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Cuemacro Finmarket

by Tang Weigang · GitHub ↗ · v0.3.3 · MIT-0
cross-platform ⚠ suspicious
100
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0
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3
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Install in OpenClaw
/install cuemacro-finmarket
Description
金融市场回测框架，支持FX G10货币对技术指标策略回测、ArcticDB高频tick数据本地与S3云端存储、Quandl等数据源的市场数据获取与缓存。
README (SKILL.md)

Cuemacro 市场工具 (cuemacro-finmarket)

金融市场回测框架，支持FX G10货币对技术指标策略回测、ArcticDB高频tick数据本地与S3云端存储、Quandl等数据源的市场数据获取与缓存。

Pipeline

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

Top Use Cases (4 total)

ArcticDB Tick Data Storage (UC-101)

Provides persistent storage for high-frequency tick market data using ArcticDB, supporting both local LMDB and S3 cloud storage backends for efficient Triggers: arcticdb, tick data storage, time series database

Market Data Fetching from Vendors (UC-103)

Fetches economic and financial market data from external vendors like Quandl, demonstrating how to request and cache market data with specific fields Triggers: market data, quandl, fetch data

S3 Cloud Storage for Tick Data (UC-104)

Demonstrates writing and reading tick market data to/from AWS S3 cloud storage using Parquet format for efficient compression and retrieval of histori Triggers: s3 storage, aws, parquet

For all 4 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-108. Evidence verify ratio = 32.0% and audit fail total = 18. 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-108 blueprint at 2026-04-22T13:00:51.768652+00:00. See human_summary.md for non-technical overview.

Usage Guidance
This skill appears to be a legitimate finance backtesting blueprint, but several things don't add up: (1) SKILL.md requires Python 3.12+ / 'uv' package manager but the registry lists no required binaries — verify you have the correct runtime before running checks. (2) The skill discusses S3 and vendor APIs (Quandl, joinquant), yet declares no required environment variables for AWS or vendor API keys — do not provide credentials blindly; assume you must configure them yourself if needed. (3) The SKILL.md and seed.yaml instruct the agent to run Python precondition commands that will check imports and write a test file in ~/.zvt — run these in a sandbox or review them manually first if you are cautious. (4) SKILL.md references LICENSE.txt but that file is not present in the manifest; confirm licensing and source provenance before use. Recommended actions: inspect seed.yaml and references locally, run the preconditions manually in a controlled environment (virtualenv or disposable VM), never paste secrets into a skill prompt, and only provide AWS/Quandl credentials to code you trust. If you need higher assurance, ask the publisher for a source repository or signed release and verify artifacts before installing or running.
Capability Analysis
Type: OpenClaw Skill Name: cuemacro-finmarket Version: 0.3.3 The cuemacro-finmarket skill bundle is a legitimate financial backtesting and data management framework designed for the ZVT and finmarketpy ecosystems. It provides structured instructions for an AI agent to handle market data collection (ArcticDB, S3, Quandl) and FX strategy backtesting. The bundle is notable for its extensive safety guardrails, including 'Semantic Locks' (e.g., SL-01, SL-02) and 'Fatal Constraints' (e.g., finance-C-011) specifically designed to prevent common quantitative trading errors like look-ahead bias and incorrect position accounting. No evidence of malicious intent, data exfiltration, or unauthorized execution was found; the instructions focus entirely on technical correctness and preventing the agent from misrepresenting backtest results as live trading performance.
Capability Tags
cryptorequires-sensitive-credentials
Capability Assessment
ℹ Purpose & Capability
The name, description and bundled reference files consistently describe a financial backtest / market-data pipeline (ArcticDB, ZVT, Quandl, S3). That aligns with the advertised purpose. Minor inconsistency: SKILL.md explicitly states 'Requires Python 3.12+ with uv package manager', but the registry metadata declares no required binaries or environment variables.
ℹ Instruction Scope
SKILL.md instructs the agent to run environment precondition checks (python3 -c 'import zvt...' and filesystem write tests against ZVT_HOME), to reload seed.yaml, and to follow strict semantic locks. Those runtime checks are reasonable for a backtest skill, but they perform local command execution and touch the user's ~/.zvt by design. The instructions also reference S3/AWS and vendor data sources (Quandl, joinquant, etc.) but do not declare the need for corresponding credentials in the skill metadata.
✓ Install Mechanism
This is an instruction-only skill with no install spec and no code files to execute. That is the lowest install risk. The SKILL.md suggests installing Python packages (e.g., pip install zvt) in preconditions, but the skill itself does not perform downloads or provide an installer.
⚠ Credentials
The skill references cloud storage (AWS S3), ArcticDB S3 backend, and third-party data providers (Quandl, joinquant, etc.) which normally require API keys/credentials. Yet requires.env and primary credential fields are empty. Also SKILL.md references ZVT_HOME and a write-test that will write to the user's filesystem. The absence of declared credential requirements (AWS/Quandl) is a proportionality gap and should be clarified before granting secrets.
✓ Persistence & Privilege
always is false and the skill is user-invocable. The skill does not request permanent platform presence, nor does it modify other skills' configurations. It does instruct agents to read seed.yaml and run preconditions, which is normal for an instruction-heavy blueprint.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install cuemacro-finmarket
  3. After installation, invoke the skill by name or use /cuemacro-finmarket
  4. 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 Cuemacro 市场工具; 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 cuemacro-finmarket
Version 0.3.3
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 3
Frequently Asked Questions

What is Cuemacro Finmarket?

金融市场回测框架，支持FX G10货币对技术指标策略回测、ArcticDB高频tick数据本地与S3云端存储、Quandl等数据源的市场数据获取与缓存。 It is an AI Agent Skill for Claude Code / OpenClaw, with 100 downloads so far.

How do I install Cuemacro Finmarket?

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

Is Cuemacro Finmarket free?

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

Which platforms does Cuemacro Finmarket support?

Cuemacro Finmarket is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Cuemacro Finmarket?

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

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