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strategy-translator

Translate trading strategies between different frameworks and languages. Use when converting Pine Script to Python, porting strategies to NautilusTrader, or cross-platform migration.

ahuserious0 来源安装数☆ 0 仓库 Starsv1.0.0

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

SkillHub CLI

skillhub install ahuserious/strategy-translator

打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

Strategy Translator

Source: https://mcpmarket.com/tools/skills/strategy-translator

Use When

  • You need to apply this capability as part of trading research workflows (data, features, backtests, ML, reporting).
  • You want a reproducible output that can be committed to this repo (code, configs, docs).

Inputs To Ask For

  • Objective: what success looks like (metric, constraints, time horizon).
  • Data: symbols, timeframe, sampling, data sources, and leakage risks.
  • Constraints: compute budget, latency, interpretability, and deployment requirements.

Outputs

  • A concrete plan (steps + checks).
  • A minimal implementation sketch (files to create/change) and verification steps.
  • If applicable: a risk checklist (leakage, overfitting, evaluation pitfalls).

Workflow

  1. Restate the task in measurable terms.
  2. Enumerate required artifacts (datasets, features, configs, scripts, reports).
  3. Propose a default approach and 1-2 alternatives.
  4. Add validation gates (walk-forward, Monte Carlo, sanity checks).
  5. Produce repo-ready deliverables (code + docs) and a run command.

来源信息

Source
github
Repository
openclaw/skills
Path
skills/ahuserious/strategy-translator
Version
1.0.0
Commit
ef0665541cc92d595ce22b33e82e7a9588490698
分类来源
rules
状态
active
可见性
public

这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。

来源内容

SKILL.md
# Strategy Translator

Source: https://mcpmarket.com/tools/skills/strategy-translator

## Use When

- You need to apply this capability as part of trading research workflows (data, features, backtests, ML, reporting).
- You want a reproducible output that can be committed to this repo (code, configs, docs).

## Inputs To Ask For

- Objective: what success looks like (metric, constraints, time horizon).
- Data: symbols, timeframe, sampling, data sources, and leakage risks.
- Constraints: compute budget, latency, interpretability, and deployment requirements.

## Outputs

- A concrete plan (steps + checks).
- A minimal implementation sketch (files to create/change) and verification steps.
- If applicable: a risk checklist (leakage, overfitting, evaluation pitfalls).

## Workflow

1. Restate the task in measurable terms.
2. Enumerate required artifacts (datasets, features, configs, scripts, reports).
3. Propose a default approach and 1-2 alternatives.
4. Add validation gates (walk-forward, Monte Carlo, sanity checks).
5. Produce repo-ready deliverables (code + docs) and a run command.

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