Open Source Windows Algorithmic Trading Platforms
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Algorithmic Trading Platforms for Windows
View 56 business solutionsBrowse free open source Algorithmic Trading platforms and projects for Windows 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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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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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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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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TradingGym
Trading backtesting environment for training reinforcement learning
TradingGym is a toolkit (in Python) for creating trading and backtesting environments, especially for reinforcement learning agents, but also for simpler rule-based algorithms. It follows a design inspired by OpenAI Gym, offering various environments, data formats (tick data and OHLC), and tools to simulate trading with costs, position limits, observation windows etc. Licensed under MIT. This training environment was originally designed for tickdata, but also supports OHLC data format. WIP. The list contains the feature columns to use in the trading status. -
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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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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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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. -
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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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StockSharp
Algorithmic trading and quantitative trading open source platform
Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).StockSharp (shortly S#) – are free programs for trading at any markets of the world (American, European, Asian, Russian, stocks, futures, options, Bitcoins, forex, etc.). You will be able to trade manually or automated trading (algorithmic trading robots, conventional or HFT). FIX/FAST, ITCH (LSE, NASDAQ), Blackwood/Fusion, BarChart, CQG, E*Trade, IQFeed, InteractiveBrokers, LMAX, MatLab, Oanda, FXCM, OpenECry, Rithmic, RSS, Sterling, BTCE, BitStamp, Bitfinex, Coinbase, Kraken, Poloniex, GDAX, Bittrex, Bithumb, HitBTC, OKCoin, Coincheck, Binance, Liqui, CEX.IO, Cryptopia, OKEx, BitMEX, YoBit, Livecoin, EXMO, Deribit, Huobi, KuCoin, BITEXBOOK, CoinExchange, QuantFEED and many other. -
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LXTrader
C# algorithmic trading platform
This project is no longer supported. It is redesigned and upgraded to AlgoSpace, a C++ based extreme fast algorithmic trading platform with lots of cool features. LXTrader (formerly AlgoTrader) is a free algorithmic trading platform programmed in C#. It aim to help traders design/test/optimize your automatic trading strategies with ultra speed. It is developed by Ranye Lu and Yu Xia. -
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PyAlgoTrade
Python Algorithmic Trading Library
PyAlgoTrade is a Python library for backtesting stock trading strategies. -
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Open Exchange (OpEx)
The open source Algorithmic Trading System
OpEx is an application suite that includes the main building blocks of commercial electronic trading systems. All OpEx applications run on distributed system architectures. -
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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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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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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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EliteQuant
A list of online resources for quantitative modeling, trading, etc.
EliteQuant is a curated directory of online resources for quantitative finance: trading, portfolio management, quantitative modeling, data sources, libraries, platforms, and communities. It is not a software library per se, but a “list of things” - i.e., an aggregator of open source projects, blogs, tools etc., intended to help practitioners find useful resources. It is licensed under Apache-2.0, and maintained by volunteers. A list of online resources for quantitative modeling, trading, and portfolio management. Has criteria for recommending projects/resources to help keep quality up. -
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A suite of libraries and applications using genetic algorithms and AI for financial analysis and simulation. Currently the focus is to route FIX messages to an exchange simulator and use genetic algorithms to explore algorithmic trading strategies.
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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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Gekko-Strategies
Strategies to Gekko trading bot with backtests results
Gekko-Strategies is a community repository of strategies (JavaScript files plus configuration) for the Gekko trading bot. It contains a variety of trading strategy scripts, backtest results, and tools or helpers for strategy evaluation. It is not itself a standalone trading engine but contains strategy modules to use with Gekko. Results are sorted by amount of best profit/day on unique DATASETS. Includes an install script (install.sh) to facilitate installing strategies into the user’s Gekko setup under Unix-like systems. Backtest results included alongside strategies (via backtest_database.csv) so users can compare performance. -
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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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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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The Marketcetera Trading Platform is a comprehensive open-source software infrastructure for algorithmic trading, that is a true alternative to expensive, monolithic proprietary systems or brittle software mashups.
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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.
