3 repositorios
AI frameworks that coordinate specialized agents to execute complex financial tasks like research, analysis, and forecasting.
Distinct from Multi-Agent Research Frameworks: Distinct from Multi-Agent Research Frameworks: focuses on financial domain tasks with specialized financial data processing, not general research analysis.
Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Financial Analysis Frameworks. Refine with filters or upvote what's useful.
FinRobot is an AI-powered financial analysis framework that coordinates multiple specialized agents to automate equity research, financial analysis, and investment risk assessment. At its core, it functions as a multi-agent orchestration system where a director and task manager allocate financial tasks to the most suitable large language models based on performance metrics and task requirements. The framework distinguishes itself through its ability to execute complex multi-step financial workflows by routing tasks through perception, reasoning, and action modules. It generates professional e
Coordinates specialized agents to execute complex financial tasks like research, analysis, and forecasting.
ms-agent is an LLM agent framework and multi-agent orchestration system designed to build autonomous entities that combine large language models with tool calling and structured workflows. It serves as a tool integration platform and workflow engine for executing complex tasks through the coordination of specialized agents. The project distinguishes itself through a multimodal agent workflow engine capable of automating the production of text, images, and video. It features a sandboxed code execution environment for running generated code and quantitative data analysis in isolated containers,
Coordinates specialized agents to combine quantitative market data and sentiment analysis for professional financial research.
QuantMuse is an algorithmic trading platform and quantitative trading framework that integrates large language models with mathematical analysis to automate market insights and trading strategies. It functions as a system for building, backtesting, and executing strategies using both historical and real-time market data. The framework is distinguished by its use of large language models for financial analysis and sentiment extraction from news and social media. It utilizes autonomous agents with chain-of-thought reasoning to generate market intelligence and strategic reports, while employing
Coordinates specialized AI agents to execute complex financial research, sentiment analysis, and forecasting.