| --- |
| language: |
| - en |
| license: mit |
| size_categories: |
| - 1GB < n < 10GB |
| tags: |
| - finance |
| - portfolio-management |
| - multi-asset |
| - benchmark |
| - LLM |
| - correlation |
| task_categories: |
| - other |
| --- |
| |
| [](https://arxiv.org/abs/2605.27887) |
| [](https://github.com/AgenticFinLab/portbench) |
| [](https://portbench.github.io/) |
|
|
| # PortBench-RawData |
|
|
| This repository contains the **raw collected data and preprocessed asset files** for [PortBench](https://github.com/AgenticFinLab/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 ranges |
| ``` |
|
|
| --- |
|
|
| ## Data Sources |
|
|
| | Source | Coverage | Content | Tickers/Series | |
| |--------|----------|---------|----------------| |
| | [Yahoo Finance](https://finance.yahoo.com/) | 2015–2025 | Daily OHLCV, adjusted close, volume for ETFs and stocks across all 6 asset classes | 72 tickers | |
| | [FRED](https://fred.stlouisfed.org/) | 2015–2025 | Macroeconomic indicators: yield curve (DGS1–DGS30), TIPS real yields (DFII5/10/30), breakeven inflation (T5YIE/T10YIE), Fed funds rate (DFF/FEDFUNDS), CPI (CPIAUCSL/CPILFESL), GDP, employment (PAYEMS), housing (CSUSHPINSA), VIX | 60 series | |
| | [Kaggle](https://www.kaggle.com/) | 2015–2025 | Supplementary cryptocurrency OHLCV, commodity futures, equities with news sentiment, real estate data | ~45 datasets | |
| | [SEC EDGAR](https://www.sec.gov/edgar/) | 2015–2025 | 10-K and 10-Q filings for US equities, parsed text | Equity filings | |
|
|
| --- |
|
|
| ## Coverage by Asset Class |
|
|
| | Asset Class | Raw Tickers | Processed | Role in Portfolio | |
| |-------------|------------|-----------|-------------------| |
| | **Equities** | 127 | 126 | Return engine; diversified via sector/factor ETFs (SPY, QQQ, XLE, XLF, VXUS, etc.) | |
| | **Bonds** | 15 | 15 | Fixed-income hedging; Treasury, corporate, high-yield (TLT, IEF, HYG, LQD, etc.) | |
| | **Commodities** | 16 | 16 | Inflation hedge; gold, oil, natural gas, agriculture (GLD, USO, UNG, DBC, etc.) | |
| | **Real Estate** | 10 | 10 | Diversification; REIT sector ETFs (VNQ, IYR, SCHH, etc.) | |
| | **Cryptocurrency** | 12 | 12 | High-risk allocation; major + mid-cap (BTC, ETH, SOL, DOGE, etc.) | |
| | **Cash** | 4 | 4 | Capital preservation; money market, short-term Treasuries (BIL, SGOV, SHV) | |
|
|
| 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](https://huggingface.co/datasets/AgenticFinLab/PortBench-Market) — The processed market base dataset (`portbench.csv`) with all six asset classes merged at daily frequency, plus visualization figures. |
| - [PortBench-QA](https://huggingface.co/datasets/AgenticFinLab/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](https://huggingface.co/collections/AgenticFinLab/portbench). |
|
|
| --- |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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. |
