Market-Bench: Quantitative Market Benchmarks

• Market-Bench is a set of research benchmarks that standardize evaluations of quantitative reasoning, algorithmic trading, and decentralized market aggregation.
• It employs rigorous protocols and metrics such as pass@k, MAE, cumulative return, and Sharpe ratio to assess performance under live market conditions.
• Applications include LLM trading backtesting, live decision-making in POMDP environments, and robust aggregation against manipulation in decentralized settings.

Market-Bench is a term that designates a class of research benchmarks, tools, and architectures used for evaluating quantitative reasoning, aggregation processes, AI coding competence, and sequential decision-making under live market conditions. Its instantiations span the evaluation of LLMs in algorithmic trading, robust market aggregation in decentralized marketplaces, optimal benchmark construction against manipulation, and real-time portfolio management under uncertainty. The following sections outline the principal Market-Bench paradigms, their formal specifications, methodological design, empirical findings, and open challenges.

1. Formal Definitions and Structural Paradigms

Market-Bench frameworks operationalize market evaluation tasks across diverse domains by specifying canonical task families, input modalities, output metrics, and agent/aggregator protocols.

Market-Bench for LLM Trading and Backtesting

Market-Bench introduces a protocol where LLMs receive natural-language strategy descriptions (scheduled equity trading, pairs mean-reversion, or options delta-hedging), input data (L10 order book snapshots, deltas), and output requirements (profit & loss, drawdown, position paths). Each agent must generate a single Python backtester (restricted to specific libraries), whose outputs match a reference implementation in both structure and numerical metrics. Evaluation centers on pass@k metrics and mean absolute error (MAE) across all scalar outputs: $$\mathrm{MAE}(y,\hat y)\;=\;\frac{1}{d}\sum_{i=1}^d |y_i-\hat y_i|$$ where $d$ indexes the required metrics (Srivastava et al., 13 Dec 2025).

LiveTradeBench: Sequential Decision Making under Live Uncertainty

In LiveTradeBench, the evaluation environment is formalized as a POMDP: $$\mathcal{E} = \langle \mathcal{S},\mathcal{A},\mathcal{O},\mathcal{T},\Omega \rangle$$ where agents, at each time step $t$, observe live prices, news snippets, and current portfolios, then output allocation vectors $\mathbf{a}_t \in \mathcal{A}$ (the $N$-dimensional simplex). Key financial performance metrics include cumulative return, volatility, Sharpe ratio, maximum drawdown, and win rate (Yu et al., 5 Nov 2025).

Robust Aggregation in Decentralized Marketplaces

Market-Bench in federated learning settings models a buyer with a small private baseline dataset $D_B \subset D$, a large pool of sellers partitioned by noisy class histograms, and marketplace rounds where sellers submit gradients for selection and aggregation. Evaluation augments accuracy and attack success rates with economic efficiency (cost-of-convergence), fairness (malicious selection rate, Gini), and selection dynamics (diversity, stability) (Song et al., 6 Sep 2025).

2. Benchmarking Methodologies and Evaluation Metrics

Market-Bench frameworks emphasize rigorous, reproducible protocols that distinguish between agent reliability, semantic correctness, economic properties, and robustness against manipulation.

Code Generation and Reliability Metrics

• Structural Reliability: Measures whether generated code is syntactically valid and executable.
• Numerical Accuracy: Evaluates closeness to reference results via MAE.
• pass@k: The fraction of rounds solved within $k$ attempts, disaggregated by strategy and model:

$$\mathrm{pass@3}_{s,m} = \frac{1}{5}\sum_{r=1}^5 \mathrm{solved}_{s,m,r}$$

(Srivastava et al., 13 Dec 2025).

Marketplace Aggregation and Fairness

• Cost per round, Cost-of-Convergence: Track the economic burden on the buyer for achieving a target accuracy.
• Malicious Selection Rate (MSR): Fraction of accepted gradients from malicious sellers.
• Payment Gini, Diversity, Stability: Quantify reward equity, entropy of selection, and cross-round overlap (Song et al., 6 Sep 2025).

Portfolio Management Evaluation

• Cumulative Return (CR), Volatility ($\sigma$), Sharpe, Max Drawdown (MDD), Win Rate (WR):

$$CR = \frac{v_T-v_0}{v_0},\;\; SR = \frac{\bar r - r_f}{\sigma},\;\; MDD = \max_t \frac{\max_{i\le t}v_i - v_t}{\max_{i\le t}v_i}$$

Rolling-$k$ delta analyses assess the impact of stale actions versus live adaptation (Yu et al., 5 Nov 2025).

3. Optimal Benchmark Construction under Manipulation

Market-Bench models for aggregation in contract and oracle settings analyze the cost structure of manipulation and provide principled rules for maximal manipulator deterrence (Hernando-Veciana, 27 Jun 2025):

• Weighted Mean: Optimal when fixed costs are negligible; weights proportional to marginal variable manipulation costs.
• Median: Optimal when variable costs are negligible; robust to fixed per-feed attack costs.
• Trimmed Mean ($\tau$-Trimmed): Degrees of tail trimming rise as the fixed-vs-variable cost ratio grows; optimal trimming parameter

$$\tau^* = \frac{1}{2}\left[1 - \frac{c_v'(2P)}{\overline c(2P)} \right]$$

Implementation complexity correlates with gas costs: weighted mean is $O(n)$; median and trimmed mean scale as $O(n^2)$ due to sorting and selection (Hernando-Veciana, 27 Jun 2025).

4. Empirical Results and Comparative Analysis

LLM Model Performance on Quantitative Trading

Evaluated across scheduled execution, pairs mean-reversion, and delta-hedging, LLMs exhibit:

• Strategy 1 (scheduled execution): High reliability (average pass@3=0.80), some models (GPT-5.1 Codex-Max, Gemini 3 Pro) achieve perfect pass@1 and low best-run MAE, but mean MAE can explode due to outlier failure rounds.
• Strategy 2/3: Greater semantic complexity, higher MAE variance, recurrent errors in spread computation and position management. Structural reliability does not imply semantic correctness (Srivastava et al., 13 Dec 2025).

Live Market Decision-Making

LiveTradeBench shows no correlation between general reasoning scores and realized trading returns or Sharpe ratios. Disparate portfolio styles emerge: risk-seeking agents achieve higher returns but incur greater drawdown and volatility, conservative agents maintain lower risk (Yu et al., 5 Nov 2025).

Marketplace Aggregation Robustness

Under gradient attacks, MartFL, FLTrust, and SkyMask filter malicious sellers to differing degrees:

Dataset	MartFL BSR→MSR	FLTrust BSR→MSR	SkyMask BSR→MSR
FMNIST	0.29→0.18	0.30→0.29	0.30→0.30
CIFAR-10	0.29→0.46	0.30→0.27	0.30→0.30
TREC	0.29→0.24	0.30→0.24	–

Sybil attacks exploit similarity-based filters, appearing to improve cost/convergence but increasing attack success rates. Fairness measures reveal hidden equity loss among benign sellers under adversarial pressure (Song et al., 6 Sep 2025).

5. Implementation Considerations and Practical Challenges

Market-Bench deployment in smart contracts, live environments, and federated systems must attend to:

• Resource Constraints: Weighted mean is computationally efficient, median/trimmed mean incur sorting overhead and higher gas/memory usage.
• Oracle Selection and Weighting: Accurate estimation of fixed and variable manipulation costs per feed is essential for robust aggregate selection (Hernando-Veciana, 27 Jun 2025).
• Live Data Streaming: Real-time ingestion and prompt design must ensure zero information leakage and true temporal causality (Yu et al., 5 Nov 2025).
• Aggregation Strategy Integration: Plug-in architectures (MartFL, FLTrust, SkyMask) facilitate comparative benchmarking within a unified simulation environment (Song et al., 6 Sep 2025).
• Validation and Hyperparameter Tuning: Rolling retraining and diversified validation schemes optimize out-of-sample performance and robustness, as shown in QuantBench (Wang et al., 24 Apr 2025).

6. Future Directions and Open Problems

Market-Bench paradigms illuminate several research challenges and avenues:

• Extending Task Coverage: Current benchmarks cover a narrow set of strategies; key areas omitted include option pricing, multi-asset portfolios, and cost-sensitive algorithms (Srivastava et al., 13 Dec 2025).
• Robustness under Manipulation: Needed advances include multi-stage provenance, reputation-tracking, anomaly detection, and economic-incentive aligned aggregation (Song et al., 6 Sep 2025).
• Continual Learning and Distribution Shift: Efficient online adaptation to regime changes is critical for sustained real-world performance (Wang et al., 24 Apr 2025).
• Relational Modeling and Interpretability: Improvements in leveraging relational, causal, and dynamic data structures may enhance modeling power and robustness.
• Beyond Generation to Critique: Aspirationally, agent frameworks should support not just implementation but deeper critique and refinement of trading strategies (Srivastava et al., 13 Dec 2025).

Market-Bench benchmarks, in their various instantiations, serve as foundational platforms for evaluating quantitative reasoning, aggregation security, and decision-making intelligence under realistic market constraints. They advance methodological rigor, empirical reproducibility, and a more complete understanding of both AI model and human agent performance in algorithmic financial settings.