Credit Lgd Model
📖 技能介绍
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)
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AP-CREDIT-RISK-001: Empty DataFrame passed to bucketing pipelineAP-CREDIT-RISK-002: Multi-dimensional target array causing WoE shape mismatchAP-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.
🤖 AI 评测
这个 Skill 存在严重的"表里不一"问题：文档说是做信用风险建模的，但里面实际内容全是股票量化交易。证据质量很低（验证率只有两成），可用场景极少，而且很多规则和这个技能的实际用途根本不匹配。整体质量较差，不建议直接使用。
