Best AI tools for< Financial Modeling >

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.

Sensey.ai is a personal AI assistant designed to help startups with a variety of tasks, from scheduling meetings to managing finances. It uses natural language processing and machine learning to understand your needs and provide personalized recommendations.

Slidebean is an all-in-one pitch deck software that helps startups create professional and visually appealing pitch decks to raise funds from investors. It offers a range of features including an AI-powered pitch deck builder, collaboration tools, automated design, and analytics. Slidebean also provides pitch deck services where a team of experts helps founders with writing, design, financial modeling, and go-to-market strategy.

Jyotax.ai is an AI-powered tax solution that revolutionizes tax compliance by simplifying the tax process with advanced AI solutions. It offers comprehensive bookkeeping, payroll processing, worldwide tax returns and filing automation, profit recovery, contract compliance, and financial modeling and budgeting services. The platform ensures accurate reporting, real-time compliance monitoring, global tax solutions, customizable tax tools, and seamless data integration. Jyotax.ai optimizes tax workflows, ensures compliance with precise AI tax calculations, and simplifies global tax operations through innovative AI solutions.

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and high-level mathematical functions to perform operations on these arrays. It is the fundamental package for scientific computing with Python and is used in a wide range of applications, including data science, machine learning, and image processing. NumPy is open source and distributed under a liberal BSD license, and is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.

Drivetrain is a Strategic Finance Platform designed for modern businesses. It offers real-time tracking and reporting, continuous planning and forecasting, and a single source of truth by combining accounting and business data effortlessly. The platform empowers finance teams globally with AI-powered FP&A software, enabling users to accelerate planning, tracking, and forecasting. Drivetrain provides integrations with ERP, CRM, HRIS, and other systems, along with over 200 pre-built connectors. The platform is praised for its collaborative features, user-friendly interface, and ability to make data-driven decisions quickly.

Underwrite.ai is a platform that leverages advances in artificial intelligence and machine learning to provide lenders with nonlinear, dynamic models of credit risk. By analyzing thousands of data points from credit bureau sources, the application accurately models credit risk for consumers and small businesses, outperforming traditional approaches. Underwrite.ai offers a unique underwriting methodology that focuses on outcomes such as profitability and customer lifetime value, allowing organizations to enhance their lending performance without the need for capital investment or lengthy build times. The platform's models are continuously learning and adapting to market changes in real-time, providing explainable decisions in milliseconds.

SheetBot AI is an AI data analyst tool that enables users to analyze data quickly without the need for coding. It automates repetitive and time-consuming data tasks, making data visualization and analysis more efficient. With SheetBot AI, users can generate accurate and visually appealing graphs in seconds, streamlining the data analysis process.

ChartFast is an AI Data Analyzer tool that automates data visualization and analysis tasks, powered by GPT-4 technology. It allows users to generate precise and sleek graphs in seconds, process vast amounts of data, and provide interactive data queries and quick exports. With features like specialized internal libraries for complex graph generation, customizable visualization code, and instant data export, ChartFast aims to streamline data work and enhance data analysis efficiency.

Simudyne is an enterprise simulation software powered by AI technology. It allows large financial institutions to simulate various future scenarios efficiently and measure their impact in a safe virtual environment. The software offers solutions for environment, social and governance issues, market execution, financial crime analytics, and risk management. Simudyne's technology is secure, distributable, and Cloudera certified, providing a robust library of code for specialized functions. The platform also utilizes agent-based modeling to bridge the gap between theoretical and real-world scenarios in the financial services sector.

Julius is an AI-powered tool that helps users analyze data and files. It can perform various tasks such as generating visualizations, answering data questions, and performing statistical modeling. Julius is designed to save users time and effort by automating complex data analysis tasks.

EnterpriseAI is an advanced computing platform that focuses on the intersection of high-performance computing (HPC) and artificial intelligence (AI). The platform provides in-depth coverage of the latest developments, trends, and innovations in the AI-enabled computing landscape. EnterpriseAI offers insights into various sectors such as financial services, government, healthcare, life sciences, energy, manufacturing, retail, and academia. The platform covers a wide range of topics including AI applications, security, data storage, networking, and edge/IoT technologies.

Navalai.co is an AI-powered platform that offers cutting-edge solutions for data analysis, predictive modeling, and decision-making. It leverages advanced algorithms and machine learning techniques to provide actionable insights from complex datasets. The platform is designed to streamline the process of extracting valuable information from data, enabling users to make informed decisions and drive business growth. With a user-friendly interface and powerful features, Navalai.co empowers users to harness the power of AI for a wide range of applications.

The Predict API is a powerful tool that allows you to forecast your data with simplicity and accuracy. It uses the latest advancements in stochastic modeling and machine learning to provide you with reliable projections. The API is easy to use and can be integrated with any application. It is also highly scalable, so you can use it to forecast large datasets. With the Predict API, you can gain valuable insights into your data and make better decisions.

Plat.AI is an automated predictive analytics software that offers model building solutions for various industries such as finance, insurance, and marketing. It provides a real-time decision-making engine that allows users to build and maintain AI models without any coding experience. The platform offers features like automated model building, data preprocessing tools, codeless modeling, and personalized approach to data analysis. Plat.AI aims to make predictive analytics easy and accessible for users of all experience levels, ensuring transparency, security, and compliance in decision-making processes.

Galaxy.ai is an all-in-one AI platform that offers a wide range of AI tools and applications to streamline and enhance various business processes. From data analysis to predictive modeling, Galaxy.ai provides advanced AI solutions to help businesses make data-driven decisions and improve efficiency. With its user-friendly interface and powerful algorithms, Galaxy.ai is designed to cater to the needs of both small businesses and large enterprises, making AI technology accessible and easy to implement.

meb.ai is an innovative AI tool designed to streamline and enhance various business processes through advanced automation and data analytics. It leverages cutting-edge artificial intelligence algorithms to provide actionable insights and optimize decision-making. With a user-friendly interface and robust features, meb.ai empowers organizations to drive efficiency, improve productivity, and achieve strategic goals. Whether it's data analysis, predictive modeling, or process automation, meb.ai offers a comprehensive solution to meet diverse business needs.

Aitodata.com is an AI tool designed to streamline data processing and analysis tasks. It offers a user-friendly interface that allows users to easily upload, clean, analyze, and visualize data sets. The tool leverages advanced machine learning algorithms to provide accurate insights and predictions, making it a valuable asset for data scientists, analysts, and researchers. With features such as data cleaning, exploratory data analysis, predictive modeling, and interactive visualizations, aitodata.com simplifies the data analysis process and helps users make informed decisions based on data-driven insights.

Julius AI is an advanced AI data analyst tool that allows users to analyze data with computational AI, chat with files to get expert-level insights, create sleek data visualizations, perform modeling and predictive forecasting, solve math, physics, and chemistry problems, generate polished analyses and summaries, save time by automating data work, and unlock statistical modeling without complexity. It offers features like generating visualizations, asking data questions, effortless cleaning, instant data export, creating animations, and supercharging data analysis. Julius AI is loved by over 1,200,000 users worldwide and is designed to help knowledge workers make the most out of their data.

Super AI is a generative AI tool designed as a copilot for data analysts. It is trained by top-tier product company experts and domain experts to provide unparalleled expertise in research, visualization, and data delivery. The tool goes beyond data processing by generating a comprehensive Business Decision Canvas tailored to specific challenges. Super AI offers guided insights, data modeling suggestions, and effortless integration with legacy BI systems. It is designed to convert business requirements into concrete objectives and is supported by a team of domain experts to mentor the AI. With applications in various industries, Super AI accelerates the process of finding business KPIs and generating data stories with expert intelligence.

The Data Scientist Roadmap2024 provides a comprehensive guide to mastering essential tools for data science success. It includes programming languages, machine learning libraries, cloud platforms, and concepts categorized by difficulty. The roadmap covers a wide range of topics from programming languages to machine learning techniques, data visualization tools, and DevOps/MLOps tools. It also includes web development frameworks and specific concepts like supervised and unsupervised learning, NLP, deep learning, reinforcement learning, and statistics. Additionally, it delves into DevOps tools like Airflow and MLFlow, data visualization tools like Tableau and Matplotlib, and other topics such as ETL processes, optimization algorithms, and financial modeling.

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

The AI Data Science Team of Copilots is an AI-powered data science team that uses agents to help users perform common data science tasks 10X faster. It includes agents specializing in data cleaning, preparation, feature engineering, modeling, and interpretation of business problems. The project is a work in progress with new data science agents to be released soon. Disclaimer: This project is for educational purposes only and not intended to replace a company's data science team. No warranties or guarantees are provided, and the creator assumes no liability for financial loss.

The `sec-parser` project simplifies extracting meaningful information from SEC EDGAR HTML documents by organizing them into semantic elements and a tree structure. It helps in parsing SEC filings for financial and regulatory analysis, analytics and data science, AI and machine learning, causal AI, and large language models. The tool is especially beneficial for AI, ML, and LLM applications by streamlining data pre-processing and feature extraction.

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.

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.

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.

LLaMA Factory is a unified framework for fine-tuning 100+ large language models (LLMs) with various methods, including pre-training, supervised fine-tuning, reward modeling, PPO, DPO and ORPO. It features integrated algorithms like GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, LoRA+, LoftQ and Agent tuning, as well as practical tricks like FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA. LLaMA Factory provides experiment monitors like LlamaBoard, TensorBoard, Wandb, MLflow, etc., and supports faster inference with OpenAI-style API, Gradio UI and CLI with vLLM worker. Compared to ChatGLM's P-Tuning, LLaMA Factory's LoRA tuning offers up to 3.7 times faster training speed with a better Rouge score on the advertising text generation task. By leveraging 4-bit quantization technique, LLaMA Factory's QLoRA further improves the efficiency regarding the GPU memory.

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.

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.

The 'gritlm' repository provides all materials for the paper Generative Representational Instruction Tuning. It includes code for inference, training, evaluation, and known issues related to the GritLM model. The repository also offers models for embedding and generation tasks, along with instructions on how to train and evaluate the models. Additionally, it contains visualizations, acknowledgements, and a citation for referencing the work.

OpenSPG is a knowledge graph engine developed by Ant Group in collaboration with OpenKG, based on the SPG (Semantic-enhanced Programmable Graph) framework. It provides explicit semantic representations, logical rule definitions, operator frameworks (construction, inference), and other capabilities for domain knowledge graphs. OpenSPG supports pluggable adaptation of basic engines and algorithmic services by various vendors to build customized solutions.

LLMs4TS is a repository focused on the application of cutting-edge AI technologies for time-series analysis. It covers advanced topics such as self-supervised learning, Graph Neural Networks for Time Series, Large Language Models for Time Series, Diffusion models, Mixture-of-Experts architectures, and Mamba models. The resources in this repository span various domains like healthcare, finance, and traffic, offering tutorials, courses, and workshops from prestigious conferences. Whether you're a professional, data scientist, or researcher, the tools and techniques in this repository can enhance your time-series data analysis capabilities.

This repository is an updating survey for Continual Learning of Large Language Models (CL-LLMs), providing a comprehensive overview of various aspects related to the continual learning of large language models. It covers topics such as continual pre-training, domain-adaptive pre-training, continual fine-tuning, model refinement, model alignment, multimodal LLMs, and miscellaneous aspects. The survey includes a collection of relevant papers, each focusing on different areas within the field of continual learning of large language models.

This repository serves as a comprehensive survey on the application of Large Language Models (LLMs) on tabular data, focusing on tasks such as prediction, data generation, and table understanding. It aims to consolidate recent progress in this field by summarizing key techniques, metrics, datasets, models, and optimization approaches. The survey identifies strengths, limitations, unexplored territories, and gaps in the existing literature, providing insights for future research directions. It also offers code and dataset references to empower readers with the necessary tools and knowledge to address challenges in this rapidly evolving domain.

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.

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

FinWiz-GPT is designed for finance professionals. It assists in market analysis, financial modeling, and understanding complex financial instruments. It's a great tool for financial analysts, investment bankers, and accountants.

Guides financial decisions by monitoring and enforcing policies and procedures.

Guides financial strategy through predictive scenario planning.

Specialist in corporate finance, offering strategic insights and best practices.

Automates DCF Analysis with internet-sourced data and summary table.

Can value any given company with traditional finance methods

Analyzes tech stocks with in-depth, qualitative and quantitative analysis

Serve as a dedicated tutor for a CFA exam candidate

Get an objective valuation of your startup.

Analyzes earnings reports using Warren Buffett's investment model.

Freak in the Sheets!

Conversational advisor for VC memos

Designing Strategic M&A Blueprints for Success in buying, selling or merging companies. Use this GPT to simplify, speed up and improve the quality of the M&A process. With custom data - 100s of creative options in deal flow, deal structuring, financing and more. **Version 2.2 - 28012024**

designed to assist day traders in making informed and profitable trading decisions. It leverages a combination of real-time data analysis, predictive modeling, and personalized trading recommendations to enhance the trading experience and maximize success.

中文数学建模辅助专家，提供策略和专业术语解答，帮助你完美写出一篇数模论文

A financial expert building optimized macroeconomic models from a renowned book.

清华大学级别的经济数学建模中文教师。

是一个专门用于建立数学模型并撰写python代码的gpt.

stunspot's advisor for all things Financial Cybersec