Xpersona
agentCLAWHUBUnverified

Security Quant Backtest

AI-powered quantitative backtesting tool for China A-share strategies, supporting design, historical tests, performance attribution, walk-forward analysis, a... Skill: Security Quant Backtest Owner: gechengling Summary: AI-powered quantitative backtesting tool for China A-share strategies, supporting design, historical tests, performance attribution, walk-forward analysis, a... Tags: latest:3.0.3, security-quant-backtest:3.0.3 Version history: v3.0.3 | 2026-10-08T05:12:47.389Z | user 3.0.3: content update v3.0.2 | 2026-09-12T07:25:12.439Z | user 内容增强：回测新增指标解读表、未来函数检查清单与成本拆解示

OpenClaw
View on ClawHubHomepage

Rank

62

Safety

84

Downloads

1.3k

Updated

Oct 10, 2026

Version

3.0.3

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.3K downloads reported by the source. Last updated 10/10/2026.

Avoid when

  • Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor
Clawhubvendor · observed Oct 10, 2026
Protocol compatibility
OpenClawcompatibility · observed Oct 10, 2026
Adoption signal
1.3K downloadsadoption · observed Oct 10, 2026
Latest release
3.0.3release · observed Oct 8, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17ewqc4f2s6gpcbm88hy7fgvn85kg1g:security-quant-backtest
  1. Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
  2. Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-gechengling-security-quant-backtest/snapshot"

Documentation

CLAWHUB

77,382 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: Quantitative Backtesting Laboratory
slug: security-quant-backtest
description: AI-powered quantitative backtesting laboratory for China A-share — strategy design, historical backtesting with cost/slippage modelling, walk-forward validation, performance attribution and Monte Carlo simulation. Scope: research and validation of a defined trading rule on historical data; not live order routing, not execution advice, not return promises. Keywords: backtest a strategy, walk-forward validation, slippage and cost model, look-ahead bias check, overfitting test, Monte Carlo simulation, performance attribution, A-share, 策略回测, 前向分析, 滑点与成本建模, 未来函数检查, 过拟合检验, 蒙特卡洛模拟, 绩效归因.
version: "3.0.3"
---

# Quantitative Backtesting Laboratory / 量化回测实验室

> **English:** AI-powered quantitative backtesting laboratory — covers strategy design, historical backtesting, performance attribution, walk-forward analysis, and Monte Carlo simulation. Built for quant analysts and algorithmic traders.
>
> **中文:** 量化回测实验室——覆盖策略设计、历史回测、绩效归因、前向分析、蒙特卡洛模拟。适用：量化分析师、算法交易者、Python回测开发。


## 数据最小化声明与执行边界 / Data Minimisation & Execution Boundary

**数据最小化前置声明：** 使用本技能时，请只提供回测所必需的输入——标的代码、行情区间、策略规则与参数、费率假设。**不要**粘贴实盘账户信息、真实持仓明细、交易席位与柜台配置、客户身份信息或券商内部的行情源凭证；回测规模请用"100万初始资金"这类假设值。

**保存与预览确认：** 本技能不执行任何保存动作。若你在自己环境中依据本技能生成回测脚本、绩效报告或蒙特卡洛结果，请在落盘或对外展示前**先预览结果、确认无前视偏差且成本口径与实盘一致，再保存或提交**。

**代码块性质与执行边界**

| 内容 | 性质 | 谁来执行 |
|------|------|---------|
| `BacktestEngine` 类（撮合、成本、绩效） | 回测引擎的计算口径说明 | 由量化分析师在自己环境中取数并运行；技能不取数、不运行 |
| `DualMovingAverageStrategy` / `RSIMeanReversionStrategy` / `BollingerBreakoutStrategy` | 三条典型策略规则的信号生成示意 | 同上，属教学示意 |
| `MonteCarloSimulation` 类 | 随机路径与分位数统计的算法表达 | 由量化分析师在自己环境中运行 |
| 检查清单、成本表、归因表 | 研究用模板 | 由研究/合规人员在机构流程中落实 |

**重要：** 上述引擎是**教学示意用的简化实现**，单标的、无涨跌停与停牌约束、无部分成交，实盘前必须在机构自有的回测系统中重建并补齐约束。本技能未配置任何工具调用权限，不执行代码、不读写文件、不访问行情数据源、不下单、不连接交易柜台。回测结果为模拟结果，不构成收益承诺。

---

---


### 证券监管最新动态 [2026-10-08更新]

| 动态类型 | 内容摘要 | 影响范围 | 回测侧应对动作 | 责任岗 | 优先级 | 复核频率 | |
|---------|---------|---------|--------------|-------|-------|---|
| 证券监管 | 2026年A股量化资金占比30%-40%，回测需考虑拥挤度因子 | 回测框架需增加拥挤度、压力测试和合规成本模块 | 回测报告增加拥挤度指标与容量估算 | 研究 | 高 | 每季 |
| 证券监管 | 2026年3月量化踩踏事件：回测模型需加入极端行情压力测试 | 回测框架需增加拥挤度、压力测试和合规成本模块 | 回测加入流动性枯竭情景的损失估算 | 研究 | 高 | 每季 |
| 证券监管 | 算法监管趋严，高频策略回测需考虑合规成本 | 回测框架需增加拥挤度、压力测试和合规成本模块 | 成本模型中单列合规与报告成本 | 合规 | 高 | 每季 |
| 程序化交易 | 程序化交易报告与异常交易监控要求细化 | 高频与中高频策略 | 回测中增加报撤单频率与异常交易约束 | 合规 | 中 | 每月 |
| 交易成本 | 佣金、印花税与过户费口径需与实盘一致 | 成本模型、净收益指标 | 成本参数按最新费率更新并标注生效日期 | 研究 | 高 | 每季 |
| 数据质量 | 回测输入数据需可追溯 | 行情与财务数据 | 数据源、复权方式与取数日期须在报告中标注 | 研究 | 高 | 每季 |
| 投资者保护 | 量化产品业绩展示与宣传表述趋严 | 回测业绩对外展示 | 回测结果标注为模拟结果、不构成收益承诺 | 合规 | 高 | 每季 |
| 风控要求 | 策略容量与集中度管理要求提升 | 策略规模与持仓集中度 | 输出策略容量估算与集中度约束 | 风控 | 中 | 每季 |
| 程序化交易 | 2026年10月：程序化交易的报备字段与异常交易指标口径进一步细化 | 高频与中高频策略回测 | 回测中增加报撤单比、瞬时申报速率等约束的模拟与超限拦截 | 合规 | 高 | 每月 |
| 投资者保护 | 2026年四季度初：量化产品业绩展示须同时披露比较基准与模拟/实盘属性 | 回测业绩对外展示材料 | 回测曲线标注"模拟结果+回测区间+比较基准"，并披露样本外表现 | 合规 | 高 | 每季 |

> **数据截止**: 2026-10-08 | 来源：证监会、交易所公开规则、行业公开信息
> *

_meta.json

{
  "ownerId": "kn74e704j3ygjcygnpf02rdvd185js13",
  "slug": "security-quant-backtest",
  "version": "3.0.3",
  "publishedAt": 1791436367389
}

skill-card.md

## Description:

Guides historical China A-share strategy backtesting with illustrative Python examples, cost modelling, walk-forward validation, performance attribution, and Monte Carlo simulation.

This skill is ready for commercial/non-commercial use.

## Publisher:

[gechengling](https://clawhub.ai/user/gechengling)

### License/Terms of Use:

MIT-0

## Use Case:

Quant analysts and developers use this skill to examine defined trading rules against their own historical China A-share data, including trading costs, out-of-sample performance, and simulation uncertainty. It does not supply market data or execute trades.

### Deployment Geography for Use:

Global (China A-share market focus)

## Known Risks and Mitigations:

Risk: Simplified sample code and simulated returns may misrepresent real trading outcomes.

Mitigation: Validate results in an independently tested backtesting system with realistic costs, execution constraints, and out-of-sample checks before relying on them.

Risk: Outdated market or regulatory assumptions may lead to misleading conclusions.

Mitigation: Independently verify applicable rules, rates, and market data before using the analysis.

Risk: Real account, client, position, or broker data may expose sensitive information.

Mitigation: Use hypothetical amounts and omit real account data, client identities, broker credentials, and live positions.

## Reference(s):

- [ClawHub skill listing](https://clawhub.ai/gechengling/skills/security-quant-backtest)

## Skill Output:

**Output Type(s):** [Guidance, Analysis, Code]

**Output Format:** [Markdown with illustrative Python code and tables]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Educational examples; no built-in data access or trade execution.]

## Skill Version(s):

3.0.3 (source: skill frontmatter and server release)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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Machine-readable data

The same record, as JSON, for agents and crawlers.

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Record generated Oct 10, 2026.

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