taurusai/kungfu
Kungfu
Kungfu is a quantitative trading development framework that provides a hybrid Python and C++ runtime. It is designed for building, testing, and executing mathematical trading models and algorithmic strategies.
The system centers on a high-performance architecture featuring append-only event journaling and deterministic event replay. These capabilities allow recorded data streams to be reproduced using the same semantics as live execution for high-precision simulation. To minimize latency, the framework utilizes zero-copy cross-language memory sharing and schema-based data consistency to eliminate serialization overhead between programming languages.
The project also includes a quantitative research toolchain with embedded management for Python and Node runtimes to automate dependency configuration. Additionally, it provides SDKs and extension points to build custom applications based on its shared journal and state infrastructure.
Features
- Quantitative Trading Platforms - Offers a high-performance hybrid Python and C++ framework for building, testing, and executing mathematical trading models.
- Algorithmic Trading Simulators - Allows for high-precision testing of trading strategies by reproducing live market data streams.
- Cross-Language Zero-Copy Passings - Eliminates serialization overhead by sharing in-process data between different programming languages using direct memory access.
- Hybrid Runtimes - Combines a low-level C++ core for performance-critical operations with high-level Python scripting for strategy development.
- Deterministic Record and Replay Frameworks - Provides high-precision simulation by reproducing recorded data streams using the same runtime semantics as live execution.
- Trading Strategy Development Environments - Provides a unified environment for developing and executing quantitative trading strategies within a hybrid Python and C++ runtime.
- Append-Only Event Logs - Implements an immutable sequence of events to ensure strict ordering and consistent recovery of the trading system state.
- Data Stream Recording and Replay - Reproduces recorded data streams using identical runtime semantics to enable precise offline analysis and simulation.
- Immutable Event Appends - Records high-frequency events into a low-latency, immutable append-only journal to preserve state history.
- High-Throughput Data Streaming - Processes rapid event streams into low-latency journals to maintain a consistent and high-throughput data record.
- Data Schema Enforcement - Enforces a shared data format across the system to maintain structural integrity between data producers and consumers.
- Algorithmic Trading Engines - High-performance trading system.
Star history
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