AgenticFinLab/PortBench-RawData
PortBench-RawData This repository contains the raw collected data and preprocessed asset files for PortBench. The data spans 2015–2025 across six heterogeneous asset classes: Equities, Bonds, Commodities, Real Estate, Cryptocurrency, and Cash. Repository Structure PortBench-RawData/ ├── raw_data/ # Raw collected data (~4.6 GB) │ ├── fred/ # FRED macroeconomic indicators (60 series) │ │ ├── bonds/… See the full description on the dataset page: https://huggingface.co/datasets/AgenticFinLab/PortBench-RawData.
  
PortBench-RawData
This repository contains the raw collected data and preprocessed asset files for PortBench.
The data spans 2015–2025 across six heterogeneous asset classes: Equities, Bonds, Commodities, Real Estate, Cryptocurrency, and Cash.
Repository Structure
PortBench-RawData/
├── raw_data/ # Raw collected data (~4.6 GB)
│ ├── fred/ # FRED macroeconomic indicators (60 series)
│ │ ├── bonds/ # Yield curve, credit spreads, TIPS
│ │ ├── cash/ # Fed funds rate, CPI, GDP, employment
│ │ ├── commodities/ # Oil, gold, agriculture spot indices
│ │ └── real_estate/ # Case-Shiller, HPI, REIT indices
│ ├── kaggle/ # Kaggle supplementary data (~4 GB)
│ │ ├── commodities/ # Commodity futures and spot prices
│ │ ├── cryptocurrency/ # Crypto OHLCV data
│ │ ├── equities/ # Stock data with news text
│ │ └── real_estate/ # REIT and property data
│ ├── sec/ # SEC EDGAR filings
│ │ └── equities/ # 10-K, 10-Q filings for US equities
│ ├── yahoo/ # Yahoo Finance price data
│ │ ├── bonds/ # Bond ETF prices and yields
│ │ ├── cash/ # Money market and treasury ETF data
│ │ ├── commodities/ # Commodity ETF data
│ │ ├── cryptocurrency/ # Crypto ETF and trust data
│ │ ├── equities/ # 72 tickers (broad-market, sector, factor ETFs)
│ │ └── real_estate/ # REIT ETF data
│ └── metadata.json # Dataset metadata summary
│
└── processed/ # Preprocessed asset data
├── equities.csv # 126 equity tickers, aligned daily
├── bonds.csv # 15 bond series, aligned daily
├── commodities.csv # 16 commodity series, aligned daily
├── real_estate.csv # 10 real estate series, aligned daily
├── cryptocurrency.csv # 12 cryptocurrency series, aligned daily
├── cash.csv # 4 cash equivalent series, aligned daily
├── correlation_matrix.csv # 183×183 Pearson correlation matrix
├── asset_class_map.json # Ticker-to-asset-class mapping
└── time_ranges.json # Per-ticker date coverage rangesData Sources
Coverage by Asset Class
Cross-asset correlations exhibit the key structural property exploited by PortBench's dual-layer scoring: intra-class correlations are strongly positive (0.4–0.6+), while inter-class correlations are near-zero or negative, meaning true diversification requires cross-class allocation, not just many tickers within one class.
Preprocessing Details
- Calendar alignment: All series aligned to a common business-day calendar; gaps ≤5 days forward-filled; longer gaps retained as NaN for pairwise-complete correlation estimation.
- Market regime labels: Each asset class labeled as bull/bear/sideways/crisis using MA crossover (50/200-day) + 15% max-drawdown crisis threshold.
- Data splits: Train (2015–2022), Validation (2023–2024), Test (2025), with year-end boundaries.
- Correlation matrix: 183×183 Pearson correlation matrix computed from daily simple returns over the full training period using pairwise-complete observations; frozen and not re-estimated.
Related Datasets
This repository contains the raw data foundation. See also:
- PortBench-Market — The processed market base dataset (
portbench.csv) with all six asset classes merged at daily frequency, plus visualization figures. - PortBench-QA — 6,269 question-answer pairs across 7 templates (T1–T7) probing correlation-based financial reasoning, with train/val/test splits.
Both are part of the PortBench collection.
Citation
@article{zhao2026portbench,
title={PortBench: A Correlation-Aware, Full-Pipeline Benchmark for LLM-Driven Portfolio Management},
author={Zhao, Yuxuan and Chen, Sijia and Su, Ningxin},
journal={arXiv preprint arXiv:2605.27887},
year={2026}
}License
This dataset is released under the MIT License. Data sourced from Yahoo Finance, FRED, Kaggle, and SEC EDGAR is subject to their respective terms of service.
