metadata
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
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
{
"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.