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.

3,891 stars

byopenclaw

View on GitHub Installation ↓

Best use case

strategy-translator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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

Teams using strategy-translator should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/strategy-translator/SKILL.md --create-dirs "https://raw.githubusercontent.com/openclaw/skills/main/skills/ahuserious/strategy-translator/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/strategy-translator/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How strategy-translator Compares

Feature / Agent	strategy-translator	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

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

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

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SKILL.md Source

# 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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