Best AI tools for< Quant Trader >

CHAI AI Platform is an AI tool designed for quant traders to build and implement AI algorithms for trading. The platform combines chat functionality with AI capabilities to enhance the trading experience. Based in Palo Alto, CA, CHAI Research Corp. developed the platform to empower traders with advanced AI technology for better decision-making and performance in the financial markets.

StockHero is a highly-rated stock trading bot that allows users to trade intelligently with a high win rate. The platform offers preset bots from the Marketplace for users to deploy in minutes, automating trades on leading brokerages. StockHero caters to users of all levels, from beginners to experienced traders, providing features like TradingView integration, Strategy Designer, and advanced indicator settings. The platform also offers a White-Glove Service for personalized guidance, paper trading for risk-free practice, and an AI ChatBot for financial queries. With a focus on risk management and user satisfaction, StockHero aims to enhance the trading experience for all users.

BigShort is a real-time stock charting platform designed for day traders and swing traders. It offers a variety of features to help traders make informed decisions, including SmartFlow, which visualizes real-time covert Smart Money activity, and OptionFlow, which shows option blocks, sweeps, and splits in real-time. BigShort also provides backtested and forward-tested leading indicators, as well as live data for all NYSE and Nasdaq tickers.

TrendSpider is a comprehensive trading software suite designed for traders by traders. It offers a wide range of tools and features to help users make informed trading decisions, including charting and analysis tools, trade idea generation tools, strategy development tools, and trade timing and execution tools. TrendSpider also provides access to a variety of third-party indicators, scanners, and strategies. The platform is available as a web-based application and as a mobile app for iOS and Android devices.

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.

Composer is an AI-powered platform designed for investing, allowing users to build trading algorithms with AI, backtest them, and execute trades all in one place. The platform does not require coding skills and offers features like AI-powered strategy creation, automation of trading strategies, pre-built strategies, and customizable strategies. Composer aims to make sophisticated quant investing accessible to all users, providing a fun, stimulating, and creative investing experience.

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.

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.

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.

Probz is an AI-powered end-to-end Insights Platform that helps brands gather detailed insights on their ideas through Quant, Qual, and Video Research. With a panel of over 10 million users worldwide, Probz enables brands to supercharge their decision-making process, understand customer behavior, collect feedback for product development, optimize marketing strategies, explore new initiatives, and obtain feedback on technology preferences and designs. The platform offers AI-generated hypotheses, customizable surveys, and real-user tests to enhance sales, customer satisfaction, and brand loyalty.

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.

Wondershare Filmora is a powerful and intuitive video editing application that offers a wide range of features and tools to create professional-looking videos. With AI-powered features like AI copywriting, text-to-speech, and smart trimming, Filmora simplifies the video editing process for users of all skill levels. The application provides a seamless editing experience across multiple platforms, allowing users to edit, save, and share their content effortlessly. Filmora also offers a variety of pre-designed templates, customizable content, and abundant formats for social media platforms, enhancing productivity and creativity in video editing.

Focus Group Simulator is an AI tool designed to generate market insights instantly by simulating focus groups. By combining the power of LLMs to personate target segments with marketing quants analysis and best marketing frameworks, the tool provides valuable insights for businesses, especially startups. It helps identify low-hanging-fruit segments and offers guidance on product development, pricing, and promotion strategies to create more value and avoid waste. Users can customize simulations and engage with the team for further enhancements.

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.

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.

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.

This repository provides a comprehensive collection of resources, open-source tools, and knowledge related to quantitative analysis. It serves as a valuable knowledge base and navigation guide for individuals interested in various aspects of quantitative investing, including platforms, programming languages, mathematical foundations, machine learning, deep learning, and practical applications. The repository is well-structured and organized, with clear sections covering different topics. It includes resources on system platforms, programming codes, mathematical foundations, algorithm principles, machine learning, deep learning, reinforcement learning, graph networks, model deployment, and practical applications. Additionally, there are dedicated sections on quantitative trading and investment, as well as large models. The repository is actively maintained and updated, ensuring that users have access to the latest information and resources.

Since the emergence of chatGPT in 2022, the acceleration of Large Language Model has become increasingly important. Here is a list of papers on LLMs inference and serving.

Awesome LLM compression research papers and tools to accelerate LLM training and inference.

Fairseq is a sequence modeling toolkit that enables researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. It provides reference implementations of various sequence modeling papers covering CNN, LSTM networks, Transformer networks, LightConv, DynamicConv models, Non-autoregressive Transformers, Finetuning, and more. The toolkit supports multi-GPU training, fast generation on CPU and GPU, mixed precision training, extensibility, flexible configuration based on Hydra, and full parameter and optimizer state sharding. Pre-trained models are available for translation and language modeling with a torch.hub interface. Fairseq also offers pre-trained models and examples for tasks like XLS-R, cross-lingual retrieval, wav2vec 2.0, unsupervised quality estimation, and more.

Awesome-LLM-Inference: A curated list of 📙Awesome LLM Inference Papers with Codes, check 📖Contents for more details. This repo is still updated frequently ~ 👨‍💻‍ Welcome to star ⭐️ or submit a PR to this repo!

RAG with txtai is a Retrieval Augmented Generation (RAG) Streamlit application that helps generate factually correct content by limiting the context in which a Large Language Model (LLM) can generate answers. It supports two categories of RAG: Vector RAG, where context is supplied via a vector search query, and Graph RAG, where context is supplied via a graph path traversal query. The application allows users to run queries, add data to the index, and configure various parameters to control its behavior.

This repository includes papers and blogs about Efficient Transformers, Length Extrapolation, Long Term Memory, Retrieval Augmented Generation(RAG), and Evaluation for Long Context Modeling.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

OpenRedTeaming is a repository focused on red teaming for generative models, specifically large language models (LLMs). The repository provides a comprehensive survey on potential attacks on GenAI and robust safeguards. It covers attack strategies, evaluation metrics, benchmarks, and defensive approaches. The repository also implements over 30 auto red teaming methods. It includes surveys, taxonomies, attack strategies, and risks related to LLMs. The goal is to understand vulnerabilities and develop defenses against adversarial attacks on large language models.

ExLlamaV2 is an inference library for running local LLMs on modern consumer GPUs. It is a faster, better, and more versatile codebase than its predecessor, ExLlamaV1, with support for a new quant format called EXL2. EXL2 is based on the same optimization method as GPTQ and supports 2, 3, 4, 5, 6, and 8-bit quantization. It allows for mixing quantization levels within a model to achieve any average bitrate between 2 and 8 bits per weight. ExLlamaV2 can be installed from source, from a release with prebuilt extension, or from PyPI. It supports integration with TabbyAPI, ExUI, text-generation-webui, and lollms-webui. Key features of ExLlamaV2 include: - Faster and better kernels - Cleaner and more versatile codebase - Support for EXL2 quantization format - Integration with various web UIs and APIs - Community support on Discord

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.

Qwen-TensorRT-LLM is a project developed for the NVIDIA TensorRT Hackathon 2023, focusing on accelerating inference for the Qwen-7B-Chat model using TRT-LLM. The project offers various functionalities such as FP16/BF16 support, INT8 and INT4 quantization options, Tensor Parallel for multi-GPU parallelism, web demo setup with gradio, Triton API deployment for maximum throughput/concurrency, fastapi integration for openai requests, CLI interaction, and langchain support. It supports models like qwen2, qwen, and qwen-vl for both base and chat models. The project also provides tutorials on Bilibili and blogs for adapting Qwen models in NVIDIA TensorRT-LLM, along with hardware requirements and quick start guides for different model types and quantization methods.

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