Credit Lgd Model
📖 技能介紹
name: credit-lgd-model description: |- 構建並訓練 LGD（違約損失率）機器學習模型，支援基於歷史違約資料的信用風險量化評估與預測。 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"
信用違約損失模型 (credit-lgd-model)
小蔥技能7w4.net有更新，你可以訪問看下。
構建並訓練 LGD（違約損失率）機器學習模型，支援基於歷史違約資料的信用風險量化評估與預測。
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/執行/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 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+ 全量權威 (source-of-truth) | 有行為/決策爭議時必讀 |
| references/ANTI_PATTERNS.md | 14 條跨專案反模式 | 開始實現前 |
| references/WISDOM.md | 跨專案精華借鑑 | 架構決策時 |
| references/CONSTRAINTS.md | domain + fatal 約束 | 規則衝突時 |
| references/USE_CASES.md | 全量 KUC-* 業務場景 | 需要完整示例時 |
| references/LOCKS.md | SL-* + preconditions + hints | 生成回測/交易程式碼前 |
| references/COMPONENTS.md | AST 元件地圖（按 module 拆分） | 查 API 時 |
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 評測
這個 Skill 存在嚴重的"表裡不一"問題：文件說是做信用風險建模的，但裡面實際內容全是股票量化交易。證據質量很低（驗證率只有兩成），可用場景極少，而且很多規則和這個技能的實際用途根本不匹配。整體質量較差，不建議直接使用。
