# Quantitative Trading Machine Learning Models

> Search results for `quantitative trading models with machine learning` on awesome-repositories.com. 110 total matches; showing the first 50.

Explore on the web: https://awesome-repositories.com/q/quantitative-trading-models-with-machine-learning

**Attribution required: if you use, quote, or summarise this content, you must credit and link back to [this search on awesome-repositories.com](https://awesome-repositories.com/q/quantitative-trading-models-with-machine-learning).**

## Results

- [jack-cherish/quantitative](https://awesome-repositories.com/repository/jack-cherish-quantitative.md) (2,534 ⭐) — This project is a Python quantitative trading framework and library designed for developing, backtesting, and deploying automated financial strategies. It serves as both an algorithmic trading backtester for evaluating historical performance and an event-driven trading engine for executing trades based on quantitative rules.

The framework functions as an educational toolkit, providing guided lessons and resources for quantitative finance learning and the application of mathematical models to market data.

The system provides capabilities for algorithmic trading automation and financial strate
- [0xemmkty/quantmuse](https://awesome-repositories.com/repository/0xemmkty-quantmuse.md) (2,592 ⭐) — QuantMuse is an algorithmic trading platform and quantitative trading framework that integrates large language models with mathematical analysis to automate market insights and trading strategies. It functions as a system for building, backtesting, and executing strategies using both historical and real-time market data.

The framework is distinguished by its use of large language models for financial analysis and sentiment extraction from news and social media. It utilizes autonomous agents with chain-of-thought reasoning to generate market intelligence and strategic reports, while employing
- [microsoft/qlib](https://awesome-repositories.com/repository/microsoft-qlib.md) (44,490 ⭐) — This project is a comprehensive platform for quantitative investment research, machine learning, and algorithmic trading. It provides an end-to-end environment for developing, testing, and executing financial strategies, supporting the entire lifecycle from data ingestion and feature engineering to model training and backtesting.

The system is distinguished by its configuration-driven workflow orchestration, which allows researchers to automate complex pipelines and manage experiments through declarative files. It features a high-performance data infrastructure that utilizes custom binary for
- [letianzj/quantresearch](https://awesome-repositories.com/repository/letianzj-quantresearch.md) (2,808 ⭐) — QuantResearch is a quantitative research framework and specialized toolkit for algorithmic simulation, financial time-series analysis, and systematic trading. It provides an event-driven backtesting environment for validating strategies against historical tick and bar data, alongside a dedicated portfolio optimization engine for calculating asset weights and risk metrics.

The project distinguishes itself through a machine learning finance toolkit that implements recurrent neural networks for price prediction and reinforcement learning for derivative pricing. It also features advanced statisti
- [jamesmawm/high-frequency-trading-model-with-ib](https://awesome-repositories.com/repository/jamesmawm-high-frequency-trading-model-with-ib.md) (2,891 ⭐) — This is a containerized algorithmic trading system that connects to Interactive Brokers to execute high-frequency pairs trading strategies on forex instruments. The project implements a mean-reversion model that maintains long-short position pairs, continuously recalculating a beta hedge ratio to profit from temporary divergences in correlated price spreads.

The system processes each incoming market tick through a signal pipeline that immediately evaluates indicators and triggers market orders without batching or aggregation. It includes an irregular tick resampling engine that converts inhom
- [stefan-jansen/machine-learning-for-trading](https://awesome-repositories.com/repository/stefan-jansen-machine-learning-for-trading.md) (16,552 ⭐) — This project is a comprehensive framework for engineering financial data pipelines, designed to automate the collection, cleaning, and synchronization of large-scale market datasets. It functions as a quantitative trading data engine, providing the infrastructure necessary to manage historical and real-time asset pricing information for research and machine learning workflows.

The system distinguishes itself through a configuration-driven approach to orchestration, allowing users to manage complex data acquisition tasks across multiple financial providers. It features resilient middleware tha
- [lazyprogrammer/machine_learning_examples](https://awesome-repositories.com/repository/lazyprogrammer-machine-learning-examples.md) (8,823 ⭐) — This project is a comprehensive collection of practical code examples and implementation libraries for machine learning. It provides a wide array of reference materials for building supervised, unsupervised, and reinforcement learning algorithms.

The repository serves as a multi-domain resource, featuring specific implementation suites for financial AI, Bayesian statistical modeling, and deep learning architectures. It includes a framework for training intelligent agents using policy gradients and actor-critic models, as well as practical guides for fine-tuning transformers and utilizing larg
- [jack-cherish/machine-learning](https://awesome-repositories.com/repository/jack-cherish-machine-learning.md) (10,333 ⭐) — This project is a collection of supervised and unsupervised machine learning algorithms implemented from scratch using Python. It serves as an educational resource for studying model training, parameter optimization, and the implementation of core predictive models.

The library provides a variety of supervised learning tools, including linear and logistic regression, decision trees, and support vector machines. It also features unsupervised learning capabilities for discovering patterns in unlabeled datasets through clustering algorithms.

Broad capability areas include ensemble learning thro
- [aladdinpersson/machine-learning-collection](https://awesome-repositories.com/repository/aladdinpersson-machine-learning-collection.md) (8,465 ⭐) — This project is a machine learning educational repository providing a collection of implementations and guides for machine learning and deep learning algorithms. It serves as a deep learning model library and a reference for training workflows, covering foundational machine learning, convolutional, recurrent, and transformer architectures.

The collection includes a generative adversarial network suite for synthesizing realistic images and performing image-to-image translation. It also functions as a computer vision implementation guide for object detection and semantic segmentation, alongside
- [edtechre/pybroker](https://awesome-repositories.com/repository/edtechre-pybroker.md) (3,191 ⭐) — pybroker is a Python algorithmic trading framework and quantitative technical analysis library designed for developing, testing, and optimizing trading strategies using historical market data. It functions as a trading strategy backtester and a financial performance evaluator, providing a structured environment to simulate trading rules and analyze their statistical reliability.

The framework distinguishes itself through a market data integration layer that handles the fetching and caching of historical price data from external providers. It incorporates an event-driven backtesting engine and
- [ceruleanacg/quantitative-trading](https://awesome-repositories.com/repository/ceruleanacg-quantitative-trading.md) (39 ⭐) — 💸 Papers and Code Implements for Quantitative-Trading
- [grananqvist/awesome-quant-machine-learning-trading](https://awesome-repositories.com/repository/grananqvist-awesome-quant-machine-learning-trading.md) (3,701 ⭐) — Quant/Algorithm trading resources with an emphasis on Machine Learning
- [jesse-ai/jesse](https://awesome-repositories.com/repository/jesse-ai-jesse.md) (7,438 ⭐) — Jesse is a Python algorithmic trading framework used for developing, backtesting, and executing quantitative trading strategies. It functions as a trading strategy backtester and a machine learning trading platform, providing an environment to train predictive models on historical market data and deploy them into live strategies.

The framework features a standardized crypto exchange connectivity layer that allows for the execution of automated spot and futures trades across multiple cryptocurrency exchanges via an exchange-agnostic interface. It includes a quantitative risk analysis toolset t
- [packtpublishing/machine-learning-for-algorithmic-trading-bots-with-python](https://awesome-repositories.com/repository/packtpublishing-machine-learning-for-algorithmic-trading-bots-with-python.md) (405 ⭐) — This is the code repository for [Machine Learning for Algorithmic Trading Bots with Python [Video]](https://www.packtpub.com/application-development/machine-learning-algorithmic-trading-bots-python-video), published by Packt. It contains all the supporting project files necessary to work through…
- [freqtrade/freqtrade](https://awesome-repositories.com/repository/freqtrade-freqtrade.md) (51,527 ⭐) — This project is an algorithmic trading engine designed for the automated execution of cryptocurrency strategies. It provides a modular execution core that connects to multiple centralized and decentralized exchanges, allowing users to deploy rule-based trading logic across various spot and futures markets. The platform serves as a comprehensive environment for the entire trading lifecycle, from initial strategy development to live market operations.

What distinguishes this platform is its integrated suite for quantitative analysis and predictive modeling. It features a robust backtesting engi
- [kamranahmedse/developer-roadmap](https://awesome-repositories.com/repository/kamranahmedse-developer-roadmap.md) (357,434 ⭐) — Developer Roadmap is a community-driven platform that provides structured, graph-based learning paths for software engineering. It serves as a comprehensive knowledge repository where technical domains are organized into visual sequences to guide professional skill acquisition and career growth.

The project distinguishes itself through a collaborative ecosystem that enables users to contribute roadmaps, curate industry best practices, and maintain professional profiles. It integrates diagnostic assessment frameworks to evaluate technical proficiency, helping developers identify knowledge gaps
- [trainindata/deploying-machine-learning-models](https://awesome-repositories.com/repository/trainindata-deploying-machine-learning-models.md) (895 ⭐) — Accompanying repo for the online course Deployment of Machine Learning Models.
- [avelino/awesome-go](https://awesome-repositories.com/repository/avelino-awesome-go.md) (175,576 ⭐) — This project serves as a comprehensive language ecosystem index, functioning as a centralized, community-curated directory for the Go programming language. It organizes a vast landscape of software components, libraries, and development tools into a structured, navigable hierarchy, enabling developers to efficiently discover resources tailored to specific functional domains.

The repository distinguishes itself through a decentralized contribution model, where community-driven updates ensure the index remains current with the rapidly evolving software landscape. Beyond simple resource listing,
- [susanli2016/machine-learning-with-python](https://awesome-repositories.com/repository/susanli2016-machine-learning-with-python.md) (4,583 ⭐) — Python codes for common Machine Learning Algorithms
- [arbox/machine-learning-with-ruby](https://awesome-repositories.com/repository/arbox-machine-learning-with-ruby.md) (2,215 ⭐) — Curated list: Resources for machine learning in Ruby
- [humansignal/label-studio](https://awesome-repositories.com/repository/humansignal-label-studio.md) (27,619 ⭐) — Label Studio is a multi-modal data annotation platform designed to create and manage high-quality training datasets for machine learning. It functions as a self-hosted, containerized environment that supports secure, private deployments, including air-gapped configurations. The platform provides a centralized workspace for labeling diverse media types, such as images, text, audio, and time-series data, to support supervised and reinforcement learning workflows.

The platform distinguishes itself through deep integration with machine learning backends, enabling active learning loops, automated
- [shiyu-coder/kronos](https://awesome-repositories.com/repository/shiyu-coder-kronos.md) (30,502 ⭐) — Kronos is a financial time-series forecasting framework and quantitative trading strategy simulator. It functions as a research environment designed to analyze historical market data, train predictive models, and evaluate the performance of automated trading signals.

The platform distinguishes itself through its deep learning sequence predictors and probabilistic market modeling tools. By utilizing sequence-based architectures and statistical sampling, the system generates multiple potential price trajectories and volatility estimates to quantify uncertainty. It also supports transfer learnin
- [wilsonfreitas/awesome-quant](https://awesome-repositories.com/repository/wilsonfreitas-awesome-quant.md) (26,818 ⭐) — 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
- [packtpublishing/hands-on-machine-learning-for-algorithmic-trading](https://awesome-repositories.com/repository/packtpublishing-hands-on-machine-learning-for-algorithmic-trading.md) (1,851 ⭐) — Hands-On Machine Learning for Algorithmic Trading, published by Packt
- [h2oai/h2ogpt](https://awesome-repositories.com/repository/h2oai-h2ogpt.md) (12,016 ⭐) — h2oGPT is a self-hosted platform designed for running large language models and executing retrieval-augmented generation workflows locally. It provides a comprehensive web interface that allows users to index private document collections into searchable databases, enabling context-aware question answering and summarization without exposing sensitive data to external services.

The platform distinguishes itself by offering a modular architecture that supports both local model execution and connections to external inference servers. It facilitates the development of autonomous agents capable of
- [ashishpatel26/500-ai-machine-learning-deep-learning-computer-vision-nlp-projects-with-code](https://awesome-repositories.com/repository/ashishpatel26-500-ai-machine-learning-deep-learning-computer-vision-nlp-projects.md) (34,579 ⭐) — This repository serves as a comprehensive, curated collection of open-source implementations focused on artificial intelligence, machine learning, and computer vision. It functions as a centralized knowledge base and technical resource index, providing students and professional engineers with a structured directory of code examples for educational and practical reference.

The project distinguishes itself through a community-driven curation model, relying on manual updates and contributions to maintain a relevant and expansive archive. By organizing these resources into categorized lists, the
- [humphd/have-fun-with-machine-learning](https://awesome-repositories.com/repository/humphd-have-fun-with-machine-learning.md) (5,110 ⭐) — An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks
- [longonly/quantitative-notebooks](https://awesome-repositories.com/repository/longonly-quantitative-notebooks.md) (1,371 ⭐) — Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy
- [ai4finance-foundation/fingpt](https://awesome-repositories.com/repository/ai4finance-foundation-fingpt.md) (20,507 ⭐) — FinGPT is a suite of specialized financial tools and a framework for adapting large language models to the financial domain. It provides a set of pipelines for financial entity extraction, sentiment analysis, and retrieval-augmented generation to improve the accuracy of financial information systems.

The project distinguishes itself through efficient training workflows, utilizing low-rank adaptation and quantized low-rank adaptation to fine-tune models on consumer-grade hardware. It employs market-labeled datasets and reinforcement learning that uses actual stock price movements as reward sig
- [ethen8181/machine-learning](https://awesome-repositories.com/repository/ethen8181-machine-learning.md) (3,445 ⭐) — :earth_americas: machine learning tutorials (mainly in Python3)
- [virattt/ai-hedge-fund](https://awesome-repositories.com/repository/virattt-ai-hedge-fund.md) (60,143 ⭐) — This project is an algorithmic trading platform designed to automate financial market analysis and the execution of investment strategies. It provides an end-to-end environment for processing real-time market data through automated decision models, allowing for the triggering of financial transactions based on predefined quantitative signals and risk parameters without manual intervention.

The platform distinguishes itself through a modular pipeline architecture that decouples data ingestion, signal generation, and trade execution, facilitating the iterative refinement of investment models. I
- [clearml/clearml](https://awesome-repositories.com/repository/clearml-clearml.md) (6,740 ⭐) — ClearML is a comprehensive MLOps platform designed to manage the end-to-end machine learning lifecycle, from initial experimentation to production deployment. It provides a suite of integrated tools including a pipeline orchestrator for automating workflows, an experiment tracking tool for logging hyperparameters and metrics, and a metadata-driven data versioning system for managing large-scale datasets and model artifacts.

The platform is distinguished by its advanced compute management and serving capabilities. It features a GPU compute manager that supports fractional resource slicing and
- [ai4finance-llc/finrl-library](https://awesome-repositories.com/repository/ai4finance-llc-finrl-library.md) (15,443 ⭐) — FinRL-Library is a reinforcement learning trading framework and algorithmic trading library used to develop and backtest automated financial trading strategies. It functions as a quantitative trading pipeline and financial market simulator, allowing users to build decision policies that optimize asset trading across various financial markets.

The framework features a modular integration system for swapping reinforcement learning algorithms through a consistent API. It utilizes a standardized environment wrapper to encapsulate market dynamics into a state-action-reward interface, facilitating
- [allegroai/clearml](https://awesome-repositories.com/repository/allegroai-clearml.md) (6,733 ⭐) — ClearML is a comprehensive MLOps platform designed to manage the entire machine learning lifecycle. It functions as an experiment tracking tool, a data versioning system, and a pipeline orchestrator, while providing infrastructure for GPU cluster management and model serving.

The platform is distinguished by its ability to handle hybrid-cloud compute scheduling and fractional GPU allocation, allowing multiple workloads to share a single hardware accelerator. It employs a metadata-based approach to data versioning, using virtual views to track large datasets and artifacts without duplicating r
- [harvard-edge/cs249r_book](https://awesome-repositories.com/repository/harvard-edge-cs249r-book.md) (20,217 ⭐) — This project is a comprehensive educational framework designed to teach the design, deployment, and performance optimization of machine learning systems. It provides a structured curriculum that covers the full stack of artificial intelligence engineering, ranging from the construction of core framework components like tensors and automatic differentiation engines to the orchestration of large-scale distributed training clusters.

The platform distinguishes itself through its integration of physics-grounded systems modeling and interactive simulation environments. Users can experiment with dis
- [trademaster-ntu/trademaster](https://awesome-repositories.com/repository/trademaster-ntu-trademaster.md) (2,484 ⭐) — TradeMaster is a reinforcement learning trading framework and algorithmic trading simulator designed for designing and testing quantitative trading strategies. The system provides a platform for developing reinforcement learning agents, managing quantitative portfolios, and optimizing trade execution using financial market data.

The project features specialized components for multi-modality data preprocessing, a high-fidelity market environment simulation for strategy backtesting, and a quantitative portfolio manager for capital reallocation across multiple assets. It includes a trade executi
- [machinelearningmindset/machine-learning-course](https://awesome-repositories.com/repository/machinelearningmindset-machine-learning-course.md) (7,043 ⭐) — ################################################### A Machine Learning Course with Python ###################################################
- [jakevdp/pythondatasciencehandbook](https://awesome-repositories.com/repository/jakevdp-pythondatasciencehandbook.md) (48,561 ⭐) — This project is an interactive data science environment that combines code execution, rich media visualization, and narrative documentation into a persistent, browser-based platform. It serves as a comprehensive educational resource for scientific computing, providing a framework for iterative data analysis and machine learning prototyping.

The environment is distinguished by its focus on high-performance numerical computing, utilizing vectorized array operations and memory-mapped data structures to handle large-scale computations efficiently. It features a unified estimator interface that st
- [josephmisiti/awesome-machine-learning](https://awesome-repositories.com/repository/josephmisiti-awesome-machine-learning.md) (72,867 ⭐) — This project is a comprehensive, community-driven directory of machine learning resources, software libraries, and educational materials. It serves as a centralized knowledge base for developers and researchers, organizing tools and frameworks by their primary programming language and technical domain to simplify discovery across the artificial intelligence ecosystem.

The collection distinguishes itself by providing a cross-language development index that spans diverse programming environments, including C, C++, Rust, Clojure, and Python. It covers a wide range of specialized capabilities, fr
- [instillai/machine-learning-course](https://awesome-repositories.com/repository/instillai-machine-learning-course.md) (7,043 ⭐) — ################################################### A Machine Learning Course with Python ###################################################
- [unslothai/unsloth](https://awesome-repositories.com/repository/unslothai-unsloth.md) (66,628 ⭐) — Unsloth is a high-performance training and inference platform designed to optimize the lifecycle of large language and multimodal models. It provides a comprehensive engine for fine-tuning, executing, and managing models locally, with a focus on reducing memory consumption and increasing compute speed on consumer-grade hardware.

The platform distinguishes itself through hand-optimized kernels and automated computational graph techniques that maximize hardware throughput. It supports advanced training methodologies, including reinforcement learning for reasoning and efficient adapter-based fin
- [shunliz/machine-learning](https://awesome-repositories.com/repository/shunliz-machine-learning.md) (1,424 ⭐) — 机器学习原理笔记整理. Gitbook地址https://shunliz.gitbooks.io/machine-learning/content/ 前半部分关注数学基础，机器学习和深度学习的理论部分，详尽的公式推导。 后半部分关注工程实践和理论应用部分
- [dragonflydb/dragonfly](https://awesome-repositories.com/repository/dragonflydb-dragonfly.md) (30,688 ⭐) — Dragonfly is a high-performance, multi-model in-memory data store designed to serve as a drop-in replacement for existing database infrastructures. By utilizing a multi-threaded, shared-nothing architecture and a fiber-based concurrency model, it maximizes CPU utilization and minimizes latency for read and write operations. The system supports a wide range of data structures, including strings, hashes, lists, sets, sorted sets, and JSON documents, while maintaining full compatibility with standard industry wire protocols and client libraries.

What distinguishes Dragonfly is its focus on effic
- [wshobson/agents](https://awesome-repositories.com/repository/wshobson-agents.md) (36,830 ⭐) — This project is an automated trading and agentic workflow platform designed to orchestrate complex financial tasks through state-based graphs. It provides a comprehensive framework for building, deploying, and managing autonomous agents that execute multi-step analytical processes, monitor real-time market conditions, and perform high-speed trade execution.

The platform distinguishes itself through a robust agentic plugin ecosystem that integrates directly with popular AI-powered development environments and command-line interfaces. It features a specialized financial analysis engine capable
- [aws/aws-cdk](https://awesome-repositories.com/repository/aws-aws-cdk.md) (12,817 ⭐) — The AWS Cloud Development Kit is an infrastructure-as-code framework that enables developers to define and provision cloud resources using familiar programming languages. By utilizing construct-based synthesis, it translates high-level, object-oriented code into declarative templates, allowing for the automated management of complex cloud environments through a centralized, code-driven control plane.

The framework distinguishes itself through its ability to model infrastructure as a dependency-aware resource graph, ensuring that components are provisioned and updated in the correct order. It
- [microsoft/rd-agent](https://awesome-repositories.com/repository/microsoft-rd-agent.md) (11,266 ⭐) — RD-Agent is an autonomous framework designed to orchestrate multi-step software engineering and data science workflows. By leveraging large language models, the system decomposes complex technical requirements into actionable research, planning, and execution phases, ultimately generating and running code to solve specific development tasks.

The platform distinguishes itself through a containerized execution sandbox that ensures secure dependency management and system stability for all autonomously generated code. It employs multi-agent orchestration to manage iterative feedback loops, allowi
- [jeff1evesque/machine-learning](https://awesome-repositories.com/repository/jeff1evesque-machine-learning.md) (258 ⭐) — Web-interface + rest API for classification and regression (https://jeff1evesque.github.io/machine-learning.docs)
- [chrisconlan/algorithmic-trading-with-python](https://awesome-repositories.com/repository/chrisconlan-algorithmic-trading-with-python.md) (3,405 ⭐) — Source code for Algorithmic Trading with Python (2020) by Chris Conlan
- [bytebytegohq/system-design-101](https://awesome-repositories.com/repository/bytebytegohq-system-design-101.md) (83,491 ⭐) — This project is a centralized engineering knowledge repository that provides a structured curriculum for mastering system design, architectural patterns, and fundamental software development workflows. It serves as a professional development resource for engineers, offering foundational knowledge and real-world case studies to support the design of scalable, secure, and efficient distributed systems.

The repository distinguishes itself through a visual-first approach to knowledge synthesis, distilling complex technical concepts into high-density graphical diagrams and succinct illustrations.
- [voltagent/awesome-claude-code-subagents](https://awesome-repositories.com/repository/voltagent-awesome-claude-code-subagents.md) (21,906 ⭐) — This project provides a framework for managing multi-agent systems, designed to automate complex software development, infrastructure, and business workflows. It functions as a multi-agent workflow orchestrator that routes tasks to domain-specific workers while maintaining state persistence and infrastructure automation. By leveraging large language models, the system decomposes high-level objectives into actionable plans, ensuring that complex operations are executed with consistency and reliability.

The framework distinguishes itself through its hierarchical agent registry and policy-driven