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
Venue: ICLR 2026 Poster (2026)
Keywords: Multi-Agent System
Algorithmic Trading
Mathematical Reflection
Large Language Models
Primary area: neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)
TL;DR: We introduce TiMi (Trade in Minutes), a rationality-driven multi-agent system that decouples strategy development from real-time deployment, enabling a policy-deployment-optimization chain to navigate market dynamics in financial trading.

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.

Published as a conference paper at ICLR 2026
TRADE IN MINUTES! RATIONALITY-DRIVEN AGENTIC SYSTEM FOR QUANTITATIVE FINANCIAL TRADING
Zifan Song!.
Kaitao Song"
Guosheng Hu?
Ding Qi
Junyao Gao"
Xiaohua Wang'
Dongsheng Li
Cairong Zhao 1+
Tongji University
Microsoft Research Asia
University of Bristol
'Fudan University
ABSTRACT
Recent advancements in large language models (LLMs) and agentic systems have shown exceptional recision-makinne 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 strategic depth in agents with the mechanical rationality essential for quantita- tive 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 L.MM capabilities of semantic analysis, code programming, and mathematical reasoning within comprehensive letrroptiiiiaaiiodeeplloeeee
chain. Specifically, we propose a two-tier analyti- cal paradigm from macro patterns to miero 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.
INTRODUCTION
Recent breakthroughs in large language models (LLMs) (OpenAI, 2023; Grattafiori et al., 2024) have demonstrated significant potential for solving complex tasks. Researchers are continuously advancing the fundamental capabilities of LLMs through innovations in model architectures (Cai et al., 2024; Guo et al., 2025), training paradigms (Ding et al., 2023; Wang et al., 2025), and data scaling (Song et al., 2024; Zhu et al., 2025). Concurrently, a systematic research paradigm is emerging: leveraging LLMs as core cognitive engines to construct agentic systems (Zhang et al., 2025; Hu al., 2024) with autonomous decision- decision-making and execution capabilities. This approach transcends the limitations of single-model improvements by integrating semantic understanding. logical reasoning, and tool utilization abilities into dynamic workflows through modular architectural design and strategic task decomposition, aiming to track long-term challenges in real-world scenarios.
This paper focuses on quantitative finance (Wilmott, 2013; Sun et al., 2023), where the required composite capabilities (e.g., real-time recision-making risk control, and strategy iteration) present highly practical yet challenging domain for autonomous agent research. Classical based strategies (Platen & Heath, 2006), while maintaining stable performance under specific market patterns, struggle to adapt complex dynamics such non-linear fluctuations and black swan events in the financial ecosystem. Notably, existing research on LLM-powered financial trading agents (Ding al., 2024; Li et al., 2023; Xiao et al., 2025) emphasizes role-playing analysis and decision- decision-making, including financial assistants and news-driven or debate-driven variants. Although these anthropomorphic approaches effectively leverage the strengths of LLMs in processing textual information, they pay less attention to the advancements in code programming and mathematical reasoning capabilities, which can be the key to achieving mechanical rationality in financial trading.
"We don't 't let anyone predict the market we let the models speak." James Simons
"Corresponding author (zhaocairongetongji. hwtpe/ooonniieuuue..o cn). Work done during Zifan's internship MSRA.

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Published as a conference paper at ICLR 2026
OFFLINE ENVIRONMENT
LIVE ENVIRONMENT
I. Policy Stage
Optimization Stage
Deployment Stage
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Figure 1: Architecture of the proposed TiMi system comprising three stages: and deployment. TiMi implements a decoupling mechanism where the initial two stages develop and optimize prototype trading bots through offline simulations by leveraging specialized LLM capabilities, while the deployment stage executes thoroughly refined bots with tuned parameters in live trading. This paradigm separates complex reasoning from time-sensitive execution, enabling both comprehensive stralegy development and quantitativ--level efficiency across market dynamics.
policy, optimization,
To delve deeper, we identify three key aspects driving this exploration: (1) market analysis paradigms the simulation of human trading organizations (e.g., sentiment/news analysts, traders with varying risk preferences) in previous research inadvertently introduces interference from emotional biases and subjective judgments simulated by agents; (2) supporting data selection unstructured peripheral information regarding target trading pairs (e.g.. heterogeneous news on social media, project reports) frequently contains misleading signals and temporal lags, which is particularly problematic for retail investors, as dependence on such publicly available information may lead to missed trading opportumities or substantial exposure to adverse market movements: (3) system deployment efficiency the lengthy reasoning and negotiation among multiple agents significantly increase computational costs and action delays during practical deployment, which, in high-volatility trading environments, can manifest as execution slippage and opportunity costs.
In light of these considerations, we introduce TiMi (Trade in Minutes) depicted in Figure a novel agentic system that achieves minute-level dynamic trading through rational decision-making. Regarding market analysis, we design top-level agents to capture and analyze patterns, deriving macro strategies from technical indicators, while specialized agents optimize strategies at the micro level based on specific trading pair characteristics. For data selection, we utilize objective technical indicators of target pairs (e.g.. volume and amplitude) with dynamically updated time windows to adapt to market fluctuations. To enhance deployment efficiency, we decouple analysis from execution by transforming strategies into programmatic trading bots through bot evolution agents (i.e., Code LLMs). This approach enables minute-level quantitative trading with low latency, eliminating the computational costs and time consumption associated with continuous multi-agent inference. Ei- sentially, we collect deployment feedback and employ reflection agents with reasoning capabilities, formulating mathematical problems (e.g., linear programming) from representative cases to determine optimal parameters. These parameters are then submitted to bot evolution agents for hierarchical refinement across parameter, function, and strategy layers. Through this architecture, we effectively leverage the specialized capabilities of agents in semantic analysis, code programming, and mathemat- ical reasoning, establishing a complete closed-loop system encompassing market analysis, strategy customization, programmatic deployment, and feedback iteration.
We perform live trading experiments on over 200 trading pairs across the U.S. stock index and cryptocurrency markets, reporting comprehensive metrics including Annual Rate of Return, Sharpe ratio, and Maximum Drawdown. The proposed TiMi demonstrates a competitive advantage among quantitative, -bssed, and I.L.Magent methods, particularly in challenging altcoin markets. Crucially, we present the systematic evolution of trading bots and conduct in-depth analytical studies with visualizing representative transactions from actual deployments, thereby examining the capacity of TiMi under various market dynamics. The core contributions of our work are threefold:
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Published as a conference paper at ICLR 2026
We introduce TiMi (Trade in Minutes), aationality-driver agentic system for quantitative financial trading that effectively leverages the complementary capabilities of different LLM variants across semantic analysis, code programming, and mathematical reasoning.
Our TiMi system pioneers several key innovations: (1) strategic decoupling of strategy devel- opment from real-time deployment: (2) a two-tier analytical paradigm from macro patterns to micro customization; (3) a layered programming design for trading bot implementation; and (4) a closed-lood loop optimization system driven by mathematical reflection.
Through comprehensive evaluations across 200+ diverse trading pairs, empirically validate the efficacy of TiMi in profitability, deployment efficiency, and risk mitigation, offering an exploration for developing customizable agentic trading systems.
2 RATIONALIT-DRIVEE MULTI-AGENT SYSTEM
2.1 PRELIMINARY
We aim to develop an agentic system with a comprehensive selicy-dpployment-oopiinnn to navigate market dynamics. Theoretically, each trading environment can be modeled as a tuple M,W.S. J, where M represents the market, W represents the targeted time window, S defines the strategy space, FF denotes feedback signals, and J denotes evaluation functions. The trading system is expected to achieve: (1) amalysis: M W + S, which transforms observed market patterns into trading strategies; (2) deployment: M + F, which converts strategies into transactions and collects feedback during actions; and (3) optimization: S x F - S*, which refines strategies based on transaction feedback. Given a trading policy æ E S parameterized by θ, our 7iMi is dedicated to maximizing I (TT) through a rationality-driven agentic system detailed in subsequent sections.
chain
2.2 MULTTI-GEET ARCHITECTURF WITH DECOUPLED ANALYSIS AND DEPLOYMENT
Building upon the mechanical rationality, we formulate a ageet architecture leveraging spe- cialized LLM capabilities in semantic analysis, code programming, and mathematical reasoning, thereby mitigating the inherent limitations of lacking adaptability crulebbased approaches) or intro- ducing emotional biases (anthropomorphic simulations). Simultaneously, we advocate for decoupling analysis from deployment to separate strategy preparation from time-sensitive execution.
Multi-agent design. As presented on the left side of Figure 1, our TiMi comprises four specialized agents that interact in a coordinated workflow to transform market data into executable trading actions: (1) macro analysis agent Ama identifies macro-level market patterns and formulates general trading strategies S based on technical indicators; (2) strategy adaptation agent A... customizes macro strategies S into pair-specific rules Sp with initialized parameters Op by analyzing characteristics of trading pairs P: (3) bot evolution agent Ane - creates and optimizes programmatic trading bots B from trading strategies and feedback reflection; (4) feedback reflection agent Ar reflects upon action feedback F to obtain more precise and hierarchical feedback F with refined parameters O* w.r.f. B. Let Φ, sh, and y respectively represent the capabilities of semantic analysis, code programming, and mathematical reasoning, the complete TiMi system can be formulated as composition of these agent functions:
Ama oOop: vb M W S, Amodoy: : S
Sp x Op.
Ave o cobb : S θ 0x 1x B, An :B B x F F x T(M.
(Am (M) (B
where indicates a conceptual combination of functionalities (i.e., the agents perform defined mapping tasks by invoking embedded core capabilities), T(M, W) represents the system operating on the market M with the time window W. and L denotes programming laws (detailed in Section 2.4).
Decoupling mechanism. We achieve the decoupling of analysis and deployment through three- stage process: (1) policy stage - complex reasoning and strategy development occur in an offline environment, fully leveraging the capabilities of specialized agents, including actions of Ama. Ass. and Abe to generate prototype trading bots with initialized parameter θ; (2) optimization stage the prototype bots undergoes simulation in offline environments (e.g., live or historical markets) to gather feedback JF including technical exccution traceback and risk corner cases, to iteratively conduct offline agent interactions and achieve advanced trading bots 5 Abe (B; An (3, F, Q)); (3)
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following thorough optimization, the trading bot that has successfully passed simulation tests can be deployed in live trading environments with low latency and execution costs (a concrete implementation is discussed in Section 3). This mechanism climinates the requirement for continuous model inference during actual deployment and creates an efficiency advantage, quantified as Opolay Cepimaanio Fra where Cagent/bon is the agent/bot inference cost per trade, n is the number of trading actions, and Canalysis is the offline analysis cost. As increases in high-volatility markets, the efficiency ratio approaches lim Caeeet
deployment stage
Curees
Given that typically Cbot & Cagents this represents an efficiency and responsiveness improvement that scales with trading frequency. Concurrently, the decoupling enables in-depth strategy refinement during the optimization stage without temporal constraints, contributing to enhanced efficacy and more robust trading performance.
Chus
2.3 ANALYTICAL PARADIGM FROM MACRO PATTERNS TO MICRO CUSTOMIZATION
We implement& two-tier paradigm for strategy initialization: from market-wide analysis pair- specific customization. It is designed to offer advantages in both statistical significance and strategic adaptability compared monolithic approaches.
Macro strategy analysis. In theory (Hasbrouck, 2007: Lo et al., 2000), financial markets funda- mentally possess periodic behavioral patterns under specific conditions (e.g., within short-term time windows), which can be identified through a combination of technical indicators and statistical meth- ods. Consequently, the foundation of our system is the macro analysis agent Ama. which performs rational analysis of market patterns. Initialized through the definition of technical indicators I, Ama captures the state space encompassing all observable market conditions across time scales W. This enables the generation of general strategy set S, specifically oriented toward patterns demonstrating statistical significance. Formally, the operational mechanism of Ama can be expressed as:
(MM W;Z) = ( w) E W,i E I}) + S.
(2)
here, the function (,M, w) denotes programming process that extracts relevant market data within time window w and applies indicator to transform this data into analytically useful features.
Pair-specific customization. Different trading pairs often exhibit heterogeneous behaviors due to their unique characteristics. To track this, we introduce strategy adaptation agent Asa that systemati- cally refines the general strategy set for specific trading pairs. Our methodology employs two-step process: initially, we perform semantic analysis o(S, p)lp P + S, to select and adapt strategies from the general set S, thereby creating ppir-specific strategy candidates Sp: subsequently, we conduct mathematical reasoning (Sp, p)lp E P - Oр to optimize the parameter set θ₂ for these strategies. Crucially, this customization encompasses strategy prioritization based on historical performance, parameter calibration tailored to pair-specific volatility profiles, and adaptive risk management rules that account for critical factors such market liquidity.
2.4 IMPLEMENTATION OF TRADING BOTS WITH LAYERED PROGRAMMING DESIGN
To transform strategic insights into executable trading bots, we implement a layered programming policy that enhances modularity and facilitates systematic refinement. Our bot evolution agent Abe constructs trading bots B by decomposing them into three hierarchical layers: strategy, function, and parameter. The strategy layer encapsulates decision-making logie derived from Sy. including signal generation, position sizing, and entry/exit criteria. The functional layer provides computational mechanisms required by the strategy, implementing technical indicators, data preprocessing, and order execution routines that are reusable across different strategies. The parameter layer manages the adjustable parameters that fine-tune the behavior of the trading strategy and its functions. This architecture enables Ane to efficiently transform ppir-specific strategies into algorithmic procedures while facilitating the decoupling mechanism between policy and development stages.
Programming laws. We present three core laws L that govern the code programming 1 of Abe: (1)
functional cohesion law - each functional component must address exactly one responsibility; (2)
unidirectional dependency law dependencies flow strictly from higher to lower layers; and (3)
parameter externalization law - all adjustable values must be extracted from implementation code
and centrally managed. These principles are designed for Are to enable systematic construction of trading bots that support the bedbback-rrvver refinement process initiated by As while maintaining architectural integrity across optimization cycles.
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2.5 CLOSED-LOOP OPTIMIZATION DRIVEN BY MATHEMATICAL REFLECTION
In the optimization stage, trading bots are simulated live or historical markets to periodically collect action feedback F, encompassing trading performance metries, risk event records, and execution statistics. The feedback reflection agent Ar deconstructs this feedback and formulates precise optimization plans, which are then transmitted to Are for programmatic refinement. In this way, establish a rationality-driven evolutionary process toward a robust and reliable system.
Mathematical reasoning for parameter solving. Our feedback reflection agent Ar employs mathematical reasoning y in a three-step optimization process: first organizing risk scenarios from feedback F and transforming them into linear programming problems; then solving for the feasible parameter solution space; and finally optimizing parameters within the constrained space to maximize performance. This optimization can be formally expressed as (exemplified in Appendix A):
θ = arg max
Σ w:J.(O s.t. C(O) {0 R=" A = b(R)).
(3)
(EC(@)
where C(@) defines the feasible parameter space, w, and Te denote i-th objective weight and evalua- tion metric (e.g,, win rate) respectively, while AR) and b(R) represent the constraint matrix and threshold vector derived from risk scenarios R. = (F), implementing parameter restrictions through component-wise inequality 3. Critical to this process is the ability of Ar to recognize trade-offs between competing objectives and establish Pareto -efecceefficient parameter configurations.
Hierarchical optimization. We propose a hierarchical optimization scheme that propagates refine- ments from the parameter level (i.e., the parameter layer of B) upward through the trading system. At the parameter level, we focus on fine-tuning numerical values within constraints. When parameter adjustments prove insufficient to meet requirements (e.g., failing risk simulations), Ar escalates to function level and substitutes algorithmic components. The highest level of intervention occurs at the strategy layer, where fundamental decision-making rules encoded in Sp undergo structural modifications. This tiered manner, exemplified in Figure 2, offers dual advantages: it adheres to the principle of minimal intervention by prioritizing lower-level adjustments that preserve strategic continuity, and it establishes natural complexity progression that enables testing less disruptive modifications before implementing more fundamental changes.
Algorithm 1: TiMi Implementation for B*
Input: market M. minute-level doployment time 2 Parameter: execution intervals T,, look back period Ta,
[Perr] Update ds Update
Update My:
P. [Riss]]
Update Vi:
[Risk]
Update Mig
P. [Ped]]]]
volumicvolatility threshold Vieg/B capital allocation A.
Update Mg-
Update
Apply [g)
4 pricelquantity distribution Mp/Mq. profivless points H. for each exccution retrieve market M for all trading pairs;
E TT
F. [Stabilit]] Refine API connector.
Restrict pair
S. [Rule]
Update-9reg
Update Mys
F. [Regulation]
Control API
seleet pairs (p|Vn. v/....2. 2 Van A A. 2 Pma)) where volatility p.tt, calculatedd muxteT, (Op())C((()) winteT, (0p(0.c(()) Cp(tt))
applicability:
[Risk]
concurrency;
Modify order
[[fficienne]] Add thread
Adjust order
sequenee:
F. [Stability] Add exception
attributes.
Clear
evecutor; Add dynamic
S. [Rule] Design order deactivation mechaniiem.
positions.
8 for each qualified pair p E do for each onder level
2.... m) de
eache.
F. [Interface] Update real- time feedbocck.
P. P Premi (1 + +,, pil);
Fussion Loyer
10
11
Q = A x My [5] x Cm x ezi
Rlow
Striteyy
12
place
(P..Q.).
Figure 2: Evolution map of the trading bots B. We present deliberate optimization cycles (C1-C4) w.r.f. parameter, function, and strategy layers showcasing how hierarchical optimization progres- sively drives sophisticated trading capabilities.
while 31
[t I do
14
for each position do
15
monitor profitlloss at Panay X (1 + ,. Hil);
close where Peany x A/A when profitable.
Return: action feetlhack F.
3 TRADE IN MINUTES
In this section, we present a concrete implementation of advanced trading bots B* (detailed in Algorithm 1) that demonstrates the practical deployment of the proposed TiMi system, with particular emphasis on parameter configuration, order execution logic, position management, and risk control.
Parameter configuration. TiMi establishes crucial parametric variables governing trading operations, including temporal constraints T,, T2 defining minute-level execution intervals and volatility look- back period respectively, and risk allocation amount A for capital distribution. Additionally, minimum
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trading volume threshold Veg ensures sufficient liquidity, while parameter Payy serves the volatility criterion for trading pair qualification. The system incorporates matrix- -baidd parameters Mp = [pi, Pr...... Pm. and Mg [9), , 92. .... qm controlling order distribution alongside quantity scaling coefficient (Cm, Cr, Ce } for adjustment of market capitalization, funding rates, and position entry. Here, profit/loss thresholds H [h,, h22,- .-.. hig] are adopted for position management.
Order execution logic. Starting with retrieving market data through API endpoints, the system calculates essential indicators within the execution period [&e, Le A6] Ti, including price metrics, volatility indices, and funding rates. Then trading pairs P = {p]V pif: 2 Veeg A A 2 P satisfying volume and volatility requirements are qualified by filtering rules, estimated through & p.fe maxeeTy (O)C)())))))mmm))))) (y((t)
(O,))),,(())) where T2 { T, - T + At2...,,ee te]
represents a sequence of time points determined by the estimation window and time step At2, and (p(tCCpptt) represents the opening/closing price of the K-line corresponding to pair during interval [ At2, t). Specifically, TiMi implements a precision-engineeree grid strategy with minute-level dynamics, placing orders optimized price levels P. = Precent x (1 + 4 Mp for selected pairs. Order quantity Qi is calculated by Q2 = A x Mº[ x Cm x c,. For positioned assets, TiMi applies a proportional scaling factor P y to dynamically adjust allocation when positions move.
Panry
Position management. During deployment, the system continuously monitors positions and market dynamics. Upon reaching profit/loss thresholds Pentry x (1 + +)H(i TiMi executes partial position closures through a progressive realization manner. Meanwhile, positions where Pennry x Q < A/A (with À as position size divisor) are automatically closed when profitable, optimizing capital efficiency.
Risk control. TiMi integrates sophisticated mechanisms for risk mitigation, ensuring robust trading across varied market dynamics. Essentially, the system employs mathematically optimized parameter matrices Mp and Mę that have undergone rigorous refinement through the feedback reflection agents Aa. with these parameters solved within a constrained feasible solution space derived from extensive risk scenario simulations. Simultancously, capital allocation is precisely governed by the A parameter, limiting exposure per asset and preventing concentration risk. Additionally, our TiMi performs price deviation control to prevent order placement during abnormal market conditions when significant discrepancies exist between the latest and mark prices.
4
EXPERIMENTS
4.1
IMPLEMENTATION DETAILS
Backbone LLMs. As articulated in Section 2, the core design philosophy of TiMi leverages special- ized LLM capabilities. We strategically adopt DeepSeek-V3 V3 for semantic analysis, Qwen2.5-Coder- for code programming, and DeepSeek-RI for mathematical reasoning. Besides, develop a hybrid implementation that combines local inference (small models) and API-based infer- ence (large models), facilitating flexible upgrading and optimal performance-efficiennyy trade-off's
Agentic Implementation. We develop a hybrid communication protocol that combines XML-based message envelopes with ISON payloads to facilitate inter-agent data exchange. The XMI. layer encapsulates essential metadata (e.g., sender identity and contextual domain), while the JSON payload carries domain-specifie content. Our agents operate within deterministic environment equipped with system-level capabilities, including local file operations and verifiable API invocations. Crucially, we implement procedural posterior checks where TiMi programmatically verifies generated scripts and mathematical solutions within controlled sandboxes, capturing execution tracebacks to ensure computational outputs and parameter derivations satisfy predefined constraints before deployment.
Deployment. Benefiting from our decoupling mechanism, TiMi requires CPU-only runtime environment during the deployment stage. The trading bots developed by the agents Are and Arr are implemented in Python and integrated with exchange APIs through standardized connectors. Additionally, TiMi achieves hanor-anndiig routines to manage connectivity issues, rate limits, and unexpected market conditions, ensuring operational continuity under suboptimal circumstances.
Simulation and live trading. We conduct extensive experiments across both U.S. stock index futures and cryptocurrency markets to evaluate the versatility and robustness of TiMi under diverse market conditions. We implement a progressive validation: initial strategy development using historical data, followed by trading simulation with real-time market data, and culminating in live trading evaluation.

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Table 1: Backtesting comparison on 2024 historical data across U.S. stock index futures and cryptocurrency markets. Opimal/suboptimal results are indicated by bold/underline, respectively.
U.S. Stock Index Futures
Mainstream Coin Futures
Altcoin Futures
Method
ARR%1
SRt M ARR%
SRT MDD%
ARR%1
SRT M
Quantitative Methods
MACD
14.5
12.6
0.84
32.8
Momentum
16.8
15.7
0.93
21.4
-2.8
-0.40
38.3
Grid Trading
10.2
-8.3
-0.72
9.4
18.7
Pairs Trading
2.3
0.40
7.4
-6.8
-0.67
16.3
-5.7
-0.46
14.5
ETF&PCA
4.6
0.49
13.9
7.2
0.62
15.8
6.3
0.69
17.2
TSMOM
10.5
0.81
19.7
18.4
1.15
17.2
1.5
0.05
41.6
OFI
2.1
0.34
11.8
8.9
0.66
19.3
7.8
0.64
23.9
ML&RL Methods
LSTM
3.2
0.36
15.3
9.4
0.88
17.8
-16.8
-0.89
25.4
DQN
8.3
0.69
18.6
9.7
0.83
20.5
-9.3
-0.80
38.7
DDPG
5.4
0.52
17.4
14.8
1.09
14.0
8.6
0.65
26.2
Autoformer
4.9
0.43
16.5
13.2
0.97
16.4
17.5
0.98
24.8
PatchTST
6.7
0.58
18.1
12.5
0,90
17.6
11.2
0.82
23.5
LLM based Agents
FinGPT
5.8
0.57
15.7
6.6
0.59
19.8
7.4
0.61
31,2
FinMem
5.2
0.54
14.8
11.3
0.96
17.4
-8.9
-0.60
19.6
TradingAgents
6.3
0.60
16.2
17.9
1.12
20.3
9.7
0.72
29.6
TiMi (ours)
10.5
16.5
1.25
1.27
26.0
Evaluation metrics. The primary metrics include Annual Rate of Return (ARR), which measures the change in investment value year ARR Vang- Vanial where Vinat and Vinuall represent final Vinial
and initial values; Sharpe Ratio (SR), quantifying excess return per unit of risk as SR R- where 7 is average portfolio return, R2 risk-free rate, and op standard deviation of excess return; and Maximum Drawdown (MDD), representing the largest peak-to-trouge decline MDD where Vpeak and Viroggh are the highest value before and lowest value after the largest drop.
dp
Vinuet Veea Vpok
Baselines. We compare TiMi against three representative categories: (I) quantitative methods, includ- ing MACD (Wang& Kim, 2018) optimized by historical volatility, momentum strategy (Jegadeesh & Titman, 1993), grid trading (Griffin et al., 2003), pairs trading (Gatev et al., 2006), ETF&PCA- based statistical arbitrage (Avellaneda & Lee, 2010), time-series momentum (TSMOM) (Moskowitz et al., 2012), and order flow imbalance (OFI) strategy (Cont & De Larrard, 2013); (2) ML&RL methods, spanning time-series forecasting (LSTTM (Sunny al., 2020), Autoformer (Wu al., 2021), PatchTST (Nie et al., 2022)) and reinforcement learning (DQN (Mnih et al., 2013), DDPG (Liu et al., 2020)); (3) LLM-based agents, including news-driven FinGPT (Liu ct al., 2023), memory- FinMem (Yu et al., 2024a), and multi-agent TradingAgents (Xiao et al., 2025).
4.2 EMPIRICAL RESULTS
Backtesting comparison. We evaluate on 2024 historical data to establish baseline performance. As detailed in Table 1, trend- following methods (e.g.. TSMOM) capitalize on ETF-driven trends in mainstream coin markets, while I.L.-agents exploit news and potential posterior information. Crucially, TiMi harmonizes high profitability with rigorous risk control, achieving superior stability and risk-adji -djjustd returns (notably SR 1.27 in Altcoins). This high- lights the robustness of our system in high-volaiilit,, reflexive assets where traditional momentum or pure semantic analysis struggle.
Table 2: Data (type&duration) requirement and Sortino Ratio comparison (altcoin). M: market indicators; N: peripheral news.
Method
Data Req.
Sortino"
Grid
M > 30m
0.16
ETF&PCA
M 7d
-0.33
DDPG PatchTST
M 3d
0.67
FinMem
M&N
0.41
Live trading comparison. Table 2 and Table present comprehen- sive performance metrics evaluated in live trading environments. TiMi appears outperform competing approaches, achieving ARR
Trddingggets
M&N > 3d
0.58
TIMi (ours)
M 4h
0.91
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Table 3: Live trading comparison of mainstream methods across U.S. stock index futures and cryptocurrency markets from 2025 January to April. TF and Ne represent the trading frequency and number of supported trading pairs for each method ("*" denotes estimated results from partial experiments). The optimal and suboptimal results are indicated by bold and underline, respectively.
U.S. Stock Index Futures MDD%
Mainstream Coin Fatures MDDS
Altcoin Futures
Nrt
TF
ARR
SRT
ARR%
SRT
ARR%!
SRT
MDD%
Quantitative Methods
MACD
2.1
0.32
22.4
-0.66
38.3
-12.5
-0.85
41.3
213
daily
Momentum
1.5
0.23
25.5
-0.58
31.0
-8.4
-0.67
37.5
213
daily
Grid Trading
3.2
0.42
17.2
3.2
0.25
25.9
1.8
0.15
28.4
213
hourly
Pairs Trading
0.22
27.4
4.5
daily
ETE&PCA
4.1
0.50
19.1
-2.5
-0.26
22.3
-4.8
-0.31
27.3
75
minde
TSMOM
3.8
0.44
24.9
-9.5
-0.77
40.8
-10.2
-0.78
42.9
213
daily
-1.9
0.58
27.8
5.4
xecond
ML&RL Methods
LSTM
1.2
0.12
18.4
1.8
0.14
28.5
2.8
0.26
28.2
70
daily
DQN
1.7
0.11
25.2
-1.0
-0.06
31.7
-2.3
-0.18
39.0
70"
daily
DDPG
5.1
0.53
22.7
0.63
27.9
5.9
0.54
38.1
150"
daily
Autoformer
4,4
0.48
21.1
4.9
0.47
28.4
8.3
0.66
42.5
120"
daily
PatchTST
5.5
0.62
22.8
2.7
0.25
29.0
6.4
35.4
120"
daily
LLM-based Agents
FinGPT
0.57
22.6
-0.31
29.5
-6.2
-0.60
30.6
81
daily
FinMem
3,6
0.45
19.7
4.4
0.45
27.3
3.8
0.39
23.7
50"
daily
TradingAgents
4.8
0.50
20.4
0.63
25.6
5.5
0.57
28.3
28"
daily
TIMi (ours)
0.74
20.3
0.79
13.7
ProBt/Los Ratio (Capitul)
Grih: 22
Trddacigett:: 152
DIEPS Bass TST
Tafh: LS3
40
Arnuel Rate of Return (APER %%)
20
25-4
-20
400
3.0
tiilsisk
80
Latenoy (ms)
Capital
-80
Grdd
ETF&PCA
DDPG
PatchTST
FinMdem TradingAgerts
7IW
Figure 3: Comparison of action la- tency (left) and capital utilization (right) for representative methods.
Figure 4: Comparative performance (ARR) distributions of different methods across trading pairs.
of 6.4%, 8.0% and 13.7% across U.S. stock index futures, mainstream cryptocurrencies, and altcoin markets, respectively. Notably, our system demonstrates stable risk-adjusted returns with promising Sharpe&Sortino Ratios and competitive drawdown control, indicating robust trading sustainability. Crucially, the minute-level trading frequency enables deployed bots capitalize short-term market inefficiencies that daily-frequency methods necessarily overlook. Furthermore, TiMi's ex- tensive market coverage (Np 213) matches that of quantitative approaches while surpassing previous ML&RL and agent methods, which typically support fewer trading pairs due to convergence challenges and data requirements for trading action (evidenced in Table 2, m/h/d: minute/hour/day)) These empirical results thus confirm the transformation our rationality-driven paradigm towards demonstrable trading efficacy market dynamics.
Action efficiency and capital management. On the left side of Figure 3, we record the inference-only time of action cycle per trading pair. Benefiting from architectural decoupling, our TiMi achieves latency on par with guantitative methods, which is fundamentally unattainable for continuous model- inference approaches. On the right side, we calculate Capital Utilization Rate as avgp available capital
deployed capital ).
TiMi shows clear advantages among learning-based approaches, indicating the ability to capitalize on a broader range of trading opportunities while maintaining strategic position sizing. Additionally, we provide the ratio between profits/losses generated per unit of invested capital, and TiMi possesses competitive ratio of 1.53, outperforming both Grid (1.22) and TradingAgents (1.32) approaches. This metric is significant as it quantifies the efficacy to balance profitable and loss-making trades.
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Table 4: Component-wise ablation studies simulated within the 2024 crytocurreney market.
Method
Configuration
ARR% t
SR
MDD%
Live Deployment
TiMi
full system
20.9
1.23
15.3
stable
Af variant
purameter-only optimization
12.5
0.92
16.3
logic inconsist
Ag variant
semantic-only reflection
1.1
0.05
25.1
stable
w/o Aua
unified strategy
15.2
0.95
28.4
stable
w/o Air
prototype bot B
1.1
0.05
25.1
runtime unstable
w/o Asa Au
minimal baseline
-455
-0.21
34.2
runtime unstable
4.3 ANALYTICAL STUDY
In-depth analysis of performance distribution. According to the distribution results in Figure 4, most significantly, TiMi exhibits markedly performance stability with reduced variance (a 11.03%) and rare tail events (<2%), indicating more consistent returns over market dynamics compared to alternatives. This characteristic is particularly valuable in algorithmic trading where catastrophic drawdowns often negate long-term performance advantages. It is evident when contrasting with RI. approaches like DDPG, which despite showing competitive median returns, suffers from extreme volatility (a 29.29.. 29.64% that undermines its reliability in practical deployment. The adiionalitydriveer multi-agent design of TiMi appears to effectively navigate the inherent trade-off between return maximization and risk minimization that challenges trading systems, achieving more favorable risk-adjusted profile through its hierarchical optimization and mathematical reflection.
Ablation study of agents and optimization. Employ- ing the macro analysis agent Ama and bot evolution agent conduct granular
(100
Ane as the operational backbone, component-wise ablation experiments simulated in the 2024 crytocurrency market to isolate the contributions of agent specialization (i.e., Asa and Aq) and our optimization mechanism. As quantified in Table 4, removing As nearly doubles Maximum Drawdown to 28.4%, underscoring its critical role in harmonizing diverse assets for consistent risk exposure (e.g., stable utility tokens vs. high-volatility meme coins). Regarding optimization, the semantic-only Ai, ensures stability via syntax resolution yet stagnates in profitability. Conversely, the parameter-only AI, yields theoretical gains but proves logically inconsistent for live deployment due to code parammarretter dissonance, while the unoptimized baselines succumb runtime instability. Consequently, only the full system sustains the synergy corroborated by the evolutionary trajectory Figure 5; here, the unoptimized B stagnates near break-even and ((1) suffers degradation despite transient 35% peaks, exposing the brittleness of shallow parameter tuning without structural adaptation, whereas the stabilized (3(3) culminates in B achieving consistent returns over 20%, validating the iterative efficacy of constraint-based solving and hierarchical interventions detailed in Section 2.5 and Figure 2.
Cumulativ Rotum (CR
15
-5
-15 Jun.
Mar.
May.
Jul.
Sept.
Nov.
Figure 5: Comparison of trading bot variants: B, B and their intermediate versions (1/3-eycle optimization), simu- lated in 2024 cryptocurrency markets.
cedepplyymeenoopiiniittte chain, a
Transaction visualization. We present empirical evidence of TiMi efficacy upon minute-level transactions across four representative cryptocurrency pairs visualized in Figure 6. The candlestick charts illustrate the adaptive order strategy implemented in B*, with buy (t) and sell (J) indicators precisely demarcating transaction points. Notably, higher-volatility pairs such SIGN/USITT (82.21%) and OM/USDT (74.39%) yield superior profitability metrics (+32.75% and +10.78% PnL respectively) with correspondingly higher order densities (39 and 28 valid orders), thereby demonstrating the capability of the system to capitalize on price oscillations. Conversely, lower- volatility assets like TRUMP/USDT and XRP/USDT have more conservative trading patterns. These visualizations substantiate that the parameter matrices Mp and MΩ. tuned by deep optimization cycles with mathematical feedback, can effectively modulate order execution intensity to pair-specific volatility while maintaining robust risk management over diverse market conditions, including sustained directional movements, consolidation phases, and extreme price actions.

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Published as a conference paper at ICLR 2026
ON/UUNDT ACT01-36
TROMPASHIE SORISEEN Intrako wiitee 1507%
SINGUSIT achers B6- aniltty
Inteoday beletttt 2039%)
wolatiit 76.39%
Coro Roalled Consolidation
Realbod +22/171
Case
Cane
Shoulders Patter
Shaath Docline& Vertical Rovertal
Figure 6: Detailed transactions of TiMi on four representative eryptocurrency trading pairs. These 15-minute candlestick charts display market movements with green candles indicating price increases and red candles showing price decreases. Buy (t) and sell (4) actions performed by TiMi are marked each chart, demonstrating its robust trading capabilities across various market dynamics including uptrends, downtrends, consolidation periods, and extreme price movements.
RELATED WORK
LLM-powered agentic system. Agentic systems built upon LLMs can be categorized into agentic workflows and autonomous agents (Zhuge al., 2023; Hong et al., 2024a; Zhang al., 2024b) by autonomy level. The former follows predefined processes with multiple LLM invocations, while the latter employs flexible decision-making Agentic workflows can be broadly categorized into general and domain-speciff types. Workflows further separate into general approaches (Wei et al., 2022; Madaan et al., 2023) and domain-speciffic ones (e.g., code generation (Hong et al., 2024b; Zhong et al., 2024a), data analysis (Xie et al., 2024; Li et al., 2024a), and mathematical problem-solvingg (Zhong et al., 2024b; Xin et al., 2024)). Research advances agentic optimization through automated prompt optimization (Fernando et al., 2024; Yang et al., 2024). hyperparameter optimization (Saad-Falcon et al., 2024), and workflow optimization (Hu et al., 2024; Zhang et al., 2025). The proposed TiMi for financial trading exemplifies domain-specific implementation, while its hierarchical reflection provides insights into agentic optimization, and we will continuously explore the potential of our rationality-driver agentic system as a generalist.
Agents for financial trading. Financial trading agents fall into three architectures (Ding et al., 2024): news-driven, Infectioondrivenn and factor optimization frameworks. News-driven agents (Zhang et al., 2023; Wang et al., 2024a) incorporate up-to-date news and events to make informed decisions, with approaches like FinMem (Yu et al., 2024a), FinAgent (Zhang et al., 2024c), and CryptoTraae (Li et al., 2024b). Reflection-driven agents (Xing, 2025; Koa et al., 2024) enhance decisions through reflection and debating. For instance, StockAgent (Zhang et al., 2024a) and TradingAgents (Xiao et al., 2025) implement multi-agent frameworks to simulate investor trading behavior and conduct role-based collaboration, and Fincon (Yu et al., 2024b) introduces conceptual verbal reinforcement to refine decision making. Beyond direct trading, other agents (Wang et al., 2024b; 2023) function as alpha factor optimizers for quantitative strategies. In this paper, we harmonize the strategic depth of agents with the mechanical rationality expected for quantitative trading, and pioneer decoupled paradigm emphasizing progressive strategy development and quantitative-level deployment.
6 CONCLUSION
In this paper, we present TiMi, multi-agent system designed with mechanical rationality for algo- rithmic trading that decouples complex analysis from time-sensitive execution. Through a three-stage process (policy, optimization, and deployment), TiMi demonstrates promising and stable performance across diverse financial markets. Our key innovations lie in: (1) multi-agent architecture leveraging specialized LLM capabilities in semantic analysis, code programming, and mathematical reasoning; (2) decoupling mechanism separating analysis from deployment; (3) a two-tier analytical paradigm from macro patterns to micro customization; (4) layered programming design for trading bot implementation; and (5) a closed-loop optimization system driven by mathematical reflection.
Limitations and ethics statement. The necessity of the optimization stage limits the zero-shot performance of trading bots developed by TiMi when porting to new markets. From broader perspec- tive, advancements in automatic trading systems may affect market dynamics and liquidity provision, while issues of market faimess and accessibility may also widen the gap between institutional and retail investors. We aim to explore the development of customizable agentic trading systems, and this paper does not constitute investment advice investment is risky, be cautious before entering.
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7 ACKNOWLEDGMENT
This work was supported by National Natural Science Fund of China (No. U25A20527, 62473286). This work was also supported by Shanghai Municipal Science and Technology Major Project (No. 202SHHZDZX025100,
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A DELVE INTO MATHEMATICAL REASONING FOR PARAMETER SOLVING
In this section, we provide three practical cases of TiMi implementation for 8 Each case illustrates a distinct mode commonly encountered in algorithmic trading. Through these representative scenarios, we aim to show how TiMi converts qualitative risk into quantitative optimization, thereby bridging the gap between observed trading pathologies and systematic parameter refinement.
A.1 CASE #1: POSITION SIZE CONTROL UNDER MARKET VOLATILITY
After simulation period, the system collects feedback F for trading bot operating OM/USDT pair. The feedback (organized in structured data) includes: (1) performance metrics of final return, max drawdown, and Sharpe Ratio; (2) trade logs with detailed records of valid transactions and positions; (3) market data including K-lines for OM/USDT (typically including minute-level and hourly data), volatility, liquidity, funding rate changes, and market capitalization.
The feedback records that the bot incurred a significant drawdown over 50% during a sharp. 30- minute market downturn. An excessively dense series of buy orders was executed as the price fell, leading to an oversized and deeply underwater position. The feedback reflection agent Ai analyzes this feedback and identifies a risk scenario R = (F) that the order density and size do not adapt sufficiently to sudden volatility spikes.
Subsequently, Ar translates this risk scenario into a formal mathematical constraint (linear pro- gramming problem), with the goal of limiting potential losses in similar future cases. As illus- trated in Section 3, the relevant parameters appear to be the order quantity distribution matrix MQ 191, 92, .... 9ml with the capital allocation A, and the quantity for the i-th level order is Q: = A x Mqli] x Cm x q. Consequently, the agent can establish a direct constraint on the total position size, where the size of all filled buy orders under the extreme scenario must not exceed maximum size mma Specially, we obtain a linear inequality for the parameters g::
Q max
Σ Q1 S Q max 1=1
Σ A X
(4)
where Qaax can be derived from risk tolerance (i.e., determined by global capital and parallel trading volume). This forms a specific variant of the inequality A ARR) < b(R) from Equation 3. In this case, the parameter vector θ contains the elements gi to be optimized, the corresponding row the constraint matrix A (R) would be [1, 1,..., 1J. and the corresponding value in the constraint vector Qun
b(R) would be Ax
A.2 CASE #2: ORDER BOUNDARY CALIBRATION FOR PRICE SURGE EVENTS
Following simulated trading period with bot on DOGE/USIT, TiMi collects structured feedback F, including: (1) performance metrics indicating catastrophic portfolio decline and excessive maximum drawdown; (2) trade logs showing that the highest-level sell order was triggered while prices continued to surge, resulting in rapidly accumulating losses; and (3) market data containing minute-level K-line information capturing the price surge incident.
To start with, the feedback reflection agent scenario R (FF:: the upper boundary of the order group was inadequately calibrated for the volatility observed during the failure event. Then, Air translates this risk scenario into a formal mathematical constraint.
Aur analyzes this feedback and identifies a critical risk
According to Algorithm 1, price levels are determined by the order distribution matrix Mp [pi P2: ..., Pm. Thus, the agent can establish a constraint on the highest price exponent relative to the absolute peak price during the surge: Phefore X (1 + 4 > Ppeak. By taking the logarithm, we get the solvable inequality for the parameter pm:
log(
Dgg(Ppekk/ peak/ PPeeuoee
Pm
log(1 ()
(5)
where P Preak and Phefore are extracted from the market data. This constraint establishes an evidence- based lower bound for Pm within the inequality system A AR) = b(R).
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Published as a conference paper at ICLR 2026
A.3 CASE #3: ADAPTIVE TAKING UNDER TRENDING MARKET CONDITIONS
After a simulation period on NQ index futures, the system collects structured feedback F from the trading bot, comprising: (1) performance metries, including final return, profit factor, and comparison with buy-and-hor hhold return; (2) trade logs, including detailed records indicating systematic premature closure of profitable long positions; (3) market data K-lines for NQ futures, trend strength indicators (e.g., ADX), and historical volatility across trending versus range- range-bound periods.
The feedback indicates that the deployed bot, while consistently making small profits, underperformed during a sustained market rally. Its profit factor was high, but the total return was lower than simple buy-and-holle strategy. An analysis of the trade logs shows that profitable long positions were closed too early, capturing only a fraction of the actual upward price movement. The feedback reflection agent Arr analyzes this feedback and identifies a risk scenario (or, an opportunity cost) R = (F) that the profit-taking thresholds are overly conservative and not adapted to strong trend persistence.
Sequentially, Arr translates this opportunity cost scenario into a formal mathematical constraint.
As discussed in Section 3, the relevant parameters include the profit/loss threshold matrix H = [11. /u........u. n1g), which determines the exit points Pennt X (1 + + H[(] Thus, the agent can establish a constraint on the minimum profit-taking level, where the first profit target for any position must be set wide enough to capture at least the average price movement observed during prior trending phases.
Next, we obtain linear inequality for the parameter h1 The first profit-taking price, Pi Penty X (1 + 4 4)² must satisfy:
Pi Penny > rendd
(6)
where irend denotes the average profitable movement during a market trend. This leads to the inequality (1+ 2 1+ Afont, and it can be simplified by taking logs to h 2 logi+ (1+ Af ).
Panry
B DETAILS ON IMPLEMENTATION SPECIFICS FOR DEPLOYMENT
Reproducibility statement. To facilitate replication, we provide further implementation details of the TiMi system, covering order specifics, exchange selection, risk control mechanisms, transaction cost modeling, and action latency control. These technical details demonstrate the practical considerations necessary for deploying the system in live trading environments, Crucially, we will open-source the implementation of TiMi product for deployment and release the key corner case list (from both back simulation and live deployment).
Order types and exchange selection. TiMi employs three order types for specific trading functions: LIMTT orders serve as the exclusive mechanism for opening positions based on volatility-derive formulas; (2)
and STOP orders are dynamically placed and cancelled during position monitoring: and (3) MARKET orders are utilized for risk management purposes, including liquidating low-risk positions and executing global profit/loss events. We select top-tier exchanges with high liquidity, i.e,, CME for stock index futures and Binance for cryptocurrencies.
Transaction costs and slippage modeling. We model and mitigate two primary costs: or- der fees and periodic funding rates. TiMi employs LIMIT orders for entry to capture favor- able maker fees and eliminate entry slippage. Beyond static fees, TiMi adapts to funding rates on a per-pair basis. For instance, high funding rates trigger order reduction (even deacti- vation) to avoid accumulating positions with prohibitive holding costs. And a pre-trade price deviation check is conducted to prevent unintended actions during extreme volatility and poten- tial slippage. Real records of transaction costs can be found in the Supplementary Material,
Action latency control. In Table 5, we present detailed records of latency with variance. The primary source of latency and, more importantly, tail latency, exter- nal: network I/O associated with RTT to the exchange. In a stable network environment, the action latency of TiMi is robust. To track the external issues, TiMi has progressively implemented engineering optimizations (e.g., dynamic cache and thread executor as shown in Figure 2) with a series of safeguards, including timeouts, state checks, and circuit breakers.
Table 5: Decomposition of action latency
(ms) for the deployed TiMi.
Latency Source
Avg.
Std. Dev.
P99
(1) Market Retrieval
85
12
115
(2) Internal Logic
5
<1
5
Trade Request
47
8
65
Total End-to-End
137
15
185
16

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Published as a conference paper at ICLR 2026
Table 6: Ablation study of the strategy adaptation agent Asa simulated in 2025 altcoin markets. aRR represents the standard deviation of annualized returns across pairs, indicating cross sross-pair& stability.
Method
Configuration
SR
MDD%
JARR %6
TiMi
full system
13.7
0.86
32.8
11.0 19.5
w/o Aa
unified strategy
10.4
0.71
38.2
Failover mechanisms for error handling. At the function level, all external API interactions are wrapped in exception exception-handlin handling logic with rate limit management. At the process level, failures in concurrent tasks are isolated to guarantec service continuity. Besides, TiMi integrates price deviation check and periodically clears orphaned orders, preventing erroneous actions under market anomalies. At the system level, state information is fetched directly from the exchange, enabling stateless execution logic. This allows the bots recover at any time without losing trading context.
C ABLATION ANALYSIS OF AGENT COMPONENTS
Above all, our TiMi system designed as a highly synergistic architecture. The agents are not merely collection of components, their functionalities are deeply interlinked to perform the policy- deployment-oo cilloymmenn-oimmizatioon chain. To further understand the contribution of each agent, we offer a component component-wise wise ablation study below:
Macro analysis agent Ama and bot evolution agent Abe: Ama provides the initial strategic hypothesis from market data, and Ase translates abstract strategies into executable code, serving as the essential bridge deployment. Consequently, these agents form the indis- pensable backbone our TiMi, and the absence of them would render the entire system non-operational, precluding their ablation.
Strategy adaptation agent Asa: Table 6, we further provide the ablation results of Asa simulated in the altcoin future market (2025). The key insight is that while Asa provides improvement in rijk-adjusted returns, its most critical contribution (lower variance returns across pairs) is enhancing robustness and performance consistency in diverse market.
Feedback reflection agent Ar: The efficacy of Ar is empirically validated by the ablation study in Table and Figure 5. The prototype bot B, lacking the optimization stage, stagnates near break-even performance. In contrast, the advanced bot B", progressively refined by Ar. achieves consistent growth and a final return exceeding 20%. Crucially, Air achieves parameter solving by operating within the proposed hierarchical optimization scheme. As illustrated Section 2.5 and visualized in Figure 2, the necessity of this scheme is confirmed by the unstable transient gains of bots after only shallow parameter- level tuning versus the sustained profitability of those refined at higher functional and strategic layers.
D THE USE LARGE LANGUAGE MODELS (LLMs)
We use LLMs solely for checking grammar and polishing writing. Importantly, LLMs did not contribute the conception of the research problem or the development of the core methodology.
17


Reviews:
[{"review_id": "2kxNtu0kw9", "reviewer": "Reviewer_ZzEo", "summary": "TiMi is a rationality-driven, multi-agent LLM-based trading system designed for minute-level quantitative trading. TiMi decouples complex strategic analysis (policy and optimization stages) from real-time execution (deployment stage) via a modular framework of specialized agents (semantic analysis, code programming, mathematical reasoning). The system uses macro-to-micro strategy formulation, layered bot programming, and hierarchical optimization.", "questions": "Can the authors provide ablation studies on agent components (e.g., remove Afr or skip hierarchical optimization) to isolate their impact?\n\nBesides, could TiMi be extended to generate interpretable rationales for its trades?", "limitations": "", "rating": 4, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- Clear decoupling between inference and execution phases enables simultaneous low-latency deployment and high-frequency efficiency.\n- The closed-loop optimization mechanism based on mathematical reflection (e.g., parameter solving via linear programming) is innovative and holds significant practical value.\n- Supports large amounts of assets (NP=213) while demonstrating stable operational efficiency and capital utilization—achievements beyond the reach of most reinforcement learning and large language model approaches.", "weaknesses": "- Roles of individual agents (e.g., Afr vs. Asa) lack clear ablation to quantify their individual importance to final performance.\n- Unlike some LLM-trading papers that provide human-readable rationales, TiMi’s decisions seem more black-boxed in deployment.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "2Aa5EEPTWo", "reviewer": "Reviewer_V2m2", "summary": "This paper proposes TiMi, a rationality-driven multi-agent trading system that leverages LLM specialization across three dimensions: semantic analysis (ϕ), code programming (ψ), and mathematical reasoning (γ). The core idea is to decouple strategy analysis from deployment, building trading bots offline through multi-agent reasoning (macro → micro → bot evolution) and deploying lightweight, deterministic bots for real-time trading. The architecture includes:\n\nStrategic decoupling of reasoning and execution.\n\nA two-tier analytical paradigm (macro pattern discovery → pair-specific customization).\n\nA layered programming design for bot implementation.\n\nA closed-loop optimization mechanism based on mathematical reflection (Linear Programming-style constraints).\n\nEmpirical results across 200+ trading pairs show better profitability and latency compared to rule-based, ML, and prior LLM-agent systems.", "questions": "Same as what I mentioned in the weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1） The paper identifies a central challenge in LLM-based trading: continuous multi-agent reasoning causes latency and instability that limit minute-level decision-making. Framing this as a policy–deployment decoupling problem establishes a clear and theoretically relevant research direction.\n\n2）The four-agent design (macro analysis, strategy adaptation, bot evolution, feedback reflection) aligns with the three capability dimensions of semantic analysis, code generation, and mathematical reasoning, yielding a systematic and interpretable framework rather than an ad hoc workflow. It is a relevantly reasonable design.\n\n3) The experiments encompass both stock and cryptocurrency trading markets, which makes the evaluation relatively more comprehensive.", "weaknesses": "1) Although the paper focuses on higher-frequency trading (minute-level), the experimental time span of only 3–4 months may be too short to adequately evaluate the system’s performance under diverse market conditions. Extending the time horizon to at least half or a full trading year would provide a more robust assessment.\n\n2) More necessary ablation studies can be needed. The experimental section does not isolate the contribution of the main components. In particular, there is no ablation for LLM specialization (ϕ vs ψ vs γ), for the layered programming design, or for the LP-style reflection; the reported ablation is mainly on optimization depth.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "uhNmTCI58P", "reviewer": "Reviewer_QZtH", "summary": "This paper introduces TiMi (Trade in Minutes), a multi-agent system designed for quantitative financial trading. This system includes (1) Policy Stage (Offline), a Macro Analysis Agent identifies market patterns from technical data to form \"general strategies,\" which a Strategy Adaptation Agent then customizes for specific trading pairs. (2) Optimization Stage (Offline): a Bot Evolution Agent translates these strategies into programmatic trading bots. These bots are then simulated, and a Feedback Reflection Agent analyzes their performance, using mathematical reasoning to solve for optimal parameters and refine the bots. (3) Deployment Stage (Live): the final, optimized programmatic bots are deployed. These bots are low-latency and execute trades at a minute-level frequency using technical indicators, without requiring any live LLM inference.", "questions": "Is the strategy space limited to grid-trading archetypes, or can your agentic system invent and implement entirely different strategies (e.g., a momentum-crossover strategy or a statistical arbitrage strategy) from scratch?\n\nWhat is the total offline computational cost to fully train and optimize one bot for a single trading pair, from the Policy stage to the Deployment stage?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The core contribution—decoupling offline, high-cognition strategy development (Policy & Optimization) from online, low-latency execution (Deployment)—is a significant and practical innovation. It directly addresses the primary bottleneck (latency and cost) of applying agentic LLM systems to real-time, high-frequency domains.\n\nThe \"Optimization Stage\" is a major strength. The use of a Feedback Reflection Agent to translate simulation feedback (e.g., high-risk scenarios) into formal mathematical constraints (as shown in Appendix A) is a novel and powerful mechanism for feedback, far superior to simple parameter-tuning.\n\nThe authors validate their system comprehensively. The experiments span multiple markets (stocks, crypto), a large number of trading pairs (200+), and include strong baselines from three distinct categories (Quant, ML/RL, and LLM-Agents). The results, particularly the low latency (Fig. 3), stable returns (Fig. 4), and effective ablation study (Fig. 5), compellingly support the architectural design.", "weaknesses": "The paper describes the agents' functions at a high, conceptual level but provides few implementation details. To be convincing, the authors can show their work. For example, what prompts are used? What does the \"general strategy\" output by Ama actually look like? Is it text, JSON, or code?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)