Credit Lgd Model

核心功能为构建并训练LGD（违约损失率）机器学习模型，基于历史违约数据完成信用风险的量化评估与预测。典型用于金融领域信用风险分析场景，使用时需提供目标市场、数据源等参数，适配Doramagic-host生态，要求Python3.12+及uv包管理器，输出训练完成的模型及风险评估结果。

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热度 46.50

最近更新 2026-05-11 17:45:14

分类：开发依赖：代码执行 / 第三方 API / 读写文件 / 内部调用大模型应用场景：代码生成 / 财务 / 数据处理

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add credit-lgd-model

Metadata

name: credit-lgd-model
description: 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。
license: Proprietary. See LICENSE.txt in project root.
compatibility: Designed for Doramagic-host ecosystem (Claude Code / openclaw / Cursor). Requires Python 3.12+ with uv package manager.
metadata: {"version":"v6.1","blueprint_id":"finance-bp-112","compiled_at":"2026-04-22T13:00:54.441302+00:00","capability_markets":"global","capability_activities":"credit-risk","sop_version":"crystal-compilation-v6.1"}

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.