15 best Java Algorithmic Trading libraries in 2024

Over the past few years, we’ve witnessed a significant rise in the usage of algorithmic trading bots, especially by sophisticated institutions.

These bots follow a set of predefined rules and place and place trades to generate high-frequency profits in the stock market. However, the cryptocurrency market is still nascent, and yet algorithmic trading is now accessible to all retail and individual traders alike. Many of these trading bots are developed using Java libraries. Here are some of the best Java algorithmic trading libraries. Released under the Apache license, Coin Trader is a Java-based backend for trading cryptocurrencies. It is connected to many crypto exchanges and offers various features, including but not limited to simulated crypto trading live order execution, 24-hour OHLC bars, and backorder routing.

Knowm’s XChange offers a simple and consistent API connecting more than 60 crypto exchanges with modular components which are under active development. Another one, ta4j, is an open-source, 100% pure Java library with 130+ indicators essential to technical analysis. It comes equipped with basic components which are necessary for creating, testing, and executing a range of trading strategies. Developers tend to use some of the following trending open-source libraries:

XChange  

• Provides a consistent API for interacting with various cryptocurrency exchanges.  
• Cryptocurrency exchanges can access market data, place orders, and manage accounts.  
• Supports various cryptocurrency exchanges like Coinbase Pro, Bitstamp, Binance, and Kraken.  

ta4j  

• Provides tools for performing technical analysis on financial data.   
• Helps build and test trading methods in Java.  
• Helps you create and test trading methods that use technical analysis indicators.   

Used-Trading-Platform  

• Is a Java library used for Object-Relational Mapping, Utilities, and Spring applications.  
• Allows developers to integrate trading functionalities into their applications.  
• Supports the development of custom indicators and trading methods.   

bateman  

• Designed to facilitate the development and execution of algorithmic trading methods.   
• Provides various functions for strategy development, live trading, data analysis, and backtesting.  
• Helps automate trading decisions based on predefined rules and market indicators.  

tsharding  

• Used by algorithmic trading firms, individual developers, and quantitative traders.  
• Helps connect their trading algorithms directly to their brokerage accounts.  
• Provides unified API to access many cryptocurrency exchanges.  

bxbot  

• Makes algorithmic trading bots easier to develop with new features.  
• Users could implement and customize their trading methods using the provided framework.  
• Fetches real-time market data, enabling trading decisions based on the latest information.  

CoinExchange_CryptoExchange_Java  

• Provides a simple and easy-to-use interface for strategy development.  
• Allows users to develop and test trading algorithms on historical data.  
• Helps connect their trading algorithms directly to their brokerage accounts.   

univocity-trader  

• Helps create and test trading strategies using past and current market information.  
• Test their trading methods on past data to see how well they performed.  
• Helps implement risk management rules to control position sizing and risk exposure.   

parity  

• Designed to facilitate the development of high-performance, ultra-fast trading applications for financial markets.  
• Can handle market data feeds, manage risks, execute orders, and handle financial instruments.  
• Provides functions for order submission, modification, cancellation, and tracking.  

Axon-trader  

• Helps build event-driven, Command Query Responsibility Segregation applications.  
• Helps build scalable and maintainable applications.  
• Can save and find an application's status using a sequence of events.  

cointrader  

• Is a Java-based backend for algorithmically trading cryptocurrencies.  
• Provides data collection and export, complex event processing, triggering, and backtesting.  
• Enables developers to integrate cryptocurrency trading functionality into their applications.   

trading-backtest  

• Helps in backtesting and live trading of financial strategies.  
• Offers extensive features for strategy development and optimization.  
• Provides built-in backtesting capabilities for their supported trading strategies.  

binance-trader  

• A flexible and feature-rich library for backtesting and live trading of financial strategies.  
• Provides a simple and easy-to-use interface for strategy development.  
• Allows you to develop and test trading algorithms on historical data.  

xmarket  

• Includes functions for pricing and risk management of financial instruments.  
• Support for automated trading strategies, connectivity to various exchanges, and risk management.  
• Helps connect their trading algorithms directly to their brokerage accounts.   

philadelphia  

• Designed to facilitate the development and execution of algorithmic trading strategies.  
• Offers functions for strategy development, live trading, backtesting, and data analysis.  
• Helps automate trading decisions based on predefined rules and market indicators.  

FAQ  

What is backtesting and paper trading, and how can they be used for algorithmic trading with Java?  

Backtesting and paper trading are essential components of algorithmic trading. Before trading, traders and developers can improve their methods by reviewing them. Backtesting is when you test a trading strategy on old market data to see how it would have worked before. Paper trading is a practice where traders execute trades without using real money.   

Which Python libraries are commonly used for algorithmic trading?  

Python is a popular programming language for algorithmic trading. It's simple, flexible, and has many libraries. Here are some popular Python libraries frequently used for algorithmic trading:  

• Backtrader  
• Zipline  
• QuantConnect  
• PyAlgoTrade  
• CCXT  
• TA-Lib  
• pandas  
• NumPy and SciPy  
• TensorFlow and PyTorch  
• vn.py  
• AlgoTrader  
• Lean Algorithm Framework  

How does the engine work in an algorithmic trading system?  

The trading system's engine executes trading methods, processes orders, and manages trades. It is a critical part of the overall system architecture. It directly interacts with the markets. It ensures that trading methods are executed efficiently and accurately. The trading engine has two parts. These parts are the Order Management System and the Execution Management System.  

Here is how the engine works in an algorithmic trading system:  

• Strategy Execution  
• Order Management System  
• Risk Management  
• Execution Management System  
• Order Routing and Matching  
• Real-time Monitoring  
• Record Keeping  

Are there any advantages to using automated trading bots over manual trades?  

Automated trading bots and manual trading each have their benefits. Automated trading bots have pros and cons compared to manual trades. 

Advantages:  

1. Speed and Efficiency – Bots can execute trades in milliseconds. This can be crucial in high-frequency trading, where quick reactions are needed.  
2. Consistency – Bots follow predefined rules without emotions. It avoids the impact of human biases and erratic decision-making.  
3. Backtesting & Optimization – Bots can improve their strategies by testing with old data. This helps identify potential issues before deploying them in live trading.  
4. Diversification – Bots can manage many strategies and trading pairs simultaneously, providing diversification benefits.  
5. Algorithm Complexity – Bots can handle complex strategies with many indicators and conditions. Manual traders may find this challenging.  

Are there any open-source solutions for developing an algorithmic trading library using Java?  

You can use Java to create an algorithm trading library with open source solutions. Java is a popular programming language for making trading applications. It's fast, flexible, and has many useful libraries. Here are a few open source options to consider:  

• QuantConnect Lean  
• AlgoTrader  
• Tradelink  
• QuickFIX/J  
• XChange  

Which electronic products are suitable for algorithmic traders using Java libraries for trading?  

Java library users can apply their strategies to various electronic trading products. Traders choose products based on knowledge, goals, risk tolerance, and library capabilities. These products are traded electronically using Java libraries and algorithmic strategies. 

• Equities and Stocks  
• Futures and Options  
• Foreign Exchange (Forex)  
• Cryptocurrencies  
• Fixed Income  
• Commodities  
• Exchange-Traded Funds (ETFs)  
• Spread Betting and Contracts for Difference (CPDs)  
• Index Funds and ETF Rebalancing  
• Algorithmic Market-Making  

If traders help trading algorithms, where can they find financial information? 

Alternative financial data can provide valuable insights for algorithmic trading strategies. If you use a Java library for trading, you can find data sources to help with decisions. 

• News APIs and Feeds  
• Bloomberg API  
• Reuters Connect API  
• Alpha Vantage API  
• Financial News Websites  
• Social Media Platforms  
• Event Calendars  
• Government and Regulatory Websites  
• Specialized News Providers  
• Web Scraping  
• Natural Language Processing  
• Data Vendors  
• Financial Data Platforms