Open-source alternatives to Kungfu

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

OpenTSDB is a distributed time series database and metrics engine designed for storing and managing massive volumes of high-cardinality system metrics. It functions as a data store and analytics platform that enables large-scale metric ingestion and infrastructure performance monitoring across a distributed cluster. The system distinguishes itself through a distributed storage abstraction that supports multiple backends such as HBase, Cassandra, and Google Bigtable. It utilizes a hierarchical metric tree to organize time series and employs numeric identifier indexing to reduce storage footpri

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

StockSharp is an algorithmic trading platform and quantitative framework used for developing and deploying trading robots across stock, forex, and cryptocurrency markets. It functions as a multi-asset trading gateway and a dedicated development environment for building, debugging, and scheduling automated strategies. The platform includes a visual strategy workflow editor that maps logic blocks to executable code and a simulation engine that replays historical tick data to validate trading logic. It utilizes a plugin-based broker integration system to normalize diverse exchange protocols into

quant-wiki is a comprehensive knowledge base and structured reference for quantitative finance, financial engineering, and algorithmic trading. It serves as a centralized library of documentation covering mathematical models, financial instruments, and systematic trading strategies. The project integrates AI-driven capabilities through a modular retrieval-augmented generation framework that extracts structured data from research papers and news. It features a multi-agent workflow engine designed to discover and validate predictive alpha factors, alongside tools for local large language model

Hikyuu is a quantitative trading framework designed for developing, backtesting, and executing systematic trading strategies. It functions as a high-speed system that combines a financial time-series library, a multi-factor analysis tool, and a quantitative backtesting engine to support comprehensive trading research. The framework is distinguished by its high-speed computing core, which utilizes multi-threaded execution to process large volumes of market data for technical indicator generation. It supports a modular strategy composition model where signal, risk, and fund management component

This project is a futures algorithmic trading system designed to execute high-performance trading strategies through direct API integrations and low-latency message routing. It features a strategy execution engine that automates order placement and manages trade flows based on predefined logic and API triggers. The system utilizes a native trading API bridge and a low-latency message bus to interface internal logic with external exchange APIs while minimizing execution delays. Monitoring is handled through a web-based trading dashboard for real-time activity tracking and remote management. B

This project is a high-performance C++ trading engine designed for automated cryptocurrency market making and high-frequency trading. It functions as a liquidity provision tool that executes rapid order adjustments and quote placements to capture the bid-ask spread. The system utilizes a fair-value pricing model to estimate target asset prices based on real-time exchange data. It features a self-hosted trading dashboard that provides a web interface for monitoring portfolio holdings, visualizing market metrics, and manually controlling automated trading instances. The software includes capab

tqsdk-python is a quantitative trading SDK and framework designed for developing automated strategies for futures, options, and stocks using Python. It functions as an algorithmic trading engine and financial market data API, providing the tools necessary to backtest strategies, analyze historical data, and execute live trades across multiple brokerage accounts. The project distinguishes itself through a specialized option analytics library that calculates Greeks, implied volatility, and volatility surfaces using the Black-Scholes model. It further supports complex order execution patterns, s

This is an open-source, crowd-sourced wiki textbook that teaches Linux system programming in C. It covers the core operating system concepts of process management through the fork-exec-wait model, dynamic memory allocation using implicit free list heap allocators, inode-based file systems, inter-process communication via pipes and shared memory, POSIX threads with synchronization primitives, signal-based asynchronous notification, virtual memory with page table translation, and runtime diagnostics using Valgrind and GDB. The textbook distinguishes itself by providing practical, implementation

This project is a comprehensive framework for engineering financial data pipelines, designed to automate the collection, cleaning, and synchronization of large-scale market datasets. It functions as a quantitative trading data engine, providing the infrastructure necessary to manage historical and real-time asset pricing information for research and machine learning workflows. The system distinguishes itself through a configuration-driven approach to orchestration, allowing users to manage complex data acquisition tasks across multiple financial providers. It features resilient middleware tha

This project is a Python quantitative trading framework and library designed for developing, backtesting, and deploying automated financial strategies. It serves as both an algorithmic trading backtester for evaluating historical performance and an event-driven trading engine for executing trades based on quantitative rules. The framework functions as an educational toolkit, providing guided lessons and resources for quantitative finance learning and the application of mathematical models to market data. The system provides capabilities for algorithmic trading automation and financial strate

finmarketpy is a quantitative trading framework and financial market analysis tool. It provides a Python-based library for simulating trading strategies against historical market data, computing the value of options contracts, and extracting trends from financial datasets. The system includes specialized engines for financial options pricing using numerical calculations and a backtesting library to assess risk and performance before live deployment. It further enables the detection of market seasonality and the execution of event studies to measure asset price behavior around specific time wi

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 elimi

Apache IoTDB is a time-series database designed for the Internet of Things, purpose-built to ingest high-volume data from millions of low-power devices and store timestamp-value pairs with configurable data types and encoding schemes. It organizes time series data and device metadata in a tree-like hierarchy, enabling efficient management of complex industrial sensor networks. The database supports rich querying capabilities, including time-aligned data retrieval across multiple devices, time-based aggregation like downsampling, and frequency-domain signal analysis. It provides high-throughpu

FinRL-Trading is a modular framework designed for the development, training, and deployment of quantitative trading strategies using reinforcement learning and autonomous agent workflows. It provides a comprehensive infrastructure for managing the entire lifecycle of financial models, from data ingestion and strategy generation to live market execution. The platform distinguishes itself through a multi-agent architecture that coordinates specialized tasks such as sentiment analysis, risk assessment, and collaborative research. By utilizing a standardized environment abstraction, it allows rei

InfluxDB is a high-performance time-series database designed for collecting, storing, and querying time-stamped metrics and event data. It functions as a columnar time-series store and a real-time analytics engine, providing a network-accessible interface for retrieving and analyzing temporal records. The system utilizes a specialized columnar storage format to support high ingestion rates and efficient data retrieval. It incorporates a programmable runtime for executing custom plugins and triggers, including integration for processing and transforming incoming data streams. The platform cov

Kronos is a financial time-series forecasting framework and quantitative trading strategy simulator. It functions as a research environment designed to analyze historical market data, train predictive models, and evaluate the performance of automated trading signals. The platform distinguishes itself through its deep learning sequence predictors and probabilistic market modeling tools. By utilizing sequence-based architectures and statistical sampling, the system generates multiple potential price trajectories and volatility estimates to quantify uncertainty. It also supports transfer learnin

This project is a cross-language quantitative trading framework designed to implement and execute trading strategies consistently across Python, JavaScript, C++, and PineScript. It functions as a polyglot trading strategy translator and a multi-language algorithmic trading engine that maps high-level scripting and block-based logic to executable binaries. The system features a financial domain-specific language parser that translates specialized trading syntax and visual programming blocks into a standardized internal representation. It includes a technical analysis pattern library providing

Quantaxis is a quantitative trading framework designed for building, backtesting, and executing automated strategies across global equities, futures, and cryptocurrencies. It integrates an event-driven backtesting engine, a multi-market execution gateway for order routing, and a quantitative data pipeline for ingesting and storing multi-asset market data. The system features a Rust-accelerated financial library that utilizes Apache Arrow for high-performance technical indicator calculation and zero-copy data processing. It provides a containerized infrastructure model designed for orchestrati

This project is an algorithmic trading engine designed for the automated execution of cryptocurrency strategies. It provides a modular execution core that connects to multiple centralized and decentralized exchanges, allowing users to deploy rule-based trading logic across various spot and futures markets. The platform serves as a comprehensive environment for the entire trading lifecycle, from initial strategy development to live market operations. What distinguishes this platform is its integrated suite for quantitative analysis and predictive modeling. It features a robust backtesting engi

QuantMuse is an algorithmic trading platform and quantitative trading framework that integrates large language models with mathematical analysis to automate market insights and trading strategies. It functions as a system for building, backtesting, and executing strategies using both historical and real-time market data. The framework is distinguished by its use of large language models for financial analysis and sentiment extraction from news and social media. It utilizes autonomous agents with chain-of-thought reasoning to generate market intelligence and strategic reports, while employing

This project provides technical documentation and reference guides for spot trading, including specifications for REST, WebSocket, and FIX protocols. It serves as a comprehensive resource for integrating with spot trading endpoints to execute trades, query account data, and fetch market statistics. The project distinguishes itself by supporting institutional-grade connectivity through the Financial Information eXchange standard and simple binary encoding to reduce latency and payload size. It also includes a dedicated sandbox environment for validating trading logic and strategies without fin

ElegantRL is a deep reinforcement learning framework and quantitative trading platform designed for automating financial decision making. It provides a system for designing and training agents using massively parallel GPU execution and includes a coordination layer for multi-agent reinforcement learning. Additionally, it features a GPU-based solver for NP-complete and nonconvex mathematical optimization problems. The platform distinguishes itself through GPU-accelerated environments that simulate thousands of parallel market interactions on a single device to accelerate data collection. It in

QuantResearch is a quantitative research framework and specialized toolkit for algorithmic simulation, financial time-series analysis, and systematic trading. It provides an event-driven backtesting environment for validating strategies against historical tick and bar data, alongside a dedicated portfolio optimization engine for calculating asset weights and risk metrics. The project distinguishes itself through a machine learning finance toolkit that implements recurrent neural networks for price prediction and reinforcement learning for derivative pricing. It also features advanced statisti

Lean is an algorithmic trading engine and quantitative finance platform designed for the development, backtesting, and live execution of automated trading strategies. It provides a comprehensive framework for processing time-series market data, managing multi-asset portfolios, and conducting quantitative research across diverse financial markets. The platform distinguishes itself through a modular, event-driven architecture that decouples strategy logic from data ingestion and brokerage connectivity. By utilizing standardized interfaces for data providers and brokerage abstractions, it enable

Backtrader is a Python framework designed for the development, backtesting, and live execution of algorithmic trading strategies. It provides a comprehensive environment for quantitative finance, allowing users to simulate trading logic against historical market data or connect directly to brokerage platforms for automated real-time trading. The project distinguishes itself through a unified event-driven architecture that treats backtesting and live trading with the same API. This consistency is supported by a flexible data-feed abstraction layer that normalizes diverse financial sources, ena