README.md

---
language:
- en
license: apache-2.0
task_categories:
- question-answering
- text-generation
tags:
- finance
- portfolio-management
- multi-asset
- benchmark
- financial-reasoning
- correlation
size_categories:
- 1M<n<10M
---

[![Paper](https://img.shields.io/badge/arXiv-2605.27887-b31b1b)](https://arxiv.org/abs/2605.27887)
[![Code](https://img.shields.io/badge/GitHub-PortBench-blue)](https://github.com/AgenticFinLab/portbench)

# PortBench QA Dataset

## Dataset Description

**6,269** structured question-answer pairs probing correlation-based financial reasoning for multi-asset portfolio management, generated from the PortBench Market Base Dataset.

### Task Templates

| Template | Task | Complexity | Pairs |
|----------|------|:----------:|------:|
| T1 | Return prediction — direction for next N days | 1 (single asset) | 1,000 |
| T2 | Risk assessment — VaR at given confidence level | 1 | 1,000 |
| T3 | Position sizing — given max drawdown constraint | 1 | 1,000 |
| T4 | Pairwise allocation — minimize variance for 2 assets | 2 (pairwise) | 1,000 |
| T5 | Multi-asset optimization — maximize Sharpe for 3+ assets | 3 (multi-asset) | 1,000 |
| T6 | Rebalancing decision — threshold-based trigger | 3 | 778 |
| T7 | Regime detection — identify bull/bear/sideways + adjust allocation | 4 (full portfolio) | 491 |

### Splits

| Split | Period | Pairs |
|-------|--------|------:|
| Train | 2015-01-02 – 2022-12-31 | 1,941 |
| Val | 2023-01-01 – 2024-12-31 | 2,700 |
| Test | 2025-01-01 – 2025-12-31 | 1,628 |

### Data Fields

Each JSONL record contains:

| Field | Type | Description |
|-------|------|-------------|
| `id` | string | Unique identifier (`{template}_{class}_{date}_{seq}`) |
| `template` | string | Task template (T1–T7) |
| `complexity` | int | Difficulty level (1–4) |
| `split` | string | train / val / test |
| `market_regime` | string | bull / bear / sideways / crisis |
| `asset_class` | string | Target asset class |
| `assets` | list[string] | Ticker symbols involved |
| `decision_date` | string | Point-in-time decision date (YYYY-MM-DD) |
| `context_summary` | string | Market context window (prices, macro, correlations, news) |
| `question` | string | The question with all necessary numerical context |
| `answer` | string | Ground-truth answer |
| `answer_numeric` | float | Numeric ground-truth (for scoring) |
| `explanation` | string | Step-by-step explanation of the answer |
| `metadata` | object | Additional fields (future_return, horizon, volatility, text coverage, etc.) |

### Example

```json
{
  "id": "T1_all_20251226_0002",
  "template": "T1",
  "complexity": 1,
  "split": "test",
  "market_regime": "sideways",
  "assets": ["DBB"],
  "decision_date": "2025-12-26",
  "question": "Asset: DBB\nHistorical prices (past 60 trading days): start=20.40, end=22.01, cumulative_return=+7.9%, annualized_volatility=14.0%\n...\nPredict whether the return of DBB over the next 21 trading days will be: positive (>+1%), negative (<-1%), or flat (within ±1%).",
  "answer": "flat",
  "answer_numeric": 0.0,
  "explanation": "The actual 21-day forward return for DBB starting 2025-12-26 was +0.00%, which classifies as 'flat'."
}
```

### Text Coverage

85.3% of QA pairs include associated news text in the context window (avg 3,997 chars).

### Market Regime Distribution

QA pairs are stratified by market regime (bull/bear/sideways/crisis) to enable per-regime performance decomposition.

### Intended Use

- Evaluating LLM financial reasoning capabilities across four difficulty levels
- Benchmarking correlation-based multi-asset decision-making
- Comparing static knowledge (QA accuracy) with dynamic pipeline performance (CEPS)

### Point-in-Time (PiT) Constraint

All questions use only information available at or before the `decision_date`. Ground-truth answers are computed from realized future data that is never included in the question or context.