Best AI tools for< Analyze Financial Data >

Datafitai is a community platform for ChatGPT prompting, where users can find and share top-rated prompts for various topics such as marketing, coding, finance, writing, gaming, and art. The platform aims to improve the accuracy of ChatGPT responses by providing high-quality prompts. Users can explore, share, and contribute to a wide range of prompts to enhance their ChatGPT experience.

Investiment.io is a financial news and data platform that uses AI to help investors make better decisions. The platform provides access to real-time news, earnings transcripts, and financial data, as well as AI-powered insights and analysis. Investiment.io is designed to help investors of all levels, from beginners to experienced professionals.

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.

Quill is an AI-powered SEC filing platform that allows users to extract key information from filings, answer questions about public investor materials, access historical financial data, and receive real-time SEC filings and earnings call transcripts. The platform leverages financially-tuned AI to provide accurate and up-to-date information, making it a valuable tool for analysts and professionals in the finance industry.

Castello.ai is a financial analysis tool that uses artificial intelligence to help businesses make better decisions. It provides users with real-time insights into their financial data, helping them to identify trends, risks, and opportunities. Castello.ai is designed to be easy to use, even for those with no financial background.

CityFALCON is a financial and business due diligence platform that provides a range of solutions for the needs of a wide audience, including retail investors, retail traders, daily business news readers, brokers, students, professors, academia, wealth managers, financial advisors, P2P crowdfunding, VC, PE, institutional investors, treasury, consultancy, legal, accounting, central banks, and regulatory agencies. The platform offers a variety of features and content, including a CityFALCON Score, watchlists, similar stories, grouping news on charts, key headlines, sentiment content translation, content news premium publications, insider transactions, official company filings, investor relations, ESG content, and languages.

FinChat.io is a comprehensive AI-powered stock research platform that provides institutional-quality data and insights to investors. With FinChat.io, you can access accurate financial data on over 100,000 global public companies, as well as company revenue and profit segments, KPIs, analyst estimates, price targets, and ratings. FinChat.io also utilizes cutting-edge AI to build summaries, models, and visualizations, making it easy to understand complex financial data. Additionally, FinChat.io offers a customizable terminal, allowing you to track what matters most to you and auto-save your research. With FinChat.io, you can work faster than ever and make better investment decisions.

Fama.one is an AI-powered platform that helps businesses automate their financial processes. It uses machine learning to analyze financial data and identify patterns, which can then be used to automate tasks such as invoice processing, expense management, and financial reporting. Fama.one also provides businesses with insights into their financial performance, which can help them make better decisions.

Novus Writer is a customizable, on-premise AI and LLM solution designed to enhance efficiency in various business functions, including sales, finance, customer support, and more. It offers a range of features such as plagiarism detection, long-form generation, proofreading, fact checking, and custom AI agents. Novus Writer is trusted by enterprises for its streamlined processes, advanced data analysis capabilities, and compliance with industry standards.

Flavor is an AI-powered accounting automation tool that revolutionizes the month-end close process. It automates tasks such as book closure management, general ledger reconciliations, consolidation & reporting, financial analysis, and accruals management. Flavor combines AI technology with human validation to ensure accurate, compliant, and audit-ready financial records. The tool offers customizable checklists, real-time insights, and dynamic reports for faster decision-making. With Flavor, users can reduce manual workload, eliminate errors, and focus on strategic growth initiatives.

PrometAI is an AI-powered business plan generator that helps entrepreneurs and businesses create comprehensive and professional business plans. It offers a range of features and tools to guide users through each step of the planning process, including strategy development, financial analysis, and valuation. PrometAI's platform is designed to simplify and streamline the business planning process, making it accessible to users of all levels of experience.

Baselinemag is an AI tool that provides news and insights on software, business, finance, and project management. It covers a wide range of topics related to technology and business, offering valuable information to professionals and enthusiasts in the industry.

Thomson Reuters is a leading provider of business information services. The company provides a wide range of products and services to professionals in the legal, tax, accounting, and risk management industries. Thomson Reuters' products and services include news and information, research and analysis, software and technology, and education and training. The company has a global presence with operations in over 100 countries.

LedgerBox is an AI tool that specializes in converting bank statements into digital formats. It simplifies the process of managing financial data by automatically extracting and organizing information from bank statements. With LedgerBox, users can easily convert paper-based bank statements into digital files, enabling quick and efficient financial analysis and reporting. The tool is designed to save time and reduce errors associated with manual data entry, making it a valuable asset for individuals and businesses looking to streamline their financial processes.

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.

Daloopa is an AI financial modeling tool designed to automate fundamental data updates for financial analysts working in Excel. It helps analysts build and update financial models efficiently by eliminating manual work and providing accurate, auditable data points sourced from thousands of companies. Daloopa leverages AI technology to deliver complete and comprehensive data sets faster than humanly possible, enabling analysts to focus on analysis, insight generation, and idea development to drive better investment decisions.

ChartPixel is an AI-assisted data analysis platform that empowers users to effortlessly generate charts, insights, and actionable statistics in just 30 seconds. The platform is designed to demystify data and analysis, making it accessible to users of all skill levels. ChartPixel combines the power of AI with domain expertise to provide secure and reliable output, ensuring trustworthy results without compromising data privacy. With user-friendly features and educational tools, ChartPixel helps users clean, wrangle, visualize, and present data with ease, catering to both beginners and professionals.

Roic AI is an AI tool designed to provide users with essential financial data for analyzing companies. It offers comprehensive company summaries, 30+ years of financial statements, and earnings call transcripts in a single location. Users can access crucial information about popular companies like Apple Inc. and Microsoft Corporation through this platform.

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.

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.

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.

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.

LetMeDoIt AI is a virtual assistant designed to revolutionize the way you work. It goes beyond being a mere chatbot by offering a unique and powerful capability - the ability to execute commands and perform computing tasks on your behalf. With LetMeDoIt AI, you can access OpenAI ChatGPT-4, Google Gemini Pro, and Microsoft AutoGen, local LLMs, all in one place, to enhance your productivity.

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.

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.

FreeGenius AI is an ambitious project offering a comprehensive suite of AI solutions that mirror the capabilities of LetMeDoIt AI. It is designed to engage in intuitive conversations, execute codes, provide up-to-date information, and perform various tasks. The tool is free, customizable, and provides access to real-time data and device information. It aims to support offline and online backends, open-source large language models, and optional API keys. Users can use FreeGenius AI for tasks like generating tweets, analyzing audio, searching financial data, checking weather, and creating maps.

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.

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.

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.

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.

AIlice is a fully autonomous, general-purpose AI agent that aims to create a standalone artificial intelligence assistant, similar to JARVIS, based on the open-source LLM. AIlice achieves this goal by building a "text computer" that uses a Large Language Model (LLM) as its core processor. Currently, AIlice demonstrates proficiency in a range of tasks, including thematic research, coding, system management, literature reviews, and complex hybrid tasks that go beyond these basic capabilities. AIlice has reached near-perfect performance in everyday tasks using GPT-4 and is making strides towards practical application with the latest open-source models. We will ultimately achieve self-evolution of AI agents. That is, AI agents will autonomously build their own feature expansions and new types of agents, unleashing LLM's knowledge and reasoning capabilities into the real world seamlessly.

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.

This repository is a collection of papers on Tabular Large Language Models (LLMs) specialized for processing tabular data. It includes surveys, models, and applications related to table understanding tasks such as Table Question Answering, Table-to-Text, Text-to-SQL, and more. The repository categorizes the papers based on key ideas and provides insights into the advancements in using LLMs for processing diverse tables and fulfilling various tabular tasks based on natural language instructions.

The LLMEvaluation repository is a comprehensive compendium of evaluation methods for Large Language Models (LLMs) and LLM-based systems. It aims to assist academics and industry professionals in creating effective evaluation suites tailored to their specific needs by reviewing industry practices for assessing LLMs and their applications. The repository covers a wide range of evaluation techniques, benchmarks, and studies related to LLMs, including areas such as embeddings, question answering, multi-turn dialogues, reasoning, multi-lingual tasks, ethical AI, biases, safe AI, code generation, summarization, software performance, agent LLM architectures, long text generation, graph understanding, and various unclassified tasks. It also includes evaluations for LLM systems in conversational systems, copilots, search and recommendation engines, task utility, and verticals like healthcare, law, science, financial, and others. The repository provides a wealth of resources for evaluating and understanding the capabilities of LLMs in different domains.

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.

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

Enhances financial decision-making by analyzing, interpreting and reporting financial data.

Enhances financial decision-making by analyzing, interpreting and presenting financial data.

Analyzes annual reports, focusing on specific user-provided data

Guides financial strategy through data analysis and forecasting.

Investment Co-Pilot: Portfolio backtesting and access to in-depth financial data and historical closing prices of US-listed companies. (Pulse formerly)

Specializes in financial analysis with DALL-E 3 imagery

Drives financial performance improvement via strategic analysis and advice.

Analyzes r/wallstreetbets for top topics, trends, and potential financial advice.

Expert in financial accounting, clarifying complex concepts with academic sources.

Expert in financial modeling for valuation, budgeting, and forecasting.

Structured financial analysis with investment advisory and simulations.

Financial analysis expert for annual report summaries.

The Sign of Money: A SherLockian Quest for Decoding the Financial Market’s Mysteries

Guides financial decisions by monitoring and enforcing policies and procedures.

Guides financial strategy through predictive scenario planning.

Great help for financial success

DeFi analyst specializing in insightful financial narratives.

GPT-4-based assistant that remembers and saves conversations

总结纲要，结合财报及经营数据，生成总结点评分析

Simple stock insights. Not financial advice.