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Credit Lgd Model

Name: Credit Lgd Model
Author: tangweigang-jpg

作者 Tang Weigang · GitHub ↗ · v0.3.3 · MIT-0

cross-platform ⚠ suspicious

总下载

当前安装

版本数

在 OpenClaw 中安装

/install credit-lgd-model

功能描述

构建并训练 LGD（违约损失率）机器学习模型，支持基于历史违约数据的信用风险量化评估与预测。

使用说明 (SKILL.md)

信用违约损失模型 (credit-lgd-model)

构建并训练 LGD（违约损失率）机器学习模型，支持基于历史违约数据的信用风险量化评估与预测。

Pipeline

data_collection -> data_storage -> factor_computation -> target_selection -> trading_execution -> visualization

Top Use Cases (1 total)

Sphinx Documentation Configuration (`UC-101`)

This file configures the Sphinx documentation builder for the openLGD project, setting up project metadata, version information, and path configuratio Triggers: documentation, sphinx, configuration

Execute trigger: When user intent matches intent_router.uc_entries[].positive_terms AND user uses action verb (run/execute/跑/执行/backtest/fetch/collect)

What I'll Ask You

Target market: A-share (default), HK, or crypto? (US stocks in ZVT are half-baked — stockus_nasdaq_AAPL exists but coverage is thin)
Data source / provider: eastmoney (free, no account), joinquant (account+paid), baostock (free, good history), akshare, or qmt (broker)?
Strategy type: MACD golden-cross, MA crossover, volume breakout, fundamental screen, or custom factor?
Time range: start_timestamp and end_timestamp for backtest period
Target entity IDs: specific stocks (stock_sh_600000) or index components (SZ1000)?

Semantic Locks (Fatal)

ID	Rule	On Violation
`SL-01`	Execute sell orders before buy orders in every trading cycle	halt
`SL-02`	Trading signals MUST use next-bar execution (no look-ahead)	halt
`SL-03`	Entity IDs MUST follow format entity_type_exchange_code	halt
`SL-04`	DataFrame index MUST be MultiIndex (entity_id, timestamp)	halt
`SL-05`	TradingSignal MUST have EXACTLY ONE of: position_pct, order_money, order_amount	halt
`SL-06`	filter_result column semantics: True=BUY, False=SELL, None/NaN=NO ACTION	halt
`SL-07`	Transformer MUST run BEFORE Accumulator in factor pipeline	halt
`SL-08`	MACD parameters locked: fast=12, slow=26, signal=9	halt

Full lock definitions: references/LOCKS.md

Top Anti-Patterns (14 total)

AP-CREDIT-RISK-001: Empty DataFrame passed to bucketing pipeline
AP-CREDIT-RISK-002: Multi-dimensional target array causing WoE shape mismatch
AP-CREDIT-RISK-003: OptimalBucketer receiving high-cardinality numerical features

All 14 anti-patterns: references/ANTI_PATTERNS.md

Evidence Quality Notice

[QUALITY NOTICE] This crystal was compiled from blueprint finance-bp-112. Evidence verify ratio = 21.0% and audit fail total = 23. Generated results may have uncaptured requirement gaps. Verify critical decisions against source files (LATEST.yaml / LATEST.jsonl).

Reference Files

File	Contents	When to Load
references/seed.yaml	V6+ 全量权威 (source-of-truth)	有行为/决策争议时必读
references/ANTI_PATTERNS.md	14 条跨项目反模式	开始实现前
references/WISDOM.md	跨项目精华借鉴	架构决策时
references/CONSTRAINTS.md	domain + fatal 约束	规则冲突时
references/USE_CASES.md	全量 KUC-* 业务场景	需要完整示例时
references/LOCKS.md	SL-* + preconditions + hints	生成回测/交易代码前
references/COMPONENTS.md	AST 组件地图（按 module 拆分）	查 API 时

Compiled by Doramagic crystal-compilation-v6.1 from finance-bp-112 blueprint at 2026-04-22T13:00:54.441302+00:00. See human_summary.md for non-technical overview.

安全使用建议

This package mixes credit LGD model artifacts with trading/backtesting (ZVT/MACD/Sphinx) and directs the agent to run Python checks, pip installs, and create files under ~/.zvt. Before installing or invoking: 1) Confirm whether you actually want a trading/backtest workflow or a pure LGD credit model — the skill appears to attempt both. 2) If you proceed, run it in an isolated environment (VM/container) because the skill may install packages and write to your home directory. 3) Inspect seed.yaml and references/LOCKS.md yourself to verify the enforced semantic locks (trading rules) are acceptable. 4) Ask the author/owner to clarify scope (remove trading/backtest prompts if only LGD is desired) or provide a trimmed skill that only contains credit-modeling instructions. 5) If you lack trust or need least privilege, avoid allowing the agent to run the precondition install commands automatically; perform those steps manually after review.

功能分析

Type: OpenClaw Skill Name: credit-lgd-model Version: 0.3.3 The bundle provides a framework for building federated Loss Given Default (LGD) credit risk models using scikit-learn and Flask. It contains extensive security-focused constraints (e.g., finance-C-085 and finance-C-106 in seed.yaml) that explicitly instruct the AI agent to avoid Remote Code Execution (RCE) vulnerabilities by disabling Flask debug mode and using safe YAML loading. The architecture focuses on local data processing and parameter averaging, which is consistent with the stated purpose of federated learning, and no evidence of data exfiltration or malicious intent was found.

能力标签

crypto

能力评估

⚠ Purpose & Capability

The name/description state 'LGD (违约损失率) machine learning model' for credit risk, but SKILL.md, human_summary, seed.yaml and many reference files repeatedly reference ZVT, backtesting, trading pipelines (MACD, trading_execution) and Sphinx documentation. Semantic locks and preconditions include trading rules (execute sell before buy, next-bar execution, MACD param locks) that are unrelated to a pure LGD credit model. This mixed purpose is incoherent: a credit-LGD modelling skill normally would not ask about markets/strategies or enforce trading semantics unless it's intentionally a dual-purpose blueprint.

⚠ Instruction Scope

Although instruction-only, the SKILL.md and referenced files direct the agent to run precondition checks that execute Python commands (e.g. 'python3 -c "import zvt..."'), to install packages via pip if checks fail, and to create/write files under a ZVT_HOME (~/.zvt). It also instructs the agent to reload seed.yaml before decisions and to follow an execution protocol. These runtime actions access the host filesystem and package manager and go beyond merely describing modelling steps; they also include trading/backtest question prompts. The instructions are not strictly scoped to LGD estimation and give broad discretion to run installs and filesystem touches.

ℹ Install Mechanism

There is no formal install spec (lowest static install risk). However, SKILL.md and seed.yaml explicitly instruct running pip installs and zvt initialization as preconditions if checks fail. Those commands would pull packages from external registries at runtime even though no install recipe is declared in the registry metadata — this is an implicit install path the agent may perform.

ℹ Credentials

The skill declares no required environment variables or credentials (good). Still, runtime preconditions reference ZVT_HOME and instruct touching files under that directory (~/.zvt) and checking writability. That implies the agent will read/write local configuration directories. No secrets are requested, but the skill expects filesystem access which is proportionally reasonable for a ZVT/backtest setup but unexpected for a pure LGD model.

✓ Persistence & Privilege

The skill does not request always:true or other elevated persistent privileges. It is user-invocable and allows model invocation (normal). It does not declare modifications to other skills or system-wide agent settings in the metadata. Seed.yaml indicates the host should reload it during execution, but that is a documentation/execution protocol instruction, not a registry-level persistence flag.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install credit-lgd-model
安装完成后，直接呼叫该 Skill 的名称或使用 /credit-lgd-model 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

v0.3.3

v0.3.3: bilingual metadata injected. H1 shows 信用违约损失模型; tagline replaced with skill-specific Chinese hook; tags upgraded to Level 1-4.

v0.3.1

Remove install.sh — knowledge-only bundle. Host AI consumes directly from URL; no user-side installation needed. Fixes ClawHub suspicious flag.

v0.3.0

Doramagic crystal portfolio v0.3.0. Full 5-layer bp-009 standard. github.com/tangweigang-jpg/doramagic-skills

v0.2.0

Doramagic crystal portfolio v0.2.0. Full 5-layer bp-009 standard. github.com/tangweigang-jpg/doramagic-skills

元数据

Slug credit-lgd-model

版本 0.3.3

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 4

常见问题

Credit Lgd Model 是什么？

构建并训练 LGD（违约损失率）机器学习模型，支持基于历史违约数据的信用风险量化评估与预测。它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 76 次。

如何安装 Credit Lgd Model？

在 OpenClaw 或 Claude Code 对话框中运行命令「/install credit-lgd-model」即可一键安装，无需额外配置。

Credit Lgd Model 是免费的吗？

是的，Credit Lgd Model 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。

Credit Lgd Model 支持哪些平台？

Credit Lgd Model 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。

谁开发了 Credit Lgd Model？

由 Tang Weigang（@tangweigang-jpg）开发并维护，当前版本 v0.3.3。