For auto copy trading crypto ai, the first results are vnpy/vnpy (VeighNa is an algorithmic trading platform offering exchange integration, backtesting, and real-time execution, though it focuses on quantitative development rather than built-in AI strategies and social copy trading out of the box), charliedream1/ai_quant_trade (This repository provides an AI-driven quantitative trading platform with machine learning support, backtesting, and automated execution, fitting the trading bot and strategy requirements well despite lacking explicit mention of copy trading) and hummingbot/hummingbot (Hummingbot is an open-source framework for building, backtesting, and deploying algorithmic crypto trading bots with support for AI assistant integrations and exchange connectors, though it focuses more on modular strategy building than native copy trading out of the box). deviavir/zenbot and askmike/gekko round out the shortlist. Compare the match explanations and check the project documentation against your requirements.
Compare open-source crypto copy trading bots and AI-powered automated trading repositories on GitHub to find your solution.
VeighNa is an event-driven, modular platform designed for the development, backtesting, and execution of automated financial trading strategies. It provides a comprehensive suite of tools that includes a centralized trading terminal for monitoring portfolios and market conditions, alongside a robust algorithmic trading engine that manages real-time data processing and order execution. The platform distinguishes itself through a highly decoupled architecture that isolates algorithmic logic from market connectivity, allowing for independent strategy development and testing. It utilizes a dynami
VeighNa is an algorithmic trading platform offering exchange integration, backtesting, and real-time execution, though it focuses on quantitative development rather than built-in AI strategies and social copy trading out of the box.
aiquanttrade is an AI-driven quantitative trading platform that enables the development, backtesting, and deployment of trading strategies powered by machine learning and artificial intelligence. It provides a complete local environment for quantitative research, simulation, and automated live trading through brokerage APIs, supporting both historical backtesting and real-time paper trading without capital risk. The platform distinguishes itself through a modular, event-driven architecture that separates strategy logic from execution, allowing rule-based and machine learning models to be co
This repository provides an AI-driven quantitative trading platform with machine learning support, backtesting, and automated execution, fitting the trading bot and strategy requirements well despite lacking explicit mention of copy trading.
Hummingbot is an open-source framework designed for building, backtesting, and deploying autonomous trading agents and algorithmic strategies across centralized and decentralized cryptocurrency exchanges. It provides a modular environment where users can orchestrate containerized bots to execute complex market-making, grid trading, and arbitrage operations. The platform distinguishes itself through a skill-based architecture that integrates large language models, enabling users to monitor market conditions and control trading operations via natural language commands. It features a unified con
Hummingbot is an open-source framework for building, backtesting, and deploying algorithmic crypto trading bots with support for AI assistant integrations and exchange connectors, though it focuses more on modular strategy building than native copy trading out of the box.
Zenbot is an automated cryptocurrency trading bot designed to execute trades on exchanges based on technical analysis and predefined risk parameters. It functions as a technical analysis engine that processes market data through mathematical indicators to generate actionable trade signals. The system includes a genetic algorithm strategy optimizer to automatically discover the most profitable parameter configurations. It provides multiple simulation environments, including a trading strategy backtester for replaying historical data and a paper trading simulator for testing strategies against
Zenbot is an established automated cryptocurrency trading bot featuring exchange integration, backtesting, and risk management parameters, though it lacks built-in copy trading functionality.
Gekko is a Node.js trading platform and automated Bitcoin trading bot designed to execute buy and sell orders across multiple cryptocurrency exchanges. It functions as an algorithmic trading system that uses a standardized exchange integration gateway to connect with various external trading platforms. The system includes a backtesting engine that simulates trading strategies against historical market data to evaluate performance before live deployment. It employs an adapter-based integration model to normalize diverse exchange API responses into a consistent internal format. The platform pr
Gekko is a JavaScript-based automated cryptocurrency trading bot that includes exchange integration, a backtesting engine, and algorithmic execution, though it focuses on traditional technical analysis strategies rather than built-in AI or copy trading features.
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
Quantmuse is a quantitative algorithmic trading framework that integrates large language models for financial analysis and strategy automation, matching the core AI requirement though lacking explicit built-in copy trading features.
This project is an automated trading and agentic workflow platform designed to orchestrate complex financial tasks through state-based graphs. It provides a comprehensive framework for building, deploying, and managing autonomous agents that execute multi-step analytical processes, monitor real-time market conditions, and perform high-speed trade execution. The platform distinguishes itself through a robust agentic plugin ecosystem that integrates directly with popular AI-powered development environments and command-line interfaces. It features a specialized financial analysis engine capable
This project is an automated trading and agentic workflow platform driven by AI, though it focuses more on agent orchestration and multi-step analytical processes rather than out-of-the-box copy trading.
This project is an automated cryptocurrency trading platform and server-side framework designed for executing trades and managing digital asset portfolios. It provides a unified environment for running algorithmic strategies across multiple exchanges, allowing for the automated placement of buy and sell orders without manual intervention. The system distinguishes itself through a modular plugin architecture that separates trading logic from core execution, enabling users to customize or swap algorithms as needed. It utilizes an event-driven execution loop that processes real-time market data
This repository provides an automated cryptocurrency trading platform with exchange integration, backtesting, and algorithmic execution, though it lacks dedicated native support for AI strategies and copy trading out of the box.
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
This Python-based algorithmic trading engine provides exchange integration, backtesting, and automated execution for cryptocurrency strategies, though it lacks built-in native copy trading functionality out of the box.
LLM-Trading-Lab is a trading framework designed to execute equity trades and manage portfolios using large language models while adhering to strict investment constraints. The system distinguishes itself by integrating an algorithmic trading auditor that logs the reasoning behind model-driven decisions for retrospective analysis. It also includes a quantitative research reporter that transforms experimental results into portable reports and weekly summaries for long-term archiving. The framework covers several core functional areas, including automated risk management to enforce stop-loss ac
LLM-Trading-Lab is a Python-based trading framework that leverages large language models for automated trading strategies and risk management, though it focuses on equities rather than cryptocurrency and lacks built-in copy trading.
OctoBot is an open-source automated trading platform that connects to over 15 cryptocurrency exchanges, enabling users to deploy grid, dollar-cost averaging, market-making, and AI-driven trading strategies. It functions as a unified multi-exchange trading platform, a TradingView alert executor, and a crypto trading bot, all within a single system. The platform is built on an event-driven trading loop with a plugin-based strategy engine, an exchange-agnostic connector layer, and a cloud-synced profile store for multi-device consistency. What distinguishes OctoBot is its integration of large la
OctoBot is an open-source automated crypto trading platform featuring exchange integration, backtesting, and AI-driven strategies, though it lacks dedicated copy trading functionality.
Jesse is a Python algorithmic trading framework used for developing, backtesting, and executing quantitative trading strategies. It functions as a trading strategy backtester and a machine learning trading platform, providing an environment to train predictive models on historical market data and deploy them into live strategies. The framework features a standardized crypto exchange connectivity layer that allows for the execution of automated spot and futures trades across multiple cryptocurrency exchanges via an exchange-agnostic interface. It includes a quantitative risk analysis toolset t
Jesse is a Python algorithmic trading framework designed for developing, backtesting, and executing quantitative and machine learning strategies with exchange integration and risk management, though it does not natively support copy trading.
This project is an algorithmic trading platform designed to automate financial market analysis and the execution of investment strategies. It provides an end-to-end environment for processing real-time market data through automated decision models, allowing for the triggering of financial transactions based on predefined quantitative signals and risk parameters without manual intervention. The platform distinguishes itself through a modular pipeline architecture that decouples data ingestion, signal generation, and trade execution, facilitating the iterative refinement of investment models. I
This project is an algorithmic trading platform that automates financial market analysis and execution using quantitative models, fitting the core cryptocurrency and automated trading intent well despite its focus on general financial markets and lack of explicit native copy trading.
This project is an automated cryptocurrency trading platform for the Binance exchange. It functions as a technical analysis trading tool and grid trader, executing strategies and managing assets without manual intervention. The platform is distinguished by its multi-service containerized architecture, which orchestrates a listener, cache, and database. It utilizes a secure web dashboard for monitoring active trades and adjusting bot parameters, protected by password and token-based authentication. The system covers a broad range of trading capabilities, including grid and trailing order auto
This project is an automated cryptocurrency trading platform focused on grid trading and technical analysis for Binance, fitting the category well despite lacking advanced AI strategies and copy trading features.
Superalgos is a cryptocurrency algorithmic trading platform used for designing, backtesting, and deploying automated trading bots. It centers on a visual strategy designer that allows users to create indicators and trading logic through a graphical interface instead of writing manual code. The platform features a token-gated signal network that enables a decentralized marketplace for broadcasting and monetizing trading intelligence. Access to these signals and predictions is managed via digital tokens and reputation scores, while a distributed trading infrastructure allows users to coordinate
Superalgos is an algorithmic trading platform featuring visual strategy design, backtesting, and decentralized signal sharing that supports exchange integration and trading automation, though it lacks direct turnkey AI strategy execution out of the box.
Valuecell is an artificial intelligence financial trading platform and market analysis engine. It functions as a multi-exchange trading bot and financial data orchestrator, designed to analyze market data and execute automated trades across global financial exchanges. The system utilizes a modular agent plugin framework that allows for the integration of third-party tools and agents through a shared community registry. It incorporates a retrieval-augmented generation approach to analyze fundamental financial documents and historical patterns, grounding AI responses in factual data. The platf
Valuecell is an AI-powered financial trading platform and automated trading engine with exchange integration and market analysis, though it lacks explicit mention of copy trading and built-in backtesting features.
Building a population of models that trade crypto and mutate iteratively
This project is an automated cryptocurrency trading bot that uses evolutionary algorithms inspired by neural networks to iteratively train trading models, though it lacks the requested copy trading and backtesting features.
Create your Java crypto trading bot in minutes. Our Spring boot starter takes care of exchange connections, accounts, orders, trades, and positions so you can focus on building your strategies.
Cassandre is a Java-based cryptocurrency trading bot framework with exchange integrations and technical analysis support, though it requires custom implementation for advanced AI models and native copy trading features.
| Repository | Stars | Language | License | Last push |
|---|---|---|---|---|
| vnpy/vnpy | 41.7K | Python | MIT | |
| charliedream1/ai_quant_trade | 5.1K | Jupyter Notebook | apache-2.0 | |
| 18.9K |
| Python |
| Apache-2.0 |
| deviavir/zenbot | 8.3K | HTML | MIT |
| askmike/gekko | 10.2K | JavaScript | MIT |
| 0xemmkty/quantmuse | 2.6K | Python | mit |
| wshobson/agents | 36.8K | Python | MIT |
| haehnchen/crypto-trading-bot | 3.5K | TypeScript | MIT |
| freqtrade/freqtrade | 51.5K | Python | GPL-3.0 |
| luckyone7777/llm-trading-lab | 7.5K | Python | — |