Datasets:

xpertsystems
/

fin-qa-001-sample

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

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

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:

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.
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.
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.
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.
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.
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.
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

@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.