Datasets:

aslcai
/

OpenPM-Bench

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):

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

@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}
}