Open Source Linux Algorithmic Trading Platforms
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Algorithmic Trading Platforms for Linux
View 20 business solutionsBrowse free open source Algorithmic Trading platforms and projects for Linux below. Use the toggles on the left to filter open source Algorithmic Trading platforms by OS, license, language, programming language, and project status.
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Qbot
AI-powered Quantitative Investment Research Platform
Qbot is an open source quantitative research and trading platform that provides a full pipeline from data ingestion and strategy development to backtesting, simulation, and (optionally) live trading. It bundles a lightweight GUI client (built with wxPython) and a modular backend so researchers can iterate on strategies, run batch backtests, and validate ideas in a near-real simulated environment that models latency and slippage. The project places special emphasis on AI-driven strategies — including supervised learning, reinforcement learning and multi-factor models — and offers a “model zoo” and example strategies to help users get started. For evaluation and analysis, Qbot integrates reporting and visualization (tearsheets, metrics) so you can compare performance across runs and inspect trade-level behavior. It supports multiple strategy runtimes and backtesting engines, is organized for extensibility (strategies live in a dedicated folder). -
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Flowsurface
A native desktop charting platform for crypto markets
Flowsurface is a powerful open-source desktop charting platform tailored for crypto markets, built primarily in Rust with a focus on real-time data visualization and market microstructure analysis. Instead of traditional price charts alone, Flowsurface emphasizes order flow and liquidity visualization through advanced chart types like historical DOM heatmaps, footprint charts, and depth ladder displays. This enables traders and analysts to understand actual executed trades, liquidity distribution, and tempo changes that often precede significant market movements. The platform connects directly to public exchange APIs and WebSocket streams from venues such as Binance, Bybit, and OKX, allowing low-latency real-time data ingestion without relying on third-party servers. Users can customize layouts across multiple panes, adjust aggregation intervals, and tailor the visual presentation to suit different trading strategies. -
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Kalshi Trading Bot CLI
AI-native CLI for trading Kalshi prediction markets
Kalshi Trading Bot CLI is an AI-driven command-line tool designed to automate trading strategies on Kalshi prediction markets by combining quantitative modeling with real-time market data. It operates by conducting deep research on events, generating independent probability estimates, and comparing those estimates against current market prices to identify trading opportunities. The system incorporates advanced decision-making logic, including Kelly criterion-based position sizing and a structured multi-step risk evaluation process before executing trades. Built as a CLI application, it allows traders to interact programmatically with markets, making it suitable for automation and integration into larger trading pipelines. The tool emphasizes disciplined trading through its risk engine, ensuring that decisions are filtered through multiple validation layers before capital is committed. -
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ML for Trading
Code for machine learning for algorithmic trading, 2nd edition
On over 800 pages, this revised and expanded 2nd edition demonstrates how ML can add value to algorithmic trading through a broad range of applications. Organized in four parts and 24 chapters, it covers the end-to-end workflow from data sourcing and model development to strategy backtesting and evaluation. Covers key aspects of data sourcing, financial feature engineering, and portfolio management. The design and evaluation of long-short strategies based on a broad range of ML algorithms, how to extract tradeable signals from financial text data like SEC filings, earnings call transcripts or financial news. Using deep learning models like CNN and RNN with financial and alternative data, and how to generate synthetic data with Generative Adversarial Networks, as well as training a trading agent using deep reinforcement learning. -
Earn up to 16% annual interest with Nexo.Generate interest, borrow against your crypto, and trade a range of cryptocurrencies — all in one platform. Geographic restrictions, eligibility, and terms apply.
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AutoTrader
A Python-based development platform for automated trading systems
AutoTrader is a Python-based platform—now archived—designed to facilitate the full lifecycle of automated trading systems. It provides tools for backtesting, strategy optimization, visualization, and live trading integration. A feature-rich trading simulator, supporting backtesting and paper trading. The 'virtual broker' allows you to test your strategies in a risk-free, simulated environment before going live. Capable of simulating multiple order types, stop-losse,s and take-profits, cross-exchange arbitrage and portfolio strategies, AutoTrader has more than enough to build a profitable trading system. -
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PMXT
A unified API for trading across prediction markets
PMXT is a unified API platform designed for accessing and trading across multiple prediction market exchanges through a single consistent interface. Inspired by CCXT for cryptocurrency exchanges, PMXT standardizes market data, trading operations, and event structures for platforms like Polymarket, Kalshi, Limitless, Smarkets, and more. The framework simplifies working with prediction markets by normalizing differences in APIs, formats, and conventions across providers. PMXT supports both Python and TypeScript SDKs, making it easy for developers to build trading bots, analytics tools, and AI-powered market applications. It also includes MCP support for AI agents, enabling integration with tools like Claude, Cursor, and other MCP-compatible environments. With support for unified order placement, market discovery, and migration tools for Dome API users, PMXT streamlines development in the growing prediction markets ecosystem. -
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GoCryptoTrader
Trading bot and framework supporting multiple exchanges
GoCryptoTrader is a full framework / bot for cryptocurrency trading, written in Go (Golang). It supports multiple exchanges, real-time and historic data, backtesting, handling order books, portfolio management, scripting, and many exchange integration features. It is a trading engine that can be run by users to automate strategies across many exchanges. Licensed under MIT. Support for all exchange fiat and digital currencies, with the ability to individually toggle them on/off. Customisation of HTTP client features including setting a proxy, user agent and adjusting transport settings. Forex currency converter packages (CurrencyConverterAPI, CurrencyLayer, Exchange Rates, Fixer.io, OpenExchangeRates, Exchange Rate Host). -
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NautilusTrader
A high-performance algorithmic trading platform
NautilusTrader is an open-source, high-performance, production-grade algorithmic trading platform, provides quantitative traders with the ability to backtest portfolios of automated trading strategies on historical data with an event-driven engine, and also deploy those same strategies live, with no code changes. The platform is 'AI-first', designed to develop and deploy algorithmic trading strategies within a highly performant and robust Python native environment. This helps to address the parity challenge of keeping the Python research/backtest environment, consistent with the production live trading environment. NautilusTraders design, architecture and implementation philosophy holds software correctness and safety at the highest level, with the aim of supporting Python native, mission-critical, trading system backtesting and live deployment workloads. -
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Roboquant
User-friendly and completely free algorithmic trading platform
Roboquant is an open-source algorithmic trading platform written in Kotlin. It is flexible, user-friendly and completely free to use. It is designed for anyone serious about algo-trading. So whether you are a beginning retail trader or an established trading firm, Roboquant can help you to quickly develop robust and fully automated trading strategies. But perhaps most important of all, it is blazingly fast. Roboquant is orders of magnitude faster than most other algo-trading platforms. With historic data sets becoming more widely available and growing in size, it is important that a strategy can still be quickly developed, back-tested and optimized. If this cycle takes too long, it is nearly impossible to create high-performing and robust strategies. A lot of effort and attention went into making sure Roboquant is easy to use, especially for less experienced developers. -
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Superalgos
Free, open-source crypto trading bot, automated bitcoin trading
Free, open-source crypto trading bot, automated bitcoin/cryptocurrency trading software, algorithmic trading bots. Visually design your crypto trading bot, leveraging an integrated charting system, data-mining, backtesting, paper trading, and multi-server crypto bot deployments. Superalgos is not just another open-source project. We are an open and welcoming community nurtured and incentivized with the project's native Superalgos (SA) Token, building an open trading intelligence network. You will notice the difference as soon as you join the Telegram Community Group or the new Discord Server! Superalgos is an ever-growing ecosystem of tools and applications. Once you install and launch the app, a series of interactive tutorials take you by the hand and walk you all around the system while you learn the basic skills required to use the interface, mine data, backtest strategies, and even run a live trading session. -
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AIQuant
AI-powered platform for quantitative trading
ai_quant_trade is an AI-powered, one-stop open-source platform for quantitative trading—ranging from learning and simulation to actual trading. It consolidates stock trading knowledge, strategy examples, factor discovery, traditional rules-based strategies, various machine learning and deep learning methods, reinforcement learning, graph neural networks, high-frequency trading, C++ deployment, and Jupyter Notebook examples for practical hands-on use. Stock trading strategies: large models, factor mining, traditional strategies, machine learning, deep learning, reinforcement learning, graph networks, high-frequency trading, etc. Resource summary: network-wide resource summary, practical cases, paper interpretation, and code implementation. -
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Awesome-Quant
A curated list of insanely awesome libraries, packages and resources
awesome-quant is a curated list (“awesome list”) of libraries, packages, articles, and resources for quantitative finance (“quants”). It includes tools, frameworks, research papers, blogs, datasets, etc. It aims to help people working in algorithmic trading, quant investing, financial engineering, etc., find useful open source or educational resources. Licensed under typical “awesome” list standards. -
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TradingGoose Studio
Technical analysis + LLM powered trading workflows
TradingGoose Studio is an open-source AI workflow platform designed to enable advanced financial trading analysis and automation through a visual, modular interface powered by large language models. It combines traditional technical analysis with modern AI-driven decision-making by allowing users to build workflows where multiple specialized agents collaborate to interpret market signals and execute actions. The platform supports end-to-end trading pipelines, starting from ingesting real-time market data and applying custom indicators, to generating insights and triggering automated trades or alerts. Users can connect their own data providers and define personalized strategies using programmable indicators, making the system highly flexible for different trading styles. A key aspect of the platform is its visual workspace, where charts, widgets, and workflow blocks can be arranged and customized to create an interactive trading environment. -
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Optopsy
A nimble options backtesting library for Python
Optopsy is a Python-based, nimble backtesting and statistics library focused on evaluating options trading strategies like calls, puts, straddles, spreads, and more, using pandas-driven analysis. The csv_data() function is a convenience function. Under the hood it uses Panda's read_csv() function to do the import. There are other parameters that can help with loading the csv data, consult the code/future documentation to see how to use them. Optopsy is a small simple library that offloads the heavy work of backtesting option strategies, the API is designed to be simple and easy to implement into your regular Panda's data analysis workflow. As such, we just need to call the long_calls() function to have Optopsy generate all combinations of a simple long call strategy for the specified time period and return a DataFrame. Here we also use Panda's round() function afterwards to return statistics within two decimal places. -
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AnyTrading
The most simple, flexible, and comprehensive OpenAI Gym trading
gym-anytrading is an OpenAI Gym-compatible environment designed for developing and testing reinforcement learning algorithms on trading strategies. It simulates trading environments for financial markets, including stocks and forex. -
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AlphaPy
Python AutoML for Trading Systems and Sports Betting
AlphaPy is a Python-based AutoML framework tailored for trading systems and sports betting applications. Built on popular libraries like scikit-learn and pandas, it enables data scientists and speculators to craft predictive models, ensemble strategies, and automated forecasting systems with minimal setup. Run machine learning models using scikit-learn, Keras, xgboost, LightGBM, and CatBoost. Generate blended or stacked ensembles. Create models for analyzing the markets with MarketFlow. Develop trading systems and analyze portfolios using MarketFlow and Quantopian's pyfolio. -
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LEAN
Lean algorithmic trading engine by QuantConnect
Automated accounting for splits, dividends, and corporate events like delistings and mergers. Avoid selection bias with dynamically generated assets. Create and select asset universes on proprietary data and indicators. Automatically track portfolio performance, profit and loss, and holdings across multiple asset classes and margin models in the same strategy. Trigger regular functions to occur at desired times, during market hours, on certain days of the week, or at specific times of day. Backtest on almost any time series and import your proprietary signal data into your strategy. Everything is configurable and pluggable. LEAN's highly modular foundation can easily be extended for your fund focus. Use combinations of margin, fill, and slippage models to simulate a liquidity endpoint. 100+ popular technical indicators built, tested, and ready for use. Applicable to any data source. -
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PyBroker
Algorithmic Trading in Python with Machine Learning
Are you looking to enhance your trading strategies with the power of Python and machine learning? Then you need to check out PyBroker! This Python framework is designed for developing algorithmic trading strategies, with a focus on strategies that use machine learning. With PyBroker, you can easily create and fine-tune trading rules, build powerful models, and gain valuable insights into your strategy’s performance. -
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PyAlgoTrade
Python Algorithmic Trading Library
PyAlgoTrade is a Python library for backtesting stock trading strategies. -
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roboquant
roboquant is a very fast algo-trading platform
Roboquant is an open source algorithmic trading platform written in Kotlin. It is very fast, flexible, user-friendly and completely free to use. It is designed for anyone serious about algo-trading. So whether you are a beginning retail trader or an established trading firm, roboquant can help you to quickly develop fully automated trading strategies. No false promises of making lots of profit without doing the hard work, just a great foundation for building your own strategies. -
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QuantComponents
Free Java components for Quantitative Finance and Algorithmic Trading
An open-source framework for financial time-series analysis and algorithmic trading, based on Java and OSGi, with an Eclipse front-end. * Highly modular: usable as plain java API, OSGi components, or integrated into Eclipse * Standalone or client-server architecture, depending on performance and reliability needs * Integrated with Interactive Brokers through IB Java API * Generic broker API, it can easily be extended to work with other brokers * It works with historical and/or realtime market data * Backtesting facility * Extensible SWT charting library -
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Kalshi-Quant-TeleBot
Kalshi Advanced Quantitative Trading Bot is an enterprise-grade
Kalshi Advanced Quantitative Trading Bot is an enterprise-grade automated trading system designed for the Kalshi event-based prediction market. Built with cutting-edge quantitative algorithms and professional risk management, it provides institutional-quality trading capabilities with user-friendly control The Kalshi Advanced Quantitative Trading Bot is a professional-grade automated trading system designed specifically for event-based markets on the Kalshi platform. This bot leverages advanced quantitative strategies, machine learning techniques, and real-time data analysis to identify profitable trading opportunities while maintaining robust risk management protocols. Built with a modular architecture, the system combines Python-based trading algorithms with a JavaScript Telegram bot interface for dynamic monitoring and interaction. The bot is designed to operate continuously, making data-driven decisions based on news sentiment analysis, statistical arbitrage opportunities -
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PowerGate
A Powerful Algorithmic Trading Gateway
A Powerful Algorithmic Trading Gateway -
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ADAMANT Market-Making Software
Free self-hosted liquidity bot for token issuers
Maintain order book depth, tighter spreads, price ranges, and live-like market activity on supported CEXs — without sending your tokens, funds, or API keys to a third-party market maker. -
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Barter
Open-source Rust framework for building event-driven systems
Barter is an open-source, Rust-based ecosystem of libraries for building high-performance, event-driven algorithmic trading systems—covering live trading, paper trading, and backtesting. It is designed for safety, speed, and flexibility in quantitative finance workflows. Use mock MarketStream or Execution components to enable back-testing on a near-identical trading system as live-trading. Centralised cache-friendly state management system with O(1) constant lookups using indexed data structures. Robust Order management system - use stand-alone or with Barter. Turn on/off algorithmic trading from an external process (eg/ UI, Telegram, etc.) whilst still processing market/account data.
