thehardwarestore.ai

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.

TqSdk (天勤量化) is a comprehensive Python SDK for quantitative trading in Chinese futures, options, and stock markets. It provides real-time market data via WebSocket, multi-broker account management (CTP, simulation, backtest), order execution, position management, and a full backtesting engine with replay capabilities. The architecture follows a client-server diff protocol: the SDK maintains an in-memory snapshot of business data (quotes, orders, positions) and processes incremental updates delivered via an async event loop. Users write strategies as Python scripts using `TqApi`, `wait_update()`, and helper classes like `TargetPosTask`. The SDK includes a rich set of demo strategies, technical indicators (60+), options pricing (Black-Scholes Greeks), risk management rules, scenario trials, and a web GUI for visualization. It also provides AI skill packs for integration with coding assistants. While primarily an application library, it contains strong data engineering components for time-series handling and backtesting.

This repository is a production-grade, AI-powered stock analysis platform for CN, HK, US, JP, KR, and TW markets. In production it runs a scheduled analysis pipeline that fetches market data from multiple providers (akshare, yfinance, tushare, efinance, futu, etc.) with priority-based fallback, feeds the data to LLM backends via LiteLLM, generates structured decision reports, and pushes them through 15+ notification channels (WeChat, Feishu, DingTalk, Telegram, email, Discord, Slack, etc.). It also exposes a FastAPI REST API, a React/TypeScript web dashboard, an Electron desktop client, and bot integrations for Feishu/DingTalk/Discord. The system includes agent-based chat with tool use, screening, backtesting, alerts, portfolio management, decision-signal tracking, and market review capabilities.

This is a Python library for technical analysis of financial markets based on 'Chan Theory' (缠论). It computes complex structural elements such as fractals, strokes (bi), line segments, trading ranges (zhongshu), and buy/sell points from candlestick data. The library supports multiple data sources (BaoStock, Akshare, CSV, CCXT), multi-level analysis, customizable algorithms, and plotting via Matplotlib. The code is well-structured with clear separation of concerns, extensive use of caching for performance, and a flexible configuration system. It includes a GUI scanner application for A-shares and demo strategies. The library is designed for both research and production use, with incremental calculation capabilities for real-time updates. While the open-source version omits trading engine and ML features described in the full version, the core analytical engine is robust and reusable.

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.

goex is a Go library that provides unified interfaces for cryptocurrency exchanges (OKX, Binance, Bitget). It abstracts exchange-specific API differences behind common interfaces for both public market data and private trading operations. The architecture features modular exchange-specific packages, shared utilities for HTTP clients, logging, and model definitions, and uses dependency injection for configuration. Key strengths include clean separation of concerns, proper implementation of cryptographic signing, and use of the functional options pattern for configuration.

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.

This repository is a Chinese futures algorithmic trading system centered on the CTP (Commodity Trading Platform) native protocol. It comprises a C++ pybind11 bridge for low-latency market data and order execution, Python trading strategies (e.g., channel breakout, experimental transformer-based models), and a Django web dashboard for monitoring positions, orders, signals, performance, and correlations. The architecture is hybrid: the native module handles real-time CTP communication, while Python strategies run on an in-process event bus with Redis-like pub/sub. The dashboard visualizes trading metrics with ECharts and provides admin CRUD via Django admin. The system is production-oriented but exhibits several maintenance concerns: hardcoded secrets, outdated frontend libraries (Bootstrap 3, jQuery 3.1), duplicate config loading, and an incomplete ML integration. Despite these, the core CTP bridge and strategy framework are well-constructed and demonstrate solid engineering for the niche domain of Chinese futures trading.