Add Dark Pool sample (10K fraud/AML sequences) with README, SCHEMA, parquet, JSONL

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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+  - tabular-regression
+  - time-series-forecasting
+language:
+  - en
+tags:
+  - synthetic
+  - finance
+  - fraud-detection
+  - aml
+  - anti-money-laundering
+  - anomaly-detection
+  - transaction-monitoring
+  - fintech
+  - banking
+  - crypto
+  - market-manipulation
+  - account-takeover
+  - false-positive-reduction
+pretty_name: Aestrea Dark Pool Financial Anomaly Pack
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: dark_pool_sample.parquet
+---
+
+# Aestrea Dark Pool Financial Anomaly Pack (Sample)
+
+**A synthetic financial-anomaly and fraud-detection dataset for AML / transaction-monitoring model training, false-positive reduction research, and fintech anomaly-detection pipelines.** Each row is a complete financial activity sequence — a dormant warm-up period followed by a sudden activity burst — labeled as account takeover, market-manipulation spoofing, or legitimate-but-high-risk (false positive) behavior, with telemetry, identity context, detection logic, and financial exposure.
+
+Built by [SolsticeAI](https://www.solsticestudio.ai/datasets) as a free sample of a larger commercial pack. 100% synthetic. No real transactions, accounts, addresses, order books, or customer data.
+
+## What is included
+
+| File | Rows | Format | Purpose |
+|---|---:|---|---|
+| `dark_pool_sample.parquet` | 10,000 | Parquet | Columnar, typed, best for analytics |
+| `dark_pool_sample.jsonl` | 10,000 | JSON Lines | Streaming / LLM training friendly |
+
+**This sample:** 10,000 financial activity sequences, stratified 3,333 per fraud class.  
+**Fraud classes (3):** `Account_Takeover_Sequence`, `Market_Manipulation_Spoofing`, `Legitimate_High_Risk_Activity` (false positive)  
+**Label distribution:** ~6,666 `fraudulent` / ~3,334 `benign` (by design — one of three scenarios is a benign high-risk lookalike)  
+**Severity tiers:** `low`, `medium`, `high`, `critical` — scenario-weighted so spoofing skews mid, ATO skews high, false-positives skew low  
+**Assets covered:** BTC, ETH, USDC, USDT, SOL, USD, EUR, GBP, JPY  
+**Geographic coverage:** 20 country codes across high-risk and low-risk jurisdictions
+
+## Record structure
+
+Each record is one financial activity sequence with 7 top-level fields:
+
+| Field | Type | Contents |
+|---|---|---|
+| `schema_version` | string | Pack schema version (`1.0.0-dark-pool-sample`) |
+| `event` | struct | `id`, `trace_id`, `timestamp`, `severity`, `label`, `label_confidence` |
+| `identity_context` | struct | `account_id`, `baseline_risk_score`, `session_entropy`, `account_age_days`, `kyc_tier` |
+| `telemetry_stream` | list<struct> | Ordered financial events: `timestamp`, `event_name` (e.g., `LARGE_WITHDRAWAL`, `LIMIT_ORDER_CANCELLED`, `LOGIN_NEW_COUNTRY`), `asset`, `transaction_amount_usd`, `geo_country`, `latency_ms`, `burst_indicator` |
+| `vulnerability` | struct | `fraud_class`, `scenario_description`, `exposure_vector` |
+| `detection` | struct | `anomaly_type`, `baseline_deviation`, `detection_logic` (SQL-like), `anomaly_score`, `confidence_band` |
+| `impact` | struct | `financial_exposure_usd`, `customer_funds_at_risk_usd`, `recoverable_pct` |
+| `simulation` | struct | `synthetic`, `engine`, `chaos_profile`, `ground_truth_classification` |
+
+See [SCHEMA.md](./SCHEMA.md) for the full nested field breakdown.
+
+## Why this dataset is useful
+
+Most public financial-fraud datasets are either flat tabular snapshots (IEEE-CIS, credit-card fraud) or narrow single-event labels. Real AML / transaction-monitoring models need something these don't provide: **sequences** (warm-up → burst), **ambiguous labels** (legitimate-but-high-risk that looks like fraud), and **detection logic grounded in behavioral patterns** rather than just feature engineering.
+
+- Full pre-event dormant and check-in telemetry — not just the burst
+- `burst_indicator` flags at each step so models can learn temporal dynamics explicitly
+- A dedicated false-positive class (`Legitimate_High_Risk_Activity`) with benign ground truth but fraud-adjacent behavior — the exact class that drives real-world FP rates
+- Per-record `detection_logic` in SQL-like form shows how the scenario would be detected in production
+- Per-record `anomaly_score` and `label_confidence` so models can train on calibration targets, not just hard labels
+- Cross-asset (crypto + fiat), cross-geography coverage
+
+## Typical use cases
+
+- Fraud-detection model training
+- AML / transaction-monitoring pipelines
+- False-positive-reduction research (reducing customer-experience pain)
+- Risk-scoring systems
+- Account-takeover detection
+- Spoofing / market-manipulation classifiers
+- Temporal-burst anomaly detection
+- LLM fine-tuning on fraud-investigation narratives
+- Benchmarking anomaly scoring against ambiguous ground truth
+
+## Quick start
+
+```python
+import pandas as pd
+import pyarrow.parquet as pq
+
+df = pq.read_table("dark_pool_sample.parquet").to_pandas()
+
+# Label distribution (mix of fraudulent and benign)
+print(df["event"].apply(lambda e: e["label"]).value_counts())
+
+# Average financial exposure by fraud class
+df["cls"] = df["vulnerability"].apply(lambda v: v["fraud_class"])
+df["exposure"] = df["impact"].apply(lambda i: i["financial_exposure_usd"])
+print(df.groupby("cls")["exposure"].mean().round(0))
+
+# Burst density by scenario (bursts per sequence)
+df["burst_count"] = df["telemetry_stream"].apply(
+    lambda s: sum(1 for step in s if step["burst_indicator"])
+)
+print(df.groupby("cls")["burst_count"].mean().round(2))
+
+# False-positive rate signal — proportion of fraudulent-label vs benign per severity
+df["severity"] = df["event"].apply(lambda e: e["severity"])
+df["label"] = df["event"].apply(lambda e: e["label"])
+print(pd.crosstab(df["severity"], df["label"], normalize="index").round(3))
+```
+
+Streaming form:
+
+```python
+import json
+
+with open("dark_pool_sample.jsonl") as f:
+    for line in f:
+        sequence = json.loads(line)
+        # one financial activity sequence per line
+```
+
+## Responsible use
+
+This dataset is intended for **defensive / monitoring** use cases: fraud-detection model training, AML research, false-positive reduction, and academic benchmarks. It contains synthesized transaction amounts, synthetic account IDs, and fictional scenario narratives — it does **not** contain real customer data, real transactions, real wallet addresses, or any PII. Models trained on this data will learn fraud-pattern shape and temporal dynamics; downstream deployment for live transaction monitoring requires calibration against institution-specific real-world data under appropriate compliance review.
+
+## License
+
+Released under **CC BY 4.0**. Use freely for research, fraud-tool prototyping, education, and commercial development with attribution.
+
+## Get the full pack
+
+This Hugging Face repo is a **10K-sequence sample**. The production pack scales to 5M+ sequences with expanded scenario coverage (layering, smurfing, wash trading, sanctions-evasion routing, trade-based ML, bust-out fraud), additional asset classes (equities, FX, derivatives, NFTs), multi-entity transaction graphs, richer ambiguous-label distribution tuning, parquet + JSONL + SAR-narrative-aligned delivery, and buyer-specific variants.
+
+**Self-serve (Stripe checkout):**
+- [**Sample Scale tier — $5,000**](https://buy.stripe.com/7sY5kD2j85QTfSb5lfeEo03) — ~25K records, one subject, 72-hour delivery.
+
+**Full pack + enterprise scope:**
+- [www.solsticestudio.ai/datasets](https://www.solsticestudio.ai/datasets) — per-SKU pricing across Starter / Professional / Enterprise tiers, plus commercial licensing, custom generation, and buyer-specific variants.
+
+**Procurement catalog:**
+- [SolsticeAI Data Storefront](https://solsticeai.mydatastorefront.com) — available via Datarade / Monda.
+
+## Citation
+
+```bibtex
+@dataset{solstice_dark_pool_pack_2026,
+  title        = {Aestrea Dark Pool Financial Anomaly Pack (Sample)},
+  author       = {SolsticeAI},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  url          = {https://huggingface.co/datasets/solsticestudioai/dark-pool-pack}
+}
+```

SCHEMA.md

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+# Aestrea Dark Pool Financial Anomaly Pack — Schema
+
+One row = one financial activity sequence (warm-up → burst). All records share the same seven top-level fields.
+
+Schema version: `1.0.0-dark-pool-sample`
+
+## Top-level fields
+
+### `schema_version` — string
+Schema identifier. Constant within a sample release.
+
+### `event` — struct
+Identifier fields and the overall label/severity for the sequence.
+
+| Field | Type | Notes |
+|---|---|---|
+| `id` | string | Stable event identifier, e.g., `DARKPOOL-100000`. |
+| `trace_id` | string (UUID) | Cross-links telemetry within the sequence. |
+| `timestamp` | string (ISO-8601) | Sequence anchor time (burst start). |
+| `severity` | string | `low`, `medium`, `high`, `critical`. |
+| `label` | string | `fraudulent` or `benign`. |
+| `label_confidence` | double | 0–1. Lower on ambiguous scenarios (false-positive class skews lower). |
+
+### `identity_context` — struct
+Synthetic account-level context.
+
+| Field | Type | Notes |
+|---|---|---|
+| `account_id` | string | Synthetic account identifier (e.g., `ACCT-A1B2C3D4E5F6`). |
+| `baseline_risk_score` | double | 0–1. Pre-sequence risk signal. |
+| `session_entropy` | double | 0–1. Unpredictability of session behavior. |
+| `account_age_days` | int | Account age (days). |
+| `kyc_tier` | string | `tier_1`, `tier_2`, `tier_3`. |
+
+### `telemetry_stream` — list<struct>
+Ordered financial telemetry events. One struct per event.
+
+Event struct:
+
+| Field | Type | Notes |
+|---|---|---|
+| `timestamp` | string (ISO-8601) | Event time. |
+| `event_name` | string | Event label (scenario-specific, e.g., `LARGE_WITHDRAWAL`, `LIMIT_ORDER_CANCELLED`, `LOGIN_NEW_COUNTRY`, `PASSWORD_CHANGE`, `OPPOSITE_SIDE_FILL`, `VACATION_PAYMENT`). |
+| `asset` | string | `BTC`, `ETH`, `USDC`, `USDT`, `SOL`, `USD`, `EUR`, `GBP`, `JPY`. |
+| `transaction_amount_usd` | double | USD-equivalent transaction amount. |
+| `geo_country` | string | ISO-2 country code. |
+| `latency_ms` | int | Observed latency for the event. |
+| `burst_indicator` | bool | `true` during high-frequency burst phase. |
+
+### `vulnerability` — struct
+Scenario classification and exposure vector.
+
+| Field | Type | Notes |
+|---|---|---|
+| `fraud_class` | string | `Account_Takeover_Sequence`, `Market_Manipulation_Spoofing`, `Legitimate_High_Risk_Activity`. |
+| `scenario_description` | string | Short English description of the scenario. |
+| `exposure_vector` | string | Primary exposure dimension (e.g., `credential_abuse`, `order_book_spoof`, `geo_anomaly`, `rapid_burst`, `session_hijack`, `benign_travel_pattern`). |
+
+### `detection` — struct
+Anomaly-signature metadata describing how this sequence would be detected in production.
+
+| Field | Type | Notes |
+|---|---|---|
+| `anomaly_type` | string | One of: `temporal_burst_sequence`, `cancel_burst_with_opposite_fill`, `geo_travel_with_elevated_spend`. |
+| `baseline_deviation` | string | Short English description of the deviation pattern (includes burst count and geo diversity). |
+| `detection_logic` | string | SQL-like rule expression showing the production-style detector. |
+| `anomaly_score` | double | 0–1. |
+| `confidence_band` | string | `low` / `medium` / `high` / `very_high`. |
+
+### `impact` — struct
+Financial exposure attributable to the sequence.
+
+| Field | Type | Notes |
+|---|---|---|
+| `financial_exposure_usd` | double | USD exposure during the burst (scaled down for benign sequences). |
+| `customer_funds_at_risk_usd` | double | USD exposure to customer funds specifically. |
+| `recoverable_pct` | double | 0–1. Estimated recoverable fraction. Lower for account takeovers. |
+
+### `simulation` — struct
+Simulation engine provenance and ground-truth label.
+
+| Field | Type | Notes |
+|---|---|---|
+| `synthetic` | bool | Always `true`. |
+| `engine` | string | Simulation engine label (`dark_pool_sim_v1`). |
+| `chaos_profile` | string | `Temporal_Burst_Mode`, `Geo_Travel_Mode`, `Market_Stress_Mode`, `Calm_Baseline`, `Holiday_Season_Spike`. |
+| `ground_truth_classification` | string | `fraudulent` or `benign`. Matches `event.label`. |
+
+## Distribution of this sample
+
+- 10,000 sequences total.
+- Fraud class: balanced 3,333 per class across 3 classes (account takeover, spoofing, false-positive).
+- Label: 2/3 `fraudulent`, 1/3 `benign` (by design — only the false-positive class is benign).
+- Severity: scenario-weighted so spoofing skews mid, ATO skews high, false-positive skews low.
+- Anomaly types: each fraud class maps to its own anomaly type.
+
+## Sanitization notes
+
+- Event IDs are synthetic (`DARKPOOL-*`).
+- Account IDs are UUID-derived synthetic identifiers (`ACCT-A1B2C3...`).
+- No real customer data, real transactions, real wallet addresses, or PII is present.
+- `detection_logic` is illustrative SQL-style rule syntax; it is not connected to any real detection system.
+- Country codes are used as generic geo labels, not as allegations about any specific jurisdiction's transaction profile.
+
+## Relationship to the full pack
+
+The production pack scales to 5M+ sequences with expanded scenario coverage (layering, smurfing, wash trading, sanctions-evasion routing, trade-based ML, bust-out fraud), additional asset classes (equities, FX, derivatives, NFTs), multi-entity transaction graphs, richer ambiguous-label distribution tuning, and SAR-narrative-aligned delivery. See the pack card for commercial access.

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