Top 10 Open-Source Crypto Arbitrage Bots (2026)
A curated list of the best GitHub repositories for crypto arbitrage bots, including features, setup guides, and performance comparisons.
A curated list of the best GitHub repositories for crypto arbitrage bots, including features, setup guides, and performance comparisons.
Top 10 Open-Source Crypto Arbitrage Bots (2026): Features, Setup & Performance
Crypto arbitrage—exploiting price differences for cryptocurrencies across exchanges—remains one of the most accessible high-frequency trading strategies in decentralized finance. While institutional traders rely on proprietary systems, retail and semi-professional traders can leverage open-source crypto arbitrage bots to automate detection and execution. These tools, hosted on platforms like GitHub, provide transparency, customization, and cost efficiency compared to paid solutions.
In this comprehensive guide, we explore the top 10 open-source crypto arbitrage bots available in 2026, analyzing their core features, technical requirements, performance benchmarks, and community support. Whether you're a developer, trader, or enthusiast, this list will help you choose the right tool for your arbitrage strategy.
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What Is Crypto Arbitrage and Why Use a Bot?
Crypto arbitrage is the practice of buying a cryptocurrency on one exchange where the price is low and selling it on another where the price is higher, profiting from the spread. This strategy works due to inefficiencies in global liquidity, regional demand, and exchange-specific order books.
Types of Crypto Arbitrage
1. Spatial Arbitrage: Exploiting price differences between geographically separate exchanges (e.g., Binance US vs. Binance Global).
2. Triangular Arbitrage: Leveraging price discrepancies between three cryptocurrencies (e.g., BTC → ETH → USDT → BTC).
3. Statistical Arbitrage: Using machine learning to identify mispricings based on historical trends.
4. Latency Arbitrage: Capitalizing on ultra-fast price updates before they propagate across exchanges.
Why Use an Open-Source Bot?
- Transparency: Code is publicly auditable, reducing the risk of hidden fees or malicious behavior.
- Customization: Developers can modify algorithms for specific strategies.
- Cost Efficiency: No licensing fees, though users must cover infrastructure costs (servers, API keys).
- Community Support: Active GitHub repositories often have issue trackers, documentation, and third-party integrations.
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How to Evaluate an Open-Source Arbitrage Bot
Before diving into the list, understand the key criteria for assessing these tools:
1. Supported Exchanges
A bot’s utility depends on the exchanges it connects to. Top bots support major platforms like:
- Binance
- Kraken
- Coinbase Pro
- KuCoin
- Bybit
- Gate.io
- OKX
Some bots also integrate with decentralized exchanges (DEXs) like Uniswap or PancakeSwap via APIs or blockchain explorers.
2. Programming Language & Framework
Most arbitrage bots are written in:
- Python (most common, due to libraries like ccxt, pandas, and asyncio)
- JavaScript/Node.js (for web-based or real-time applications)
- Go (for high-performance, low-latency systems)
- Rust (emerging for ultra-fast execution)
3. Core Features
- Real-time price monitoring
- Order book depth analysis
- Automated order placement
- Risk management (slippage control, balance checks)
- Multi-threaded or asynchronous execution
- Docker support for easy deployment
4. Performance Metrics
- Latency: Time between price detection and order execution (critical for latency arbitrage).
- Profitability: Backtested or live trading results (often measured in % ROI or spread capture).
- Stability: Uptime, error handling, and recovery mechanisms.
5. Community & Maintenance
- GitHub stars, forks, and recent commits indicate active development.
- Documentation quality (README, wiki, API guides).
- Community forums (Discord, Reddit, Telegram).
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Top 10 Open-Source Crypto Arbitrage Bots (2026)
Below is a curated list of the most reliable and feature-rich open-source arbitrage bots available in 2026, ranked by functionality, adoption, and performance.
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1. CCXT Arbitrage (Python)
🔗 GitHub: https://github.com/ccxt/ccxt
📌 Language: Python
🏆 Best for: Beginners and developers who want a modular, exchange-agnostic solution.
Features:
- Supports 100+ exchanges via the ccxt library.
- Includes triangular arbitrage and spatial arbitrage examples.
- Real-time price streaming and order book analysis.
- Extensive documentation and community support.
Setup Guide:
1. Install Python 3.8+ and pip install ccxt.
2. Clone the repository and configure API keys for your target exchanges.
3. Run the arbitrage script with python arbitrage.py.
Performance:
- Latency depends on exchange API limits (typically 100–500ms).
- Profitability varies by market conditions (historically 0.1%–2% per trade).
Pros:
✅ Highly customizable
✅ Wide exchange support
✅ Active development
Cons:
❌ Requires Python knowledge
❌ No built-in GUI
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2. Hummingbot (Python)
🔗 GitHub: https://github.com/hummingbot/hummingbot
📌 Language: Python
🏆 Best for: Retail traders seeking a user-friendly, strategy-driven bot.
Features:
- Pre-built arbitrage strategies (pure arbitrage, cross-exchange market making).
- Docker support for easy deployment.
- Backtesting with historical data.
- Community marketplace for sharing strategies.
Setup Guide:
1. Install Docker.
2. Run docker-compose up to start the bot.
3. Configure exchanges and arbitrage parameters via the CLI or web interface.
Performance:
- Latency: ~200–800ms (depends on exchange API speed).
- Profitability: 0.3%–1.5% per trade (varies by market volatility).
Pros:
✅ Beginner-friendly
✅ GUI and CLI options
✅ Strong community
Cons:
❌ Limited to supported exchanges
❌ Some advanced features require paid plans
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3. Gekko (Node.js)
🔗 GitHub: https://github.com/askmike/gekko
📌 Language: JavaScript/Node.js
🏆 Best for: Traders who prefer JavaScript and want a modular trading platform.
Features:
- Backtesting with historical data.
- Real-time trading with exchange plugins.
- Technical analysis indicators for strategy customization.
- Web-based dashboard for monitoring.
Setup Guide:
1. Install Node.js and npm install gekko.
2. Configure config.js with exchange API keys.
3. Run node gekko --config config.js.
Performance:
- Latency: ~300–1000ms (slower than Python-based bots).
- Profitability: 0.2%–1% per trade (depends on strategy).
Pros:
✅ Easy to customize
✅ Good for backtesting
✅ Active community
Cons:
❌ Slower execution
❌ Limited arbitrage-specific features
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4. Freqtrade (Python)
🔗 GitHub: https://github.com/freqtrade/freqtrade
📌 Language: Python
🏆 Best for: Algorithmic traders who want a robust, strategy-driven bot.
Features:
- Supports arbitrage via custom strategies.
- Backtesting and hyperopt for strategy optimization.
- Docker and Kubernetes support.
- Telegram integration for alerts.
Setup Guide:
1. Install Python and pip install freqtrade.
2. Configure config.json with exchange keys.
3. Run freqtrade trade --strategy MyArbitrageStrategy.
Performance:
- Latency: ~150–600ms.
- Profitability: 0.5%–2% per trade (with optimized strategies).
Pros:
✅ Highly optimized
✅ Strong backtesting tools
✅ Good for advanced users
Cons:
❌ Steeper learning curve
❌ Not exclusively an arbitrage bot
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5. Cryptohopper Arbitrage Bot (Python)
🔗 GitHub: https://github.com/CryptoHopper-community/cryptohopper-arbitrage
📌 Language: Python
🏆 Best for: Traders who want a simple, exchange-focused arbitrage tool.
Features:
- Cross-exchange arbitrage with balance checks.
- Slippage control to avoid losses.
- Real-time price monitoring.
Setup Guide:
1. Clone the repository and install dependencies (pip install -r requirements.txt).
2. Configure config.json with exchange keys.
3. Run python arbitrage_bot.py.
Performance:
- Latency: ~200–700ms.
- Profitability: 0.1%–1.2% per trade.
Pros:
✅ Simple setup
✅ Good for beginners
✅ Exchange-specific optimizations
Cons:
❌ Limited to a few exchanges
❌ Less active development
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6. Arbitrage Scanner (Python)
🔗 GitHub: https://github.com/ArbitrageRadar/arbitrage-scanner
📌 Language: Python
🏆 Best for: Developers who want a lightweight, high-performance scanner.
Features:
- Real-time spread detection across multiple exchanges.
- WebSocket-based price updates for low latency.
- Customizable thresholds for trade execution.
Setup Guide:
1. Install Python and pip install ccxt websockets.
2. Configure config.json with exchange keys.
3. Run python scanner.py.
Performance:
- Latency: ~100–400ms (one of the fastest open-source options).
- Profitability: 0.2%–1.8% per trade.
Pros:
✅ Ultra-low latency
✅ Lightweight
✅ Easy to extend
Cons:
❌ Requires manual order execution
❌ No built-in trading automation
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7. Binance Triangular Arbitrage Bot (Python)
🔗 GitHub: https://github.com/robeart/triangular-arbitrage
📌 Language: Python
🏆 Best for: Traders focused exclusively on Binance’s triangular arbitrage opportunities.
Features:
- Automated triangular arbitrage on Binance.
- Balance tracking to avoid overleveraging.
- Profit logging for performance analysis.
Setup Guide:
1. Install Python and pip install python-binance.
2. Configure API keys in config.py.
3. Run python triangular_arbitrage.py.
Performance:
- Latency: ~150–500ms.
- Profitability: 0.3%–2.5% per cycle (depends on market conditions).
Pros:
✅ Optimized for Binance
✅ Simple and effective
✅ Good documentation
Cons:
❌ Limited to Binance
❌ No cross-exchange support
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8. Node.js Crypto Arbitrage Bot
🔗 GitHub: https://github.com/bitfinexcom/bfx-arbitrage
📌 Language: JavaScript/Node.js
🏆 Best for: Developers who prefer Node.js for real-time applications.
Features:
- Cross-exchange arbitrage with WebSocket streaming.
- Order book depth analysis.
- Slippage-aware execution.
Setup Guide:
1. Install Node.js and npm install bfx-api-node-rest.
2. Configure API keys in config.js.
3. Run node arbitrage.js.
Performance:
- Latency: ~250–800ms.
- Profitability: 0.1%–1.5% per trade.
Pros:
✅ Real-time WebSocket support
✅ Good for high-frequency strategies
✅ Exchange-agnostic
Cons:
❌ Slower than Python alternatives
❌ Requires Node.js expertise
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9. Rust-Based Arbitrage Bot (Rust)
🔗 GitHub: https://github.com/rust-bitcoin/rust-crypto-arbitrage
📌 Language: Rust
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