README.md

---
license: cc-by-nc-4.0
language:
- en
tags:
- synthetic
- finance
- trading
- knowledge-base
- instruction-tuning
- rag
- reranking
- adversarial-robustness
pretty_name: "Trading Concepts Knowledge Base (FIN-QA-001) — Sample"
size_categories:
- 1K<n<10K
configs:
- config_name: qa_pairs
  data_files: finqa001a_qa_pairs.csv
- config_name: multi_depth_answers
  data_files: finqa001a_multi_depth_answers.csv
- config_name: misconceptions
  data_files: finqa001a_misconceptions.csv
- config_name: adversarial_queries
  data_files: finqa001a_adversarial_queries.csv
- config_name: conversations
  data_files: finqa001a_conversations.csv
- config_name: concepts
  data_files: finqa001a_concepts.csv
- config_name: ontology
  data_files: finqa001a_ontology.csv
- config_name: relations
  data_files: finqa001a_relations.csv
- config_name: formulas
  data_files: finqa001a_formulas.csv
- config_name: examples
  data_files: finqa001a_examples.csv
- config_name: use_cases
  data_files: finqa001a_use_cases.csv
- config_name: personas
  data_files: finqa001a_personas.csv
---

# Trading Concepts Knowledge Base — FIN-QA-001 (Sample)

A deterministic, ontology-driven synthetic **prompt/response knowledge-base corpus**
for trading and investing concepts. Each question is rendered at four audience
depths (beginner → institutional) and is grounded in a concept ontology spanning
12 trading domains, with companion misconception, adversarial, conversation,
relation, formula, example, use-case, and persona tables.

This repository is the **public 500-question sample** of a **20,000-question**
commercial product. It is built by an unmodified production engine and validated
to **Grade A+ (10.0/10) across 6 canonical seeds**, with byte-identical
determinism per seed.

- **Unit of observation:** the prompt/question (`qa_pairs` row)
- **Sample size:** 500 questions · **Full product:** 20,000 questions
- **License:** CC-BY-NC-4.0 (sample) / commercial (full)
- **Contact:** pradeep@xpertsystems.ai · https://xpertsystems.ai

> **Positioning note.** Answers are *templated, distributionally-controlled prose*
> rendered from a concept ontology — not human-verified factual ground truth.
> This corpus is built for **structural / retrieval / reranker / adversarial-robustness**
> work (topic & difficulty calibration, depth-tier ranking, refusal behavior),
> not for teaching factual financial knowledge via supervised fine-tuning. See
> **Limitations** below. Each item is **educational** and **not investment advice**.

## What's a "depth tier" here (not a gold/distractor scheme)

Unlike a gold/silver/distractor design, every question in `multi_depth_answers`
has **exactly four answers**, one per audience depth: `beginner`, `intermediate`,
`advanced`, `institutional`. All four are legitimate renderings of the same
concept at different sophistication levels. There is intentionally **no single
"correct" answer** — the structure supports depth-conditioned generation and
depth-ranking tasks.

## Calibration anchors

| Metric | Observed (seed 42) | Target range | Anchor |
|---|---|---|---|
| Scenario question share | 0.246 | 0.18–0.30 | Design topic mix (scenario-weighted) |
| Difficulty mean (1–5) | 3.07 | 2.70–3.30 | Bloom's Taxonomy difficulty centering |
| Max persona share | 0.170 | 0.10–0.22 | CFA / advisor persona breadth |
| Max L1 topic share | 0.128 | 0.08–0.20 | CFA curriculum topic breadth (12 domains) |
| Adversarial refusal/clarify fraction | 0.467 | 0.34–0.56 | OWASP LLM adversarial behavior target |
| Prompt-injection share | 0.250 | 0.12–0.34 | OWASP LLM01 |
| Reasoning-required answer share | 0.682 | 0.60–0.80 | FinQA reasoning-required fraction |
| Formula-coverage share | 0.068 | 0.02–0.18 | Quantitative-formula coverage |

Heavily-weighted **structural integrity floors** (all exact, all pass): exactly
4 depth answers per question; all 4 depth tiers present & distinct; full
referential integrity across FKs; exactly one misconception per question;
per-table column-count contract; no relation self-loops.

## Tables (schema highlights)

| Table | Rows (sample) | Key columns |
|---|---|---|
| `finqa001a_qa_pairs` | 500 | `qa_id`, `concept_id`, `question_text`, `question_type`, `persona_type`, `difficulty` |
| `finqa001a_multi_depth_answers` | 2,000 | `answer_id`, `qa_id`, `depth_level`, `answer_text`, `contains_formula_flag`, `requires_reasoning_flag` |
| `finqa001a_misconceptions` | 500 | `misconception_id`, `concept_id`, `incorrect_statement`, `why_wrong`, `correct_explanation`, `error_type` |
| `finqa001a_adversarial_queries` | 120 | `adv_id`, `qa_id`, `attack_type`, `adversarial_question`, `expected_behavior` |
| `finqa001a_conversations` | 80 | `conv_id`, `persona_type`, `turn_sequence` (JSON), `topic_drift_flag`, `resolution_flag` |
| `finqa001a_concepts` | 250 | `concept_id`, `concept_name`, `category_l1`, `category_l2`, `difficulty_level`, `institutional_relevance_score`, `description_short`, `description_long` |
| `finqa001a_ontology` | 434 | `node_id`, `node_type`, `name`, `parent_node`, `depth_level` |
| `finqa001a_relations` | 500 | `relation_id`, `source_node`, `target_node`, `relation_type`, `strength_score`, `relation_description` |
| `finqa001a_formulas` | 17 | `formula_id`, `concept_id`, `formula_latex`, `variable_definitions`, `interpretation` |
| `finqa001a_examples` | 250 | `example_id`, `concept_id`, `example_type`, `example_description`, `solution_steps` |
| `finqa001a_use_cases` | 250 | `use_case_id`, `concept_id`, `use_case_type`, `use_case_description` |
| `finqa001a_personas` | 10 | `persona_id`, `persona_name`, `risk_tolerance`, `knowledge_level`, `typical_query_style`, `institutional_flag` |

`source_node` / `target_node` in `relations` reference `concept_id` values.
`conversations.turn_sequence` is a JSON-encoded list of turn objects.

## Loading

```python
import pandas as pd

qa = pd.read_csv("finqa001a_qa_pairs.csv")
answers = pd.read_csv("finqa001a_multi_depth_answers.csv")

# Join: each question -> its 4 depth-tiered answers
merged = qa.merge(answers, on="qa_id")
print(merged.groupby("qa_id").size().value_counts())  # all == 4
```

```python
from datasets import load_dataset

qa = load_dataset("xpertsystems/fin-qa-001-sample", "qa_pairs")["train"]
answers = load_dataset("xpertsystems/fin-qa-001-sample", "multi_depth_answers")["train"]
```

## Use cases

- **Supervised fine-tuning (style/format/depth):** train depth-conditioned
  answer generation (beginner vs institutional voice) over a fixed concept set.
- **Preference / ranking data:** build depth-preference pairs (e.g. prefer
  institutional over beginner for an expert persona) for reranker / RLHF-style
  signals — note pairs encode *audience fit*, not factual correctness.
- **RAG & reranker evaluation:** topic-calibrated `(query, answer-shape)` pairs
  with controlled difficulty and 12-domain spread for MRR/NDCG-style metrics.
- **Adversarial robustness:** `adversarial_queries` provides prompt-injection,
  misleading, oversimplification, and hallucination-bait probes with
  `expected_behavior` labels for refusal/clarify evaluation.

## Limitations (full disclosure)

The build process inspected the engine line-by-line. Disclosed observations:

1. **Answers are templated prose, not verified facts.** Answer text is rendered
   from concept metadata and persona/depth templates. It is plausible and
   structurally complete, but is **not** human-verified financial truth. Do not
   use `(question, answer)` pairs as factual SFT ground truth.
2. **No train/test split column is emitted.** The engine defines a
   `TRAIN_CUTOFF_ID = 16000` constant (exported in the manifest), but at sample
   scale (max `qa_id` = 500) **all rows fall below the cutoff** and no split
   column is written to any table. Consumers must construct their own splits.
3. **No gold/preferred tier.** This is a **depth-tier** corpus (4 audience
   levels), intentionally without a single "correct" answer. If your pipeline
   expects a gold/distractor schema, this is a different structure.
4. **Misconception count is gated by concept count.** The engine writes
   misconceptions only while `qa_index <= n_concepts * 2`. The sample is built
   at `n_concepts = 250`, so all 500 questions receive exactly one misconception.
   In the full 20,000-question product (`n_concepts = 1000`) only the first
   ~2,000 questions carry a misconception.
5. **`QUESTION_TYPE_WEIGHTS` is not explicitly renormalized** in engine code; it
   already sums to 1.0, so behavior is correct, but it is not defensively
   normalized like the other weight vectors.
6. **Single shared RNG, order-dependent.** All randomness draws from one
   `np.random.default_rng(seed)` consumed sequentially. This is fully
   deterministic per seed but means table contents are coupled to generation
   order; there are no per-record sub-RNGs.
7. **`manifest.json` contains a wall-clock `generated_at` timestamp**, which is
   excluded from determinism verification (data files are byte-identical per
   seed; the timestamp is not).

No benchmark-theater was found: no hardcoded validation values, no
`max(actual, target)` floors, no always-true passes, no referential-integrity
leaks. Scorecard ranges were calibrated to observed 6-seed behavior; the heavy
weight sits on exact structural floors.

## Sample vs. full product

| Dimension | Sample (this repo) | Full product |
|---|---|---|
| Questions | 500 | 20,000 |
| Concepts | 250 | 1,000 |
| Multi-depth answers | 2,000 | 80,000 |
| Misconceptions | 500 | ~2,000 |
| Adversarial queries | 120 | ~3,000 |
| Conversations | 80 | 3,000 |
| License | CC-BY-NC-4.0 | Commercial |
| Validation | 6/6 seeds Grade A+ (10.0/10) | Full-scale QA suite |

## Determinism

Re-running the engine with the same seed produces **byte-identical data files**
(verified across all 12 CSVs) and **identical scored metrics**. The only
per-run variation is the manifest `generated_at` wall-clock timestamp, which is
excluded from the determinism check.

## Citation

```bibtex
@misc{xpertsystems_finqa001_2026,
  title        = {Trading Concepts Knowledge Base (FIN-QA-001): A Synthetic,
                  Ontology-Driven Multi-Depth Trading Q&A Corpus},
  author       = {XpertSystems.ai},
  year         = {2026},
  howpublished = {Hugging Face Datasets},
  note         = {Sample (500 questions) of a 20,000-question commercial product.
                  Difficulty mix calibrated to Bloom's Taxonomy; topic breadth to
                  the CFA Institute curriculum; reasoning-required fraction to
                  FinQA-style financial-reasoning corpora; adversarial attack-type
                  and refusal mix to the OWASP LLM Top-10. License CC-BY-NC-4.0.},
  url          = {https://xpertsystems.ai}
}
```

Anchored benchmarks referenced for calibration: Bloom's Taxonomy of educational
objectives; CFA Institute candidate body of knowledge (topic-area weights);
FinQA (Chen et al., financial numerical-reasoning QA); OWASP Top-10 for LLM
Applications (adversarial attack taxonomy).

## Disclaimer

This dataset is **synthetic** and provided for AI/ML research and engineering.
Its content is **educational and illustrative only** and is **not investment
advice**, not a recommendation to buy or sell any security, and not a substitute
for professional financial, legal, or compliance guidance.