert rate == 1.0 

tests/unit/test_fd_utils.py

@@ -0,0 +1,11 @@
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')))
+
+from utils import fd_utils
+from datetime import date
+
+def test_calculate_fd_returns():
+    result = fd_utils.calculate_fd_returns(1000, 0.05, date(2020, 1, 1), date(2021, 1, 1))
+    # 1 year at 5% interest
+    assert abs(result - 1050) < 1 

tests/unit/test_yfinance_utils.py

@@ -0,0 +1,23 @@
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../..')))
+
+import pytest
+from unittest.mock import patch, MagicMock
+from utils import yfinance_utils
+import pandas as pd
+
+def test_fetch_yfinance_daily_success():
+    with patch('yfinance.download') as mock_download:
+        df = pd.DataFrame({'Adj Close': [1, 2, 3]}, index=pd.date_range('2020-01-01', periods=3))
+        mock_download.return_value = df
+        result = yfinance_utils.fetch_yfinance_daily('MSFT', '2020-01-01', '2020-01-03')
+        assert isinstance(result, pd.Series)
+        assert not result.empty
+
+def test_fetch_yfinance_daily_empty():
+    with patch('yfinance.download') as mock_download:
+        df = pd.DataFrame()
+        mock_download.return_value = df
+        result = yfinance_utils.fetch_yfinance_daily('MSFT', '2020-01-01', '2020-01-03')
+        assert result is None 

utils/currency_utils.py

@@ -0,0 +1,11 @@
+import yfinance as yf
+
+
+def get_usd_sgd_rate():
+    try:
+        fx = yf.download('USDSGD=X', period='5d', interval='1d')
+        if not fx.empty:
+            return float(fx['Close'][-1])
+    except Exception:
+        pass
+    return 1.0  # fallback to 1 if failed 

utils/fd_utils.py

@@ -0,0 +1,4 @@
+def calculate_fd_returns(initial_amount, rate, start_date, end_date):
+    days = (end_date - start_date).days
+    years = days / 365
+    return initial_amount * (1 + rate) ** years 

utils/yfinance_utils.py

@@ -0,0 +1,16 @@
+import yfinance as yf
+import pandas as pd
+
+def fetch_yfinance_daily(ticker, start_date, end_date):
+    try:
+        data = yf.download(ticker, start=start_date, end=end_date)
+        if data.empty:
+            print(f"No data found for {ticker} between {start_date} and {end_date}")
+            return None
+        print("data type returned:", type(data['Close']))
+        return data['Close'][ticker]
+    except Exception:
+        return None 
+    
+if __name__ == "__main__":
+    print(fetch_yfinance_daily("MSFT", "2020-01-01", "2020-01-10"))