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kungfu-origin/kungfu

0
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3,876 stars·1,207 forks·C++·Apache-2.0·28 views

Kungfu

Kungfu is a high-performance quantitative trading framework and algorithmic execution engine. It provides a development kit for writing trading logic and a dedicated in-memory financial time-series database for recording and analyzing tick-by-tick market data with high temporal accuracy.

The system integrates a market gateway layer to interface with stock and futures exchange protocols, enabling the routing of orders and receipt of price updates. It features a trading operations interface that allows for the management of strategies in both manual and automated headless modes.

The framework focuses on low-latency execution and high-precision timing. Its capabilities cover quantitative strategy development, high-frequency trading execution, and the storage of financial time series data.

Features

  • Low-Latency Trading Infrastructures - Provides a high-performance infrastructure capable of processing market data with microsecond latency for high-frequency trading.
  • Market Trading Integrations - Provides connectors to financial market exchanges and counters for executing trades across stocks and futures.
  • Quantitative Trading Platforms - Offers an integrated environment for writing and testing quantitative algorithmic trading logic using computational libraries.
  • Trading Execution Engines - Ships a low-latency algorithmic trading execution engine that processes market data and routes orders with microsecond response times.
  • Time Series Data Storage - Provides high-performance in-memory storage for recording financial tick data with nanosecond accuracy.
  • Financial Tick Databases - Provides a dedicated in-memory financial time-series database for high-precision recording and analysis of tick data.
  • High-Precision Time Series Storage - Records high-precision market tick data in memory for real-time analysis and historical auditing.
  • Memory-Mapped Time Series Storage - Ships an in-memory financial time-series database using memory-mapped formats for low-latency tick data retrieval.
  • Multi-Exchange Trading Connectors - Implements an integration layer with connectors for various stock and futures exchange protocols.
  • Trading Logic Development Kits - Provides a development kit for writing trading logic using computational libraries and general purpose programming languages.
  • Trading Strategy Management Interfaces - Includes a trading operations interface for monitoring and managing algorithmic strategies in both manual and headless modes.
  • Shared Memory IPC - Implements a shared-memory data bus using memory-mapped files for zero-copy, nanosecond-latency inter-process communication.
  • Non-Blocking I/O Interfaces - Implements non-blocking I/O interfaces for exchange connectivity to maintain high throughput without stalling the execution engine.
  • Process Isolation - Executes trading strategies in separate operating system processes to ensure a single strategy failure does not crash the system.
  • Lock-Free Ring Buffers - Uses lock-free ring buffers with atomic pointers to minimize synchronization overhead during high-frequency data bursts.
  • Nanosecond Event Timestamping - Uses high-resolution hardware clocks to provide nanosecond precision timestamping for precise event sequencing and latency analysis.

Star history

Star history chart for kungfu-origin/kungfuStar history chart for kungfu-origin/kungfu

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Frequently asked questions

What does kungfu-origin/kungfu do?

Kungfu is a high-performance quantitative trading framework and algorithmic execution engine. It provides a development kit for writing trading logic and a dedicated in-memory financial time-series database for recording and analyzing tick-by-tick market data with high temporal accuracy.

What are the main features of kungfu-origin/kungfu?

The main features of kungfu-origin/kungfu are: Low-Latency Trading Infrastructures, Market Trading Integrations, Quantitative Trading Platforms, Trading Execution Engines, Time Series Data Storage, Financial Tick Databases, High-Precision Time Series Storage, Memory-Mapped Time Series Storage.

Which projects share features with kungfu-origin/kungfu?

Projects with overlapping indexed features include: ai4finance-foundation/finrl — FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated… nautechsystems/nautilus_trader — Nautilus Trader is a high-performance algorithmic trading framework built in Rust, designed for the development,… opentsdb/opentsdb — OpenTSDB is a distributed time series database and metrics engine designed for storing and managing massive volumes of… michaelgrosner/tribeca — Tribeca is a Node.js cryptocurrency trading platform designed for high-frequency trading and automated market making.… m3db/m3 — m3 is a distributed time series database designed for high-resolution metrics and high-cardinality data management. It… shidenggui/easytrader — Easytrader is a quantitative trading automation framework and brokerage API wrapper designed to programmatically…

Projects sharing features with Kungfu

These projects share indexed features with Kungfu. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • ai4finance-foundation/finrlAI4Finance-Foundation avatar

    AI4Finance-Foundation/FinRL

    13,964View on GitHub↗

    FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated trading strategies. It functions as a quantitative finance toolkit that integrates deep learning algorithms with financial market simulations to address complex portfolio management and asset allocation tasks. The platform provides an end-to-end pipeline for transforming raw market data into actionable trading models. The project distinguishes itself through a layered, modular architecture that separates data processing, environment simulation, and agent training. This design allow

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    View on GitHub↗13,964
  • nautechsystems/nautilus_tradernautechsystems avatar

    nautechsystems/nautilus_trader

    20,056View on GitHub↗

    Nautilus Trader is a high-performance algorithmic trading framework built in Rust, designed for the development, backtesting, and live execution of automated trading strategies. It provides a comprehensive platform for managing multi-asset portfolios and interacting with diverse financial markets through a standardized connectivity suite. The system is engineered to handle high-frequency data processing and complex order execution while maintaining precise numerical accuracy across various asset classes. The framework distinguishes itself through an architecture centered on deterministic even

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    View on GitHub↗20,056
  • m3db/m3m3db avatar

    m3db/m3

    4,895View on GitHub↗

    m3 is a distributed time series database designed for high-resolution metrics and high-cardinality data management. It functions as a scalable storage system and a multi-cluster query engine, providing a distributed metrics aggregator capable of downsampling and summarizing data before it is committed to storage. The project distinguishes itself through a coordinated cluster model using etcd for node membership and shard placement. It supports multiple ingestion protocols, including the Prometheus remote write protocol, InfluxDB line protocol, and Graphite Carbon plaintext protocol, and provi

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    View on GitHub↗4,895
  • michaelgrosner/tribecamichaelgrosner avatar

    michaelgrosner/tribeca

    4,112View on GitHub↗

    Tribeca is a Node.js cryptocurrency trading platform designed for high-frequency trading and automated market making. It functions as a low-latency execution engine that automates the process of providing liquidity and capturing price spreads across multiple cryptocurrency exchanges. The system employs a connectivity layer to stream real-time market data and execute trades via persistent network connections. It uses an adapter-based integration to normalize diverse venue APIs into a unified format, allowing for coordinated activity across several different cryptocurrency venues. The platform

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