Description

提供多市场财务分析能力，涵盖历史数据获取、财务报表解析、财务比率计算、固定收益分析、投资组合绩效评估和股票基本面筛选等核心功能。。

README (SKILL.md)

财务比率工具 (financial-ratios-toolkit)

Name: Financial Ratios Toolkit
Author: tangweigang-jpg

提供多市场财务分析能力，涵盖历史数据获取、财务报表解析、财务比率计算、固定收益分析、投资组合绩效评估和股票基本面筛选等核心功能。

Pipeline

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

Top Use Cases (13 total)

Multi-Module Financial Analysis Overview (`UC-101`)

Demonstrating comprehensive financial analysis capabilities covering multiple domains including historical data, financial statements, ratios, models, Triggers: financial analysis, overview, multi-module

Fixed Income Analysis and Bond Valuation (`UC-103`)

Analyzing fixed income securities including bond statistics, duration calculations, derivative pricing models, and government/corporate bond yield com Triggers: bond, fixed income, yield

Financial Ratio Analysis (`UC-106`)

Evaluating company financial health through profitability ratios, solvency ratios, liquidity ratios, valuation ratios, and custom ratio calculations f Triggers: ratio, profitability, solvency

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-PORTFOLIO-ANALYTICS-001: Division by zero in price ratio calculations corrupts rebalancing
AP-PORTFOLIO-ANALYTICS-002: Look-ahead bias from unshifted signal generation and position calculations
AP-PORTFOLIO-ANALYTICS-003: Non-positive-semidefinite covariance matrix breaks CVXPY optimization

All 14 anti-patterns: references/ANTI_PATTERNS.md

Evidence Quality Notice

[QUALITY NOTICE] This crystal was compiled from blueprint finance-bp-118. Evidence verify ratio = 33.3% and audit fail total = 62. 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-118 blueprint at 2026-04-22T13:00:57.393924+00:00. See human_summary.md for non-technical overview.

Usage Guidance

This skill appears to be a genuine finance/quant toolkit, but there are important mismatches you should resolve before installing: (1) SKILL.md expects Python 3.12+, the 'uv' package manager, and the zvt ecosystem (including a writable ZVT_HOME directory ~/.zvt) — yet the registry lists no required binaries/env vars. Ask the publisher to declare required binaries, explicit install steps, and any environment variables (e.g., ZVT_HOME) and required provider credentials. (2) Expect the skill to run python commands and possibly pip install zvt or invoke zvt recorders that may contact external data providers; run it in an isolated environment (container/VM) until you've validated behavior. (3) If you will provide data-provider credentials (eastmoney/joinquant/qmt), only provide the minimum required and confirm where they are stored; prefer short-lived or least-privilege keys. (4) Ask for a clear install spec or packaged release (PyPI/GitHub release) and a copy of LICENSE.txt; if the author cannot provide these, treat the package as incomplete. Providing those clarifications would raise confidence that the skill is coherent and reduce the need for caution.

Capability Analysis

Type: OpenClaw Skill Name: financial-ratios-toolkit Version: 0.3.3 The financial-ratios-toolkit is a highly structured skill bundle designed to enable an AI agent to perform quantitative financial analysis using the ZVT library. It employs a sophisticated 'Doramagic' framework (blueprints, crystals, and semantic locks) to enforce strict financial logic, such as preventing look-ahead bias (SL-02) and ensuring T+1 trading rules (SL-10). The bundle includes extensive documentation on financial anti-patterns, constraints, and use cases. No evidence of malicious intent, data exfiltration, or harmful prompt injection was found; the instructions are entirely focused on the domain-specific task of financial modeling and data acquisition from legitimate sources like Yahoo Finance and FinancialModelingPrep.

Capability Tags

cryptocan-make-purchasesrequires-sensitive-credentials

Capability Assessment

ℹ Purpose & Capability

The name, description, and included reference files consistently describe a finance/quant toolkit (ratios, backtests, fixed income, portfolio analytics). That purpose is coherent with the instructions and listed components. However the SKILL.md explicitly states runtime requirements (Python 3.12+, 'uv' package manager, Doramagic host expectations and heavy reliance on the zvt ecosystem) while the registry metadata lists no required binaries or env vars — an inconsistency between what it claims it needs and what it declares.

⚠ Instruction Scope

The SKILL.md contains concrete preconditions that instruct the agent to run shell/python commands (e.g., python3 -c 'import zvt', run zvt.recorders, run zvt.init_dirs) and to check/create ~/.zvt (ZVT_HOME). Those runtime steps go beyond pure conversational guidance and will cause the host to check/install packages and touch files. The skill also references external data providers (eastmoney, joinquant, qmt) which imply account credentials may be required at runtime, but these credentials are not declared.

⚠ Install Mechanism

There is no install spec in the registry (instruction-only), but seed.yaml's execution_protocol mentions install recipes and host install triggers. SKILL.md suggests installing Python packages (pip install zvt) as part of precondition remediation. The absence of a clear, declared install mechanism in the registry while the instructions expect pip installs and host actions is an incoherence that can surprise users and hosts.

⚠ Credentials

Registry metadata declares no required environment variables or credentials, but SKILL.md/preconditions reference ZVT_HOME and expect zvt to be present; the skill also expects access to external data providers (some requiring accounts/API keys). The skill therefore implicitly requires filesystem write access (~/.zvt), package installation privileges, and possibly provider API keys — none of which are documented in requires.env. That mismatch increases the risk of unexpected credential or environment access.

ℹ Persistence & Privilege

The skill is not always-enabled and is user-invocable (normal). It requests the agent to create/read its own data directory (~/.zvt) and run package installation checks; it does not request system-wide persistent privileges or modify other skills. This is expected for a toolkit that uses a local data store, but users should be aware the skill will attempt to create/modify ~/.zvt and may run pip commands if preconditions fail.

Version History

v0.3.3

v0.3.3: bilingual metadata injected. H1 shows 财务比率工具; 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 financial-ratios-toolkit

Version 0.3.3

License MIT-0

All-time Installs 0

Active Installs 0

Total Versions 3

Frequently Asked Questions

What is Financial Ratios Toolkit?

提供多市场财务分析能力，涵盖历史数据获取、财务报表解析、财务比率计算、固定收益分析、投资组合绩效评估和股票基本面筛选等核心功能。。 It is an AI Agent Skill for Claude Code / OpenClaw, with 108 downloads so far.

How do I install Financial Ratios Toolkit?

Run "/install financial-ratios-toolkit" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Financial Ratios Toolkit free?

Yes, Financial Ratios Toolkit is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Financial Ratios Toolkit support?

Financial Ratios Toolkit is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Financial Ratios Toolkit?

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

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Financial Ratios Toolkit