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Singular Spectrum Analysis Time Series Decomposition (
Stationary Bootstrap for Interest Rate Swap Inference (
Insurance Actuarial Python
by
Tang Weigang
· GitHub ↗
· v0.3.3
· MIT-0
113
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3
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Install in OpenClaw
/install insurance-actuarial-python
Description
使用奇异谱分析和平稳自助法对利率时间序列进行分解与统计推断，构建 NSS 曲线模型并校准利率衍生品参数。
README (SKILL.md)
保险精算建模 (insurance-actuarial-python)
使用奇异谱分析和平稳自助法对利率时间序列进行分解与统计推断，构建 NSS 曲线模型并校准利率衍生品参数。
Pipeline
data_collection -> data_storage -> factor_computation -> target_selection -> trading_execution -> visualization
Top Use Cases (2 total)
Singular Spectrum Analysis Time Series Decomposition (UC-101)
Decomposes time series data into interpretable components (trend, seasonality, noise) using Singular Spectrum Analysis to identify underlying patterns Triggers: SSA, singular spectrum analysis, time series decomposition
Stationary Bootstrap for Interest Rate Swap Inference (UC-102)
Applies stationary bootstrap resampling method to Italian swap rate data for statistical inference, enabling confidence interval estimation and hypoth Triggers: stationary bootstrap, swap rates, resampling
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-INSURANCE-001: Implicit numeric format assumptions without validationAP-INSURANCE-002: Triangle axis construction with invalid temporal orderingAP-INSURANCE-003: Cumulative/incremental triangle representation misuse
All 15 anti-patterns: references/ANTI_PATTERNS.md
Evidence Quality Notice
[QUALITY NOTICE] This crystal was compiled from blueprint finance-bp-064. Evidence verify ratio = 11.6% and audit fail total = 40. 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-064 blueprint at 2026-04-22T13:00:20.990803+00:00.
See human_summary.md for non-technical overview.
Usage Guidance
Do not install or run this skill yet. Key questions to ask the publisher: (1) Why does an 'insurance-actuarial' skill include a trading/backtest pipeline and A-share/ZVT recorder instructions? (2) Exactly what install steps will be performed (package names, sources, commands)? Provide explicit install scripts and trusted upstream URLs. (3) Confirm which environment variables and filesystem paths (ZVT_HOME, ~/.zvt, host_workspace) the skill will read or write, and why they are needed. (4) Provide provenance: source repo/homepage, license file, and the actual code (not just SKILL.md) so you can audit. Until you get clear answers, run any evaluation in an isolated sandbox or VM, deny access to production credentials and sensitive env vars, and avoid granting network or file-system write access from your agent to prevent unexpected installs or data leakage.
Capability Analysis
Type: OpenClaw Skill
Name: insurance-actuarial-python
Version: 0.3.3
The skill bundle is a comprehensive framework for insurance actuarial modeling and quantitative trading using the ZVT library. It includes sophisticated implementations for Singular Spectrum Analysis (SSA), Smith-Wilson yield curve fitting, and interest rate simulations. The bundle is characterized by an extensive set of safety guardrails and domain-specific constraints (e.g., EIOPA Solvency II compliance, prevention of look-ahead bias via SL-02, and trade ordering via SL-01) designed to ensure mathematical and regulatory correctness. All executable components, such as the environment preconditions and installation recipes in seed.yaml, are standard for the OpenClaw ecosystem and aligned with the stated purpose. No evidence of data exfiltration, malicious prompt injection, or unauthorized system access was found.
Capability Tags
Capability Assessment
Purpose & Capability
The skill name/description focus on yield-curve fitting, SSA decomposition, stationary bootstrap and NSS calibration (actuarial). However the SKILL.md and human_summary also describe a full trading/backtest pipeline (data_collection -> trading_execution), ZVT recorder/backtester usage, and A-share/market-specific backtests. That mixing of actuarial and trading/backtest capabilities is incoherent for a single-scope 'insurance-actuarial-python' skill. The SKILL.md also claims compatibility requirements (Python 3.12+, uv package manager) while the registry metadata declares no required binaries/env — another mismatch.
Instruction Scope
The runtime instructions (seed.yaml + SKILL.md) instruct agents to re-load seed.yaml, run preconditions that execute Python commands to check/install zvt, verify and initialize ~/.zvt, and run recorder/test commands. Those steps access and may modify local filesystem state, require package installs, and enforce trading 'semantic locks' (sell-before-buy, next-bar execution). None of these side effects are surfaced in the minimal registry metadata; they go beyond a passive 'explain methods' skill and grant the agent directives to perform system actions.
Install Mechanism
There is no explicit install spec in the registry, yet seed.yaml's execution_protocol references host install triggers (resources.host_adapter.install_recipes[]) and SKILL.md states compatibility with Python 3.12+ and 'uv' package manager. Because the skill is instruction-only, we cannot see concrete install sources or verified release hosts; the presence of install triggers without declared, auditable install steps increases risk (agent may attempt pip/uv installs or follow implicit host adapter recipes).
Credentials
Registry metadata lists no required environment variables, but SKILL.md / seed.yaml / preconditions reference ZVT_HOME and workspace paths and require read/write access to the user's ~/.zvt directory. The skill also expects to import and possibly install zvt and will run python commands that access environment variables and filesystem — environment access is therefore understated and disproportionate to the declared metadata.
Persistence & Privilege
always:false (good) and autonomous invocation allowed (normal), but seed.yaml mandates reloading itself and running installer/precondition steps that will create files under {ZVT_HOME} or host_workspace (e.g., .zvt). The skill therefore requests the ability to modify user filesystem and install packages; while not 'always:true', that level of persistence/privilege should be explicitly declared and justified and is not in the metadata.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install insurance-actuarial-python - After installation, invoke the skill by name or use
/insurance-actuarial-python - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.3.3
v0.3.3: bilingual metadata injected. H1 shows 保险精算建模; tagline replaced with skill-specific Chinese hook; tags upgraded to Level 1-4.
v0.3.1
Remove install.sh — knowledge-only bundle. Host AI consumes directly from URL; no user-side installation needed. Fixes ClawHub suspicious flag.
v0.3.0
Doramagic crystal portfolio v0.3.0. Full 5-layer bp-009 standard. github.com/tangweigang-jpg/doramagic-skills
Metadata
Frequently Asked Questions
What is Insurance Actuarial Python?
使用奇异谱分析和平稳自助法对利率时间序列进行分解与统计推断，构建 NSS 曲线模型并校准利率衍生品参数。 It is an AI Agent Skill for Claude Code / OpenClaw, with 113 downloads so far.
How do I install Insurance Actuarial Python?
Run "/install insurance-actuarial-python" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Insurance Actuarial Python free?
Yes, Insurance Actuarial Python is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Insurance Actuarial Python support?
Insurance Actuarial Python is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Insurance Actuarial Python?
It is built and maintained by Tang Weigang (@tangweigang-jpg); the current version is v0.3.3.
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