Datasets:

xpertsystems
/

fin-qa-002-sample

license: cc-by-nc-4.0
language:
  - en
tags:
  - synthetic
  - finance
  - options
  - derivatives
  - knowledge-base
  - instruction-tuning
  - rag
  - adversarial-robustness
pretty_name: Options Strategies Knowledge Base (FIN-QA-002) — Sample
size_categories:
  - 1K<n<10K
configs:
  - config_name: qa_pairs
    data_files: finqa002_qa_pairs.csv
  - config_name: multi_depth_answers
    data_files: finqa002_multi_depth_answers.csv
  - config_name: misconceptions
    data_files: finqa002_misconceptions.csv
  - config_name: adversarial_queries
    data_files: finqa002_adversarial_queries.csv
  - config_name: conversations
    data_files: finqa002_conversations.csv
  - config_name: options_concepts
    data_files: finqa002_options_concepts.csv
  - config_name: options_strategies
    data_files: finqa002_options_strategies.csv
  - config_name: ontology
    data_files: finqa002_ontology.csv
  - config_name: relations
    data_files: finqa002_relations.csv
  - config_name: payoff_formulas
    data_files: finqa002_payoff_formulas.csv
  - config_name: greeks_scenarios
    data_files: finqa002_greeks_scenarios.csv
  - config_name: examples
    data_files: finqa002_examples.csv

Options Strategies Knowledge Base — FIN-QA-002 (Sample)

A deterministic, ontology-driven synthetic prompt/response knowledge-base corpus for options and derivatives. It pairs a concept ontology (11 option domains) and a strategy library (single-leg through multi-leg structures) with multi-depth answers (beginner → institutional), misconception corrections, adversarial probes, payoff formulas, a Greeks-scenario P&L grid, worked examples, conversations, and a typed relation graph.

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, structurally-controlled prose rendered from a concept/strategy ontology — not human-verified factual ground truth. This corpus is built for structural / retrieval / reranker / adversarial-robustness / agent-evaluation work, not for teaching factual options knowledge via supervised fine-tuning. See Limitations. Each item is educational and not investment advice.

Depth tiers, not a gold/distractor scheme

Every question in multi_depth_answers has exactly four answers, one per audience depth: beginner, intermediate, advanced, institutional. All four are legitimate renderings 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	Anchor
Difficulty mean (1–5)	3.06	2.85–3.25	Bloom's Taxonomy difficulty centering
Mid-difficulty (level 3) share	0.31	0.26–0.42	Bloom's mid-level concentration
Question-type spread	5 types, even	exactly 5 even	OCC question-type taxonomy
Persona spread	4 personas, even	exactly 4 even	retail/trader/quant/PM breadth
Reasoning-required answer share	0.80	0.78–0.82	FinQA reasoning fraction
Adversarial attack-type spread	4 types, even	exactly 4 even	OWASP LLM Top-10
Formula-coverage share	0.45	0.30–0.60	options-formula coverage

Heavily-weighted structural integrity floors (all exact, all pass): exactly 4 distinct depth answers per question; full referential integrity across all FKs; exactly one misconception per concept (all concepts covered); exactly 5 Greeks scenarios per strategy; per-table column-count contract; no relation self-loops; complete adversarial behavior coverage.

Tables (schema highlights)

Table	Rows (sample)	Key columns
`finqa002_qa_pairs`	500	`qa_id`, `concept_id` (concept or strategy id), `question_text`, `question_type`, `persona_type`, `difficulty`
`finqa002_multi_depth_answers`	2,000	`answer_id`, `qa_id`, `depth_level`, `answer_text`, `contains_formula_flag`, `requires_reasoning_flag`
`finqa002_misconceptions`	120	`misconception_id`, `concept_id`, `incorrect_statement`, `why_wrong`, `correct_explanation`, `error_type`
`finqa002_adversarial_queries`	120	`adv_id`, `qa_id`, `attack_type`, `adversarial_question`, `expected_behavior`
`finqa002_conversations`	80	`conv_id`, `persona_type`, `turn_sequence` (JSON), `topic_drift_flag`, `resolution_flag`
`finqa002_options_concepts`	120	`concept_id`, `concept_name`, `category_l1`, `category_l2`, `difficulty_level`, `institutional_relevance_score`, `description_short`, `description_long`
`finqa002_options_strategies`	30	`strategy_id`, `strategy_name`, `legs_description`, `market_outlook`, `max_profit`, `max_loss`, `breakeven_points`, `risk_profile`, `margin_requirement_estimate`
`finqa002_ontology`	117	`node_id`, `node_type`, `name`, `parent_node`, `depth_level`
`finqa002_relations`	75	`relation_id`, `source_node`, `target_node`, `relation_type`, `strength_score`
`finqa002_payoff_formulas`	30	`formula_id`, `strategy_id`, `formula_latex`, `variable_definitions`, `interpretation`
`finqa002_greeks_scenarios`	150	`scenario_id`, `strategy_id`, `underlying_move_pct`, `iv_change_pct`, `time_decay_days`, `delta_impact`, `gamma_impact`, `theta_impact`, `vega_impact`, `pnl_estimate`
`finqa002_examples`	30	`example_id`, `strategy_id`, `example_type`, `example_description`, `solution_steps`

qa_pairs.concept_id references either a concept (CON_*) or a strategy (STR_*) — the QA layer is built over a merged entity set. relations.source_node / target_node reference ontology node_id values (not concept ids). conversations.turn_sequence is JSON-encoded.

Loading

import pandas as pd

qa = pd.read_csv("finqa002_qa_pairs.csv")
answers = pd.read_csv("finqa002_multi_depth_answers.csv")
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-002-sample", "qa_pairs")["train"]
greeks = load_dataset("xpertsystems/fin-qa-002-sample", "greeks_scenarios")["train"]

Use cases

SFT (style/format/depth): depth-conditioned options-answer generation (retail-plain vs institutional-desk voice).
Preference / ranking data: depth-preference pairs encoding audience fit (not factual correctness) for reranker / RLHF-style signals.
RAG & reranker evaluation: topic-calibrated (query, answer-shape) pairs over an 11-domain options ontology for MRR/NDCG-style metrics.
Adversarial robustness: adversarial_queries provides mispricing, volatility-trap, oversimplification, and hallucination-bait probes with expected_behavior labels (clarify / correct / reject).
Scenario reasoning: greeks_scenarios gives a deterministic underlying-move × IV-change × time-decay P&L grid for sensitivity tasks.

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/strategy metadata and depth templates; it is plausible and structurally complete but not human-verified options truth. Do not use (question, answer) pairs as factual SFT ground truth.
No gold/preferred tier. This is a 4-level depth corpus by design.
Question-type and persona mixes are deterministic, not random. They are produced by index/modulo cycling, so they are exactly uniform by construction (5 question types, 4 personas, 4 adversarial attack types). Only difficulty is sampled (via an isolated random.Random(seed)).
Misconceptions are one-per-concept, drawn from 5 templates. Each concept gets exactly one misconception cycled from a 5-entry template pool, anchored to that concept's name. At full scale (800 concepts) the same 5 templates recur; treat misconceptions as concept-tagged exemplars, not 800 unique items.
greeks_scenarios is a closed-form heuristic grid, not a Black-Scholes pricer. Delta/gamma/theta/vega impacts and pnl_estimate are deterministic linear approximations for scenario-shape training, not accurate option P&L.
Strategy variants are parameterized labels. Beyond the 18 base strategies, additional rows are suffix-labeled variants ("for Earnings", "with Wider Wings", …) sharing the base payoff metadata.
relations reference ontology node ids, and the relation set is partly deterministic (curated pairs) plus a small per-strategy random edge; FK integrity to ontology.node_id is verified.
Manifest embeds the run's output_dir path and a seed, but no wall-clock timestamp — so the manifest is reproducible up to the output path. Data files are byte-identical per seed.

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; deterministic distributions are scored as exact-target floors and the heavy weight sits on structural integrity.

Sample vs. full product

Dimension	Sample (this repo)	Full product
Questions	500	20,000
Concepts	120	800
Strategies	30	400
Multi-depth answers	2,000	80,000
Misconceptions	120	800
Adversarial queries	120	3,000
Greeks scenarios	150	2,000
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 manifest is reproducible up to the embedded output-directory path; there is no wall-clock timestamp.

Citation

@misc{xpertsystems_finqa002_2026,
  title        = {Options Strategies Knowledge Base (FIN-QA-002): A Synthetic,
                  Ontology-Driven Multi-Depth Options & Derivatives 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; strategy and
                  question-type taxonomy to OCC options curricula; reasoning-required
                  fraction to FinQA-style financial-reasoning corpora; adversarial
                  attack-type taxonomy 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; OCC / standard options-strategy curricula (strategy & question-type taxonomy); 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 — including option strategies, payoff descriptions, and Greeks scenarios — is educational and illustrative only, is not investment advice, is not a recommendation to trade any option or security, and is not a substitute for professional financial, legal, or compliance guidance. Options trading involves substantial risk of loss.