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Liangwei-zhang
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strategy-generate

Automate design, coding, and backtesting of quantitative trading strategies.

Updated Apr 14, 2026
Search Tags:#python#strategy#backtesting#quantitative#config.json#signal-engine
One-click install
npx skills add https://github.com/Liangwei-zhang/six-stock --skill strategy-generate-liangwei-zhang
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: strategy-generate
Source: https://github.com/Liangwei-zhang/six-stock/tree/main/Vibe-Trading/agent/src/skills/strategy-generate
Command: npx skills add https://github.com/Liangwei-zhang/six-stock --skill strategy-generate-liangwei-zhang

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables end-to-end design, coding, and backtesting of quantitative trading strategies from user intent to runnable configurations and code.

Core Features & Use Cases

  • Create config.json from user input, defining instrument codes, time range, and backtest parameters.
  • Implement a deterministic SignalEngine in code/signal_engine.py that adheres to the SignalEngine contract for flexible strategy design.
  • Run backtests and evaluate artifacts such as metrics to guide iterative improvements.

Quick Start

Describe your trading objective to generate a runnable strategy with config.json and signal_engine.py.

Frequently Asked Questions about strategy-generate

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate backtesting a quantitative trading strategy from scratch?▼

To automate backtesting a quantitative trading strategy, provide your trading intent to generate config.json with instrument codes and time range, implement a deterministic SignalEngine, and execute backtests producing metrics artifacts for iterative evaluation.

What is a SignalEngine contract in quantitative backtesting?▼

A SignalEngine contract is a deterministic code implementation in signal_engine.py that defines flexible strategy design rules, ensuring reliable trading signal generation during quantitative backtesting and evaluation.

Do I need Python to generate and run quantitative trading configs?▼

Yes, you need Python to generate and run quantitative trading configs, as the Skill leverages Python to implement the deterministic SignalEngine code and execute the backtesting workflow.

What's the best way to design a quant strategy from a natural language description?▼

The best way to design a quant strategy from natural language is parsing intent to generate design decisions, producing config.json for parameters, and outputting deterministic code validated via AST checks.

How does backtest-driven evaluation guide iterative strategy improvements?▼

Backtest-driven evaluation guides iterative strategy improvements by analyzing generated metrics artifacts to assess quantitative performance, allowing you to refine the SignalEngine implementation and rerun configurations.

What parameters are required in config.json for quantitative backtesting?▼

Required parameters in config.json for quantitative backtesting include instrument codes, time range, and backtest parameters needed to execute the deterministic SignalEngine implementation.

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