Arch Garch Volatility

用 GARCH 族模型进行波动率建模与预测，支持夏普比率统计推断和 SPA 模型比较测试，应用于全球市场风险管理。

Security Scan

The skill appears to implement GARCH volatility workflows but its runtime instructions expect local Python packages, filesystem reads/writes, and an environment variable (ZVT_HOME) that are not declared — this mismatch and the precondition commands that may modify the host file system warrant caution.

Purpose & Capability

The skill's name/description (GARCH volatility modelling, Sharpe bootstrap, SPA test, cointegration) match the included documentation and use-cases. It reasonably depends on the ZVT ecosystem and Python tooling for data collection/backtesting. However the SKILL.md metadata mentions 'Requires Python 3.12+ with uv package manager' while the registry metadata declares no runtime requirements; that discrepancy is unexplained.

Instruction Scope

Runtime instructions and seed.yaml preconditions instruct the agent to run Python snippets (python3 -c ...), check for/import zvt, run recorders, and touch files under ZVT_HOME (create/write/delete a test file). These actions reach into the host environment and can cause writes and package installs; the SKILL.md also requires the agent to re-read seed.yaml on behavioral decisions. The instructions reference filesystem paths and env vars not declared in the registry, and grant the agent broad discretion to run host-side checks — this is out of scope for a purely analytical 'model authoring' description and should be reviewed by the user.

Install Mechanism

This is an instruction-only skill with no install spec and no bundled code files. That lowers the risk from arbitrary downloads or extracted archives — there is no explicit install recipe embedded in the skill package.

Credentials

The skill declares no required environment variables or credentials, yet its preconditions and seed.yaml reference ZVT_HOME and execute Python commands that read/write that directory and may prompt pip installs (zvt). Requesting writable access to ~/.zvt (or any configured ZVT_HOME) and performing package checks/recorders is reasonable for a data-backtest pipeline, but those capabilities are not declared up front (no required envs, no stated permission requests). This mismatch is a red flag: the skill can access and modify local files and depend on unlisted packages.

Persistence & Privilege

The skill is not 'always:true' and does not request system-wide persistent privileges in the manifest. It does, however, instruct running precondition checks that may create/modify files under the user's ZVT_HOME; these are normal for a recorder/backtest workflow but warrant running in a controlled environment.

What to consider before installing

Before installing or running this skill: (1) Treat it as a tool that expects to run Python commands on your host — it will check for and may ask you to install the 'zvt' package and create/write files under ZVT_HOME (defaults to ~/.zvt). If you don't trust that, do not run it on a production machine. (2) Inspect references/seed.yaml and references/LOCKS.md to understand the preconditions and fatal semantic locks (these can halt execution). (3) Run the skill in an isolated environment (virtualenv / container) with limited filesystem access and no sensitive credentials. (4) If you permit it to run, ensure ZVT_HOME points to a controlled writable directory and review any pip installs before allowing them. (5) Note the skill is proprietary (LICENSE referenced) and that the manifest/metadata contain some mismatches (declared no requirements vs SKILL.md saying Python 3.12+ and uv). If you need stronger assurance, ask the publisher for source/package-level install recipes and an explicit list of runtime permissions/side-effects.

GARCH 波动率模型 (arch-garch-volatility)

用 GARCH 族模型进行波动率建模与预测，支持夏普比率统计推断和 SPA 模型比较测试，应用于全球市场风险管理。

Pipeline

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

Top Use Cases (9 total)

Sharpe Ratio Bootstrap Statistical Inference (UC-101)

Computes statistical inference (confidence intervals, standard errors) for the Sharpe Ratio using bootstrap methods to quantify uncertainty in risk-ad Triggers: bootstrap, sharpe ratio, statistical inference

Multiple Model Comparison with SPA Test (UC-102)

Compares 500 predictive models against a benchmark using the Superior Predictive Ability (SPA) test to determine if any models significantly outperfor Triggers: model comparison, SPA test, multiple models

Oil Price Cointegration Analysis (UC-103)

Tests for cointegration relationships between WTI and Brent crude oil prices to identify mean-reverting spread opportunities using Engle-Granger and P Triggers: cointegration, unit root, ADF test

For all 9 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 (15 total)

• AP-DERIVATIVES-PRICING-001: Instrument NPV called without attached pricing engine
• AP-DERIVATIVES-PRICING-002: BSM forward price ignores dividend yield
• AP-DERIVATIVES-PRICING-003: Negative discount factors passed to log-domain interpolation

All 15 anti-patterns: references/ANTI_PATTERNS.md

Evidence Quality Notice

[QUALITY NOTICE] This crystal was compiled from blueprint finance-bp-124. Evidence verify ratio = 47.2% and audit fail total = 32. 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	15 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-124 blueprint at 2026-04-22T13:01:01.570350+00:00. See human_summary.md for non-technical overview.