Generate beautiful, interactive HTML reports for your investment portfolio!

PortfolioReporter fetches current and historical asset data from Yahoo Finance and presents it in an easy-to-read, modern HTML format. Get insights into your asset allocation, historical net worth compared to SPY, key performance indicators, detailed holdings, and top asset movers.

• Interactive Historical Chart: Visualize your portfolio's historical net worth against a SPY benchmark, powered by Plotly.
• Interactive Drill-Down Tables: Get high-level summaries of your portfolio by Symbol, Risk Level, and Asset Class. Click on any summary row to instantly drill down and see the specific holdings that make up that category.
• Key Performance Indicators (KPIs): At-a-glance view of current net worth, total cost basis, total gain/loss, and period changes (1-day, 5-day, 20-day, 3-month, 6-month, 1-year).
• Detailed Holdings Table: A sortable and filterable master table of all your assets, showing individual cost basis, market value, gain/loss, and asset class details, powered by DataTables.js.
• Top Asset Movers: See which assets had the most significant impact (dollar and percentage) over various periods.
• Flexible Data Input: Use a CSV file for your portfolio data. The importer is smart enough to recognize common column headers like ticker, quantity, and cost basis.
• Smart Data Caching: Fetched asset information and historical prices are cached locally to speed up subsequent report generations.
• Interactive Symbol Mapping: If an asset symbol isn't found (e.g., a typo, CUSIP, or delisted ticker), the tool prompts you to map it to a valid symbol and remembers your choice for future runs.
• Modern HTML Output: Clean, responsive HTML report with a dark/light mode theme toggle, viewable in any modern web browser.

This project is best managed using uv, a fast Python package installer and resolver from Astral.

• Python: Version 3.10 or higher. Download from python.org.
• uv: Install uv by following the instructions at astral.sh/uv.
  • For example, on macOS/Linux: curl -LsSf https://astral.sh/uv/install.sh | sh

1. Clone the Repository:

   git clone https://github.com/jianlingzhong/PortfolioReporter.git
   cd PortfolioReporter

2. Create and Activate a Virtual Environment using uv: It's highly recommended to use a virtual environment for Python projects to manage dependencies in isolation.

   uv venv .venv  # Creates a virtual environment named '.venv' in your project directory
   source .venv/bin/activate  # On macOS/Linux
   # For Windows PowerShell: .\.venv\Scripts\Activate.ps1
   # For Windows Command Prompt: .\.venv\Scripts\activate.bat

   You should see (.venv) at the beginning of your terminal prompt, indicating the environment is active.

3. Install Dependencies using uv: The required Python packages are defined in the pyproject.toml file. The uv sync command is the preferred way to install them as it ensures your environment exactly matches the project's lock file.

   uv sync

The main script to generate the report is main_reporter.py.

1. Prepare Your Portfolio Data CSV (Optional but Recommended): Create a CSV file (e.g., assets.csv - a sample is provided in the repository) with the following columns. The script is flexible with column names (e.g., it recognizes Symbol, ticker, Amount, quantity, Cost, cost basis, etc.).

   • Account: The name of the account (e.g., "Brokerage", "Retirement 401k").
   • Symbol: The stock/ETF/mutual fund ticker symbol (e.g., "AAPL", "VOO", "FXAIX"). Use "CASH" for cash holdings. Symbol lookups are case-insensitive.
   • Amount: The number of shares or units you hold. For cash, this is the dollar amount.
   • Cost: The total cost basis for that holding. For cash, this is usually 0.

   Example assets.csv row:

   Brokerage Account,MSFT,15,"$2,500.75"
   

   The script can handle currency symbols ($) and commas (,) in 'Amount' and 'Cost' columns.

2. Run the Script (using uv run): Ensure your uv virtual environment is active. uv run is a convenient way to execute scripts within the managed environment.

   • To use the default sample data embedded in the script:

     uv run main_reporter.py

   • To use your CSV input file (e.g., assets.csv in the project root):

     uv run main_reporter.py -i assets.csv

     Or specify a full path: uv run main_reporter.py -i path/to/your/assets.csv

   • Command-Line Options:

     • -i FILE or --input-file FILE: Specifies the path to your portfolio CSV file.
     • --rebuild-cache: Ignores any existing cached data and fetches fresh data from APIs for this run. The cache will be updated with the new data.
     • --non-interactive: Disables interactive prompts for unknown or problematic symbols. The script will use fallbacks (like $0 value) or skip these symbols without asking.

   Example with all options:

   uv run main_reporter.py -i assets.csv --rebuild-cache --non-interactive

3. View the Report: After the script finishes, it will generate portfolio_report.html in the project directory and attempt to open it in your default web browser.

When the tool encounters a symbol it cannot find data for (e.g., typo, delisted, non-standard ticker), it will prompt you (unless --non-interactive is used):

----------------------------------------
WARNING:root:DATA FETCH FAILED for symbol: 'NONEXISTENT' (context: current details)
Could not automatically fetch data for symbol: 'NONEXISTENT'
This might be an incorrect ticker, a delisted asset, or a non-ticker identifier.
Examples include CUSIPs (like '31617E471'), or internal fund numbers.

You can provide an alternative mapping for this session and future runs:
1. Enter a VALID Yahoo Finance ticker to use as a proxy (e.g., VTI, SPAXX).
2. (If proxy) Enter a price factor (e.g., 1.0 if direct proxy, 0.1 if proxy is 10x value).
3. Enter 'cash' to treat this symbol as a cash equivalent (price 1.0).
4. Enter 'ignore' or just press Enter to skip this symbol (will assign 0 value).
   Proxy symbol for 'NONEXISTENT' (or 'cash'/'ignore'/Enter):

How to Respond:

• Option 1 & 2: Proxy Symbol and Price Factor

  • Proxy Symbol: If NONEXISTENT actually represents an asset similar to VOO, you would type VOO.
  • Price Factor:
    • If the price of NONEXISTENT is directly comparable to VOO (i.e., 1 share of NONEXISTENT should have the value of 1 share of VOO), enter 1.0 (or just press Enter for the default of 1.0).
    • If, for example, your NONEXISTENT asset is priced such that 1 unit of it is equivalent to 0.5 units of VOO, you would enter 0.5 as the price factor.
    • Essentially, the price factor is: (Actual Price of Original Symbol) / (Price of Proxy Symbol) The tool will calculate: Value_Original = Amount_Original * (Price_Proxy * Price_Factor)

• Option 3: cash

  • Type cash. This will treat the symbol like your other "CASH" entries, with a price of $1.00 per unit of Amount. Useful for money market funds or other stable value assets.

• Option 4: ignore or Enter

  • If you type ignore or simply press Enter, the symbol will be treated as having a $0 value for the current report. The tool will also remember not to ask you again for this symbol in future runs (unless --rebuild-cache is used or you manually clear the mapping cache).

Your choices are saved in .cache/symbol_mappings/user_symbol_mappings.json and will be reused in subsequent runs, saving you from repeated prompts.

• Location: .cache/ directory in the project root.
  • asset_info/: Caches details like name, type, sector.
  • historical_prices/: Caches daily price data.
  • symbol_mappings/: Stores your interactive mapping choices.
• Purpose: Speeds up report generation significantly after the first run.
• Rebuilding: Use --rebuild-cache to force a refresh of API-fetched data (asset info and historical prices). Symbol mappings you've made will still be respected unless the mapped-to symbol also fails.
• Clearing: Manually delete the entire .cache directory to clear all cached data and mappings.

• Risk Levels: Modify determine_risk_level in portfolio_analyzer/data_processing.py.
• Asset Classes: Modify determine_asset_class in portfolio_analyzer/data_processing.py to change how your assets are categorized.
• Cash Equivalents: Expand the CASH_EQUIVALENT_SYMBOLS set in portfolio_analyzer/data_processing.py.
• New Report Sections: Add builder functions in portfolio_analyzer/section_builders.py, call them in main_reporter.py, and update templates/report_template.html.
• Styling: Edit the <style> section in templates/report_template.html.

Contributions are welcome! Please fork the repository, make your changes, and submit a pull request. For major changes, consider opening an issue first.

This project is licensed under the MIT License. See the LICENSE.txt file for details.