Awesome GitHub RepositoriesTrading Platforms

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

Awesome-quant is a curated directory of open-source software libraries and tools designed for quantitative finance, algorithmic trading, and financial data analysis. It serves as a central hub for discovering resources that support the entire lifecycle of financial modeling, from raw data ingestion to complex statistical research. The repository organizes specialized tools into categorized collections, enabling users to identify solutions for high-performance numerical computing, technical indicator calculation, and derivative pricing. It highlights frameworks that facilitate the construction

Backtrader is a Python backtesting framework and algorithmic trading platform. It provides a toolkit for developing automated trading rules and simulating investment strategies using historical financial time-series data. The system functions as a quantitative analysis tool, combining a simulation engine for testing trading rules with a financial data visualizer that generates price action charts. It allows for the calculation of technical indicators and the evaluation of portfolio performance through risk-adjusted returns. The platform covers live trading integration via brokerage APIs and

Zipline is a Python-based algorithmic trading library designed for the development and backtesting of investment strategies. It functions as a quantitative finance engine that processes historical market data to simulate trading interactions and evaluate strategy performance through custom metrics. The platform provides a modular, event-driven framework that manages portfolio state transitions based on time-series data streams. Beyond its core trading capabilities, the system includes a comprehensive financial data analysis toolkit for manipulating large-scale market datasets to support syste

Lean is an algorithmic trading engine and quantitative finance platform designed for the development, backtesting, and live execution of automated trading strategies. It provides a comprehensive framework for processing time-series market data, managing multi-asset portfolios, and conducting quantitative research across diverse financial markets. The platform distinguishes itself through a modular, event-driven architecture that decouples strategy logic from data ingestion and brokerage connectivity. By utilizing standardized interfaces for data providers and brokerage abstractions, it enable

StockSharp is an algorithmic trading platform and quantitative framework used for developing and deploying trading robots across stock, forex, and cryptocurrency markets. It functions as a multi-asset trading gateway and a dedicated development environment for building, debugging, and scheduling automated strategies. The platform includes a visual strategy workflow editor that maps logic blocks to executable code and a simulation engine that replays historical tick data to validate trading logic. It utilizes a plugin-based broker integration system to normalize diverse exchange protocols into

Portfolio and risk analytics in Python

RQAlpha is a Python-native quantitative trading backtesting framework and live trading execution system. It provides an event-driven engine for simulating trading strategies against historical market data, with realistic transaction costs, slippage models, and corporate action handling. The platform supports multi-asset class trading including stocks, futures, options, and REITs, with separate sub-accounts for different asset types and configurable margin requirements. The framework distinguishes itself through a plugin-based extensible architecture that allows users to swap out core componen

tqsdk-python هو SDK وإطار عمل للتداول الكمي مصمم لتطوير استراتيجيات آلية للعقود الآجلة، والخيارات، والأسهم باستخدام Python. يعمل كمحرك تداول خوارزمي وAPI لبيانات السوق المالية، ويوفر الأدوات اللازمة لاختبار الاستراتيجيات، وتحليل البيانات التاريخية، وتنفيذ التداولات الحية عبر حسابات وساطة متعددة. يتميز المشروع بمكتبة تحليلات خيارات متخصصة تحسب اليونانيات (Greeks)، والتقلب الضمني، وأسطح التقلب باستخدام نموذج Black-Scholes. كما يدعم أنماط تنفيذ أوامر معقدة، مثل TWAP، وIceberg، وPOV، لتقليل تأثير السوق أثناء دخول وخروج المراكز. يغطي الـ SDK سطح قدرات واسع بما في ذلك استرجاع بيانات السوق في الوقت الفعلي والتاريخية، وإدارة المخاطر الكمية، ومراقبة المحفظة. يدمج نموذج تنفيذ غير متزامن لبث البيانات وجدولة المهام، إلى جانب أدوات لمحاكاة التداول متعدد الأصول وتحليل الأداء. توفر المكتبة واجهة رسومية قائمة على الويب لمراقبة الاستراتيجية وتصور البيانات.

pyalgotrade is a Python algorithmic trading library designed for developing, backtesting, and executing automated trading strategies. It provides a comprehensive framework for financial strategy backtesting, a technical analysis library for computing mathematical indicators, and connectors for cryptocurrency exchange integration. The project distinguishes itself by supporting sentiment-based trading through the integration of real-time social media feeds and keyword streams. It features a quantitative trading visualization tool for plotting price action and portfolio equity curves, along with

Alphalens is a quantitative alpha factor analysis library designed to measure the predictive power of financial factors. It serves as a computational toolset for processing financial time series and calculating performance metrics to evaluate quantitative trading hypotheses. The library distinguishes itself through the use of quantile-based data binning to analyze return distributions across different factor strength levels. It aligns historical alpha signals with forward-looking price changes to isolate predictive effects and transforms these metrics into heatmaps and time-series charts for

finmarketpy is a quantitative trading framework and financial market analysis tool. It provides a Python-based library for simulating trading strategies against historical market data, computing the value of options contracts, and extracting trends from financial datasets. The system includes specialized engines for financial options pricing using numerical calculations and a backtesting library to assess risk and performance before live deployment. It further enables the detection of market seasonality and the execution of event studies to measure asset price behavior around specific time wi

bt - flexible backtesting for Python

QTPyLib, Pythonic Algorithmic Trading

Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.

Real-time FX trading showcase by Adaptive. THIS REPO IS NO LONGER MAINTAINED.

Python API for the Interactive Brokers on-line trading system.

Financial markets analysis framework for programmers

Scalable, event-driven, deep-learning-friendly backtesting library

Python-based framework for backtesting trading strategies & analyzing financial markets GUI :neckbeard: