| --- |
| 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. |
|
|
