Best AI tools for< Derivatives And Asset Trader >

Web3 Summary is an AI-powered platform that simplifies on-chain research across multiple chains and protocols, helping users find trading alpha in the DeFi and NFT space. It offers a range of products including a trading terminal, wallet study tool, Discord bot, mobile app, and Chrome extension. The platform aims to streamline the process of understanding complex crypto projects and tokenomics using AI and ChatGPT technology.

MarketGPT is an artificial intelligence model trained to predict stock movements based on news items. It evaluates the news and decides how the company stock is going to be affected by it. Users can access the model through the MarketGPT website or mobile app to get stock predictions and picks. The model's performance can be viewed for different time frames such as 1 week, 1 month, and 1 year. However, users are advised that investing in stocks and derivatives carries a risk of financial loss, and past performance is not a guarantee of future performance. MarketGPT is designed to assist users in making informed decisions in the stock market.

MathGPT is an AI math solver and calculator that provides users with the ability to solve various mathematical problems, including calculations, derivatives, and integrations. It also offers a question notebook feature and AI tutoring capabilities. Users can input mathematical expressions and equations, and MathGPT will provide step-by-step solutions and answers. The tool supports a wide range of mathematical functions and constants, making it a versatile and efficient tool for students, educators, and anyone needing assistance with math problems.

Rowy is a low-code backend platform that allows users to manage their database on a spreadsheet-like interface and build powerful backend cloud functions without leaving their browser. It offers a variety of features such as derivative fields, action fields, extensions, webhooks, and integrations with popular tools like Google Vision, GPT-3, Figma, and Webflow. Rowy is designed to be accessible to both developers and non-technical users, making it a versatile tool for building and managing backend applications.

OddBooks is an AI-powered platform that transforms books into scenarios and various content types such as audiobooks, webtoons, animations, and movies. It offers a simple engine to create scenarios based on books, revolutionizing the process of producing derivative works. Users can easily extract character names, emotions, spatial and sound keywords, and even infer character personalities from the text. With OddBooks, users can efficiently create scripts for secondary works, saving time and resources.

SurveyX is an advanced academic survey automation system that leverages Large Language Models (LLMs) to generate high-quality, domain-specific academic papers and surveys. Users can request comprehensive academic papers or surveys tailored to specific topics by providing a paper title and keywords for literature retrieval. The system streamlines academic research by automating paper creation, saving users time and effort in compiling research content.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.

Hexabot Community Edition is an open-source chatbot solution designed for flexibility and customization, offering powerful text-to-action capabilities. It allows users to create and manage AI-powered, multi-channel, and multilingual chatbots with ease. The platform features an analytics dashboard, multi-channel support, visual editor, plugin system, NLP/NLU management, multi-lingual support, CMS integration, user roles & permissions, contextual data, subscribers & labels, and inbox & handover functionalities. The directory structure includes frontend, API, widget, NLU, and docker components. Prerequisites for running Hexabot include Docker and Node.js. The installation process involves cloning the repository, setting up the environment, and running the application. Users can access the UI admin panel and live chat widget for interaction. Various commands are available for managing the Docker services. Detailed documentation and contribution guidelines are provided for users interested in contributing to the project.

LLM4EC is an interdisciplinary research repository focusing on the intersection of Large Language Models (LLM) and Evolutionary Computation (EC). It provides a comprehensive collection of papers and resources exploring various applications, enhancements, and synergies between LLM and EC. The repository covers topics such as LLM-assisted optimization, EA-based LLM architecture search, and applications in code generation, software engineering, neural architecture search, and other generative tasks. The goal is to facilitate research and development in leveraging LLM and EC for innovative solutions in diverse domains.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

Godot LLM is a plugin that enables the utilization of large language models (LLM) for generating content in games. It provides functionality for text generation, text embedding, multimodal text generation, and vector database management within the Godot game engine. The plugin supports features like Retrieval Augmented Generation (RAG) and integrates llama.cpp-based functionalities for text generation, embedding, and multimodal capabilities. It offers support for various platforms and allows users to experiment with LLM models in their game development projects.

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.

The Machine Learning Road Map is a comprehensive guide designed to take individuals from various levels of machine learning knowledge to a basic understanding of machine learning principles using high-quality, free resources. It aims to simplify the complex and rapidly growing field of machine learning by providing a structured roadmap for learning. The guide emphasizes the importance of understanding AI for everyone, the need for patience in learning machine learning due to its complexity, and the value of learning from experts in the field. It covers five different paths to learning about machine learning, catering to consumers, aspiring AI researchers, ML engineers, developers interested in building ML applications, and companies looking to implement AI solutions.

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.

Endia is a dynamic Array library for Scientific Computing, offering automatic differentiation of arbitrary order, complex number support, dual API with PyTorch-like imperative or JAX-like functional interface, and JIT Compilation for speeding up training and inference. It can handle complex valued functions, perform both forward and reverse-mode automatic differentiation, and has a builtin JIT compiler. Endia aims to advance AI & Scientific Computing by pushing boundaries with clear algorithms, providing high-performance open-source code that remains readable and pythonic, and prioritizing clarity and educational value over exhaustive features.

Notes on AI state of the art, with a focus on generative and large language models. These are the "raw materials" for the https://lspace.swyx.io/ newsletter. This repo used to be called https://github.com/sw-yx/prompt-eng, but was renamed because Prompt Engineering is Overhyped. This is now an AI Engineering notes repo.

A website that tries to make sense of those three-letter airport codes. It provides detailed information about each airport, including its name, location, and a description. The site also includes a search function that allows users to find airports by name, city, or country. Airport content can be found in `/data` in individual files. Use the three-letter airport code as the filename (e.g. `phx.json`). Content in each `json` file: `id` = three-letter code (e.g. phx), `name` = airport name (Sky Harbor International Airport), `city` = primary city name (Phoenix), `state` = state name, if applicable (Arizona), `stateShort` = state abbreviation, if applicable (AZ), `country` = country name (USA), `description` = description, accepts markdown, use * for emphasis on letters, `imageCredit` = name of photographer, `imageCreditLink` = URL of photographer's Flickr page. You can also optionally add for aid in searching: `city2` = another city or country the airport may be known for. Adding a `json` file to `/data` will automatically render it. You do not need to manually add the path anywhere.

DeepBattler is a tool designed for Hearthstone Battlegrounds players, providing real-time strategic advice and insights to improve gameplay experience. It integrates with the Hearthstone Deck Tracker plugin and offers voice-assisted guidance. The tool is powered by a large language model (LLM) and can match the strength of top players on EU servers. Users can set up the tool by adding dependencies, configuring the plugin path, and launching the LLM agent. DeepBattler is licensed for personal, educational, and non-commercial use, with guidelines on non-commercial distribution and acknowledgment of external contributions.

The ChatGPT Telegram Bot is a powerful Telegram bot that utilizes various GPT models, including GPT3.5, GPT4, GPT4 Turbo, GPT4 Vision, DALL·E 3, Groq Mixtral-8x7b/LLaMA2-70b, and Claude2.1/Claude3 opus/sonnet API. It enables users to engage in efficient conversations and information searches on Telegram. The bot supports multiple AI models, online search with DuckDuckGo and Google, user-friendly interface, efficient message processing, document interaction, Markdown rendering, and convenient deployment options like Zeabur, Replit, and Docker. Users can set environment variables for configuration and deployment. The bot also provides Q&A functionality, supports model switching, and can be deployed in group chats with whitelisting. The project is open source under GPLv3 license.

DataChain is an open-source Python library for processing and curating unstructured data at scale. It supports AI-driven data curation using local ML models and LLM APIs, handles large datasets, and is Python-friendly with Pydantic objects. It excels at optimizing batch operations and is designed for offline data processing, curation, and ETL. Typical use cases include Computer Vision data curation, LLM analytics, and validation.

Mistral.rs is a fast LLM inference platform written in Rust. We support inference on a variety of devices, quantization, and easy-to-use application with an Open-AI API compatible HTTP server and Python bindings.

ComfyUI Desktop is a packaged desktop application that allows users to easily use ComfyUI with bundled features like ComfyUI source code, ComfyUI-Manager, and uv. It automatically installs necessary Python dependencies and updates with stable releases. The app comes with Electron, Chromium binaries, and node modules. Users can store ComfyUI files in a specified location and manage model paths. The tool requires Python 3.12+ and Visual Studio with Desktop C++ workload for Windows. It uses nvm to manage node versions and yarn as the package manager. Users can install ComfyUI and dependencies using comfy-cli, download uv, and build/launch the code. Troubleshooting steps include rebuilding modules and installing missing libraries. The tool supports debugging in VSCode and provides utility scripts for cleanup. Crash reports can be sent to help debug issues, but no personal data is included.

The 100 Days in AI repository provides a comprehensive roadmap for individuals to learn Artificial Intelligence over a period of 100 days. It covers topics ranging from basic programming in Python to advanced concepts in AI, including machine learning, deep learning, and specialized AI topics. The repository includes daily tasks, resources, and exercises to ensure a structured learning experience. By following this roadmap, users can gain a solid understanding of AI and be prepared to work on real-world AI projects.

The Electronic Component Classifier is a project that uses machine learning and artificial intelligence to automate the identification and classification of electrical and electronic components. It features component classification into seven classes, user-friendly design, and integration with Flask for a user-friendly interface. The project aims to reduce human error in component identification, make the process safer and more reliable, and potentially help visually impaired individuals in identifying electronic components.

Cryptocurrency market data analyst, focusing on derivatives trading insights and guidance.

mfer history, derivatives, and conversation

Educational guide for understanding algebra principles and equations.

Expert in financial mathematics, providing detailed explanations and guidance.

Your Edexcel A level maths assistant. Ask for new questions. Help for the next step in your working out. Even send me a picture of a question and i can tell you what exam it is from.

Calcola e impara come si fanno i calcoli

Your sympy expert for physics

IntegriPal: Your friendly integral calculus guide!

A math problem solver that creates Julia code.