| |
| """Write README.md, LICENSE.md, CHANGELOG.md, croissant.json into the release bundle. |
| |
| Done in Python so we don't fight display-layer path substitution in the |
| shell tools. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import json |
| import hashlib |
| import os |
| import shutil |
| from pathlib import Path |
|
|
| SCRIPT_DIR = Path(__file__).resolve().parent |
| REPO = Path(os.environ.get("QUANTARENA_REPO", SCRIPT_DIR.parents[1])).resolve() |
| RELEASE = Path(os.environ.get("QUANTARENA_RELEASE_DIR", REPO / "release_data")).resolve() |
| HF_RAW_BASE_URL = "https://huggingface.co/datasets/NIPS26Repo/quantarena-artifacts/resolve/main" |
|
|
|
|
| README = """\ |
| --- |
| 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 1.1 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. |
| """ |
|
|
|
|
| LICENSE = """\ |
| # License and Redistribution Notes |
| |
| ## This bundle's contributed content |
| |
| The QuantArena artifact bundle, as a curated collection of run-level metrics, |
| (metrics, trade logs, equity curves), provenance documentation, and the |
| build scripts under `tools/`, is released under |
| **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)**. |
| |
| You may use, share, and adapt the contributed content for **academic |
| research, education, and non-commercial purposes**, provided you cite the |
| paper and preserve attribution to the authors. |
| |
| ## Underlying market data (see `README.md` "Data sources" table) |
| |
| The bundle's `trades.csv` and the per-mandate selection files contain |
| trade-level prices and ticker actions derived from third-party data providers. |
| We redistribute only the **derived, sparse, decision-level |
| records** required to verify the paper's quantitative claims. |
| |
| We do not redistribute bulk market data from any provider. Specifically: |
| |
| - **Yahoo Finance / yfinance**: bulk redistribution is forbidden by Yahoo |
| TOS. We include only the per-trade prices observed on each decision date. |
| Users who need full price history should obtain it directly from Yahoo or |
| a licensed redistributor. |
| - **Tushare**: redistribution of raw data is restricted by Tushare's |
| user agreement. Users wishing to reproduce or extend the runs must |
| register and obtain their own Tushare API token. The trade-level prices |
| included here are derived facts about portfolio decisions rather than |
| bulk feed redistribution. |
| - **Financial Modeling Prep (FMP)**, **AKShare**, **Tavily**: aggregated |
| signals and per-run summaries only; no raw feeds redistributed. |
| |
| If you redistribute or build derivative products on top of this bundle, |
| **you remain responsible for complying with each upstream provider's terms**. |
| |
| ## LLM outputs |
| |
| Short bookkeeping strings in `trades.csv` `justification` columns |
| (e.g., "Target allocation: 12.0% (current: 0 shares)") are template |
| strings emitted by the trading workflow rather than free-form LLM |
| generation. We treat them as part of the bundle's contributed content |
| under the CC BY-NC 4.0 license. |
| |
| The DeepSeek-V3.2 and GPT-5.4 backends used during runs are accessed |
| under the providers' commercial API terms; the bundle does not |
| redistribute model weights or proprietary system prompts. |
| |
| ## Disclaimers |
| |
| - **Not financial advice.** This bundle is released for research and |
| evaluation purposes only. It is not a recommendation to trade any |
| security. Past simulated performance does not guarantee future results. |
| - **Reproduction depends on third-party APIs.** Re-running the bundled |
| experiments requires market data access (Yahoo, Tushare, FMP, AKShare, |
| Tavily) and an LLM backend (DeepSeek-V3.2 or GPT-5.4); these are not |
| shipped with the bundle. |
| - **Anonymized release.** Per NeurIPS double-blind review policy, |
| authorship and institutional affiliation are not disclosed in this |
| release. |
| """ |
|
|
|
|
| CHANGELOG = """\ |
| # Changelog |
| |
| ## v1.0 — 2026-04-28 |
| |
| Initial public release prepared for NeurIPS 2026 Evaluations & Datasets |
| Track double-blind submission. |
| |
| - 28 backtest runs across 4 paper-cited experiments (Exp 1 case study US/CN, |
| Exp 2 reproducibility R2, Exp 3 mechanism ablation, Exp 4 GPT-5.4 backend |
| robustness). Exp 5 efficiency ablation is documented but its run |
| artifacts are not redistributed. |
| - Two flattened derived tables (`derived/all_metrics.csv`, |
| `derived/all_trades.csv`) for easy querying. |
| - Reproducibility manifest mirrored from the paper's `latex/audit/`. |
| - Croissant 1.1 metadata with core and RAI fields. |
| """ |
|
|
|
|
| def write_text(name: str, content: str) -> None: |
| (RELEASE / name).write_text(content) |
| print(f" wrote {name} ({len(content)} chars)") |
|
|
|
|
| def copy_build_script() -> None: |
| tools = RELEASE / "tools" |
| tools.mkdir(exist_ok=True) |
| for name in ["build_release_bundle.py", "write_release_metadata.py"]: |
| src = SCRIPT_DIR / name |
| dst = tools / name |
| if src.exists() and src.resolve() != dst.resolve(): |
| shutil.copy2(src, dst) |
| print(" verified build scripts in tools/") |
|
|
|
|
| def write_croissant() -> None: |
| """Croissant 1.1 metadata with core + RAI fields.""" |
| def content_url(path: str) -> str: |
| return f"{HF_RAW_BASE_URL}/{path}" |
|
|
| def md5(path: str) -> str: |
| digest = hashlib.md5() |
| with (RELEASE / path).open("rb") as f: |
| for chunk in iter(lambda: f.read(1024 * 1024), b""): |
| digest.update(chunk) |
| return digest.hexdigest() |
|
|
| def sha256(path: str) -> str: |
| digest = hashlib.sha256() |
| with (RELEASE / path).open("rb") as f: |
| for chunk in iter(lambda: f.read(1024 * 1024), b""): |
| digest.update(chunk) |
| return digest.hexdigest() |
|
|
| croissant = { |
| "@context": { |
| "@language": "en", |
| "@vocab": "https://schema.org/", |
| "citeAs": "cr:citeAs", |
| "column": "cr:column", |
| "conformsTo": "dct:conformsTo", |
| "cr": "http://mlcommons.org/croissant/", |
| "rai": "http://mlcommons.org/croissant/RAI/", |
| "data": "cr:data", |
| "dataType": {"@id": "cr:dataType", "@type": "@vocab"}, |
| "dct": "http://purl.org/dc/terms/", |
| "equivalentProperty": "cr:equivalentProperty", |
| "examples": {"@id": "cr:examples", "@type": "@json"}, |
| "extract": "cr:extract", |
| "field": "cr:field", |
| "fileProperty": "cr:fileProperty", |
| "fileObject": "cr:fileObject", |
| "fileSet": "cr:fileSet", |
| "format": "cr:format", |
| "includes": "cr:includes", |
| "isLiveDataset": "cr:isLiveDataset", |
| "jsonPath": "cr:jsonPath", |
| "key": "cr:key", |
| "md5": "cr:md5", |
| "parentField": "cr:parentField", |
| "path": "cr:path", |
| "prov": "http://www.w3.org/ns/prov#", |
| "recordSet": "cr:recordSet", |
| "references": "cr:references", |
| "regex": "cr:regex", |
| "repeated": "cr:repeated", |
| "replace": "cr:replace", |
| "sc": "https://schema.org/", |
| "samplingRate": "cr:samplingRate", |
| "separator": "cr:separator", |
| "sha256": "sc:sha256", |
| "source": "cr:source", |
| "subField": "cr:subField", |
| "transform": "cr:transform" |
| }, |
| "@type": "sc:Dataset", |
| "conformsTo": "http://mlcommons.org/croissant/1.1", |
| "name": "QuantArena Artifact Bundle", |
| "description": ( |
| "Reproducibility artifacts for QuantArena, a controlled live-market " |
| "evaluation protocol that fixes the LLM backend, market data stream, " |
| "analyst workflow, and execution harness while varying only the " |
| "investment doctrine (policy module) across five operational paradigms: " |
| "Fundamental Value, Macro Tactical, Behavioral Momentum, " |
| "Low-Volatility (Smart Beta), and a rule-based Equal-Weight baseline. " |
| "The bundle contains 28 individual backtest runs across four paper-cited " |
| "experiments spanning US and CN A-share equities, plus comparison " |
| "aggregates, universe definitions, and provenance documentation." |
| ), |
| "license": "https://creativecommons.org/licenses/by-nc/4.0/", |
| "url": "https://huggingface.co/datasets/NIPS26Repo/quantarena-artifacts", |
| "version": "1.0", |
| "datePublished": "2026-04-28", |
| "citeAs": ( |
| "@inproceedings{quantarena2026, title={QuantArena: Beat the Market or " |
| "Be the Market? A Live-Market Evaluation of Investment Paradigms}, " |
| "author={Anonymous Author(s)}, booktitle={NeurIPS 2026 Evaluations and " |
| "Datasets Track}, year={2026}}" |
| ), |
| "keywords": [ |
| "finance", "LLM trading", "evaluation benchmark", "investment doctrine", |
| "controlled intervention", "live-market", "fund-style policy", |
| "double-blind", "NeurIPS Datasets and Benchmarks" |
| ], |
| "creator": {"@type": "Organization", "name": "Anonymous (under double-blind review)"}, |
| "publisher": {"@type": "Organization", "name": "Anonymous (under double-blind review)"}, |
| "isLiveDataset": False, |
| |
| "distribution": [ |
| { |
| "@type": "cr:FileObject", |
| "@id": "manifest-json", |
| "name": "manifest.json", |
| "description": "Top-level run inventory (machine-readable)", |
| "encodingFormat": "application/json", |
| "contentUrl": content_url("manifest.json"), |
| "md5": md5("manifest.json"), |
| "sha256": sha256("manifest.json") |
| }, |
| { |
| "@type": "cr:FileObject", |
| "@id": "all-metrics-csv", |
| "name": "all_metrics.csv", |
| "description": "Long-format flat table of summary metrics across all 28 runs", |
| "encodingFormat": "text/csv", |
| "contentUrl": content_url("derived/all_metrics.csv"), |
| "md5": md5("derived/all_metrics.csv"), |
| "sha256": sha256("derived/all_metrics.csv") |
| }, |
| { |
| "@type": "cr:FileObject", |
| "@id": "all-trades-csv", |
| "name": "all_trades.csv", |
| "description": "Concatenated per-trade log across all 28 runs", |
| "encodingFormat": "text/csv", |
| "contentUrl": content_url("derived/all_trades.csv"), |
| "md5": md5("derived/all_trades.csv"), |
| "sha256": sha256("derived/all_trades.csv") |
| }, |
| { |
| "@type": "cr:FileObject", |
| "@id": "universe-csv", |
| "name": "sector_style_universe.csv", |
| "description": "5x4 sector/style universe (20 US + 20 CN tickers)", |
| "encodingFormat": "text/csv", |
| "contentUrl": content_url("universe/sector_style_universe.csv"), |
| "md5": md5("universe/sector_style_universe.csv"), |
| "sha256": sha256("universe/sector_style_universe.csv") |
| }, |
| { |
| "@type": "cr:FileSet", |
| "@id": "run-metrics-files", |
| "name": "Per-run metrics files", |
| "description": "Summary metrics JSON per individual backtest run", |
| "encodingFormat": "application/json", |
| "includes": "runs/*/*/metrics.json" |
| }, |
| { |
| "@type": "cr:FileSet", |
| "@id": "run-trades-files", |
| "name": "Per-run trade logs", |
| "description": "Per-trade CSV per individual backtest run", |
| "encodingFormat": "text/csv", |
| "includes": "runs/*/*/trades.csv" |
| }, |
| { |
| "@type": "cr:FileSet", |
| "@id": "run-equity-files", |
| "name": "Per-run equity curves", |
| "description": "Daily portfolio state CSV per individual backtest run", |
| "encodingFormat": "text/csv", |
| "includes": "runs/*/*/equity_curve.csv" |
| } |
| ], |
| "recordSet": [ |
| { |
| "@type": "cr:RecordSet", |
| "@id": "metrics-records", |
| "name": "metrics_records", |
| "description": "Long-format performance metrics across all 28 runs", |
| "field": [ |
| {"@type": "cr:Field", "@id": "metrics-records/experiment", |
| "name": "experiment", "description": "Experiment family (exp1_caseStudy_us_6m, exp2_reproducibility_us_6m_run2, etc.)", |
| "dataType": "sc:Text", |
| "source": {"fileObject": {"@id": "all-metrics-csv"}, "extract": {"column": "experiment"}}}, |
| {"@type": "cr:Field", "@id": "metrics-records/market", |
| "name": "market", "description": "Market identifier (us or cn)", |
| "dataType": "sc:Text", |
| "source": {"fileObject": {"@id": "all-metrics-csv"}, "extract": {"column": "market"}}}, |
| {"@type": "cr:Field", "@id": "metrics-records/mandate-dir", |
| "name": "mandate_dir", "description": "Mandate identifier in the bundle directory layout", |
| "dataType": "sc:Text", |
| "source": {"fileObject": {"@id": "all-metrics-csv"}, "extract": {"column": "mandate_dir"}}}, |
| {"@type": "cr:Field", "@id": "metrics-records/total-return", |
| "name": "total_return", "description": "Total return over the run window in percent", |
| "dataType": "sc:Float", |
| "source": {"fileObject": {"@id": "all-metrics-csv"}, "extract": {"column": "total_return"}}}, |
| {"@type": "cr:Field", "@id": "metrics-records/max-drawdown", |
| "name": "max_drawdown", "description": "Maximum drawdown in percent", |
| "dataType": "sc:Float", |
| "source": {"fileObject": {"@id": "all-metrics-csv"}, "extract": {"column": "max_drawdown"}}}, |
| {"@type": "cr:Field", "@id": "metrics-records/sharpe", |
| "name": "sharpe_ratio", "description": "Annualized Sharpe ratio", |
| "dataType": "sc:Float", |
| "source": {"fileObject": {"@id": "all-metrics-csv"}, "extract": {"column": "sharpe_ratio"}}}, |
| {"@type": "cr:Field", "@id": "metrics-records/total-trades", |
| "name": "total_trades", "description": "Number of executed trades over the run window", |
| "dataType": "sc:Integer", |
| "source": {"fileObject": {"@id": "all-metrics-csv"}, "extract": {"column": "total_trades"}}} |
| ] |
| }, |
| { |
| "@type": "cr:RecordSet", |
| "@id": "trades-records", |
| "name": "trades_records", |
| "description": "Per-trade records concatenated across all 28 runs", |
| "field": [ |
| {"@type": "cr:Field", "@id": "trades-records/experiment", |
| "name": "experiment", "dataType": "sc:Text", |
| "source": {"fileObject": {"@id": "all-trades-csv"}, "extract": {"column": "experiment"}}}, |
| {"@type": "cr:Field", "@id": "trades-records/mandate-dir", |
| "name": "mandate_dir", "dataType": "sc:Text", |
| "source": {"fileObject": {"@id": "all-trades-csv"}, "extract": {"column": "mandate_dir"}}}, |
| {"@type": "cr:Field", "@id": "trades-records/date", |
| "name": "date", "description": "Trading date (YYYY-MM-DD)", |
| "dataType": "sc:Date", |
| "source": {"fileObject": {"@id": "all-trades-csv"}, "extract": {"column": "date"}}}, |
| {"@type": "cr:Field", "@id": "trades-records/ticker", |
| "name": "ticker", "dataType": "sc:Text", |
| "source": {"fileObject": {"@id": "all-trades-csv"}, "extract": {"column": "ticker"}}}, |
| {"@type": "cr:Field", "@id": "trades-records/action", |
| "name": "action", "description": "BUY or SELL", |
| "dataType": "sc:Text", |
| "source": {"fileObject": {"@id": "all-trades-csv"}, "extract": {"column": "action"}}}, |
| {"@type": "cr:Field", "@id": "trades-records/shares", |
| "name": "shares", "dataType": "sc:Integer", |
| "source": {"fileObject": {"@id": "all-trades-csv"}, "extract": {"column": "shares"}}}, |
| {"@type": "cr:Field", "@id": "trades-records/price", |
| "name": "price", "description": "Trade price on the decision date (USD for US runs, CNY for CN runs)", |
| "dataType": "sc:Float", |
| "source": {"fileObject": {"@id": "all-trades-csv"}, "extract": {"column": "price"}}}, |
| {"@type": "cr:Field", "@id": "trades-records/value", |
| "name": "value", "description": "Trade notional (shares * price)", |
| "dataType": "sc:Float", |
| "source": {"fileObject": {"@id": "all-trades-csv"}, "extract": {"column": "value"}}} |
| ] |
| } |
| ], |
| |
| "rai:dataCollection": ( |
| "Backtest runs were executed against a closed evaluation harness using " |
| "live-style market data from licensed third-party APIs (Yahoo Finance / " |
| "yfinance, Tushare, FMP, AKShare, Tavily). Each run records the LLM's " |
| "decisions and the resulting portfolio state without modification. No " |
| "human subjects are involved at any stage." |
| ), |
| "rai:dataCollectionType": "Programmatic", |
| "rai:dataCollectionRawData": ( |
| "Raw market data from third-party providers; redistributed only as the " |
| "derived per-decision price and decision records embedded in trades.csv. " |
| "See LICENSE.md for per-source redistribution terms." |
| ), |
| "rai:dataCollectionTimeframe": "2025-09-01 / 2026-02-28 (run windows); 2026-04-09 to 2026-04-25 (run execution dates)", |
| "rai:dataImputationProtocol": ( |
| "No data imputation. Trading days are defined by the upstream price " |
| "provider's calendar; non-trading days are skipped naturally." |
| ), |
| "rai:dataPreprocessingProtocol": ( |
| "Raw API responses are normalized into a fixed schema (OHLCV daily bars, " |
| "fundamentals snapshots, news lists) inside the trading harness; only " |
| "the trades emitted and per-day portfolio state are persisted to disk. " |
| "See `tools/build_release_bundle.py` for how the public artifact is " |
| "assembled from raw run directories." |
| ), |
| "rai:dataAnnotationProtocol": "Not applicable — no human annotation.", |
| "rai:dataUseCases": [ |
| "Reproducing or extending the QuantArena evaluation protocol.", |
| "Auditing how investment doctrine alone (with LLM backend held fixed) shapes portfolio behavior.", |
| "Studying behavioral fidelity of LLM trading agents under matched execution.", |
| "Methodological research on controlled-intervention evaluation in finance." |
| ], |
| "rai:dataBiases": ( |
| "Universe is restricted to 20 liquid US tickers and 20 liquid CN A-share " |
| "tickers (5 sectors x 4 size-style cells per market); micro-cap, " |
| "illiquid, or non-equity assets are excluded by design. Performance " |
| "patterns observed here may not transfer to broader universes. " |
| "Single-seed case-study runs (with one paired re-run for the US window) " |
| "imply LLM-sampling stochasticity is bounded but not fully characterized; " |
| "see the Reproducibility table in the paper." |
| ), |
| "rai:dataLimitations": ( |
| "Trading frictions (transaction costs, market impact, short-selling, " |
| "borrowing constraints) are not modeled in the primary runs; the paper " |
| "reports a deterministic post-hoc transaction-cost sensitivity sweep " |
| "over the released trade logs. The 6M live-market window is much " |
| "shorter than typical institutional evaluation horizons. The GPT-5.4 " |
| "backend identifier reflects the API gateway label exposed at run time " |
| "and not a vendor build hash; exact backend reproducibility depends on " |
| "the gateway's routing decisions at run time. The bundle redistributes " |
| "only the derived decision-level data, not the raw third-party feeds." |
| ), |
| "rai:dataReleaseMaintenancePlan": ( |
| "v1.0 corresponds to the NeurIPS 2026 E&D Track submission. Future " |
| "versions will add longer windows, additional asset classes, and " |
| "trading-friction modeling. Bundle versioning is tracked in CHANGELOG.md." |
| ), |
| "rai:personalSensitiveInformation": "Not applicable — bundle contains no personally identifiable or sensitive information.", |
| "rai:dataSocialImpact": ( |
| "The bundle supports academic study of LLM-driven trading agents under " |
| "controlled conditions. Deploying such agents to real markets without " |
| "additional safeguards (frictions, risk controls, auditing) carries " |
| "substantial risk, including financial loss and potential amplification " |
| "of herd behavior. The bundle is explicitly scoped for research and " |
| "evaluation; it is not financial advice and should not be interpreted " |
| "as evidence of safe or profitable real-world deployment." |
| ), |
| "rai:hasSyntheticData": False, |
| "prov:wasDerivedFrom": [ |
| "Yahoo Finance / yfinance US equity price and corporate-action data", |
| "Tushare China A-share price, fundamental, and news data", |
| "Financial Modeling Prep US fundamentals and news data", |
| "AKShare China macro and policy indicators", |
| "Tavily search-API news retrieval metadata", |
| "LLM backend decision outputs from the controlled QuantArena execution harness" |
| ], |
| "prov:wasGeneratedBy": [ |
| { |
| "@type": "prov:Activity", |
| "name": "Controlled backtest execution", |
| "description": ( |
| "The QuantArena harness executed daily decisions from 2025-09-01 " |
| "to 2026-02-28 for matched US and CN 20-ticker universes, fixing " |
| "backend, analyst workflow, initial capital, portfolio accounting, " |
| "and execution rules while varying only the mandate module." |
| ) |
| }, |
| { |
| "@type": "prov:Activity", |
| "name": "Artifact derivation and anonymized release packaging", |
| "description": ( |
| "Raw run directories were filtered into decision-level artifacts: " |
| "per-run metrics.json, trades.csv, equity_curve.csv, comparison " |
| "aggregates, universe definitions, audit manifests, and flattened " |
| "all_metrics/all_trades tables. Raw third-party OHLCV/news bodies, " |
| "private credentials, author-identifying repository metadata, and " |
| "live LLM response logs are excluded." |
| ) |
| } |
| ] |
| } |
| out = RELEASE / "croissant.json" |
| out.write_text(json.dumps(croissant, indent=2, ensure_ascii=False)) |
| print(f" wrote croissant.json ({len(json.dumps(croissant))} chars)") |
|
|
|
|
| def main() -> None: |
| print(f"Writing metadata into {RELEASE}") |
| write_text("README.md", README) |
| write_text("LICENSE.md", LICENSE) |
| write_text("CHANGELOG.md", CHANGELOG) |
| write_croissant() |
| copy_build_script() |
| print() |
| |
| total = sum(os.path.getsize(os.path.join(r, f)) for r, _, fs in os.walk(RELEASE) for f in fs) |
| nfiles = sum(len(fs) for _, _, fs in os.walk(RELEASE)) |
| print(f"Final bundle: {nfiles} files, {total/1024/1024:.2f} MB") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
