# Orchestration Framework for Financial Agents: From Algorithmic Trading to Agentic Trading

arXiv: https://arxiv.org/abs/2512.02227 · HTML: https://mlsanity.com/paper/2512.02227/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 1 Dec 2025.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 17 of 17)

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- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [TradingAgents: Multi-Agents LLM Financial Trading Framework](https://mlsanity.com/paper/2412.20138/) (2024)
- [FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design](https://mlsanity.com/paper/2311.13743/) (2023)
- [FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making](https://mlsanity.com/paper/2407.06567/) (2024)
- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [Qlib: An AI-oriented Quantitative Investment Platform](https://mlsanity.com/paper/2009.11189/) (2020)
- [R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization](https://mlsanity.com/paper/2505.15155/) (2025)
- [QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model](https://mlsanity.com/paper/2402.03755/) (2024)
- [ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism](https://mlsanity.com/paper/2508.00554/) (2025)
- [FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance](https://mlsanity.com/paper/2112.06753/) (2021)
- [Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2303.11959/) (2023)

## Abstract (arXiv)

The financial market is a mission-critical playground for AI agents due to its temporal dynamics and low signal-to-noise ratio. Building an effective algorithmic trading system may require a professional team to develop and test over the years. In this paper, we propose an orchestration framework for financial agents, which aims to democratize financial intelligence to the general public. We map each component of the traditional algorithmic trading system to agents, including planner, orchestrator, alpha agents, risk agents, portfolio agents, backtest agents, execution agents, audit agents, and memory agent. We present two in-house trading examples. For the stock trading task (hourly data from 04/2024 to 12/2024), our approach achieved a return of 20.42%, a Sharpe ratio of 2.63, and a maximum drawdown of -3.59%, while the S&P 500 index yielded a return of 15.97%. For the BTC trading task (minute data from 27/07/2025 to 13/08/2025), our approach achieved a return of 8.39%, a Sharpe ratio of 0.38, and a maximum drawdown of -2.80%, whereas the BTC price increased by 3.80%. Our code is available on hrefhttps://github.com/Open-Finance-Lab/AgenticTradingGitHub.