Best AI tools for< Financial Data Scientist >

Envestnet | Yodlee is an AI-powered Conversational Banking platform that provides financial institutions and FinTech innovators with powerful data solutions. It offers a range of products and APIs for data aggregation, account verification, transaction data enrichment, financial wellness, and conversational AI. The platform enables personalized financial advice, secure account verification, and deep insights into customer needs, driving meaningful interactions. Envestnet | Yodlee revolutionizes the digital customer experience by leveraging natural language processing and machine learning to create seamless, personalized banking experiences across various channels.

Bloomberg is a leading global provider of financial data, news, and analytics. The company is known for its innovative technology solutions, including the Bloomberg Terminal, which revolutionized the industry by delivering critical insights and actionable information to financial decision-makers. Bloomberg is at the forefront of artificial intelligence, machine learning, and natural language processing, offering tools and automated workflows to help clients navigate the vast amount of data available in the financial markets. With a focus on engineering value for customers and keeping humans in the loop, Bloomberg continues to drive innovation and provide cutting-edge technology solutions for over 325,000 financial professionals worldwide.

AltIndex is an AI-powered investment analysis platform that provides unique AI stock picks, stock alerts, and alternative insights to help users make better investment decisions. The platform goes beyond traditional financial data by integrating various alternative data points such as job postings, website traffic, customer satisfaction, app downloads, and social media trends. AltIndex offers impactful insights and alerts, cutting-edge solutions to stay informed about companies in your portfolio, and advanced algorithms for real-time investment decision-making.

Vianai's Conversational Finance is an AI application that revolutionizes financial analysis by providing real-time insights through generative AI. It empowers finance teams to make informed decisions swiftly and confidently by scanning vast amounts of data, generating accurate responses, and streamlining processes. The platform offers unparalleled speed, precision, and user experience, making it easier to navigate complex financial landscapes and accomplish more in less time.

Jesse Zhang's personal website showcases his background in engineering, particularly in web development, AI/ML, and mathematics. He highlights his education at Harvard University and internships at renowned companies like Citadel, Google, and Intel. Zhang also mentions his entrepreneurial ventures, including founding Lowkey, which was acquired by Niantic, and his current work on a new company. The website features various projects he has worked on, such as real-time multiplayer implementations of Camel Up and Bananagrams, a financial data visualization tool, and a demo of Zero-Knowledge proofs in the game Mastermind. Additionally, Zhang shares his interest in writing math contest problems and his involvement in angel investing through Sequoia Scouts and Neo.

WiseData is an AI Assistant for Python Data Analytics designed to help Data Analysts and Data Scientists be 2X more productive. It offers features like data transformation with natural language, data visualization with natural language, and data transformation with SQL. WiseData ensures privacy by not sending analyzed data to its server and protects transmitted prompts and suggestions through encryption. It is a valuable tool for simplifying complex data analytics tasks and enhancing productivity.

Kudra is an AI-powered data extraction tool that offers dedicated solutions for finance, human resources, logistics, legal, and more. It effortlessly extracts critical data fields, tables, relationships, and summaries from various documents, transforming unstructured data into actionable insights. Kudra provides customizable AI models, seamless integrations, and secure document processing while supporting over 20 languages. With features like custom workflows, model training, API integration, and workflow builder, Kudra aims to streamline document processing for businesses of all sizes.

Stockpulse is an AI-powered platform that analyzes financial news and communities using Artificial Intelligence. It provides decision support for operations by collecting, filtering, and converting unstructured data into processable information. With extensive coverage of financial media sources globally, Stockpulse offers unique historical data, sentiment analysis, and AI-driven insights for various sectors in the financial markets.

Orbital Insight is a leading geospatial data analytics platform that provides users with the ability to query the world with three basic parameters: WHAT type of activity? WHERE on earth? WHEN? The platform automates the most difficult steps of deriving insights, allowing you to answer many challenging geospatial questions. Orbital Insight's GO platform is designed for enterprise collaboration and transforms multiple geospatial data sources to accelerate and streamline team member's research, reporting, due diligence, and more.

Avanzai is an AI tool designed for financial services, providing intelligent automation to asset managers. It streamlines operations, enhances decision-making, and transforms data into actionable strategies. With AI-powered reports, automated portfolio management, data connectivity, and customizable agents, Avanzai empowers financial firms to optimize portfolios and make informed decisions.

Vizly is an AI-powered data analysis tool that empowers users to make the most of their data. It allows users to chat with their data, visualize insights, and perform complex analysis. Vizly supports various file formats like CSV, Excel, and JSON, making it versatile for different data sources. The tool is free to use for up to 10 messages per month and offers a student discount of 50%. Vizly is suitable for individuals, students, academics, and organizations looking to gain actionable insights from their data.

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.

Crustdata is a platform that provides real-time LinkedIn headcount and people data for making informed investment and sales decisions. It offers curated, dynamic data refreshed weekly to help users stay updated on company performance, sales dynamics, investment intelligence, and competitive intelligence. The platform enables users to track companies of interest, enrich CRM systems, and access various datasets related to web traffic, Google search impressions, product reviews, CEO and company reviews, investment data, SEO rankings, company news, and Form D filings. Additionally, Crustdata offers services to identify and fix data gaps, modernize data pipelines, and leverage AI for market mapping and competitor identification.

The website offers a precision workflow solution for enterprises in the finance industry, combining AI technology with human oversight to empower financial decisions. It provides features such as accurate search citations, multilingual models, and complex human-in-loop automation. The application integrates seamlessly with existing platforms, uses advanced AI models, and offers meaningful time savings. Users can benefit from the application's ability to ingest unstructured data, improve over time, and avoid hallucinations.

NextGenAI is an AI application focused on the financial services industry. It aims to challenge the current perception of AI and its role in banking and financial institutions. The platform explores innovative ways to augment human intelligence and propel the financial sector into the next generation of AI. Through a combination of keynotes, panels, demos, and workshops, NextGenAI facilitates discussions on AI regulations, industry best practices, and collaboration opportunities.

Tamarack is a technology company specializing in equipment finance, offering AI-powered applications and data-centric technologies to enhance operational efficiency and business performance. They provide a range of solutions, from business intelligence to professional services, tailored for the equipment finance industry. Tamarack's AI Predictors and DataConsole are designed to streamline workflows and improve outcomes for stakeholders. With a focus on innovation and customer experience, Tamarack aims to empower clients with online functionality and predictive analytics. Their expertise spans from origination to portfolio management, delivering industry-specific solutions for better performance.

Dark Pools is a leading provider of AI-powered solutions for the financial industry. Our mission is to empower our clients with the tools and insights they need to make better decisions, improve their performance, and stay ahead of the competition. We offer a range of products and services that leverage AI to automate tasks, optimize workflows, and generate actionable insights. Our solutions are used by a wide range of financial institutions, including hedge funds, asset managers, and banks.

Saal.ai is an AI company based in Abu Dhabi that offers innovative cognitive AI solutions for various industries such as healthcare, finance, and smart cities. The company leverages artificial intelligence to develop cognitive solutions, products, and platforms that help businesses automate their operations and address challenges using advanced AI technologies. Saal.ai's framework is flexible and continuously optimized to seamlessly integrate into any business, unlocking exponential growth opportunities for humanity.

Meyka is an AI-powered stock research tool that provides users with real-time stock data and analysis. Users can explore financial health, social sentiment analysis, earnings reports, comparison of financial statements, stock market news, DCF value, stock price forecasting, and recent grades for various stocks. The tool aims to assist users in making informed investment decisions by leveraging AI technology to analyze and predict stock market trends.

Tablize is a powerful data extraction tool that helps you turn unstructured data into structured, tabular format. With Tablize, you can easily extract data from PDFs, images, and websites, and export it to Excel, CSV, or JSON. Tablize uses artificial intelligence to automate the data extraction process, making it fast and easy to get the data you need.

finagg is a Python package that provides implementations of popular and free financial APIs, tools for aggregating historical data from those APIs into SQL databases, and tools for transforming aggregated data into features useful for analysis and AI/ML. It offers documentation, installation instructions, and basic usage examples for exploring various financial APIs and features. Users can install recommended datasets from 3rd party APIs into a local SQL database, access Bureau of Economic Analysis (BEA) data, Federal Reserve Economic Data (FRED), Securities and Exchange Commission (SEC) filings, and more. The package also allows users to explore raw data features, install refined data features, and perform refined aggregations of raw data. Configuration options for API keys, user agents, and data locations are provided, along with information on dependencies and related projects.

FinRobot is an open-source AI agent platform designed for financial applications using large language models. It transcends the scope of FinGPT, offering a comprehensive solution that integrates a diverse array of AI technologies. The platform's versatility and adaptability cater to the multifaceted needs of the financial industry. FinRobot's ecosystem is organized into four layers, including Financial AI Agents Layer, Financial LLMs Algorithms Layer, LLMOps and DataOps Layers, and Multi-source LLM Foundation Models Layer. The platform's agent workflow involves Perception, Brain, and Action modules to capture, process, and execute financial data and insights. The Smart Scheduler optimizes model diversity and selection for tasks, managed by components like Director Agent, Agent Registration, Agent Adaptor, and Task Manager. The tool provides a structured file organization with subfolders for agents, data sources, and functional modules, along with installation instructions and hands-on tutorials.

PIXIU is a project designed to support the development, fine-tuning, and evaluation of Large Language Models (LLMs) in the financial domain. It includes components like FinBen, a Financial Language Understanding and Prediction Evaluation Benchmark, FIT, a Financial Instruction Dataset, and FinMA, a Financial Large Language Model. The project provides open resources, multi-task and multi-modal financial data, and diverse financial tasks for training and evaluation. It aims to encourage open research and transparency in the financial NLP field.

StockBot Powered by Groq is an AI-powered chatbot that provides lightning-fast responses with live interactive stock charts, financial data, news, screeners, and more. Leveraging Groq's speed and Vercel's AI SDK, StockBot offers real-time conversation with natural language processing, interactive TradingView charts, adaptive interfaces, and multi-asset market coverage. It is designed for entertainment and instructional use, not for investment advice.

Financial Datasets is an open-source Python library that allows users to create question and answer financial datasets using Large Language Models (LLMs). With this library, users can easily generate realistic financial datasets from 10-K, 10-Q, PDF, and other financial texts. The library provides three main methods for generating datasets: from any text, from a 10-K filing, or from a PDF URL. Financial Datasets can be used for a variety of tasks, including financial analysis, research, and education.

This repository contains the Python source code for FINMEM, a Performance-Enhanced Large Language Model Trading Agent with Layered Memory and Character Design. It introduces FinMem, a novel LLM-based agent framework devised for financial decision-making, encompassing three core modules: Profiling, Memory with layered processing, and Decision-making. FinMem's memory module aligns closely with the cognitive structure of human traders, offering robust interpretability and real-time tuning. The framework enables the agent to self-evolve its professional knowledge, react agilely to new investment cues, and continuously refine trading decisions in the volatile financial environment. It presents a cutting-edge LLM agent framework for automated trading, boosting cumulative investment returns.

Deep Lake is a Database for AI powered by a storage format optimized for deep-learning applications. Deep Lake can be used for: 1. Storing data and vectors while building LLM applications 2. Managing datasets while training deep learning models Deep Lake simplifies the deployment of enterprise-grade LLM-based products by offering storage for all data types (embeddings, audio, text, videos, images, pdfs, annotations, etc.), querying and vector search, data streaming while training models at scale, data versioning and lineage, and integrations with popular tools such as LangChain, LlamaIndex, Weights & Biases, and many more. Deep Lake works with data of any size, it is serverless, and it enables you to store all of your data in your own cloud and in one place. Deep Lake is used by Intel, Bayer Radiology, Matterport, ZERO Systems, Red Cross, Yale, & Oxford.

SambaNova AI Starter Kits is a collection of open-source examples and guides designed to facilitate the deployment of AI-driven use cases for developers and enterprises. The kits cover various categories such as Data Ingestion & Preparation, Model Development & Optimization, Intelligent Information Retrieval, and Advanced AI Capabilities. Users can obtain a free API key using SambaNova Cloud or deploy models using SambaStudio. Most examples are written in Python but can be applied to any programming language. The kits provide resources for tasks like text extraction, fine-tuning embeddings, prompt engineering, question-answering, image search, post-call analysis, and more.

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.

The `vectara-agentic` Python library is designed for developing powerful AI assistants using Vectara and Agentic-RAG. It supports various agent types, includes pre-built tools for domains like finance and legal, and enables easy creation of custom AI assistants and agents. The library provides tools for summarizing text, rephrasing text, legal tasks like summarizing legal text and critiquing as a judge, financial tasks like analyzing balance sheets and income statements, and database tools for inspecting and querying databases. It also supports observability via LlamaIndex and Arize Phoenix integration.

Phidata is a framework for building AI Assistants with memory, knowledge, and tools. It enables LLMs to have long-term conversations by storing chat history in a database, provides them with business context by storing information in a vector database, and enables them to take actions like pulling data from an API, sending emails, or querying a database. Memory and knowledge make LLMs smarter, while tools make them autonomous.

Pathway's LLM (Large Language Model) Apps provide a platform to quickly deploy AI applications using the latest knowledge from data sources. The Python application examples in this repository are Docker-ready, exposing an HTTP API to the frontend. These apps utilize the Pathway framework for data synchronization, API serving, and low-latency data processing without the need for additional infrastructure dependencies. They connect to document data sources like S3, Google Drive, and Sharepoint, offering features like real-time data syncing, easy alert setup, scalability, monitoring, security, and unification of application logic.

Agentic is a standard AI functions/tools library optimized for TypeScript and LLM-based apps, compatible with major AI SDKs. It offers a set of thoroughly tested AI functions that can be used with favorite AI SDKs without writing glue code. The library includes various clients for services like Bing web search, calculator, Clearbit data resolution, Dexa podcast questions, and more. It also provides compound tools like SearchAndCrawl and supports multiple AI SDKs such as OpenAI, Vercel AI SDK, LangChain, LlamaIndex, Firebase Genkit, and Dexa Dexter. The goal is to create minimal clients with strongly-typed TypeScript DX, composable AIFunctions via AIFunctionSet, and compatibility with major TS AI SDKs.

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.

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

Awesome GPT Prompt Engineering is a curated list of resources, tools, and shiny things for GPT prompt engineering. It includes roadmaps, guides, techniques, prompt collections, papers, books, communities, prompt generators, Auto-GPT related tools, prompt injection information, ChatGPT plug-ins, prompt engineering job offers, and AI links directories. The repository aims to provide a comprehensive guide for prompt engineering enthusiasts, covering various aspects of working with GPT models and improving communication with AI tools.

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.

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.

ML Contests is a platform that provides a sortable list of public machine learning/data science/AI contests, viewable on mlcontests.com. Users can submit pull requests for any changes or additions to the competitions list by editing the competitions.json file on the GitHub repository. The platform requires mandatory fields such as competition name, URL, type of ML, deadline for submissions, prize information, platform running the competition, and sponsorship details. Optional fields include conference affiliation, conference year, competition launch date, registration deadline, additional URLs, and tags relevant to the challenge type. The platform is transitioning towards assigning multiple tags to competitions for better categorization and searchability.

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Salam mən Alas Academy-nin Data Analitika üzrə Süni İntellekt mentoruyam. Mənə istənilən sualı verə bilərsiniz :)

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