---
license: cc-by-nc-4.0
language: en
size_categories:
- 1K<n<100K
task_categories:
- tabular-classification
- time-series-forecasting
tags:
- finance
- llm-trading
- benchmark
- evaluation
- mandate-based
- fund-style-policy
pretty_name: QuantArena Artifact Bundle
configs:
- config_name: metrics
  default: true
  data_files:
  - split: train
    path: derived/all_metrics.csv
- config_name: trades
  data_files:
  - split: train
    path: derived/all_trades.csv
---

# QuantArena Artifact Bundle

Reproducibility artifacts for the paper *QuantArena: Beat the Market or Be the
Market? A Live-Market Evaluation of Investment Paradigms* (NeurIPS 2026
Evaluations & Datasets Track submission).

## Summary

QuantArena is a controlled live-market evaluation protocol that holds the LLM
backend, market data stream, analyst workflow, capital, and execution harness
fixed across runs and varies only the **investment doctrine** (the policy
module). This bundle releases the run-level data, comparison aggregates,
universe definitions, and provenance manifests required to inspect every
figure, table, and quantitative claim in the paper.

The dataset is a structured **collection of backtest runs** rather than a
single tabular file. Each run contains daily portfolio state, the trade log,
and pre-computed performance metrics. Two flat tables in `derived/` provide a
queryable view of the full bundle for users who want to load it as a single
DataFrame.

## What's in this bundle

```
release_data/
├── README.md                       # This file
├── LICENSE.md                      # Multi-source license + redistribution notes
├── CHANGELOG.md                    # Version history
├── croissant.json                  # Croissant 1.0 metadata (core + RAI)
├── manifest.json                   # Top-level run inventory (machine-readable)
│
├── runs/                           # 28 individual backtest runs
│   ├── exp1_caseStudy_us_6m/       # US 6M main case study (5 mandates)
│   ├── exp1_caseStudy_cn_6m/       # CN 6M main case study (5 mandates)
│   ├── exp2_reproducibility_us_6m_run2/  # Independent re-run (5 mandates)
│   ├── exp3_mechanism_ablation_us_3m/    # US 3M ablation (8 variants)
│   └── exp4_backend_robustness_us_3m_gpt54/  # GPT-5.4 robustness (5 mandates)
│
├── exp5_efficiency_ablation_cn_10t_6m/   # Documented only (no run artifacts)
├── comparisons/                    # Cross-mandate aggregates per market
├── universe/                       # 5x4 sector/style ticker grid
├── derived/                        # Pre-flattened tables for easy querying
│   ├── all_trades.csv              # Concatenated trade log across all 28 runs
│   ├── all_metrics.csv             # Long-format performance metrics table
│   └── gpt54_robustness/           # Backend-comparison CSVs
│
├── audit/                          # Reproducibility manifest (mirror of paper's latex/audit/)
└── tools/                          # Scripts to rebuild the bundle from raw artifacts
```

Each `runs/<experiment>/<mandate>/` directory contains:

- `metrics.json` — summary metrics (return, drawdown, Sharpe, turnover, cash ratio, exposure, …)
- `trades.csv` — per-trade log (date, ticker, action, shares, price, value, justification)
- `equity_curve.csv` — daily portfolio state (date, total_value, daily_return, cashflow=cash_balance, benchmark_value, benchmark_return)
- `backtest_report.md` — human-readable run summary

## Loading

### As a DataFrame (recommended for most users)

```python
import pandas as pd

# Concatenated trade log across all 28 runs
trades = pd.read_csv("derived/all_trades.csv")

# Long-format metrics table
metrics = pd.read_csv("derived/all_metrics.csv")

# Filter to one experiment
us_6m = metrics.query("experiment == 'exp1_caseStudy_us_6m'")
print(us_6m[["display_name", "total_return", "max_drawdown", "total_trades"]])
```

### Hugging Face Datasets library

```python
from datasets import load_dataset

trades = load_dataset("NIPS26Repo/quantarena-artifacts", "trades", split="train")
metrics = load_dataset("NIPS26Repo/quantarena-artifacts", "metrics", split="train")
```

### Per-run artifacts (when you need the full equity curve or single trade log)

```python
import json, pandas as pd

run = "runs/exp1_caseStudy_us_6m/fundamental_value"
metrics = json.load(open(f"{run}/metrics.json"))
trades  = pd.read_csv(f"{run}/trades.csv")
equity  = pd.read_csv(f"{run}/equity_curve.csv")
```

## Experiment overview

| Experiment | Window | Universe | Backend | Mandates / variants | Purpose in the paper |
|---|---|---|---|---|---|
| **Exp 1 — Main case study (US)** | 2025-09-01 to 2026-02-28 (124 trading days) | 20 US tickers (5×4 sector/style) | DeepSeek-V3.2 | 5 mandates | Q1 returns, Q2 cross-market shift, Q3 fidelity, sector matrix |
| **Exp 1 — Main case study (CN)** | 2025-09-01 to 2026-02-28 (102 trading days) | 20 CN A-share tickers | DeepSeek-V3.2 | 5 mandates | Same as above |
| **Exp 2 — Reproducibility R2 (US)** | 2025-09-01 to 2026-02-28 | 20 US tickers | DeepSeek-V3.2 | 5 mandates | `tab:reproducibility` (Q4) |
| **Exp 3 — US 3M mechanism ablation** | 2025-12-01 to 2026-02-28 | 20 US tickers | DeepSeek-V3.2 | 8 variants (Full + ablated for FV/BM/MT, Reference for LV/EqW) | `tab:us_3m_ablation_main` (Q4) |
| **Exp 4 — Backend robustness (GPT-5.4)** | 2025-12-01 to 2026-02-28 | 20 US tickers | GPT-5.4 (Macaron Responses API gateway, run dates Apr 23–24, 2026) | 5 mandates | `tab:us_3m_backend_robustness` (Q4) |
| **Exp 5 — Execution efficiency (CN 10t)** | 2025-09-01 to 2026-02-28 | 10 CN tickers | DeepSeek-V3.2 | E0 / E1 / E2 execution paths | `tab:efficiency_ablation` (appendix) |

Mandates: **Fundamental Value**, **Macro Tactical**, **Behavioral Momentum**,
**Low-Volatility (Smart Beta)**, **Equal-Weight (Baseline)**.

Initial capital is $100,000 per run; decision cadence is daily.

## How to verify a number from the paper

1. Locate the figure / table / claim in `audit/figures.md`, `audit/tables.md`,
   or `audit/claims.md`.
2. Each entry names the source run ID(s) under
   `runs/<experiment>/<mandate>/`.
3. Open `metrics.json` (summary metrics), `trades.csv` (per-trade log), or
   `equity_curve.csv` (daily portfolio state) in that run directory.

## Data sources used by the underlying runs

| Source | What it provided | Status in this bundle |
|---|---|---|
| Yahoo Finance / yfinance | US ticker prices and corporate actions | Trade-level prices appear in `trades.csv`; not redistributed in bulk |
| Tushare | China A-share prices, fundamentals, news | Same — trade-level only, not bulk |
| Financial Modeling Prep (FMP) | US fundamentals & news | Aggregated into per-run summaries only |
| AKShare | CN macro indicators & policy news | Same |
| Tavily | Search-API-based news retrieval | Sentiment scores aggregated into ISQ signals only |
| DeepSeek-V3.2 | Default reasoning backend | LLM outputs in trades.csv `justification` are short bookkeeping templates only |
| GPT-5.4 (Macaron API gateway) | Backend-robustness reasoning backend | Same |

See `LICENSE.md` for redistribution terms.

## Citation

```bibtex
@inproceedings{quantarena2026,
  title  = {QuantArena: Beat the Market or Be the Market? A Live-Market Evaluation of Investment Paradigms},
  author = {Anonymous Author(s)},
  booktitle = {Advances in Neural Information Processing Systems Datasets and Benchmarks Track},
  year   = {2026}
}
```

## Limitations

- The 6M case studies are single-seed runs; Exp 2 quantifies seed sensitivity for the US window, but CN does not have a paired re-run in this bundle.
- Trading frictions (transaction costs, slippage, market impact) are not modeled.
- Universe is restricted to 20 liquid tickers per market; results may not transfer to micro-cap or illiquid names.
- The GPT-5.4 backend identifier follows the API gateway label exposed at run time; it is not a vendor build hash.

## Contact

Submitted via OpenReview to NeurIPS 2026 Evaluations & Datasets Track. Authors anonymized for double-blind review.