What makes AI Trading Strategy Backtester notable?

1.6K downloads. Trust evidence available

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AI Trading Strategy Backtester

AI-powered quantitative trading strategy backtesting assistant. Designs, codes, and evaluates trading strategies across historical market data. Supports A-share (China), Hong Kong, US equity markets. Covers mean reversion, momentum, breakout, pairs trading, and machine learning-based strategies. Built for quantitative analysts and retail traders. Keywords: trading backtest, quantitative strategy, algorithmic trading, Python backtesting, backtrader, vectorbt, trading strategy, momentum, mean reversion, pairs trading, A-share strategy, financial data, technical indicators. Skill: AI Trading Strategy Backtester Owner: gechengling Summary: AI-powered quantitative trading strategy backtesting assistant. Designs, codes, and evaluates trading strategies across historical market data. Supports A-share (China), Hong Kong, US equity markets. Covers mean reversion, momentum, breakout, pairs trading, and machine learning-based strategies. Built for quantitative analysts and retail traders. Keywo

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.6k

Updated

Oct 10, 2026

Version

5.0.3

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.6K 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: 1.6K downloadsadoption · observed Oct 10, 2026
Latest release: 5.0.3release · observed Oct 9, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17ewqc4f2s6gpcbm88hy7fgvn85kg1g:ai-trading-backtester

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
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-ai-trading-backtester/snapshot"

Documentation

CLAWHUB

109,846 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: "AI Trading Strategy Backtester"
description: "AI-powered quantitative trading strategy backtesting assistant. Designs, codes, and evaluates trading strategies across historical market data. Supports A-share (China), Hong Kong, US equity markets. Covers mean reversion, momentum, breakout, pairs trading, and machine learning-based strategies. Built for quantitative analysts and retail traders. Keywords: trading backtest, quantitative strategy, algorithmic trading, Python backtesting, backtrader, vectorbt, trading strategy, momentum, mean reversion, pairs trading, A-share strategy, financial data, technical indicators."
version: "5.0.3"
---

# AI Trading Strategy Backtester

## Overview

An AI-powered quantitative trading strategy design and backtesting assistant that helps you transform trading ideas into fully-coded, backtested strategies. It guides you through strategy design (mean reversion, momentum, breakout, pairs trading, ML-based), implements them in Python (backtrader, vectorbt, pandas), evaluates performance across historical data for A-share, HK, and US markets, and produces risk-adjusted performance reports.

## Market & Regulatory Context (as of 2026-10-09)

| 事项 | 对回测的直接影响 |
|------|----------------|
| **程序化交易管理要求** | 高频类策略需自行计入报备、流速与异常交易约束，回测中不能假设"无限报单" |
| **A股 T+1 与涨跌幅限制** | 当日买入不可卖出、涨跌停无法成交，回测必须对不可成交信号做剔除或顺延处理 |
| **停牌与流动性** | 停牌期间的"信号"不可成交；小市值标的需按实际成交量限制下单规模 |
| **交易成本口径** | 佣金、印花税、滑点合计常被低估，年换手率高时成本可吞掉全部超额收益 |
| **数据复权与幸存者偏差** | 使用不复权价格或仅含现存标的历史，会系统性高估策略表现 |

**新动态（截至 2026-10-09）：**
- **程序化交易监管常态化**：境内市场对程序化交易的报备、交易行为监测与异常交易认定持续细化，高频与日内回转类策略的合规成本上升。回测时应把"报单频率上限""撤单率约束"纳入假设，而非只追求收益最大化。
- **AI 选股与因子挖掘的合规关注上升**：以大模型或机器学习生成交易信号时，监管与机构风控普遍要求可解释、可复现、留痕，纯黑箱信号在实盘落地时面临额外审核成本。
- **数据要素与另类数据应用扩展**：产业、舆情、供应链等另类数据被更广泛用于因子构建，但数据可得性的时点（point-in-time）问题更突出，回测中须严格避免引入未来信息。
- 以上为公开信息综述，**具体规则、费率与执行口径以交易所、中国证监会及券商官方最新发布为准**。
- **回测口径本身成为关注点**：机构内部评审 increasingly 要求回测报告披露「数据复权方式、样本区间、成本口径、可成交性过滤、样本外划分方式」五项，缺项即视为不可采信。建议把五项写进回测报告模板的固定开头。
- **另类数据的 point-in-time 问题被单独提示**：产业、舆情、供应链类数据常见「事后回填」，直接用于回测会引入未来信息。可行做法是为每类数据记录「可获得时点」，并在回测中只允许使用该时点之前已经存在的数据版本。
- 以上新增条目为公开信息综述，**具体规则、费率与执行口径以官方最新发布为准**。

---

## 语言与适用市场声明（Language & Markets）

- **语言**：英文术语（Metrics、Sharpe、Drawdown、Walk-forward 等）保留原文，中文用于解释与结论——这是量化领域通用做法，便于与代码、公式和第三方工具对齐，属显式设计。
- **支持市场**：A 股（沪深）、中国香港市场、美股。三个市场的**交易规则差异是回测结论的前提**，不可混用：
  - A 股：T+1、涨跌停限制、停牌、卖出印花税、做空渠道有限；
  - 中国香港市场：T+0、无涨跌停、交易成本与结算周期不同；
  - 美股：T+0、可做空、税费与结算规则另行适用。
- **口径优先**：任何回测示例都必须明确标注市场；**同一段代码在不同市场下的结论不可直接套用**。
- 涉及中国香港、中国澳门、中国台湾及境外市场的具体监管要求，本技能仅提供通用回测方法，规则细节以当地监管与交易所口径为准。

---

## 数据最小化与执行边界（Data Minimization & Execution Boundary）

**数据最小化前置声明（使用本技能前请先执行）**

1. 不要粘贴实盘账户信息、持仓明细、真实成交记录；讨论时用脱敏后的结构或虚构标的。
2. 数据文件路径、数据库地址、行情接口密钥一律用占位符，不写入提示词。
3. 涉及的策略参数、因子逻辑如属机构未公开成果，只描述结构不给具体数值。
4. 本技能不运行回测、不安装依赖、不读取本地行情文件、不发起任何网络请求；所有代码需你在自己的环境中执行。
5. 生成的代码与报告如需落盘，须先预览确认无凭据与内部路径残留，再自行保存。

**代码块性质与执行边界**

| 内容 | 性质 | 谁来执行 |
|------|------|---------|
| backtrader / statsmodels 等示例 | 可运行脚手架，需本地安装依赖 | 使用者在自己的环境中运行，本技能不执行 |
| 绩效指标表中的目标值与示

_meta.json

{
  "ownerId": "kn74e704j3ygjcygnpf02rdvd185js13",
  "slug": "ai-trading-backtester",
  "version": "5.0.3",
  "publishedAt": 1791525637899
}

skill-card.md

## Description:

Guides the design of Python trading-strategy backtests and interpretation of historical performance across A-share, Hong Kong, and US equity markets.

This skill is for research and development only.

## Publisher:

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

### License/Terms of Use:

MIT-0

## Use Case:

Quantitative analysts and retail traders use this skill to draft backtesting code and compare historical strategy performance, costs, and risk assumptions. Users run and verify the generated code themselves; the skill does not execute backtests or place trades.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Sensitive account details, trading records, credentials, or proprietary strategy parameters could be exposed in prompts or saved outputs.

Mitigation: Use placeholders or anonymized examples; review generated code and reports for sensitive details before saving them.

Risk: Unverified code or unrealistic market, cost, and regulatory assumptions could make historical performance misleading or costly to apply.

Mitigation: Independently test generated code, data timing, execution constraints, fees, and current market rules before using results with real capital.

## Reference(s):

- [ClawHub skill release](https://clawhub.ai/gechengling/skills/ai-trading-backtester)

## Skill Output:

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

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

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Example performance figures are illustrative, not verified backtest results.]

## Skill Version(s):

5.0.3 (source: skill frontmatter and server-resolved 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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