Search Results for "quantitative trading"
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Showing 37 open source projects for "quantitative trading"
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Quantitative Trading System
A comprehensive quantitative trading system with AI-powered analysis
Quantitative Trading System is a comprehensive quantitative trading platform that integrates artificial intelligence, financial data analysis, and automated strategy execution within a unified software system. The project is designed to provide an end-to-end infrastructure for building and operating algorithmic trading strategies in financial markets. -
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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. ... -
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QuantDinger
AI-driven, local-first quantitative trading platform for research
QuantDinger is a local-first, open-source quantitative trading platform designed to bring AI-assisted analysis, strategy development, backtesting, and live execution into a self-hosted workspace where data and API credentials remain under your control. Unlike cloud-locked quant services, it lets users run the entire trading workflow on their own infrastructure using Docker, with a PostgreSQL database backend, a Python backend API, and a web frontend UI that supports visualization and strategy management. ... -
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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. -
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BTC Trading Since 2020
Public BTC trading context since 2020
...It is structured in a way that allows users to modify or extend strategies, making it suitable for both learning and experimentation. The system is particularly useful for individuals interested in quantitative trading and algorithmic finance. -
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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. -
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Smart Money Concepts
Discover our Python package designed for algorithmic trading
Smart Money Concepts is a Python library that implements advanced trading indicators based on the “Smart Money Concepts” methodology, which focuses on institutional market behavior and price action analysis. It is designed for algorithmic traders and quantitative analysts who want to incorporate professional trading strategies into automated systems. The library processes structured OHLC or OHLCV market data and computes indicators such as fair value gaps, order blocks, liquidity zones, and market structure changes. ... -
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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. ... -
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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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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. ... -
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AI Hedge Fund
An AI Hedge Fund Team
This repository demonstrates how to build a simplified, automated hedge fund strategy powered by AI/ML. It integrates financial data collection, preprocessing, feature engineering, and predictive modeling to simulate decision-making in trading. The code shows workflows for pulling stock or market data, applying machine learning algorithms to forecast trends, and generating buy/sell/hold signals based on the predictions. Its structure is educational: intended more as a proof-of-concept than a ready-to-use financial product, giving learners insight into the mechanics of quantitative finance automation. ... -
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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. ... -
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FlowSignal
Real-time crypto market microstructure analysis tool
FlowSignal is a standalone desktop application for real-time cryptocurrency market analysis. It connects to live market data streams and processes multiple microstructure indicators simultaneously to identify potential short-term trading setups. The system generates a signal (LONG or SHORT) with specific entry zone, stop-loss level (~1%), and two take-profit targets. FlowSignalis designed for crypto scalpers and day traders who want a quantitative "second opinion" based on market microstructure data, complementing traditional chart analysis. IMPORTANT DISCLAIMER: This is a technical analysis tool, not financial advice. -
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QuantResearch
Quantitative analysis, strategies and backtests
QuantResearch is a large educational repository dedicated to quantitative finance, algorithmic trading, and financial machine learning research. The project contains numerous notebooks and research materials demonstrating quantitative analysis techniques used in financial markets. These include implementations of factor models, statistical arbitrage strategies, portfolio optimization methods, and reinforcement learning approaches to trading. -
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fastquant
Backtest and optimize your ML trading strategies with only 3 lines
fastquant is a Python library designed to simplify quantitative financial analysis and algorithmic trading strategy development. The project focuses on making backtesting accessible by providing a high-level interface that allows users to test investment strategies with only a few lines of code. It integrates historical market data sources and trading frameworks so that users can quickly build experiments without constructing complex data pipelines. -
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quantitative
Quantized transactions python3
The “quantitative” repository by Jack-Cherish is a tutorial-style codebase for quantitative trading written in Python — essentially a learning resource that guides users through building algorithmic trading strategies step by step. It’s organized as a sequence of lessons (lesson1, lesson2, etc.), making it approachable for learners who want to understand both theory and practice in quantitative finance. -
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TradeMaster
TradeMaster is an open-source platform for quantitative trading
TradeMaster is a first-of-its-kind, best-in-class open-source platform for quantitative trading (QT) empowered by reinforcement learning (RL), which covers the full pipeline for the design, implementation, evaluation and deployment of RL-based algorithms. TradeMaster is composed of 6 key modules: 1) multi-modality market data of different financial assets at multiple granularities; 2) whole data preprocessing pipeline; 3) a series of high-fidelity data-driven market simulators for mainstream QT tasks; 4) efficient implementations of over 13 novel RL-based trading algorithms; 5) systematic evaluation toolkits with 6 axes and 17 measures; 6) different interfaces for interdisciplinary users. -
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Machine Learning Financial Laboratory
MlFinLab helps portfolio managers and traders
MlFinLab is a comprehensive Python library designed to support the development of machine learning strategies in quantitative finance and algorithmic trading. The project provides a large collection of tools that implement techniques from academic research on financial machine learning. It covers the full lifecycle of developing data-driven trading strategies, including data preprocessing, feature engineering, labeling techniques, model training, and performance evaluation. ... -
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Eiten
Statistical and Algorithmic Investing Strategies for Everyone
...It is part of the broader Tradytics ecosystem, which emphasizes AI-driven financial tools for identifying opportunities in the stock market. The repository serves both as a learning resource and as a practical toolkit for traders and developers interested in quantitative finance. It encourages experimentation with different strategies and models, allowing users to adapt techniques to their own trading goals. -
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Quantitative-Notebooks
Educational notebooks on quantitative finance, algorithmic trading
Quantitative-Notebooks is a curated set of Jupyter notebooks focused on quantitative finance, algorithmic investing, and data-driven portfolio analysis. While each individual notebook is aimed at practical finance workflows, the overall repository helps practitioners and learners use Python, pandas, and numerical libraries to build, test, and evaluate financial strategies using historical market data. The notebooks typically showcase how to perform backtesting, factor analysis, risk... -
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EliteQuant
A list of online resources for quantitative modeling, trading, etc.
...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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surpriver
Find big moving stocks before they move using machine learning
...The project is intended as a research tool for quantitative finance experiments and algorithmic trading strategy development. -
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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). -
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Strategems
Quantitative systematic trading strategy development and backtesting
Strategems is a Julia package aimed at simplifying and streamlining the process of developing, testing, and optimizing algorithmic/systematic trading strategies. This package is inspired in large part by the quantstrat1,2 package in R, adopting a similar general structure to the building blocks that make up a strategy. Given the highly iterative nature of event-driven trading strategy development, Julia's high-performance design (particularly in the context of loops) and straightforward... -
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PandoraTrader
C++ Trade Platform for quant developer
PandoraTrader is a high-frequency quantitative trading platform implemented in C++. It interfaces with real-world futures trading desks using Trade APIs and MarketData APIs and includes support for backtesting via simulated market components. We design such a trading platform with various skills given by the designer, but we do not carry wisdom; this wisdom belongs to the strategy designer.
