global-stock-data is a comprehensive financial data pipeline designed for AI coding assistants to access US and Hong Kong market data. It implements a 13-layer architecture that fetches data from 11 diverse sources including exchanges (CBOE), regulatory bodies (SEC, FINRA), financial data providers (Eastmoney, Yahoo Finance), and macroeconomic sources (Treasury, CFTC). The system normalizes data from various formats (JSON, CSV, text) into consistent schemas for real-time quotes, historical prices, technical indicators, fundamentals, options data, SEC filings, short volume, and macroeconomic indicators. Unlike a traditional application, it functions as a reusable data toolkit where each layer can be independently accessed to solve specific financial data acquisition problems.
This repository is a comprehensive, production-oriented algorithmic trading framework implemented in TypeScript. It is organized as a monorepo with six interdependent packages: `trading-signals` (100+ technical indicator implementations with streaming `add`/`replace` semantics, each verified against external reference data like Tulip Indicators and Skender.Stock.Indicators), `trading-strategies` (strategy base classes, concrete strategies such as SMA crossover, scalp, trailing stop, and a multi-indicator confluence strategy, plus a backtesting engine and report generators), `exchange` (abstract broker interface with real implementations for Alpaca and Trading212, including mock broker for backtesting, candle batching, WebSocket streaming, and a CLI tool), `messaging` (a Telegram bot for managing accounts, strategies, watches, and reports via multi-step wizards), `candles` (labeled historical candle datasets for testing), and `trading-signals-docs` (a Next.js documentation site with interactive indicator demos and a visual backtester). The architecture emphasizes clean separation between signal generation, strategy logic, broker execution, and user interface. The indicator library is the core asset, providing a consistent contract (`IndicatorSeries`, `TechnicalIndicator`, `TrendIndicator`) with rich signal detection and state rollback. The strategy framework supports live trading and backtesting via the same advice-execution pipeline. The project is actively maintained with CI/CD, mutation testing, and a well-defined broker integration template. While the codebase shows high craftsmanship in its indicator implementations and test patterns, the large number of indicators and hand-derived test fixtures introduce maintenance overhead, and the tight coupling to Telegram limits platform portability.
This repository implements a complete quantitative trading framework for A-shares, known as 'zettaranc-skill'. It is a dual-track project: (1) a Python/Rust software application providing CLI tools, a FastAPI REST API, and a React frontend for stock analysis, backtesting, simulation, screening, and strategy optimization; (2) an AI character-play skill pack that encodes a specific trading methodology (Zettaranc/Shaofu strategies) into a SKILL.md for LLM role-play. The application side dominates: it has 15+ CLI subcommands, a modular architecture with 30+ technical indicators, 20+ strategy detectors, a multi-source data layer (Tushare, Indevs, A-Stock-Data, Hithink, SQLite), a Rust-accelerated backtesting engine (via PyO3), a walk-forward simulation framework, and a self-optimization pipeline. The codebase is well-structured with clean abstractions (DataSource protocol, unified error codes, parameter registration), extensive test coverage, and CI/CD. However, it suffers from some technical drift: dual Python/Rust implementations must stay synchronized, Chinese vernacular naming reduces readability, hardcoded constants persist, and the self-optimizer's LLM judge is a stub. Overall, it is a production-grade personal quant framework with high craftsmanship in its core components.
Jesse is a mature, full-stack algorithmic trading framework designed for cryptocurrency backtesting, live/paper trading, hyperparameter optimization, Monte Carlo simulation, and rule significance testing. It combines a Python library with a FastAPI web server and a Nuxt.js dashboard, offering a comprehensive environment for developing and validating trading strategies. The repository also includes an MCP (Model Context Protocol) server for AI-assistant integration, over 180 technical indicators (many with Rust-accelerated backends), and a research API for programmatic strategy analysis. Architecture is modular with distinct layers: CLI entrypoint → web controllers / MCP tools → service layer → simulation engine (backtest/optimize/live) → exchange adapters. State management uses global singletons for in-memory simulation state, while persistent data is stored via Peewee ORM (PostgreSQL, optional TimescaleDB). The indicator library showcases a clean pattern with sequential/non-sequential modes and Rust fallback. However, the codebase shows signs of organic growth with some tightly coupled modules (global config, store singletons) and a large static frontend bundle that is opaque to analysis. Key strengths include the breadth of supported exchanges (10+), robust backtesting with realistic fee/margin/leverage modeling, and a well-designed MCP tool layer for AI-assisted development. Weaknesses include exchange driver duplication, a complex Rust subprocess for coordination, and dependency on many external APIs that require ongoing maintenance.
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.
OsEngine is a comprehensive, multi-exchange algorithmic trading platform for desktop (WPF/WinForms hybrid). It provides data downloading (OsData), backtesting (OsTester), walk-forward optimization (OsOptimizer), and live trading (OsTrader) with support for 30+ exchange connectors (MOEX, Binance, Bybit, OKX, Interactive Brokers, etc.). The platform includes a built-in technical indicator library (100+ indicators), a trading robot framework with attribute-based registration, and an MCP (Model Context Protocol) API for external AI/automation control. The architecture is modular: separate executables for different modes, a common server abstraction layer, and a consistent parameter system for bots and indicators. While the codebase is large and well-organized, it suffers from significant duplication across exchange connector entity classes and some copy-paste logic in trading robots. The UI mixes WPF and Windows Forms, and some patterns are legacy. Overall, it is a production-grade algorithmic trading platform with strong engineering practices and a rich feature set.
This repository is a Python client library for the Alpha Vantage financial data API. It provides synchronous and asynchronous wrappers for accessing stock/cryptocurrency time series, technical indicators, foreign exchange rates, commodities, economic indicators, fundamental data, options, and news sentiment. The library is designed as a straightforward SDK with a decorator-based architecture that handles API key injection, URL construction, and output formatting (JSON, pandas DataFrame, CSV). The codebase includes a comprehensive test suite with mock data and integration tests. However, it exhibits technical drift: declared support for Python 2.7 (EOL), an outdated CI configuration (Travis on Ubuntu Xenial), a hardcoded version number in documentation that differs from the library version, and duplication of core logic between synchronous and async modules. The library is functional and widely used, but not exemplary in terms of modern Python practices or maintainability.
This repository is the companion code for the Packt book 'Learn Algorithmic Trading'. It contains a collection of Python scripts, data files (pickle, CSV, HDF5), and a FIX protocol simulator organized by book chapters. The code demonstrates algorithmic trading strategies (mean reversion, trend following, pairs trading, turtle trading), technical indicators (EMA, MACD, RSI, Bollinger Bands), machine learning models (KNN, SVM, logistic regression, linear/ridge/lasso), backtesting frameworks (for-loop and event-based), risk management metrics, and a FIX 4.4 simulator with custom FX fields. The repository is primarily educational and not intended for production use. It exhibits significant technical drift: dependencies are not pinned, data sources (Yahoo Finance via pandas_datareader) are deprecated, Python 2.7 compatibility is assumed, and the code relies on very old library versions (pandas 0.14.1/0.15.2). Reproducibility is low due to missing environment definitions and hardcoded paths. However, some components (OrderManager, FIX simulator, risk measures) are well-structured and could be extracted for reuse with minimal adaptation.