Trade in Minutes! Rationality-Driven Agentic System for Quantitative Financial Trading
- URL: http://arxiv.org/abs/2510.04787v1
- Date: Mon, 06 Oct 2025 13:08:55 GMT
- Title: Trade in Minutes! Rationality-Driven Agentic System for Quantitative Financial Trading
- Authors: Zifan Song, Kaitao Song, Guosheng Hu, Ding Qi, Junyao Gao, Xiaohua Wang, Dongsheng Li, Cairong Zhao,
- Abstract summary: TiMi is a rationality-driven multi-agent system that architecturally decouples strategy development from minute-level deployment.<n>We propose a two-tier analytical paradigm from macro patterns to micro customization, layered programming design for trading bot implementation, and closed-loop optimization driven by mathematical reflection.
- Score: 57.28635022507172
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advancements in large language models (LLMs) and agentic systems have shown exceptional decision-making capabilities, revealing significant potential for autonomic finance. Current financial trading agents predominantly simulate anthropomorphic roles that inadvertently introduce emotional biases and rely on peripheral information, while being constrained by the necessity for continuous inference during deployment. In this paper, we pioneer the harmonization of strategic depth in agents with the mechanical rationality essential for quantitative trading. Consequently, we present TiMi (Trade in Minutes), a rationality-driven multi-agent system that architecturally decouples strategy development from minute-level deployment. TiMi leverages specialized LLM capabilities of semantic analysis, code programming, and mathematical reasoning within a comprehensive policy-optimization-deployment chain. Specifically, we propose a two-tier analytical paradigm from macro patterns to micro customization, layered programming design for trading bot implementation, and closed-loop optimization driven by mathematical reflection. Extensive evaluations across 200+ trading pairs in stock and cryptocurrency markets empirically validate the efficacy of TiMi in stable profitability, action efficiency, and risk control under volatile market dynamics.
Related papers
- CryptoBench: A Dynamic Benchmark for Expert-Level Evaluation of LLM Agents in Cryptocurrency [60.83660377169452]
This paper introduces CryptoBench, the first expert-curated, dynamic benchmark designed to rigorously evaluate the real-world capabilities of Large Language Model (LLM) agents.<n>Unlike general-purpose agent benchmarks for search and prediction, professional crypto analysis presents specific challenges.
arXiv Detail & Related papers (2025-11-29T09:52:34Z) - QuantAgents: Towards Multi-agent Financial System via Simulated Trading [40.636918662488505]
QuantAgents is a multi-agent system integrating simulated trading.<n> QuantAgents comprises four agents: a simulated trading analyst, a risk control analyst, a market news analyst, and a manager.<n>Our system incentivizes agents to receive feedback on two fronts: performance in real-world markets and predictive accuracy in simulated trading.
arXiv Detail & Related papers (2025-10-06T09:45:57Z) - TradingGroup: A Multi-Agent Trading System with Self-Reflection and Data-Synthesis [15.865159423176982]
TradingGroup is a multi-agent trading system designed to address limitations through a self-reflective architecture and an end-to-end data-synthesis pipeline.<n> TradingGroup consists of specialized agents for news sentiment analysis, financial report interpretation, stock trend forecasting, trading style adaptation, and a trading decision making agent.<n>Specifically, we design self-reflection mechanisms for the stock forecasting, style, and decision-making agents to distill past successes and failures for similar reasoning in analogous future scenarios.
arXiv Detail & Related papers (2025-08-25T00:29:58Z) - To Trade or Not to Trade: An Agentic Approach to Estimating Market Risk Improves Trading Decisions [0.0]
Large language models (LLMs) are increasingly deployed in agentic frameworks.<n>We develop an agentic system that uses LLMs to iteratively discover differential equations for financial time series.<n>We find that model-informed trading strategies outperform standard LLM-based agents.
arXiv Detail & Related papers (2025-07-11T13:29:32Z) - EmoDebt: Bayesian-Optimized Emotional Intelligence for Strategic Agent-to-Agent Debt Recovery [65.30120701878582]
Large Language Model (LLM) agents are vulnerable to exploitation in emotion-sensitive domains like debt collection.<n>EmoDebt is an emotional intelligence engine that reframes a model's ability to express emotion in negotiation as a sequential decision-making problem.<n>EmoDebt achieves significant strategic robustness, substantially outperforming non-adaptive and emotion-agnostic baselines.
arXiv Detail & Related papers (2025-03-27T01:41:34Z) - TradingAgents: Multi-Agents LLM Financial Trading Framework [4.293484524693143]
TradingAgents proposes a novel stock trading framework inspired by trading firms.<n>It features LLM-powered agents in specialized roles such as fundamental analysts, sentiment analysts, technical analysts, and traders with varied risk profiles.<n>By simulating a dynamic, collaborative trading environment, this framework aims to improve trading performance.
arXiv Detail & Related papers (2024-12-28T12:54:06Z) - When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments [55.19252983108372]
We have developed a multi-agent AI system called StockAgent, driven by LLMs.
The StockAgent allows users to evaluate the impact of different external factors on investor trading.
It avoids the test set leakage issue present in existing trading simulation systems based on AI Agents.
arXiv Detail & Related papers (2024-07-15T06:49:30Z) - Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines.
We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
arXiv Detail & Related papers (2023-04-15T19:22:37Z) - Quantitative Stock Investment by Routing Uncertainty-Aware Trading
Experts: A Multi-Task Learning Approach [29.706515133374193]
We show that existing deep learning methods are sensitive to random seeds and network routers.
We propose a novel two-stage mixture-of-experts (MoE) framework for quantitative investment to mimic the efficient bottom-up trading strategy design workflow of successful trading firms.
AlphaMix significantly outperforms many state-of-the-art baselines in terms of four financial criteria.
arXiv Detail & Related papers (2022-06-07T08:58:00Z) - Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics
in Limit-Order Book Markets [84.90242084523565]
Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics.
By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention.
By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
arXiv Detail & Related papers (2022-03-07T18:59:54Z) - Finding General Equilibria in Many-Agent Economic Simulations Using Deep
Reinforcement Learning [72.23843557783533]
We show that deep reinforcement learning can discover stable solutions that are epsilon-Nash equilibria for a meta-game over agent types.
Our approach is more flexible and does not need unrealistic assumptions, e.g., market clearing.
We demonstrate our approach in real-business-cycle models, a representative family of DGE models, with 100 worker-consumers, 10 firms, and a government who taxes and redistributes.
arXiv Detail & Related papers (2022-01-03T17:00:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.