Awesome AI in Finance
Section: Agents · Multi-Agents LLM Financial Trading Framework.
Entry
Appears in 4 awesome lists
Multi-agent framework for financial trading. Simulates professional trading firm operations with 6+ specialized agent roles, backtesting, risk management, and portfolio optimization. Built with LangGraph, supports multiple LLM providers.
Section: Agents · Multi-Agents LLM Financial Trading Framework.
Section: 4. Agentic AI & Multi-Agent Systems · Multi-agent framework for financial trading. Simulates professional trading firm operations with 6+ specialized agent roles, backtesting, risk management, and portfolio optimization. Built with LangGraph, supports multiple LLM providers.
Section: Other · TradingAgents: Multi-Agents LLM Financial Trading Framework
Section: Quant Research Environments · Python LLM - Multi-agent financial research framework combining fundamental, technical, news, and sentiment analysis with structured investment debates and risk assessment.
The definitive curated list of machine learning frameworks, libraries and software organized by language. Covers Python, C++, Java, JavaScript, and more with comprehensive coverage of the ML ecosystem. CC0-1.0 licensed.
books: Freely available programming books
Langchain integrates various providers like Anthropic, AWS, and OpenAI, and offers tools for components such as LLMs, chat models, and data analysis, supporting functionalities from Alpha Vantage to YouTube github | docs
Curated list of artificial intelligence courses, books, video lectures, and papers for developers and researchers. MIT licensed.
Comet's open-source AI observability and evaluation platform: deep tracing of LLM calls, conversation logging, and agent activity, plus built-in eval metrics, prompt versioning, guardrails, and the Opik Agent Optimizer. Worth including because it unifies observability, verification, and…
(MIT) provides modules for structured outputs at different levels of abstraction, including output parsers for text completion endpoints, Pydantic programs for mapping prompts to structured outputs using function calling or output parsing, and pre-defined Pydantic programs for specific output types.
🟪 - Another awesome list for mathematics, useful generally but often applicable to programming.
(from Hpcaitech) - A Unified Deep Learning System for Large-Scale Parallel Training (1D, 2D, 2.5D, 3D and sequence parallelism, and ZeRO protocol).