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Lean Cloud Backtest

作者 Tang Weigang · GitHub ↗ · v0.3.3 · MIT-0
cross-platform ⚠ suspicious
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当前安装
3
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在 OpenClaw 中安装
/install lean-cloud-backtest
功能描述
通过 LEAN 引擎搭建多市场量化研究与回测环境,支持 QuantBook 历史数据获取、技术指标计算和自定义因子建模。。
使用说明 (SKILL.md)

LEAN 云端回测 (lean-cloud-backtest)

通过 LEAN 引擎搭建多市场量化研究与回测环境,支持 QuantBook 历史数据获取、技术指标计算和自定义因子建模。

Pipeline

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

Top Use Cases (8 total)

C# QuantBook Research Environment Setup (UC-101)

Provides a foundational C# research environment template for loading QuantBook and fetching historical data across multiple asset classes for analysis Triggers: C#, QuantBook, research environment

Python QuantBook Basic Research with Indicators (UC-102)

Provides a Python research environment template demonstrating QuantBook setup, historical data fetching, price plotting, and Bollinger Bands indicator Triggers: Python, QuantBook, Bollinger Bands

C# Comprehensive QuantBook API and Data Fetching (UC-103)

Comprehensive C# template demonstrating QuantBook API cloud connectivity, project listing, and multiple methods for fetching historical data with diff Triggers: C#, QuantBook, API

For all 8 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 (25 total)

  • AP-ZVT-183: 除权因子为 inf/NaN 时直接参与乘法导致复权静默失败
  • AP-ZVT-179: 第三方数据接口超限后异常被吞噬,数据静默缺失
  • AP-ZVT-183B: HFQ(后复权)与 QFQ(前复权)K 线表使用错误导致因子计算漂移

All 25 anti-patterns: references/ANTI_PATTERNS.md

Evidence Quality Notice

[QUALITY NOTICE] This crystal was compiled from blueprint finance-bp-100. Evidence verify ratio = 23.0% and audit fail total = 20. 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 25 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-100 blueprint at 2026-04-22T13:00:45.713977+00:00. See human_summary.md for non-technical overview.

安全使用建议
This skill appears to be a legitimate LEAN/QuantBook backtest guide, but it has some transparency issues: (1) SKILL.md expects Python 3.12+, pip installs, and uses the ZVT_HOME env var and external data providers (eastmoney, joinquant, akshare, qmt) — none of these credentials or install steps are declared in the manifest. Before installing or running the skill, confirm you are comfortable letting the agent run pip installs and shell/python commands, and be prepared to supply provider API keys (if you plan to use paid providers). If you need least-privilege assurance, ask the skill author to: (a) declare required environment variables and any credential names in the manifest; (b) provide an explicit install spec (or confirm no install will occur); and (c) document exactly which external endpoints the skill will contact and when. If you plan to run this on a machine with sensitive environment variables, do not enable it until those gaps are clarified.
功能分析
Type: OpenClaw Skill Name: lean-cloud-backtest Version: 0.3.3 The 'lean-cloud-backtest' skill bundle is a highly sophisticated framework designed to provide a rigorous quantitative research and backtesting environment. It focuses heavily on preventing common financial engineering errors (such as lookahead bias, survivorship bias, and improper data normalization) through a comprehensive set of 'Semantic Locks' (SL-01 to SL-12) and 'Fatal Constraints' (finance-C-*). The bundle includes detailed documentation of anti-patterns in popular quant libraries like ZVT, Qlib, and Zipline. While it contains complex instructions for the AI agent to enforce these rules and validate outputs (e.g., checking for physically impossible returns in result.csv), all behaviors are strictly aligned with the stated purpose of financial modeling. No indicators of data exfiltration, malicious persistence, or harmful prompt injection were identified.
能力标签
cryptorequires-walletrequires-sensitive-credentials
能力评估
Purpose & Capability
Name, description, and use-cases all describe multi-market quantitative research and backtesting using LEAN/QuantBook/ZVT-like tooling. The referenced components, anti-patterns, and use-cases are coherent with that purpose.
Instruction Scope
SKILL.md instructs the agent to run runtime checks and shell/python commands (e.g. python3 -c 'import zvt...', python3 -m pip install zvt, touch/unlink tests) and to read the ZVT_HOME environment variable — but the manifest declares no required env vars or explicit runtime permissions. The instructions also direct the agent to use external data providers (eastmoney, joinquant, akshare, qmt) which may require credentials; those credentials are not declared. While these actions are expected for a backtest skill, the manifest does not surface them, creating a transparency gap.
Install Mechanism
The skill is instruction-only and has no install spec in the registry (low static risk). However SKILL.md and seed.yaml explicitly expect package installation and precondition runs (pip install zvt, pip checks, host_adapter.install_recipes). That means at runtime the agent may install packages and modify the environment despite no install metadata being declared.
Credentials
The manifest lists no required environment variables or credentials, but the runtime instructions access ZVT_HOME and expect interactions with external data providers (some of which require API keys/accounts). This mismatch means the skill may attempt to read environment variables or prompt for credentials that were not declared, increasing the chance of accidental exposure of secrets or unexpected network requests.
Persistence & Privilege
The skill does not request always:true and does not include an install spec that would force persistent system-level changes. It is user-invocable and allows autonomous invocation (platform default). No evidence it modifies other skills or global agent config.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install lean-cloud-backtest
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /lean-cloud-backtest 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.3.3
v0.3.3: bilingual metadata injected. H1 shows LEAN 云端回测; 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
元数据
Slug lean-cloud-backtest
版本 0.3.3
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 3
常见问题

Lean Cloud Backtest 是什么?

通过 LEAN 引擎搭建多市场量化研究与回测环境,支持 QuantBook 历史数据获取、技术指标计算和自定义因子建模。。 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 119 次。

如何安装 Lean Cloud Backtest?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install lean-cloud-backtest」即可一键安装,无需额外配置。

Lean Cloud Backtest 是免费的吗?

是的,Lean Cloud Backtest 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Lean Cloud Backtest 支持哪些平台?

Lean Cloud Backtest 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Lean Cloud Backtest?

由 Tang Weigang(@tangweigang-jpg)开发并维护,当前版本 v0.3.3。

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