feat: add README.md file

README.md

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+---
+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
+
+```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.
+
+### Citation
+
+```bibtex
+
+```