Actual Budget is a mature, open-source personal finance application that operates on a local-first architecture with optional multi-device sync. In production, it provides envelope and tracking budgeting, transaction management, bank account synchronization via GoCardless/SimpleFIN/Pluggy, scheduled transactions, an advanced rules engine for auto-categorization, custom reports and dashboards (including Monte Carlo forecasting), and multi-user sync server capabilities. The system is built as a monorepo with distinct packages: `loot-core` (business logic, SQLite database, CRDT sync, custom AQL query language), `desktop-client` (React SPA with Redux and TanStack Query), `desktop-electron` (Electron wrapper), `mobile-client` (Capacitor-based), `sync-server` (Express with authentication and bank integrations), `cli` (headless management), `api` (public npm package), `component-library` (design system), and `crdt` (conflict-free replicated data type). Data flows from user input or bank imports into a local SQLite store, with optional sync to other devices via a CRDT protocol over a WebSocket/HTTP server. The project demonstrates high engineering craftsmanship with extensive tests, custom but well-documented systems, and a strong modular architecture. It is not a data research project.
ojAlgo is a pure Java numerical computing library providing high-performance linear algebra (dense and sparse matrices, decompositions like LU, QR, SVD, Cholesky, eigenvalue), optimization (linear programming via simplex, convex QP via active-set/ADMM, integer programming with branch-and-cut), neural networks, statistical distributions, random processes, time series analysis, clustering, financial portfolio models, and special functions. It has zero external dependencies and is modularized via JPMS. The architecture is a classic library: users create matrix objects or optimization models, invoke solvers or decompositions, and retrieve results. Key entry points include `MatrixStore`, `ExpressionsBasedModel`, `ArtificialNeuralNetwork`, and various decomposition factories. The codebase is well-tested with extensive unit and regression tests, and demonstrates strong engineering practices such as interface-driven design, factory patterns, and careful numerical stability. While primarily a software library, it also contains data analysis and research-oriented components (clustering, financial series, random processes), making it a hybrid but dominantly an application library.
TradingAgents is a multi-agent LLM framework for financial trading research and backtesting. It orchestrates specialized agents (analysts, researchers, debaters, trader, portfolio manager) through a LangGraph state machine to produce trading decisions. The system emphasizes point-in-time data isolation to prevent lookahead bias, supports multiple LLM providers and data vendors, and includes a comprehensive backtesting engine. It is structured as a Python library with a rich CLI interface. The architecture is a hybrid of a software application and a research framework. It demonstrates high craftsmanship in its data pipeline (vendor routing, symbol normalization, date windowing), LLM integration (capability registry, structured output fallback), and concurrency handling (atomic file writes). While the core abstractions are clean and reusable, the codebase is tightly coupled to the specific behaviors of its external dependencies (LLM APIs, Yahoo Finance, SEC EDGAR), making it sensitive to ecosystem drift. The extensive test suite provides good coverage for unit and integration scenarios, but the reliance on mocking means regressions in vendor adapters may go undetected without manual integration runs.
VeighNa (formerly vnpy) is a mature, open-source quantitative trading framework written in Python. It provides a complete ecosystem for developing, backtesting, and executing algorithmic trading strategies across multiple asset classes including Chinese futures, stocks, options, and global markets via interfaces like CTP, XTP, Interactive Brokers, and more. The system is built on an event-driven architecture with a central MainEngine that orchestrates gateways (exchange connections), apps (strategy modules), and engines (services such as order management, logging, email, and WeChat notifications). It offers both a full-featured PySide6 desktop GUI and headless/scriptable operation, making it suitable for both manual and automated trading. The framework includes a dedicated alpha research module (vnpy.alpha) for factor-based strategy development, featuring built-in factor datasets (Alpha101, Alpha158), machine learning models (Lasso, LightGBM, MLP), and a backtesting engine. This gives the repository a hybrid character: primarily an application framework for trading, but with significant data/research capabilities for quantitative analysis. The codebase demonstrates strong engineering practices with plugin-based design, abstract interfaces for databases and datafeeds, and reusable utility classes like BarGenerator, ArrayManager, and OffsetConverter. However, some modules are monolithic and tightly coupled, and the large ecosystem of external gateway packages introduces version compatibility risks.
Hyperswitch is a composable open-source payments infrastructure built in Rust that provides a modular platform for processing payments through multiple connectors. The system handles the complete payment lifecycle including authorization, capture, refunds, and dispute management, while offering features like intelligent routing, 3DS authentication, revenue recovery, and analytics. Built with a microservices architecture using Actix-web, it features a plugin-based connector system, event-driven design with Redis/Kafka, and comprehensive SDK support for web and mobile platforms. The codebase demonstrates strong engineering practices with typesafe ID generation, robust encryption utilities, and well-abstracted interfaces that promote reusability and maintainability.
Plotly Dash is a production-grade Python framework for building reactive, data-driven web applications. It provides a declarative callback system (server-side, clientside, background, streaming), a rich component library (core components, data table, HTML wrappers), and a React/Redux frontend renderer. The architecture supports multiple backends (Flask, Quart, FastAPI), real-time communication via WebSocket, cross-process shared storage (local, diskcache, Redis), and an MCP (Model Context Protocol) server for AI/LLM integration. The monorepo includes component generation tooling, extensive testing infrastructure (unit, integration, visual, WebSocket, background callback), and performance benchmarks. While primarily an application framework, its data table, filtering syntax tree, and shared storage subsystems demonstrate strong data engineering patterns.
hledger is a cross-platform plain text accounting application that provides three user interfaces: a command-line tool (hledger), a terminal UI (hledger-ui), and a web UI (hledger-web). It implements double-entry bookkeeping with support for multiple currencies, investment lot tracking, budgeting, forecasting, and CSV import. Users maintain their financial data in human-readable journal files, and hledger generates reports such as balance sheets, income statements, cash flow, and transaction registers. The project is structured as a Haskell monolith with four packages: hledger-lib (core library for parsing, data models, reporting, and output), hledger (CLI with 30+ commands), hledger-ui (terminal interface using brick/vty), and hledger-web (web interface using Yesod). The core library handles reading multiple input formats (journal, CSV, timedot, timeclock), applying queries and filters, and writing reports in text, CSV, JSON, HTML, and ODS formats. The CSV import system uses a declarative rules engine for mapping external data to journal entries. The codebase demonstrates high craftsmanship with clean abstractions, extensive test coverage (unit tests, shell tests, browser-based Playwright tests), and active maintenance with CI/CD across Linux, macOS, and Windows. It has comprehensive documentation, internationalization support, and a well-defined contribution process. While the project has some technical drift (vendored old web dependencies, multiple build systems), it remains a robust and well-engineered application with several reusable components that could be extracted for other projects.
PyTimeTK is a Python time series toolkit (port of R's timetk) providing a unified API for time series analysis, feature engineering, visualization, anomaly detection, and financial technical indicators. It supports pandas, Polars, and cuDF backends via a clean engine abstraction layer, enabling transparent multi-engine execution. The library is structured around modular subpackages: core (summarization, padding, future frames), feature_engineering (lags, rolling, expanding, Fourier, wavelet, spline, holiday signatures), finance (MACD, RSI, Bollinger Bands, regime detection, risk metrics), plot (Plotly/Plotnine/Matplotlib), datasets, cross-validation, and a beta FeatureStore with MLflow integration. All functions follow a consistent augment_* pattern, register as DataFrame/GroupBy methods via pandas_flavor, and expose a .tk accessor for Polars. The codebase demonstrates strong engineering practices: extensive unit tests, CI workflows, versioned dependencies, and a clear API surface.
Fincept Terminal is a native C++20/Qt6 desktop financial intelligence platform, open-sourced under AGPL-3.0, with a proprietary Enterprise edition. It functions as a comprehensive trading, research, and analytics terminal supporting equities, options, futures, crypto, forex, and prediction markets. The architecture is a modular monolith: a single Qt Widgets process with embedded Python 3.11 for scripting, SQLite for persistence, and a DataHub pub/sub system for real-time data flows. Key entry points are the main window (WindowFrame) and screen router (DockScreenRouter), with over 100 data connectors, 20+ broker integrations, backtesting engines (QuantConnect/LEAN compatibility), AI agents (LangGraph/Agno-based), and extensive analytics (corporate finance, portfolio optimization, options pricing, economic indicators). The repository is actively maintained with a monthly release cycle, but contains a very large codebase with some boilerplate and incomplete features, making it powerful but complex to reuse partially.
Qlib is an open-source, AI-oriented quantitative investment platform developed by Microsoft. It covers the full pipeline for quantitative research and trading: data ingestion and feature engineering (Alpha158, Alpha360, high-frequency), model training (traditional ML like LightGBM/XGBoost, deep learning in PyTorch including LSTM, GRU, Transformer, GATs, and reinforcement learning), backtesting with realistic costs and slippage, portfolio optimization, and online serving for production-like deployment. The platform is highly modular and config-driven, with a core library (qlib) and a separate contrib layer for community extensions. It includes a CLI (qrun) for running full workflows from YAML configs, extensive experiment tracking via MLflow, and a rich set of evaluation metrics. While primarily a library/application, it also functions as a research benchmark suite with many example models and data collectors for global markets. The codebase is active and well-maintained, though some components (e.g., TFT model, older RL code) show signs of technical drift due to dependency version locks and deprecated patterns.
This repository is a curated list (awesome-list) of open-source quantitative finance libraries, packages, and resources. It provides a structured README.md organized by category, with inline language tags, and a pipeline that parses the list, enriches it with GitHub metadata (stars, last commit dates), and generates a static website deployed to GitHub Pages. The repo also includes automated tooling for pull-request review: a validation script that checks entry format, section placement, URL security, duplicate detection, and a full CI-driven PR review workflow. The core logic resides in parse.py (multi-threaded GitHub API enrichment), site/generate.py (static site generator), scripts/validate_readme.py (entry validation), and scripts/review_pr.py (automated PR review with offline URL reachability checks and cross-PR duplicate detection). The project is primarily a reference list, not an application or data pipeline, but it includes high-quality supporting tooling for maintainers and contributors.
Akaunting is a self-hosted accounting and invoicing platform for small businesses and freelancers. It manages invoices, bills, banking transactions, transfers, reconciliations, multi-currency, multi-tax, recurring documents, and financial reports. It includes a customer portal for invoice viewing and payment, and supports multi-company, multi-user with role-based permissions. The system is highly internationalized with 40+ language locales. The architecture is built on a custom PHP framework (Illuminate) that extends Laravel conventions. It uses an event-driven architecture with domain events, listeners, and a job queue for async processing. The frontend combines Blade templates, Vue.js components, and Livewire for interactivity. Multi-tenancy is achieved through company scoping via a global query scope. The system is extensible via a module system with an app store, and includes a CLI for maintenance tasks. Key entry points include the installation wizard (controllers under Install/), authentication flows, and core CRUD controllers for sales, purchases, banking, and settings. Data flows typically involve controllers dispatching jobs that emit events, which listeners handle to update related entities, send notifications, or trigger side effects. Reports use a filter/group/rows event system for extensibility. Import/export is handled via Excel with multi-sheet support. External dependencies include PHP 8.1+, Node 18-20, and a database (MariaDB, MySQL, PostgreSQL, SQLite). It uses Laravel Mix for asset compilation, integrates with payment gateways, email services, and error trackers (Sentry, Bugsnag), and relies on a module marketplace at api.akaunting.com.
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.
OpenBB is an open-source financial data platform that provides a unified, multi-interface access layer to over 20 data providers (FMP, FRED, CBOE, Yahoo Finance, Intrinio, etc.) through a modular provider-extension architecture. The core library (`openbb_core`) defines a standardized Fetcher pattern (transform_query -> extract_data -> transform_data), Pydantic-based standard models for equities, derivatives, fixed income, economics, and more, and a command routing system exposed via a Python SDK (OBBject), a REST API (FastAPI), a CLI (argparse translator), a desktop app (Tauri + React), and an MCP (Model Context Protocol) server for AI agents. The platform includes domain-specific extensions (equity, crypto, economy, quantitative, technical analysis, charting) and a cookiecutter template for scaffolding new providers or OBBject extensions. While the codebase is large and actively maintained, there is notable technical drift in the form of hardcoded API URLs, mixed sync/async extraction, deprecated endpoints, and code duplication across many provider implementations. The charting extension's plugin-based technical indicator system, the country/exchange validation utilities, and the LRU cache with TTL are high-craftsmanship, reusable components that demonstrate non-obvious software engineering skill.
Wallos is a self-hosted PHP web application for tracking personal subscriptions, payments, and budgets. It provides a full CRUD interface for subscriptions with categories, currencies, payment methods, and household members. The backend is PHP 8.3 with SQLite, served via Nginx and PHP-FPM in a Docker container. It supports multi-user accounts, OIDC/OAuth login, TOTP two-factor authentication, and email/password reset flows. The application includes a rich set of notification channels (email, Discord, Telegram, Pushover, Gotify, ntfy, Web Push, webhooks, and more) triggered by cron jobs for upcoming payments and cancellations. A statistics dashboard aggregates costs, trends, and budget comparisons. The codebase is organized as a flat PHP application without a framework, using custom includes for shared logic and a simple API layer for external access. The architecture follows a page-based pattern where each PHP file serves as a route, with AJAX endpoints under `/endpoints/` for dynamic actions. The application demonstrates attention to security with CSRF tokens, prepared statements for all SQL queries, input validation, and a centralized SSRF protection module for outbound HTTP requests. A notable feature is the Web Push notification implementation that follows RFC 8290-8292 using VAPID and ECDH encryption. The currency conversion system uses a WeakMap-based cache per request to avoid repeated API calls. The codebase is actively maintained with frequent releases and a comprehensive migration system. However, the code exhibits significant repetition across notification channels and logo handling, with many nearly identical endpoint files. The i18n system is manually maintained across 20+ languages, leading to potential drift. The application lacks a formal testing framework beyond a custom test harness that checks security regressions and data integrity. The absence of a Composer dependency manager and reliance on vendored libraries (PHPMailer, Parsedown) adds maintenance overhead. Overall, Wallos is a functional, feature-rich self-hosted application with some well-crafted security components but suffers from code duplication and boilerplate typical of PHP applications without a framework.
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.
Firefly III is a self-hosted personal finance management application built on the Laravel PHP framework. It provides comprehensive double-entry bookkeeping with support for multiple currencies, accounts, budgets, categories, bills, piggy banks, recurring transactions, and rules. The system exposes both a full web UI using Blade templates and Alpine.js (with legacy jQuery components) and a RESTful JSON API (v1) for programmatic access. Key features include transaction splitting, budget limits, auto-budgeting, rule-based transaction processing, exchange rate management, webhook integration, multi-user with group administration, and detailed reporting with charts. The codebase follows a repository pattern with interfaces, uses events and listeners for side effects, and includes a substantial set of artisan commands for maintenance, upgrades, and data corrections.
This repository contains the backend API for a trading platform, specifically QuantDinger. It handles strategies, indicators, and market data, and has various tests and scripts for ensuring the quality and functionality of the code.
This repository is a self-paced learning roadmap for algorithmic trading, structured as a hierarchical collection of markdown files. It curates resources, certifications, and project ideas across artificial intelligence, computer science, mathematics, finance, cloud engineering, and software engineering. The repository is entirely documentation-driven; there are no executable applications, data pipelines, or machine learning models. Its primary value lies in its organized curriculum, reusable topic templates, and role-based learning paths (e.g., quant developer, algorithmic trader). While not a software project, it demonstrates high organizational craftsmanship through consistent documentation structure, templates, and CI/CD for link checking and diagram rendering.
Cashew is a production-grade, cross-platform personal finance and budget tracking application built with Flutter/Dart. It manages transactions, multi-currency wallets, budgets with category-level spending limits, savings goals and loans, subscriptions, recurring/auto-pay rules, credit/debt tracking, and spending analytics (pie charts, line graphs, heatmaps, net worth summaries). It ships to Android (with home screen widgets), iOS, web (PWA), and desktop. Data persists locally in SQLite via the Drift ORM with 46 managed schema migrations, and synchronizes across devices through a custom Google Drive sync client, with shared-budget features on Firebase Auth/Firestore/Cloud Functions, plus optional Gmail and notification scanning for auto-generated transactions.