Kr Derivatives

B 82 completed
Library
mobile_app / python · small
59
Files
3,414
LOC
2
Frameworks
5
Languages

Pipeline State

completed
Run ID
#370987
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
61.98
Framework unique
Isolation
Last stage change
2026-05-10 03:35:31
Deduplication group #59977
Member of a group with 2 similar repo(s) — canonical #95193 view group →
Top concepts (2)
Project DescriptionMobile App
Repobility (the analyzer behind this table) · https://repobility.com

AI Prompt

Build me a Python library for Korean derivatives pricing and forensic analytics. I need functions to calculate Black-Scholes values for embedded options, specifically for CB/BWs. The library should allow users to calculate a "dilution score" by taking a raw DART parquet row, along with historical price data and a KTB rate, to determine if a CB issuance suggests minority shareholder dilution. Please include examples for pricing the embedded conversion option and scoring a specific CB issuance.
python library finance derivatives pricing black-scholes forensic analytics
Generated by gemma4:latest

Catalog Information

A Python library that provides pricing models and forensic analytics for Korean derivatives such as CB/BW, options, and futures.

Description

This library offers a comprehensive suite of tools for pricing Korean derivatives, including CB/BW contracts, options, and futures. It implements advanced mathematical models using NumPy, pandas, and SciPy to compute fair values and Greeks. The forensic analytics module helps users detect anomalies and assess counterparty risk by analyzing trade data and market conditions. Designed for quantitative analysts and risk managers, it streamlines the workflow for pricing, validation, and regulatory reporting. The modular architecture allows easy integration into existing trading or risk systems.

الوصف

توفر هذه المكتبة مجموعة شاملة من الأدوات لتسعير المشتقات الكورية، بما في ذلك عقود CB/BW والخيارات والعقود الآجلة. تنفذ نماذج رياضية متقدمة باستخدام مكتبات NumPy وpandas وSciPy لحساب القيم العادلة ومؤشرات الجري. يتيح وحدة التحليل الجنائي للمستخدمين اكتشاف الشذوذ وتقييم مخاطر الطرف المقابل من خلال تحليل بيانات التداول وظروف السوق. صممت للمحللين الكميين ومديري المخاطر لتبسيط سير العمل في التسعير والتحقق والتقارير التنظيمية. يتيح الهيكل النمطي سهولة التكامل مع أنظمة التداول أو المخاطر القائمة.

Novelty

7/10

Tags

derivative-pricing forensic-analytics korean-market options futures cb/bw financial-modeling

Technologies

numpy pandas pydantic scipy

Claude Models

claude-sonnet-4.6 claude-opus-4.6

Quality Score

B
82.0/100
Structure
81
Code Quality
100
Documentation
63
Testing
75
Practices
67
Security
100
Dependencies
60

Strengths

  • CI/CD pipeline configured (github_actions)
  • Good test coverage (41% test-to-source ratio)
  • Code linting configured (ruff (possible))
  • Consistent naming conventions (snake_case)
  • Low average code complexity \u2014 well-structured code
  • Good security practices \u2014 no major issues detected

Weaknesses

  • No LICENSE file \u2014 legal ambiguity for contributors

Recommendations

  • Add a LICENSE file (MIT recommended for open source)

Security & Health

5.8h
Tech Debt (D)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (4)
Provenance: Repobility (https://repobility.com) — every score reproducible from /scan/
Unknown
License
1.0%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
76.7%
markdown
16.3%
json
3.8%
toml
2.0%
yaml
1.3%

Frameworks

Expo pytest

Concepts (2)

Open data · scored by Repobility · https://repobility.com
CategoryNameDescriptionConfidence
Repobility · severity-and-effort ranking · https://repobility.com
auto_descriptionProject DescriptionKorean derivatives pricing and forensic analytics — CB/BW embedded option valuation and repricing coercion detection.80%
auto_categoryMobile Appmobile70%

Quality Timeline

1 quality score recorded.

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