A Python library and CLI tool for managing investment portfolios following Bogleheads principles.
Lakshmi is a Python library and command-line interface that helps individual investors manage their investment portfolios following Bogleheads principles. It provides tools for tracking asset allocation, suggesting rebalancing actions, analyzing performance, and running what-if scenarios to optimize portfolio management.
Individual investors who follow the Bogleheads philosophy and want to manage their portfolios programmatically, particularly those with multiple account types (taxable, tax-deferred, tax-exempt) and various asset classes.
Developers choose Lakshmi because it offers a programmatic, open-source alternative to manual spreadsheet tracking or proprietary portfolio management tools, with built-in support for Bogleheads investment strategies and automated data updates for ticker-based assets.
Investing library and command-line interface inspired by the Bogleheads philosophy
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Fetches current prices for ticker-based assets automatically, as shown with TickerAsset support, reducing manual data entry for stocks and ETFs.
Provides rebalancing suggestions and what-if scenarios specifically aligned with passive, low-cost investing principles, helping users maintain disciplined asset allocation.
Supports tax-lot information for assets, enabling precise tax-loss harvesting strategies, as highlighted in the features and analysis commands.
Calculates Internal Rate of Return (IRR) and tracks cash flows, offering detailed portfolio performance analysis beyond basic value tracking.
Only supports specific asset types like Vanguard funds and government bonds, excluding common investments such as Fidelity funds, cryptocurrencies, or complex derivatives.
Lacks a graphical user interface, making it inaccessible for non-technical users who prefer visual tools, as evidenced by the sole focus on CLI and library usage.
The README warns that refreshing data for large portfolios is 'extremely slow,' indicating scalability issues and reliance on cached data that may become stale.