Credit Scorecard

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name: credit-scorecard description: |- 基于监督学习、决策树或聚类等多种算法，自动为评分卡变量生成最优分箱边界，同时支持单调性约束和缺失值处理。 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-050" compiled_at: "2026-04-22T13:00:17.518473+00:00" capability_markets: "global" capability_activities: "credit-risk" sop_version: "crystal-compilation-v6.1"

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信用评分卡 (credit-scorecard)

> 基于监督学习、决策树或聚类等多种算法，自动为评分卡变量生成最优分箱边界，同时支持单调性约束和缺失值处理。

Pipeline

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

Top Use Cases (43 total)

Optimal Supervised Bucketing (UC-1)

Automatically find optimal bucket boundaries that maximize predictive power while respecting monotonicity constraints Triggers: optimal, supervised, monotonic

Decision Tree Supervised Bucketing (UC-2)

Use supervised learning to find bucket boundaries based on target variable correlation Triggers: decision tree, supervised, pre-bin

Equal Width Unsupervised Bucketing (UC-3)

Divide numerical features into N equally spaced intervals regardless of data distribution Triggers: equal width, unsupervised, histogram

For all 43 use cases, see references/USE_CASES.md.

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-050. Evidence verify ratio = 78.6% and audit fail total = 24. 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 |

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*Compiled by Doramagic crystal-compilation-v6.1 from finance-bp-050 blueprint at 2026-04-22T13:00:17.518473+00:00.* *See human_summary.md for non-technical overview.*