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Macro Economic Model

Name: Macro Economic Model
Author: tangweigang-jpg

by Tang Weigang · GitHub ↗ · v0.3.3 · MIT-0

cross-platform ⚠ suspicious

107

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Install in OpenClaw

/install macro-economic-model

Description

运行ALM资产负债管理模拟，生成组合收益、现金流报告，并通过Smith-Wilson方法校准EIOPA风险自由收益率曲线进行企业债券定价。。

README (SKILL.md)

宏观经济模型 (macro-economic-model)

运行ALM资产负债管理模拟，生成组合收益、现金流报告，并通过Smith-Wilson方法校准EIOPA风险自由收益率曲线进行企业债券定价。

Pipeline

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

Top Use Cases (13 total)

ALM Portfolio Summary Report Generator (`UC-101`)

Running a comprehensive Asset-Liability Management (ALM) simulation and visualizing portfolio returns, cash flows, and EIOPA yield curves for a mixed Triggers: ALM simulation, portfolio summary, yield curve visualization

ALM Cash Flow Visualization Dashboard (`UC-111`)

Creating visual summaries of ALM simulation results including dividend, coupon, liability cash flows, notional returns, and terminal cash flows over t Triggers: cash flow charts, portfolio visualization, ALM reporting

EIOPA Risk-Free Curve Projection and Calibration (`UC-102`)

Projecting forward interest rates and calibrating the EIOPA risk-free yield curve for long-term insurance liability discounting using Smith-Wilson met Triggers: EIOPA curve, forward rate projection, yield curve calibration

For all 13 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-MACRO-DATA-001: SEC EDGAR Rate Limit Violation
AP-MACRO-DATA-002: Temporal Knowledge Graph Look-Ahead Bias
AP-MACRO-DATA-003: Technical Indicator Look-Ahead Bias via Missing Shift

All 14 anti-patterns: references/ANTI_PATTERNS.md

Evidence Quality Notice

[QUALITY NOTICE] This crystal was compiled from blueprint finance-bp-077. Evidence verify ratio = 42.6% and audit fail total = 34. 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

Compiled by Doramagic crystal-compilation-v6.1 from finance-bp-077 blueprint at 2026-04-22T13:00:28.955724+00:00. See human_summary.md for non-technical overview.

Usage Guidance

Before installing or running this skill: (1) Treat it as an instruction-only recipe that expects Python 3.12+ and the ZVT ecosystem (zvt, recorders); the registry did not declare these requirements — confirm and install them in a controlled environment. (2) Review seed.yaml and the referenced files locally — the SKILL.md instructs the agent to read and re-load them and to run Python commands that may install packages or touch ~/.zvt. (3) Expect network access to data providers (eastmoney, joinquant, akshare, qmt) and potential need for API credentials; do not provide sensitive broker/API keys until you confirm exactly which endpoints the skill will call. (4) Because the bundle references a LICENSE.txt that is not present and declares proprietary licensing, verify licensing and provenance before trusting outputs. (5) If you will run this on a machine with real credentials or broker access, run it first in an isolated VM/container or sandbox, and avoid enabling autonomous invocation until you have validated the skill's behavior manually.

Capability Analysis

Type: OpenClaw Skill Name: macro-economic-model Version: 0.3.3 The macro-economic-model skill bundle is a highly structured financial modeling tool designed for Asset-Liability Management (ALM) and yield curve calibration using the Smith-Wilson method. It integrates with the legitimate 'zvt' quantitative trading library and contains extensive domain-specific guardrails (Semantic Locks and Fatal Constraints) to ensure regulatory compliance with EIOPA standards. There is no evidence of malicious intent, data exfiltration, or harmful prompt injection; the complex instructions are strictly focused on preventing financial modeling errors like look-ahead bias and ensuring mathematical consistency in portfolio simulations.

Capability Tags

cryptocan-make-purchases

Capability Assessment

ℹ Purpose & Capability

The name/description (ALM, EIOPA, bond pricing) are coherent with the SKILL.md contents (data pipelines, curve construction, pricing components). However SKILL.md requires Python 3.12+ and ZVT ecosystem tools (zvt, recorders, data providers), while the registry metadata lists no required binaries/dependencies — a mismatch that should be resolved before trusting automatic execution.

⚠ Instruction Scope

The runtime instructions direct the agent to run Python checks, run zvt recorder commands, read and re-load seed.yaml, check/touch the user's ZVT_HOME directory, and integrate with external data providers (eastmoney, joinquant, qmt). These operations access the host filesystem and may trigger network activity and package installs (pip). That scope is broader than the registry declares and includes actions (filesystem writes, pip installs, network calls to data/broker APIs) that warrant explicit user consent and further vetting.

✓ Install Mechanism

This is instruction-only (no install spec, no code files executed by the skill itself). That limits what the skill bundle writes to disk. Nevertheless the SKILL.md expects host-side installs (Python packages like zvt) to be present or installed by the user/agent; because installs would be performed by executing host commands, they should be reviewed before running.

⚠ Credentials

Registry declares no required environment variables or credentials, but SKILL.md and references use ZVT_HOME, recommend data providers that typically require API keys/accounts (joinquant, qmt), and instruct precondition checks that read environment variables and touch ~/.zvt. The missing declaration of these env vars/credentials (and absence of any explanation of which credentials are needed for trading/broker actions) is disproportionate and unclear.

✓ Persistence & Privilege

The skill does not request permanent/always-on presence (always: false) and does not declare modifications to other skills or global agent settings. Autonomous invocation is enabled by default (disable-model-invocation: false) which is normal; combine this with the above inconsistencies before allowing autonomous runs.

How to Use

Make sure OpenClaw is installed (local or Docker)
Run the install command in chat: /install macro-economic-model
After installation, invoke the skill by name or use /macro-economic-model
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 宏观经济模型; 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

Slug macro-economic-model

Version 0.3.3

License MIT-0

All-time Installs 0

Active Installs 0

Total Versions 3

Frequently Asked Questions

What is Macro Economic Model?

运行ALM资产负债管理模拟，生成组合收益、现金流报告，并通过Smith-Wilson方法校准EIOPA风险自由收益率曲线进行企业债券定价。。 It is an AI Agent Skill for Claude Code / OpenClaw, with 107 downloads so far.

How do I install Macro Economic Model?

Run "/install macro-economic-model" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Macro Economic Model free?

Yes, Macro Economic Model is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Macro Economic Model support?

Macro Economic Model is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Macro Economic Model?

It is built and maintained by Tang Weigang (@tangweigang-jpg); the current version is v0.3.3.

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