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Gs Quant Pricing
by
Tang Weigang
· GitHub ↗
· v0.3.3
· MIT-0
110
Downloads
0
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0
Active Installs
3
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Install in OpenClaw
/install gs-quant-pricing
Description
提供年化波动率、指数加权移动平均（EMA）和指数加权标准差等量化金融指标的专业计算能力，支持维度枚举到字符串的灵活覆盖，适用于金融时间序列分析与资产定价建模。
README (SKILL.md)
GS Quant 风险定价 (gs-quant-pricing)
提供年化波动率、指数加权移动平均（EMA）和指数加权标准差等量化金融指标的专业计算能力，支持维度枚举到字符串的灵活覆盖，适用于金融时间序列分析与资产定价建模。
Pipeline
data_collection -> data_storage -> factor_computation -> target_selection -> trading_execution -> visualization
Top Use Cases (0 total)
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
Evidence Quality Notice
[QUALITY NOTICE] This crystal was compiled from blueprint finance-bp-020. Evidence verify ratio = 57.7% and audit fail total = 4. 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 | 0 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-020 blueprint at 2026-04-22T13:00:16.803564+00:00.
See human_summary.md for non-technical overview.
Usage Guidance
This skill is a coherent quant/backtest blueprint but has important gaps and assumptions. Before installing or running it: 1) Confirm your environment: the skill expects Python 3.12+, an 'uv' package manager, and the zvt library (it may suggest 'pip install zvt'); be prepared to run package installs. 2) Prepare credentials only for the data providers you plan to use (joinquant, Marquee/GS, eastmoney, qmt, etc.) — the skill does not declare these env vars but its workflow requires them. 3) Be aware it expects writable local data directories (default ~/.zvt) and will read embedded reference files (seed.yaml, LOCKS.md) that influence behavior; consider running in an isolated container/VM. 4) Review the proprietary license and the included seed.yaml/LOCKS.md for constraints (semantic locks are 'fatal' for some rules). 5) If you need to allow package installs or network access, restrict them to a sandbox and only provide credentials you expect to expose. The inconsistencies between declared requirements and runtime instructions justify caution; ask the maintainer to (a) declare required binaries/env vars, (b) provide a reproducible install recipe, and (c) document which external credentials are needed and why.
Capability Analysis
Type: OpenClaw Skill
Name: gs-quant-pricing
Version: 0.3.3
The skill bundle provides a specialized framework for quantitative financial analysis and backtesting using the zvt library and Goldman Sachs Marquee APIs. It contains extensive domain-specific logic, including 12 semantic locks (SL-01 to SL-12) and over 200 financial constraints (finance-C-*) designed to prevent common quantitative errors like look-ahead bias and incorrect annualization. The installation recipes in seed.yaml are limited to standard libraries (numpy, pandas, zvt), and the output validation logic (OV-01 to OV-06) acts as a safety mechanism to ensure generated results are physically plausible. No evidence of malicious intent, data exfiltration, or harmful prompt injection was found.
Capability Tags
Capability Assessment
Purpose & Capability
The name/description match a quant analytics/backtesting skill (ZVT/ZVT-like pipeline). However SKILL.md explicitly states it requires Python 3.12+ and an 'uv' package manager and references zvt, Marquee/Goldman APIs and other data providers. The registry metadata declares no required binaries, no env vars, and no install steps — a mismatch. Asking for access to market-data providers and local ZVT home directories is coherent with the stated purpose, but the skill should have declared Python and provider credentials up-front.
Instruction Scope
Runtime instructions require reading several embedded reference files (seed.yaml, LOCKS.md, etc.), reloading seed.yaml on each decision, running precondition checks that invoke python3 commands, and may instruct the user/agent to run 'pip install zvt' or data recorders that contact external providers. Those actions stay within a quant-workflow scope but the instructions also assume filesystem write access (~/.zvt), network access to external data providers, and possible package installation — none of which are declared. The SKILL.md mandates semantic locks and strict preconditions (fatal halts) that the agent must enforce, which increases the operational surface.
Install Mechanism
This is instruction-only with no formal install spec (lowest installer risk). Nevertheless SKILL.md and seed.yaml reference installing Python packages (e.g., pip install zvt) and expect the host to have Python 3.12+ and 'uv' package manager. Because installs are left to runtime commands (not a curated install recipe), there is moderate operational risk from pulling third-party packages at run time, but no direct download URLs or archive extraction are present in the skill bundle.
Credentials
Registry lists no required environment variables or credentials, yet SKILL.md/preconditions reference ZVT_HOME, and data-provider choices (joinquant, Marquee/GS APIs, qmt, etc.) will typically require API keys/accounts. The skill also expects read/write access to a local data directory (~/.zvt) and may prompt users to run recorders that use network credentials. The absence of declared env vars/credentials is disproportionate to the functionality described.
Persistence & Privilege
The skill does not request always:true and does not attempt to modify other skills or system-wide configurations. Autonomous invocation is allowed (default) but not exceptional here. The skill's expectations of re-reading seed.yaml and running preconditions are internal to the skill bundle and do not imply elevated platform privileges beyond typical agent operation.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install gs-quant-pricing - After installation, invoke the skill by name or use
/gs-quant-pricing - 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 GS Quant 风险定价; 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 Gs Quant Pricing?
提供年化波动率、指数加权移动平均（EMA）和指数加权标准差等量化金融指标的专业计算能力，支持维度枚举到字符串的灵活覆盖，适用于金融时间序列分析与资产定价建模。 It is an AI Agent Skill for Claude Code / OpenClaw, with 110 downloads so far.
How do I install Gs Quant Pricing?
Run "/install gs-quant-pricing" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Gs Quant Pricing free?
Yes, Gs Quant Pricing is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Gs Quant Pricing support?
Gs Quant Pricing is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Gs Quant Pricing?
It is built and maintained by Tang Weigang (@tangweigang-jpg); the current version is v0.3.3.
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