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strategy-generate

Generate and test quantitative trading strategies into a backtest-ready signal engine.

Updated Apr 10, 2026
Search Tags:#strategy#data-analytics#trading#backtest#quantitative#signal_engine
One-click install
npx skills add https://github.com/ebrahim-sani/trading-automation --skill strategy-generate-ebrahim-sani
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: strategy-generate
Source: https://github.com/ebrahim-sani/trading-automation/tree/main/vibe-trading/agent/src/skills/strategy-generate
Command: npx skills add https://github.com/ebrahim-sani/trading-automation --skill strategy-generate-ebrahim-sani

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables users to design, modify, and backtest quantitative trading strategies, and to evaluate their performance before deployment.

Core Features & Use Cases

  • End-to-end strategy workflow from concept to backtest results using a configurable signal engine and run configuration.
  • Iterative optimization that lets you tweak parameters and compare performance across different instruments and periods.
  • Use Case: Build momentum, mean-reversion, or breakout strategies and compare metrics such as drawdown and Sharpe across backtests.

Quick Start

Define your target instruments and dates, implement signal_engine.py following the contract, then run the backtester to evaluate results.

Frequently Asked Questions about strategy-generate

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

FAQPage Schema
How do I backtest a quantitative trading strategy?▼

Backtesting a quantitative trading strategy involves implementing a signal_engine.py module, defining your target instruments and dates in config.json, and running the backtester to evaluate performance results.

Can I compare backtest results across different instruments and timeframes?▼

You can compare backtest results across different instruments and timeframes by iteratively tweaking parameters to evaluate metrics such as drawdown and Sharpe ratio across multiple backtests.

What do I need to run a strategy backtest?▼

Running a strategy backtest requires a config.json file at the run directory root and a Python module named signal_engine.py that implements the SignalEngine contract.

How do I optimize a mean-reversion or momentum trading strategy?▼

Optimizing mean-reversion, momentum, or breakout trading strategies involves iteratively tweaking parameters within your signal engine and comparing performance metrics across historical backtests.

What types of systematic trading strategies can I design and evaluate?▼

Systematic traders and researchers can design and evaluate momentum, mean-reversion, and breakout strategies, generating a backtest-ready signal engine to measure drawdown and Sharpe metrics.

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