Best AI tools for< Quant >

Quant-Tek.AI is a premier provider of conversational artificial intelligence tools, empowering businesses with human-like voice AI solutions. Their mission is to revolutionize the way businesses interact with customers by providing intelligent solutions that automate communication and enhance customer experience. They aim to drive efficiency, improve customer satisfaction, and foster growth through cutting-edge AI technology. Quant-Tek.AI values innovation, excellence, integrity, and collaboration in their pursuit of AI innovation and shaping the future of business communication.

Quant Prep AI is an AI Chrome extension designed for GRE & GMAT quantitative preparation. It assists users in identifying and practicing strategies for solving quantitative questions. The extension provides tips and detailed solutions for quant problems, helping users enhance their quantitative skills efficiently.

quantilope is an AI-driven Consumer Intelligence Platform that automates quantitative & qualitative research methodologies for actionable insights. It offers a wide range of research methods and tools for market research, consumer segmentation, brand health tracking, and more. Clients benefit from fast results, cost-effective solutions, and expert support from certified research consultants.

Google Quantum AI is a leading platform dedicated to advancing the field of quantum computing. The platform offers educational resources, research publications, open-source tools like Cirq, and career opportunities. Google Quantum AI's mission is to build best-in-class quantum computing systems with error correction capabilities, enabling the solution of complex problems that are currently intractable for classical computers. The platform also hosts the XPRIZE Quantum Applications competition, collaborates with industry and academic partners to explore future applications of quantum computing, and shares research and blog posts on quantum algorithms and advancements.

Quantarium is a pioneering force deeply rooted in data, analytics, and valuation models, the bedrock of our reputation. Fueled by this expertise, we crafted the Terraverse™ vision, which integrates Visual Technologies, Intelligent Mapping, and Large Language Models. Our dual commitment to delivering exceptional products and driving technological advancement is what sets us apart in our vast marketplace.

Quantifind is an AI-powered financial crimes automation platform that specializes in Anti-Money Laundering (AML) and Know Your Customer (KYC) solutions. It offers end-to-end automation impact, best-in-class accuracy, and powerful APIs and applications for risk screening, investigations, and compliance in the financial services and public sector industries. Quantifind's Graphyte platform leverages AI and external data to streamline AML-KYC processes, providing comprehensive data coverage, dynamic risk typologies, and seamless integrations with case management systems.

Quantexa News API is an AI-powered news data application that provides efficient, effective, and accurate access to global news at scale. It offers real-time access to enriched, tagged, and structured news feeds, enhancing risk monitoring processes and models. The application aggregates news content from over 90,000 sources and 1.3 million NLP-enriched news articles daily, with advanced AI-powered search capabilities. Users can investigate data through visualizations, automate tasks with entity and sentiment analysis, and easily share news data with relevant stakeholders.

Quantum AI is an advanced AI-powered trading platform that revolutionizes the trading experience by empowering users to make intelligent and strategic decisions. The platform offers a user-friendly interface, automated trading system, expert-designed strategies, risk-free demo mode, and top-level security. With round-the-clock expert assistance, exceptional satisfaction levels, and multilingual support, Quantum AI ensures a seamless trading experience for users worldwide.

Quanty is an AI-driven financial knowledge graph application that provides market insights on crypto and stocks news through advanced algorithms and knowledge graphs. It offers a GraphQL API for deep market understanding, smart data classification, current market insights, entity and relationship extraction, and dynamic GraphQL access. Users can access a wide range of financial news, insights, and analytics seamlessly through Quanty's robust GraphQL API.

Quanta Intelligence is a comprehensive platform providing business insights on the latest AI news. It offers in-depth analysis and expert commentary on artificial intelligence developments, helping users stay informed and competitive. With deep expertise and precision data, Quanta Intelligence is a top-quality, unbiased resource for AI news and insights. The platform sets the standard for excellence in the rapidly-evolving world of artificial intelligence.

Quanta Quest is an AI-powered personal knowledge management tool that boosts productivity by providing a universal search for all personal data sources. It seamlessly connects and searches across platforms like Gmail, Dropbox, and Notion with precision. The tool ensures privacy-first security, unified personal data management, and intelligent search capabilities. Users worldwide love how Quanta Quest simplifies personal information management and revolutionizes project and research organization.

Quantum Rise is an AI consultancy firm that believes in the power of combining human and machine intelligence to drive growth with AI and automation. They offer AI transformation services, AI for key metrics, and AI due diligence to help businesses adapt to the changing landscape. Quantum Rise focuses on delivering continuous improvement, agile development, and long-lasting results through sustainable and ethical practices. Their team of experts is dedicated to supporting businesses with data, automation, and AI solutions to achieve success in the modern era.

AI Quantum Intelligence is an AI-driven news website that focuses on delivering the latest updates and stories in the fields of AI, robotics, data analytics, data science, and IoT. The platform utilizes advanced algorithms to curate and personalize news content for its users, ensuring they stay informed in today's fast-paced world. With a commitment to accuracy and timeliness, AI Quantum Intelligence aims to be a trusted source in the digital journalism landscape, where innovation meets information.

STRATxAI is an AI-powered quantitative investment platform that offers custom AI model portfolios tailored to clients' investment philosophy, risk tolerance, and objectives. The platform harnesses machine learning to deliver data-driven insights for security analysis, portfolio construction, and management. Powered by the proprietary investment engine Alana, STRATxAI processes over 8 billion financial data points daily to uncover hidden alpha beyond traditional methods. Clients benefit from smarter decision-making, better risk-adjusted returns, optimized portfolio management, and savings on resources. The platform is designed to enhance investment decisions for forward-thinking investors by leveraging AI technology.

Three Sigma is a quantitative hedge fund that uses advanced artificial intelligence and machine learning techniques to identify and exploit trading opportunities in global financial markets.

Kuano is an AI tool that focuses on redefining drug discovery using Quantum and AI technologies. The platform offers world-class scientific expertise in quantum physics, AI, and medicinal chemistry to revolutionize the drug design process. Kuano aims to leverage cutting-edge technologies to accelerate the discovery of new drugs and improve healthcare outcomes.

Luna.ai is a sales automation tool that uses artificial intelligence (AI) to help businesses find and engage with potential customers. The tool offers a range of features, including lead generation, email marketing, and customer relationship management (CRM). Luna.ai is designed to help businesses improve their sales performance by automating tasks and providing insights into customer behavior.

LogRocket is a session replay, product analytics, and issue detection platform that helps software teams deliver the best web and mobile experiences. With LogRocket, you can see exactly what users experienced on your app, as well as DOM playback, console and network logs, errors, and performance data. You can also surface the most impactful user issues with JavaScript errors, network errors, stack traces, automatic triaging, and alerting. LogRocket also provides product analytics to help you understand how users are interacting with your app, and UX analytics to help you visualize how users experience your app at both the individual and aggregate level.

Numerai is a data science tournament platform where users can compete to build models that predict the stock market. The platform provides users with clean and regularized hedge fund quality data, and users can build models using Python or R scripts. Numerai also has a cryptocurrency, NMR, which users can stake on their models to earn rewards.

Quadrature is an AI-powered automated trading business founded by programmers in 2010. The company utilizes sophisticated data and powerful technology to trade securities globally based on predictions made by statistical models. Their long-term vision is to trade all liquid electronically tradeable asset classes across all horizons to generate consistent, significant returns on their proprietary capital. Quadrature Climate Foundation (QCF) was established in 2019 as an independent foundation dedicated to addressing climate change through science-led philanthropy and high-impact solutions.

This repo contains a comprehensive paper list of **Model Quantization** for efficient deep learning on AI conferences/journals/arXiv. As a highlight, we categorize the papers in terms of model structures and application scenarios, and label the quantization methods with keywords.

Jupyter Quant is a dockerized environment tailored for quantitative research, equipped with essential tools like statsmodels, pymc, arch, py_vollib, zipline-reloaded, PyPortfolioOpt, numpy, pandas, sci-py, scikit-learn, yellowbricks, shap, optuna, ib_insync, Cython, Numba, bottleneck, numexpr, jedi language server, jupyterlab-lsp, black, isort, and more. It does not include conda/mamba and relies on pip for package installation. The image is optimized for size, includes common command line utilities, supports apt cache, and allows for the installation of additional packages. It is designed for ephemeral containers, ensuring data persistence, and offers volumes for data, configuration, and notebooks. Common tasks include setting up the server, managing configurations, setting passwords, listing installed packages, passing parameters to jupyter-lab, running commands in the container, building wheels outside the container, installing dotfiles and SSH keys, and creating SSH tunnels.

Jupyter Quant is a dockerized environment tailored for quantitative research, equipped with essential tools like statsmodels, pymc, arch, py_vollib, zipline-reloaded, PyPortfolioOpt, numpy, pandas, sci-py, scikit-learn, yellowbricks, shap, optuna, and more. It provides Interactive Broker connectivity via ib_async and includes major Python packages for statistical and time series analysis. The image is optimized for size, includes jedi language server, jupyterlab-lsp, and common command line utilities. Users can install new packages with sudo, leverage apt cache, and bring their own dot files and SSH keys. The tool is designed for ephemeral containers, ensuring data persistence and flexibility for quantitative analysis tasks.

Awesome-LLM-Quantization is a curated list of resources related to quantization techniques for Large Language Models (LLMs). Quantization is a crucial step in deploying LLMs on resource-constrained devices, such as mobile phones or edge devices, by reducing the model's size and computational requirements.

The ai_quant_trade repository is a comprehensive platform for stock AI trading, offering learning, simulation, and live trading capabilities. It includes features such as factor mining, traditional strategies, machine learning, deep learning, reinforcement learning, graph networks, and high-frequency trading. The repository provides tools for monitoring stocks, stock recommendations, and deployment tools for live trading. It also features new functionalities like sentiment analysis using StructBERT, reinforcement learning for multi-stock trading with a 53% annual return, automatic factor mining with 5000 factors, customized stock monitoring software, and local deep reinforcement learning strategies.

Quanta is a new kind of Content Management platform, with powerful features including: Wikis & micro-blogging, ChatGPT Question Answering, Document collaboration and publishing, PDF Generation, Secure messaging with (E2E Encryption), Video/audio recording & sharing, File sharing, Podcatcher (RSS Reader), and many other features related to managing hierarchical content.

Quantus is a toolkit designed for the evaluation of neural network explanations. It offers more than 30 metrics in 6 categories for eXplainable Artificial Intelligence (XAI) evaluation. The toolkit supports different data types (image, time-series, tabular, NLP) and models (PyTorch, TensorFlow). It provides built-in support for explanation methods like captum, tf-explain, and zennit. Quantus is under active development and aims to provide a comprehensive set of quantitative evaluation metrics for XAI methods.

QuantaLogic is a ReAct framework for building advanced AI agents that seamlessly integrates large language models with a robust tool system. It aims to bridge the gap between advanced AI models and practical implementation in business processes by enabling agents to understand, reason about, and execute complex tasks through natural language interaction. The framework includes features such as ReAct Framework, Universal LLM Support, Secure Tool System, Real-time Monitoring, Memory Management, and Enterprise Ready components.

AI-LLM-ML-CS-Quant-Readings is a repository dedicated to taking notes on Artificial Intelligence, Large Language Models, Machine Learning, Computer Science, and Quantitative Finance. It contains a wide range of resources, including theory, applications, conferences, essentials, foundations, system design, computer systems, finance, and job interview questions. The repository covers topics such as AI systems, multi-agent systems, deep learning theory and applications, system design interviews, C++ design patterns, high-frequency finance, algorithmic trading, stochastic volatility modeling, and quantitative investing. It is a comprehensive collection of materials for individuals interested in these fields.

AI-LLM-ML-CS-Quant-Overview is a repository providing overview notes on AI, Large Language Models (LLM), Machine Learning (ML), Computer Science (CS), and Quantitative Finance. It covers various topics such as LangGraph & Cursor AI, DeepSeek, MoE (Mixture of Experts), NVIDIA GTC, LLM Essentials, System Design, Computer Systems, Big Data and AI in Finance, Econometrics and Statistics Conference, C++ Design Patterns and Derivatives Pricing, High-Frequency Finance, Machine Learning for Algorithmic Trading, Stochastic Volatility Modeling, Quant Job Interview Questions, Distributed Systems, Language Models, Designing Machine Learning Systems, Designing Data-Intensive Applications (DDIA), Distributed Machine Learning, and The Elements of Quantitative Investing.

This repository provides an in-depth review of industry trends in AI, Large Language Models (LLMs), Machine Learning, Computer Science, and Quantitative Finance. It covers various topics such as NVIDIA GTC conferences, DeepSeek theory and applications, LangGraph & Cursor AI, LLM essentials, system design, computer systems, big data and AI in finance, C++ design patterns, high-frequency finance, machine learning for algorithmic trading, stochastic volatility modeling, and quant job interview questions.

AutoGPTQ is an easy-to-use LLM quantization package with user-friendly APIs, based on GPTQ algorithm (weight-only quantization). It provides a simple and efficient way to quantize large language models (LLMs) to reduce their size and computational cost while maintaining their performance. AutoGPTQ supports a wide range of LLM models, including GPT-2, GPT-J, OPT, and BLOOM. It also supports various evaluation tasks, such as language modeling, sequence classification, and text summarization. With AutoGPTQ, users can easily quantize their LLM models and deploy them on resource-constrained devices, such as mobile phones and embedded systems.

HQQ is a fast and accurate model quantizer that skips the need for calibration data. It's super simple to implement (just a few lines of code for the optimizer). It can crunch through quantizing the Llama2-70B model in only 4 minutes! 🚀

Qlib is an open-source, AI-oriented quantitative investment platform that supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning. It covers the entire chain of quantitative investment, from alpha seeking to order execution. The platform empowers researchers to explore ideas and implement productions using AI technologies in quantitative investment. Qlib collaboratively solves key challenges in quantitative investment by releasing state-of-the-art research works in various paradigms. It provides a full ML pipeline for data processing, model training, and back-testing, enabling users to perform tasks such as forecasting market patterns, adapting to market dynamics, and modeling continuous investment decisions.

Qbot is an AI-oriented automated quantitative investment platform that supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning. It provides a full closed-loop process from data acquisition, strategy development, backtesting, simulation trading to live trading. The platform emphasizes AI strategies such as machine learning, reinforcement learning, and deep learning, combined with multi-factor models to enhance returns. Users with some Python knowledge and trading experience can easily utilize the platform to address trading pain points and gaps in the market.

GPTQModel is an easy-to-use LLM quantization and inference toolkit based on the GPTQ algorithm. It provides support for weight-only quantization and offers features such as dynamic per layer/module flexible quantization, sharding support, and auto-heal quantization errors. The toolkit aims to ensure inference compatibility with HF Transformers, vLLM, and SGLang. It offers various model supports, faster quant inference, better quality quants, and security features like hash check of model weights. GPTQModel also focuses on faster quantization, improved quant quality as measured by PPL, and backports bug fixes from AutoGPTQ.

KIVI is a plug-and-play 2bit KV cache quantization algorithm optimizing memory usage by quantizing key cache per-channel and value cache per-token to 2bit. It enables LLMs to maintain quality while reducing memory usage, allowing larger batch sizes and increasing throughput in real LLM inference workloads.

AIMET is a library that provides advanced model quantization and compression techniques for trained neural network models. It provides features that have been proven to improve run-time performance of deep learning neural network models with lower compute and memory requirements and minimal impact to task accuracy. AIMET is designed to work with PyTorch, TensorFlow and ONNX models. We also host the AIMET Model Zoo - a collection of popular neural network models optimized for 8-bit inference. We also provide recipes for users to quantize floating point models using AIMET.

This repository contains a collection of articles and tutorials on quantitative finance, including topics such as machine learning, statistical arbitrage, and risk management. The articles are written in a clear and concise style, and they are suitable for both beginners and experienced practitioners. The repository also includes a number of Jupyter notebooks that demonstrate how to use Python for quantitative finance.

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