Ghostfolio is an open-source wealth management application that enables individuals to track and analyze their personal investment portfolios. It supports multiple asset classes (stocks, ETFs, cryptocurrencies, bonds, real estate) and integrates with numerous financial data providers (Yahoo Finance, CoinGecko, Alpha Vantage, etc.) to automatically fetch market prices, historical data, and asset profiles. The system provides comprehensive portfolio performance calculations (ROAI, TWR, MWR), benchmark comparisons, dividend tracking, and a rule-based X-ray analysis for risk assessment (currency/asset class/regional cluster risk, emergency fund coverage, fee ratios). It also includes a FIRE calculator, watchlist, and import/export functionality for activities. The architecture follows a full-stack monorepo pattern using Nx, with an Angular 22 frontend (standalone components, Material Design, Chart.js) and a NestJS 11 backend (Prisma ORM with PostgreSQL, Redis caching, Bull queues for background data gathering). Authentication supports JWT, OAuth (Google, OIDC), API keys, and WebAuthn passkeys. The system is self-hostable via Docker Compose and includes a premium subscription tier managed through Stripe. The codebase is modular with shared libraries (`libs/common` for DTOs, interfaces, utilities; `libs/ui` for reusable Angular components) and a clear separation of concerns across API modules, services, and queue processors. Key highlights include a pluggable data provider abstraction, an abstract portfolio calculator with caching and retry logic, a comprehensive permission and scope system for fine-grained access control, and a rich UI component library with Storybook documentation. The project is actively maintained with frequent releases, extensive Prisma migrations, and a large test suite for portfolio calculations and import validation. While the codebase shows some technical drift (proliferation of environment variables, deprecated endpoints, and multiple data provider integrations), the overall engineering quality is strong, with well-structured modules and reusable patterns that could be extracted for other financial applications.
Pinkfish is a Python library for backtesting equity trading strategies, primarily used via Jupyter notebooks. It provides a modular framework with components for data fetching (Yahoo Finance via yfinance), trade logging, portfolio management, performance statistics, and signal generation. The core architecture revolves around a Strategy class pattern, a Portfolio class for multi-symbol backtesting, and a TradeLog class for simulating trade entries/exits with realistic open/close fills. The library includes a wide range of technical indicators (both custom and TA-Lib wrappers), comprehensive performance metrics (Sharpe, Sortino, drawdown, etc.), and support for signal notifications via Pushover/email. The codebase is well-structured with clear separation of concerns, but uses some global state in TradeLog and relies on a static stock market calendar, introducing minor maintenance risks. The examples cover diverse strategies such as momentum, moving average crossovers, and sector rotation, serving as both documentation and reusable templates.
OptionSuite is an options trading strategy backtesting framework implemented in Python. The system uses an event-driven architecture to simulate trading strategies by processing historical options data from CSV files. It provides modular components for option types, data handling, strategy definition, portfolio management, and risk management. The main entry point is backTester.py which orchestrates the backtesting session by initializing data handlers, portfolio managers, and strategy managers. The framework supports various options strategies including naked puts, put verticals, and strangles, with configurable risk management rules for position exits.