README.md

---
license: cc-by-nc-4.0
language:
- en
tags:
- synthetic
- finance
- portfolio-management
- asset-allocation
- risk
- knowledge-base
- instruction-tuning
- rag
- adversarial-robustness
pretty_name: "Portfolio Management Knowledge Base (FIN-QA-003) — Sample"
size_categories:
- 1K<n<10K
configs:
- config_name: qa_pairs
  data_files: finqa003_qa_pairs.csv
- config_name: multi_depth_answers
  data_files: finqa003_multi_depth_answers.csv
- config_name: misconceptions
  data_files: finqa003_misconceptions.csv
- config_name: adversarial_queries
  data_files: finqa003_adversarial_queries.csv
- config_name: conversations
  data_files: finqa003_conversations.csv
- config_name: concepts
  data_files: finqa003_concepts.csv
- config_name: ontology
  data_files: finqa003_ontology.csv
- config_name: relations
  data_files: finqa003_relations.csv
- config_name: formulas
  data_files: finqa003_formulas.csv
- config_name: examples
  data_files: finqa003_examples.csv
- config_name: use_cases
  data_files: finqa003_use_cases.csv
---

# Portfolio Management Knowledge Base — FIN-QA-003 (Sample)

A deterministic, ontology-driven synthetic **prompt/response knowledge-base corpus**
for portfolio management: asset allocation, portfolio construction and optimization,
risk and tail-risk measurement, performance attribution, institutional mandates,
rebalancing, tax-aware investing, and multi-persona portfolio reasoning. Each
question is rendered at four audience depths (beginner → institutional) over a
concept ontology, with companion misconception, adversarial, conversation,
relation, formula, worked-example, and use-case 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, structurally-controlled prose*
> rendered from a concept ontology — not human-verified factual ground truth.
> This corpus is built for **structural / retrieval / reranker /
> adversarial-robustness / agent-evaluation** work, not for teaching factual
> portfolio 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) | 3.45 | 3.30–3.75 | Bloom's Taxonomy (portfolio skew) |
| Upper-difficulty (4–5) share | 0.50 | 0.45–0.62 | Bloom's upper-level concentration |
| Reasoning-required answer share | 0.90 | 0.85–0.95 | FinQA reasoning fraction |
| Formula-coverage share | 0.03 | 0.01–0.10 | formula coverage (advanced/institutional only) |
| Question-type spread | 5 types, even | exactly 5 even | CFA PM question-type taxonomy |
| Persona spread | 5 personas, even | exactly 5 even | retail/advisor/PM/quant/CIO breadth |
| Adversarial attack-type spread | 4 types, even | exactly 4 even | OWASP LLM Top-10 |

Heavily-weighted **structural integrity floors** (all exact, all pass): exactly
4 distinct depth answers per question; full referential integrity across all FKs;
md5-derived id uniqueness; ontology parent-node integrity; per-table column-count
contract; complete adversarial behavior coverage; relation self-loops within the
disclosed bound.

## Tables (schema highlights)

| Table | Rows (sample) | Key columns |
|---|---|---|
| `finqa003_qa_pairs` | 500 | `qa_id`, `concept_id`, `question_text`, `question_type`, `persona_type`, `difficulty` |
| `finqa003_multi_depth_answers` | 2,000 | `answer_id`, `qa_id`, `depth_level`, `answer_text`, `contains_formula_flag`, `requires_reasoning_flag` |
| `finqa003_misconceptions` | 50 | `misconception_id`, `concept_id`, `incorrect_statement`, `why_wrong`, `correct_explanation`, `error_type` |
| `finqa003_adversarial_queries` | 120 | `adv_id`, `qa_id`, `attack_type`, `adversarial_question`, `expected_behavior` |
| `finqa003_conversations` | 80 | `conv_id`, `persona_type`, `turn_sequence` (JSON), `topic_drift_flag`, `resolution_flag` |
| `finqa003_concepts` | 100 | `concept_id`, `concept_name`, `category_l1`, `category_l2`, `difficulty_level`, `institutional_relevance_score`, `description_short`, `description_long` |
| `finqa003_ontology` | 145 | `node_id`, `node_type`, `name`, `parent_node`, `depth_level` |
| `finqa003_relations` | 42 | `relation_id`, `source_node`, `target_node`, `relation_type`, `strength_score` |
| `finqa003_formulas` | 6 | `formula_id`, `concept_id`, `formula_latex`, `variable_definitions`, `interpretation` |
| `finqa003_examples` | 100 | `example_id`, `concept_id`, `example_type`, `example_description`, `solution_steps` |
| `finqa003_use_cases` | 6 | `use_case_id`, `use_case_name`, `description`, `target_buyer` |

`relations.source_node` / `target_node` reference **ontology `node_id`** values
(md5-derived from `concept_id`). Root ontology nodes (depth 1) have an empty
`parent_node`. `conversations.turn_sequence` is JSON-encoded.

## Loading

```python
import pandas as pd

qa = pd.read_csv("finqa003_qa_pairs.csv")
answers = pd.read_csv("finqa003_multi_depth_answers.csv")
merged = qa.merge(answers, on="qa_id")
print(merged.groupby("qa_id").size().value_counts())  # all == 4

# ontology: preserve empty-string roots (do not coerce to NaN)
ontology = pd.read_csv("finqa003_ontology.csv", keep_default_na=False)
```

```python
from datasets import load_dataset

qa = load_dataset("xpertsystems/fin-qa-003-sample", "qa_pairs")["train"]
answers = load_dataset("xpertsystems/fin-qa-003-sample", "multi_depth_answers")["train"]
```

## Use cases

- **SFT (style/format/depth):** depth-conditioned portfolio-answer generation
  (retail-plain vs CIO/institutional 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 a portfolio-management ontology for MRR/NDCG-style metrics.
- **Adversarial robustness:** `adversarial_queries` provides prompt-injection,
  misleading, oversimplification, and hallucination-bait probes appended to
  legitimate questions, with `expected_behavior` labels (refuse/correct/clarify).

## 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 **not** human-verified portfolio truth. Do not use
   `(question, answer)` pairs as factual SFT ground truth.
2. **No gold/preferred tier.** This is a 4-level depth corpus by design.
3. **Question-type, persona, and adversarial attack-type mixes are deterministic**
   (round-robin / modulo cycling), so they are exactly uniform by construction.
   Difficulty is the principal sampled quantity (via a shared `random.Random(seed)`).
4. **One relation self-loop per run (disclosed, deterministic).** The
   category-cycle relation builder uses `target = items[(i+1) % len(items)]`; when
   a category contains a single concept this yields a self-loop. The sample
   contains exactly one such edge. The scorecard verifies self-loops stay within a
   small disclosed bound rather than requiring zero.
5. **Misconceptions cover only the first `max(50, n_concepts//2)` concepts**, cycled
   from a 5-pattern pool (50 rows at sample scale). The `incorrect_statement` text
   applies a literal `.replace("portfolio", concept_name.lower())`, which can read
   awkwardly when the substring appears mid-word; treat misconception text as
   templated exemplars.
6. **`formulas` are inherited from base concept templates only** (6 rows at sample
   scale); expanded concept variants carry the base formula metadata but most
   concepts have no formula, so `contains_formula_flag` is sparse (≈0.03) and only
   set at advanced/institutional depth.
7. **IDs are content-derived (md5) for ontology nodes, answers, and formulas**, and
   sequential for qa/adv/misconception/example. All id sets are verified unique.
8. **Manifest carries no wall-clock timestamp** and no output path, so it is fully
   reproducible per seed; 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 | 100 | 1,000 |
| Multi-depth answers | 2,000 | 80,000 |
| Misconceptions | 50 | ~500 |
| 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 11 CSVs) and **identical scored metrics**. The wrapper
reproduces the engine's exact `main()` orchestration order so the single shared
`random.Random(seed)` is consumed identically (verified byte-identical to the
native engine). The manifest carries no wall-clock timestamp.

## Citation

```bibtex
@misc{xpertsystems_finqa003_2026,
  title        = {Portfolio Management Knowledge Base (FIN-QA-003): A Synthetic,
                  Ontology-Driven Multi-Depth Portfolio-Management 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 and
                  question-type taxonomy to the CFA Institute portfolio-management
                  curriculum; 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; CFA Institute candidate body of knowledge (portfolio-management topic
and 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 allocation, optimization, risk, and performance concepts —
is **educational and illustrative only**, is **not investment advice**, is not a
recommendation to buy, sell, or allocate to any security or asset class, and is not
a substitute for professional financial, legal, or compliance guidance.