---
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}
}
```