Agent skill

Strategy Engine

by LeoYeAI in LeoYeAI/openclaw-master-skills

调用Strategy Engine MCP服务器执行量化策略。当用户需要运行因子表达式策略、回测交易策略或执行金融分析时调用此技能。基于MCP Server工具的实际默认值设置。

MITAuto-check passedAgent Workflows

Install Strategy Engine

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill strategy-engine -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills strategy-engine --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/strategy-engine .claude/skills/strategy-engine && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
strategy-engine
GitHub stars
2.2k
Token cost
~2.7k tokens
SKILL.md length
311 words
Files
2
Skills in repo
1,215
Repo updated
First seen
Licence
MIT

At a glance

调用Strategy Engine MCP服务器执行量化策略。当用户需要运行因子表达式策略、回测交易策略或执行金融分析时调用此技能。基于MCP Server工具的实际默认值设置。

  • Works in 7 steps: 仔细分析用户输入:严格区分基础周期和均线周期 → 严格遵守默认值规则:用户未明确指定基础周期时,必须使用默认值"5m" → 智能变量转换:自动将中文描述转换为FactorLang变量 → …
  • Tasks that involve MCP servers
  • SKILL.md covers 🎯 快速调用, 📋 参数说明(基于MCP Server工具实际默认值), 🔧 智能参数推断逻辑(重要) and 🎯 使用示例(基于智能推断), plus 3 more sections
  • Reaches visual.hzyotoy.com

What it does

Strategy Engine is an agent skill from LeoYeAI/openclaw-master-skills. 调用Strategy Engine MCP服务器执行量化策略。当用户需要运行因子表达式策略、回测交易策略或执行金融分析时调用此技能。基于MCP Server工具的实际默认值设置。

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `_meta.json`).

It sits in Agent Workflows, covering MCP servers. It works with Model Context Protocol. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • Tasks that involve MCP servers

Example prompts

  • “/strategy-engine”

Requirements

  • Python 3

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. 仔细分析用户输入:严格区分基础周期和均线周期
  2. 严格遵守默认值规则:用户未明确指定基础周期时,必须使用默认值"5m"
  3. 智能变量转换:自动将中文描述转换为FactorLang变量
  4. 参数验证:确保所有参数符合MCP Server工具的要求
  5. 参数结构匹配:使用新的input对象结构
  6. 手续费和滑点处理:正确处理新增的commssionFee和slippage参数
  7. 运行ID生成:自动生成随机runId用于标识每次运行

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • visual.hzyotoy.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Strategy Engine loads about 2.7k tokens when it runs. Until then it costs about 26 tokens; SKILL.md has 311 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~26
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 311 words, ~2,698 tokens.

Download SKILL.mdSave it as .claude/skills/strategy-engine/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
strategy-engine
description
调用Strategy Engine MCP服务器执行量化策略。当用户需要运行因子表达式策略、回测交易策略或执行金融分析时调用此技能。基于MCP Server工具的实际默认值设置。

Strategy Engine MCP服务器调用指南 v2.9

🎯 快速调用

当用户提供策略条件时,基于MCP Server工具的实际默认值自动组合参数:

python
# 自动组合参数示例(基于MCP Server工具实际默认值)
mcp_engine_mcp_server_run_expression_selected(
    input={
        "startDate": "2024-01-17",      # 开始日期,DateTime类型
        "endDate": "2024-04-17",         # 结束日期,DateTime类型
        "period": "5m",                 # 基础周期(默认:5m)
        "codes": "",                    # 合约代码列表(新增字段,默认:空)
        "poolId": 10,                   # 期货加权品种池(默认:10)
        "openCondition": "用户提供的开仓条件",
        "closeCondition": "用户提供的平仓条件", 
        "stopCondition": "用户提供的止损条件",
        "initCash": 10000000,            # 初始资金(默认:10000000)
        "direction": 1,                  # 多头方向(默认:1)
        "commssionFee": 0,               # 手续费%(默认:0,不需要手续费)
        "slippage": 0,                   # 跳数或跳点值(默认:0,按最小变动价格计算)
        "runId": 123456789               # 运行ID(默认:随机生成一串长整型数字)
    }
)

📋 参数说明(基于MCP Server工具实际默认值)

主要参数结构
参数类型说明实际默认值示例
inputExpressionSelectedV2Input输入参数对象-{...}
ExpressionSelectedV2Input对象属性
属性类型说明实际默认值示例
startDateDateTime开始日期当前日期-3个月"2024-01-17"
endDateDateTime结束日期当前日期"2024-04-17"
periodstring基础周期"5m"(5分钟)"1d", "60m"
codesstring合约代码列表(新增)空字符串"IF2404,IC2404"
poolIdint品种池ID10(期货加权)4(股票池)
openConditionstring开仓条件用户提供"_ma_5m_30_trend == 1"
closeConditionstring平仓条件用户提供"_ma_5m_30_trend == -1"
stopConditionstring止损条件用户提供"_palp > 10"
initCashfloat初始资金10000000500000
directionint交易方向1(多头)0(空头)
commssionFeefloat手续费%0(不需要手续费)-1(按系统设置手续费参与计算)
slippagefloat跳数或跳点值0(按最小变动价格计算)1(1个跳点)
runIdlong运行ID随机生成一串长整型数字123456789
手续费参数说明
值说明适用场景
0不需要手续费默认值,测试策略时使用
-1按系统设置手续费参与计算使用系统配置的手续费
>0按设置的手续费计算自定义手续费率
滑点参数说明
值说明适用场景
0无滑点默认值,理想交易环境
>0按设置的跳点值计算模拟真实交易环境
合约代码参数说明(新增)
值说明适用场景
空字符串使用品种池进行回测默认值,使用poolId指定的品种池
具体合约代码指定具体合约进行回测如"IF2404,IC2404",多个合约以逗号分隔
合约代码与品种池关系
情况codes值poolId值说明
使用品种池空>0默认情况,使用poolId指定的品种池
使用具体合约非空0系统自动将poolId置为0,使用指定合约
无效配置空0错误:必须提供合约代码或有效品种池
运行ID参数说明
值说明适用场景
随机长整型数字每次运行可随机生成一串长整型数字用于标识每次策略运行的唯一ID
完整周期参数映射
周期说明适用策略默认值
"1m"1分钟高频交易-
"5m"5分钟短线交易✅
"15m"15分钟中短线策略-
"30m"30分钟中短线策略-
"60m"1小时或60分钟短期趋势-
"1d"1日或日线或天中长线策略-
"1w"1周或周长期投资-
"1mon"1月或月长期投资-
品种池映射
poolId说明适用市场默认值
10期货加权品种池期货市场✅
4股票品种池股票市场-
6ETF品种池ETF市场-

🔧 智能参数推断逻辑(重要)

基础周期推断规则(关键区别)
python
# 重要概念区分:
# 1. 基础周期(period):K线数据的周期(5m、30m、1d等)
# 2. 均线周期:技术指标的计算周期(30、60、120等)

# 规则1:用户明确指定基础周期时,使用用户指定的周期
if "基础周期" in user_input or "K线周期" in user_input or "数据周期" in user_input:
    if "30分钟" in user_input:
        period = "30m"
    elif "60分钟" in user_input or "1小时" in user_input:
        period = "60m"
    elif "5分钟" in user_input:
        period = "5m"
    elif "15分钟" in user_input:
        period = "15m"
    elif "日" in user_input or "天" in user_input:
        period = "1d"
    elif "周" in user_input:
        period = "1w"
    elif "月" in user_input:
        period = "1mon"
else:
    # 规则2:用户未明确指定基础周期时,使用默认周期5m
    # 注意:用户提到"30分钟均线"指的是均线周期,不是基础周期!
    period = "5m"  # 默认值
均线周期识别规则
python
# 识别用户提到的均线周期(用于构建FactorLang表达式)
if "30分钟均线" in user_input:
    # 用户提到的是均线周期30,基础周期仍然是5m
    ma_period = "5m"  # 基础周期
    ma_length = "30"  # 均线长度
elif "60分钟均线" in user_input:
    ma_period = "5m"  # 基础周期
    ma_length = "60"  # 均线长度  
elif "120分钟均线" in user_input:
    ma_period = "5m"  # 基础周期
    ma_length = "120" # 均线长度
elif "240分钟均线" in user_input:
    ma_period = "5m"  # 基础周期
    ma_length = "240" # 均线长度
else:
    # 默认均线周期
    ma_period = "5m"
    ma_length = "30"
时间范围推断规则(重要:基于当前日期计算)
python
# 重要:所有时间范围都基于当前日期动态计算
# 当前日期:2026年3月25日(根据环境信息)

# 规则1:用户明确指定时间范围时,使用用户指定的范围
if "近5年" in user_input:
    startDate = "2021-03-25"  # 当前日期-5年
    endDate = "2026-03-25"    # 当前日期
elif "近3年" in user_input:
    startDate = "2023-03-25"  # 当前日期-3年
    endDate = "2026-03-25"    # 当前日期
elif "近1年" in user_input:
    startDate = "2025-03-25"  # 当前日期-1年
    endDate = "2026-03-25"    # 当前日期
else:
    # 规则2:用户未指定时间范围时,使用默认近3个月
    startDate = "2025-12-25"  # 当前日期-3个月
    endDate = "2026-03-25"    # 当前日期
止损条件智能修正
python
# 规则1:用户使用中文描述时,自动转换为FactorLang变量
if "盈亏点" in stop_condition or "点数" in stop_condition:
    stop_condition = stop_condition.replace("盈亏点", "_palp")
elif "盈亏%" in stop_condition or "百分比" in stop_condition:
    stop_condition = stop_condition.replace("盈亏%", "_palr")

# 规则2:确保使用正确的变量
if "_profit_loss_percent" in stop_condition:
    stop_condition = stop_condition.replace("_profit_loss_percent", "_palr")

🎯 使用示例(基于智能推断)

示例1:用户未指定基础周期(使用默认5m)
python
# 用户输入:开仓条件,30分钟均线120朝上且日级别是金叉状态
# AI推断:用户提到的是均线周期30,不是基础周期,使用默认5m基础周期

mcp_engine_mcp_server_run_expression_selected(
    input={
        "startDate": "2024-01-17",      # 近3个月(默认)
        "endDate": "2024-04-17",         # 当前日期(默认)
        "period": "5m",                 # 5分钟基础周期(默认)
        "poolId": 10,                   # 期货加权池(默认)
        "openCondition": "_ma_5m_120_trend == 1 && _dkx_1d_cross_status == 1",
        "closeCondition": "_ma_5m_120_trend == -1 && _dkx_1d_cross_status == -1",
        "stopCondition": "_palp > 10",  # 盈亏点数大于10
        "initCash": 10000000,           # 1000万初始资金(默认)
        "direction": 1,                 # 多头方向(默认)
        "commssionFee": -1,             # 手续费%(默认:按系统设置)
        "slippage": 0,                  # 滑点(默认:无滑点)
        "runId": 123456789              # 运行ID(默认:随机生成)
    }
)
示例2:用户明确指定基础周期
python
# 用户输入:开仓条件,基础周期30分钟,均线120朝上
# AI推断:用户明确指定基础周期30m

mcp_engine_mcp_server_run_expression_selected(
    input={
        "startDate": "2024-01-17",
        "endDate": "2024-04-17",
        "period": "30m",                # 用户指定基础周期30m
        "poolId": 10,
        "openCondition": "_ma_30m_120_trend == 1 && _dkx_1d_cross_status == 1",
        "closeCondition": "_ma_30m_120_trend == -1 && _dkx_1d_cross_status == -1",
        "stopCondition": "_palp > 10",
        "initCash": 10000000,
        "direction": 1,
        "commssionFee": -1,              # 手续费%(默认:按系统设置)
        "slippage": 0,                   # 滑点(默认:无滑点)
        "runId": 123456789               # 运行ID(默认:随机生成)
    }
)
示例3:用户指定手续费和滑点
python
# 用户输入:开仓条件,均线朝上,手续费0.1%,滑点1个跳点
# AI推断:用户指定手续费和滑点参数

mcp_engine_mcp_server_run_expression_selected(
    input={
        "startDate": "2024-01-17",
        "endDate": "2024-04-17",
        "period": "5m",                 # 5分钟基础周期(默认)
        "poolId": 10,
        "openCondition": "_ma_5m_30_trend == 1",
        "closeCondition": "_ma_5m_30_trend == -1",
        "stopCondition": "_palp > 10",
        "initCash": 10000000,
        "direction": 1,
        "commssionFee": 0.1,             # 用户指定手续费0.1%
        "slippage": 1,                   # 用户指定滑点1个跳点
        "runId": 123456789               # 运行ID(默认:随机生成)
    }
)
示例4:用户指定具体合约代码(新增)
python
# 用户输入:开仓条件,均线朝上,指定IF2404和IC2404合约
# AI推断:用户指定具体合约代码,系统自动将poolId置为0

mcp_engine_mcp_server_run_expression_selected(
    input={
        "startDate": "2024-01-17",
        "endDate": "2024-04-17",
        "period": "5m",                 # 5分钟基础周期(默认)
        "codes": "IF2404,IC2404",       # 用户指定具体合约代码
        "poolId": 10,                    # 系统会自动置为0,使用指定合约
        "openCondition": "_ma_5m_30_trend == 1",
        "closeCondition": "_ma_5m_30_trend == -1",
        "stopCondition": "_palp > 10",
        "initCash": 10000000,
        "direction": 1,
        "commssionFee": 0,               # 不需要手续费(默认)
        "slippage": 0,                    # 无滑点(默认)
        "runId": 123456789               # 运行ID(默认:随机生成)
    }
)

🚨 重要规则(AI必须遵守)

规则1:基础周期与均线周期区分
  • 基础周期(period):K线数据的周期,默认值"5m"
  • 均线周期:技术指标的计算周期,如30、60、120等
  • 关键区别:用户提到"30分钟均线"指的是均线周期,不是基础周期!
规则2:基础周期推断逻辑
  • 用户提到"基础周期"、"K线周期"、"数据周期" → 使用用户指定的基础周期
  • 用户未明确指定基础周期 → 必须使用默认值"5m"
  • 用户提到"30分钟均线" → 这是均线周期,基础周期仍然是"5m"
规则3:时间范围推断逻辑
  • 用户提到"近5年" → 使用5年时间范围
  • 用户提到"近3年" → 使用3年时间范围
  • 用户提到"近1年" → 使用1年时间范围
  • 用户未提到时间范围 → 必须使用"近3个月"
规则4:参数结构匹配(重要更新)
  • 必须使用新的参数结构:input对象包含所有参数
  • 参数类型变更:日期参数现在是DateTime类型
  • 新增字段:codes、commssionFee、slippage和runId参数
规则5:合约代码智能推断(新增)
  • 用户提到具体合约代码:如"IF2404"、"IC2404"等 → 自动设置codes字段
  • 用户提到品种池:如"期货加权池"、"股票池"等 → 使用poolId字段
  • 同时指定合约和品种池:优先使用合约代码,系统自动将poolId置为0

💡 最佳实践(AI执行策略)

  1. 仔细分析用户输入:严格区分基础周期和均线周期
  2. 严格遵守默认值规则:用户未明确指定基础周期时,必须使用默认值"5m"
  3. 智能变量转换:自动将中文描述转换为FactorLang变量
  4. 参数验证:确保所有参数符合MCP Server工具的要求
  5. 参数结构匹配:使用新的input对象结构
  6. 手续费和滑点处理:正确处理新增的commssionFee和slippage参数
  7. 运行ID生成:自动生成随机runId用于标识每次运行

🔄 默认值优先级(AI必须遵守)

  1. 用户明确指定值:最高优先级(必须包含"基础周期"等关键词)
  2. 智能推断值:根据用户描述推断
  3. MCP Server工具默认值:最低优先级(当用户未指定时使用)

数据来源:基于MCP Server工具类的实际默认值设置 调用时机:当用户需要运行因子表达式策略、回测交易策略或执行金融分析时自动调用此技能。

版本:v3.0(同步MCP工具最新参数结构,新增codes字段,支持具体合约代码回测)

� 运行耗时单位说明

运行耗时单位
  • 运行耗时(timeConsuming)的单位是毫秒(ms)
  • 示例:"timeConsuming": 310 表示运行耗时310毫秒(0.31秒)
运行耗时解读
耗时范围说明性能评估
< 100ms极快优秀
100-500ms快速良好
500-1000ms正常一般
> 1000ms较慢需要优化

�🔗 结果查看指南

策略结果查看方式

策略运行完成后,系统会生成一个唯一的查看链接:

markdown
https://visual.hzyotoy.com/?data_dir=xzr&data_id=123456789&initCash=10000000

点击链接或复制URL到浏览器中查看完整策略分析报告

结果查看规范(AI必须遵守)
  1. 必须提供完整的查看链接:包含协议、主机、端口和所有参数
  2. 必须明确说明链接用途:告知用户这是策略分析报告链接
  3. 必须提示用户点击操作:明确指示用户如何查看结果
  4. 必须包含运行耗时说明:明确告知运行耗时的单位是毫秒
查看链接参数说明
参数说明示例
data_dir数据目录xzr
data_id运行ID123456789
initCash初始资金10000000

AI执行要求:必须严格遵守本SKILL中的参数结构匹配规则、类型要求和结果查看规范!

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/strategy-engine of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • _meta.json

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Strategy Engine next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Strategy Engine compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Strategy Engine this skillLeoYeAI/openclaw-master-skills2.2k—~2.7kAutomated safety check: PassMIT
MCP Server Builderanthropics/skills180k62 repos~2.3kAutomated safety check: PassApache-2.0
MCP Server BuildershareAI-lab/learn-claude-code78k5 repos~1.2kAutomated safety check: PassMIT
MCP Integration for Pluginsanthropics/claude-plugins-official37k11 repos~3.1kAutomated safety check: PassApache-2.0
Fastmcp Client CLIPrefectHQ/fastmcp28k1 repos~823Automated safety check: PassApache-2.0
Crush Configurationcharmbracelet/crush29k—~3.7kAutomated safety check: PassCustom licence

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Categories

Questions about Strategy Engine

What does Strategy Engine do?

调用Strategy Engine MCP服务器执行量化策略。当用户需要运行因子表达式策略、回测交易策略或执行金融分析时调用此技能。基于MCP Server工具的实际默认值设置。. Strategy Engine is an agent skill from LeoYeAI/openclaw-master-skills.

When should I use Strategy Engine?

Strategy Engine fits situations like: tasks that involve MCP servers.

How do I install Strategy Engine in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill strategy-engine -a claude-code`. Or copy the skill folder (skills/strategy-engine in LeoYeAI/openclaw-master-skills) into .claude/skills/strategy-engine in your project. Claude Code loads it when a task matches its description.

How do I install Strategy Engine in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill strategy-engine -a codex`. Or copy the skill folder (skills/strategy-engine in LeoYeAI/openclaw-master-skills) into .agents/skills/strategy-engine in your project. Codex loads it when a task matches its description.

Can I use Strategy Engine in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill strategy-engine -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/strategy-engine, .gemini/skills/strategy-engine, .github/skills/strategy-engine and .opencode/skills/strategy-engine in your project.

What does Strategy Engine need to run?

SKILL.md names no scripts, command-line tools or credentials: Strategy Engine is instructions for the agent only. Our summary lists: Python 3.

Does Strategy Engine access the network?

SKILL.md names 1 domain. In commands or code: visual.hzyotoy.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Strategy Engine safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Strategy Engine use?

Strategy Engine is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Strategy Engine use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Strategy Engine?

Skills that share tags, products or a category with Strategy Engine: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 37k stars) and Fastmcp Client CLI (PrefectHQ/fastmcp, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Strategy Engine?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,158 GitHub stars. The repository holds 1,215 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.