OpenPM-Bench dataset release

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README.md

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+---
+license: other
+pretty_name: OpenPM-Bench
+viewer: false
+language:
+- en
+tags:
+- finance
+- portfolio-management
+- llm-agents
+- point-in-time
+- trading
+- benchmark
+size_categories:
+- 1M<n<10M
+---
+
+# OpenPM-Bench
+
+**Auditable, point-in-time evaluation data for LLM portfolio-management agents.**
+
+OpenPM-Bench is the dataset behind **OpenPM**, a benchmark for the question *"can an LLM
+manage a stock portfolio?"* A trading agent sees only point-in-time, leakage-controlled
+information for the S&P 500 — the names that were *actually* in the index on each decision
+date — and proposes target weights, which are backtested bar-by-bar on a 5-minute price panel.
+
+- **Code:** <https://github.com/aslcai/OpenPM-Bench>
+- **Paper:** *OpenPM: Auditable Point-in-Time Evaluation for LLM Portfolio-Management Agents*
+
+## What's in it
+
+A single content-hashed freeze (`FREEZE_MANIFEST.json`, `dataset_sha256` `89e154ae…`,
+**1,376 data files, ≈47 GB**) of **508 point-in-time S&P 500 members** active on at least one
+date between **2026-02-19 and 2026-05-01**. The evaluation window is **2026-03-02 → 2026-05-01**
+(44 trading days, 3,432 five-minute bars per name). The data is 2026-dated, after the knowledge
+cutoff of most evaluated model backbones.
+
+Every record carries an availability timestamp (`ts_available`); a field is visible at decision
+time *T* only if its stamp ≤ *T*. Future-looking labels are held out in a separate, agent-invisible
+layer.
+
+### Layers
+
+| Path | Contents |
+|---|---|
+| `bars/` | 5-minute IEX-derived bar/quote feature parquets |
+| `quotes/` | spread / liquidity features |
+| `panel/` | `price_panel_5m.parquet` — the bid/ask/mid panel the backtester marks against |
+| `feature_output/` | `feature_output.ndjson` — the point-in-time, label-free agent input stream |
+| `labels/` | held-out evaluation labels (step / earnings / forward / executable returns) |
+| `events/` | SEC event state (incl. PEAD), GDELT news state, earnings |
+| `context/` | rates / FX / commodities macro context |
+| `instruments/` | S&P 500 membership, sector history, corporate actions |
+| `raw/` | raw provenance archives (IEX TOPS, SEC filings, FRED, GDELT, Wikipedia revisions) |
+
+## How much do you need?
+
+The full release is **≈47 GB, but ~39 GB of that is `raw/`** — provenance archives needed **only to
+rebuild the dataset from source.** **To run the full benchmark, download everything except `raw/`
+(≈8 GB):**
+
+```python
+from huggingface_hub import snapshot_download
+
+# Run the benchmark (~8 GB) — all derived layers, skip the raw provenance archives:
+path = snapshot_download("aslcai/OpenPM-Bench", repo_type="dataset", ignore_patterns=["raw/*"])
+
+# Full release (~47 GB, incl. raw/ to rebuild from source):
+# path = snapshot_download("aslcai/OpenPM-Bench", repo_type="dataset")
+```
+
+This is a multi-layer parquet/NDJSON dataset, not a single table — read the layers directly with
+pandas/pyarrow, or use the OpenPM code (see the repo) to run an agent. Download all derived layers,
+not a subset: the flagship agent's `filings_meta` / `filings_body` analysts read `events/`, and a
+missing layer silently degrades those channels rather than erroring.
+
+## Citation
+
+```bibtex
+@misc{openpm2026,
+  title  = {OpenPM: Auditable Point-in-Time Evaluation for LLM Portfolio-Management Agents},
+  author = {Cai, Xinying and Guo, Minghao and Li, Raymond and others},
+  year   = {2026},
+  url    = {https://github.com/aslcai/OpenPM-Bench}
+}
+```

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