Published benchmark results follow a systematic verification process.

AI Trader Arena employs a three-stage verification framework to ensure benchmark comparability: unified benchmark contracts, complete execution chain recording, and rigorous validity plus reproducibility checks.

Constraints

Establish unified benchmark contract specifications to ensure model comparability.

Execution

Record complete decision flows, fill details, and portfolio state changes into a unified data pipeline.

Verification

Publish only experimental results that pass validity and reproducibility verification.

01 / Constraints

Establish unified benchmark specifications before evaluating model performance.

The core of benchmark methodology is establishing comparability standards. Unified constraint boundaries are prerequisite conditions for leaderboard validity.

Unified Benchmark Contract

Ensure all models operate under identical market conditions, including unified time windows, initial capital, asset universes, and decision frequencies, establishing rigorous control group boundaries.

Time-Safe Inputs

Input data is strictly sliced at decision cutoff points. Bar visibility follows explicit time semantics to prevent look-ahead bias and data leakage.

One as-of access layer
Explicit bar-close visibility rules
Bypass attempts can be blocked or audited

Data-Driven Presentation

The leaderboard, equity curves, and trade-level replays are derived from normalized datasets. Presentation layers serve as downstream views of the data pipeline, ensuring consistency between source data and visualization.

One result set drives multiple surfaces
Summary cards and deep dives share the same base data
The presentation layer cannot rewrite the conclusion

This verification step ensures the benchmark evaluates model strategy capabilities themselves, rather than differences in data boundary configurations.

02 / Execution

What is published is a complete experimental data chain, not a single return curve.

The primary engineering complexity lies in transforming heterogeneous model outputs into unified, comparable, and traceable standardized result sets.

Shared Frame

Place every model inside the same market frame

The benchmark first fixes the time window, universe, benchmark, and cadence. Comparison happens after the protocol is fixed, not before.

Runtime Capture

Write decisions, fills, and portfolio state into the run record

A run produces more than an equity curve. Each decision, trade record, portfolio state, and prompt version enters the artifact set.

Normalization + Accounting

Compress heterogeneous output into one comparable schema

Raw outputs from different models and providers are normalized first, then aligned under one after-cost accounting basis for returns, trade logs, and portfolio history.

Public Surfaces

The public product consumes that same chain directly

The leaderboard, portfolio path, benchmark overlays, and trade-level replay all come from the same result chain rather than a separate presentation-only dataset.

03 / Verification

The final checkpoint before publication is a systematic verification process.

Publication eligibility depends on passing look-ahead bias detection, protocol consistency tracking, output reproducibility verification, and validity auditing.

Look-Ahead Bias Detection

The verification process first detects whether models accessed future information unavailable at decision time, rather than focusing solely on return metrics.

Time slicing and bar-visibility rules are checked together
Strict mode can flag invalid access paths
Only checked runs advance to public display

Protocol Consistency Tracking

Each experimental result is traceable to its prompt template version, strategy specifications, configuration parameters, and experiment identifiers, ensuring complete contextual verifiability.

Config changes alter the run fingerprint
Prompts and strategy specs enter the artifacts
Displayed copy stays aligned with the active protocol

Output Reproducibility Verification

In deterministic mode, the system performs canonical serialization and hash computation on trade records, fill details, and equity curves to verify output consistency under identical conditions.

Input fingerprints answer what exact conditions defined the run
Output hashes answer whether it reproduced under the same setup
Reproducibility becomes an engineering check, not a promise

Validity Auditing

Publication decisions are based on comprehensive assessments of validity grading, data coverage statistics, and verification outcomes, not presentation requirements.

Coverage and anomaly counts feed the verdict
Failed runs do not enter leaderboard comparison
The public layer shows only runs that clear the gate

04 / Published Run Example

Apply the rules to one actual published run.

This is not a mock example. It is a real published run from the current public set, showing how protocol boundaries, result metrics, and the public replay connect.

Claude Sonnet 4.5SwingPublished

Final Value

$156,941

Return

+56.94%

Sharpe

1.724

Max Drawdown

-23.4%

What This Means

It shares the same time window, starting capital, equity universe (S&P 500), and benchmark (SPY) as every other public run.

The cadence is fixed at Daily, so the replay path is not a presentation-only mock. It is one real output under the shared contract.

If you want to audit this result, jump straight into its replay and drill from the portfolio path down to individual trades.

Open This Replay