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attempt-evaluation

Automate SignalAgent strategy evaluation with parameter search and backtesting.

33|2|Updated May 13, 2026
Search Tags:#backtesting#quantitative trading#strategy evaluation#parameter search#signal agent
One-click install
npx skills add https://github.com/adennng/stock_strategy_lab --skill attempt-evaluation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: attempt-evaluation
Source: https://github.com/adennng/stock_strategy_lab/tree/main/src/strategy_lab/skills/signal_agent/attempt-evaluation
Command: npx skills add https://github.com/adennng/stock_strategy_lab --skill attempt-evaluation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill streamlines the evaluation of SignalAgent strategies, automating the process of parameter search, backtesting, and summary generation, saving time and reducing manual effort.

Core Features & Use Cases

  • Automated Strategy Evaluation: Simplifies the process of evaluating SignalAgent strategies by automating parameter search, backtesting, and summary generation.
  • Single and Batch Evaluation: Supports both single attempt evaluation and batch evaluation of multiple strategies.
  • Use Case: Imagine you have a set of strategies generated by SignalAgent. Use this Skill to evaluate them and determine the best-performing strategy for further analysis.

Quick Start

Run the attempt-evaluation skill to evaluate a single strategy: python -m strategy_lab.cli signal evaluate-attempt RUN_STATE_PATH --strategy-path STRATEGY_PATH --strategy-spec-path STRATEGY_SPEC_PATH --param-space-path PARAM_SPACE_PATH --strategy-meta-path STRATEGY_META_PATH --strategy-name STRATEGY_NAME --search-method ga --max-candidates 30 --max-workers 2

Frequently Asked Questions about attempt-evaluation

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

FAQPage Schema
How do I automate backtesting and parameter search for quantitative trading strategies?▼

You can automate backtesting and parameter search for quantitative trading strategies by running the evaluation script, which executes strategy artifacts in Python to generate performance summaries and determine the best-performing strategy.

Can I evaluate multiple SignalAgent strategies in a batch?▼

Yes, batch evaluation of multiple SignalAgent strategies is supported alongside single attempt evaluation, allowing you to process a set of strategies and identify the best-performing one for further analysis.

What do I need to run automated strategy evaluation for quantitative trading?▼

To run automated strategy evaluation, you need Python installed along with specific strategy artifacts, including the strategy path, specification, parameter space, and metadata files required to execute the backtesting workflow.

How does parameter search work when evaluating trading strategies?▼

Parameter search works by applying a search method, such as a genetic algorithm, to explore up to a defined number of candidates within the parameter space, optimizing the strategy configuration for backtesting performance.

What is the best way to evaluate single and batch quantitative trading strategies?▼

The best way to evaluate quantitative trading strategies is using an automated evaluation workflow that handles parameter search and backtesting, which simplifies the process and reduces manual effort for both single and batch attempts.

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