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signal-run

Automate Signal Layer task lifecycle from data acquisition to strategy selection.

33|2|Updated May 13, 2026
Search Tags:#task automation#market analysis#backtesting#strategy generation#signal layer
One-click install
npx skills add https://github.com/adennng/stock_strategy_lab --skill signal-run
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: signal-run
Source: https://github.com/adennng/stock_strategy_lab/tree/main/src/strategy_lab/skills/signal_agent/signal-run
Command: npx skills add https://github.com/adennng/stock_strategy_lab --skill signal-run

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires DeepAgents, LangChain, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the execution and analysis of Signal Layer tasks, eliminating the need for manual processes and reducing the time spent on repetitive tasks.

Core Features & Use Cases

  • Automated Signal Run Creation: Creates a standardized directory structure and run_state.json file for Signal Layer task execution.
  • Data Acquisition: Manages data retrieval and storage, preparing data for further analysis.
  • Market Profile Generation: Generates market profiles for informed decision-making.
  • Strategy Exploration and Backtesting: Automates the exploration and backtesting of strategies.
  • Review and Selection: Facilitates the review and selection of the best strategy.
  • Use Case: Automate the end-to-end process of creating a signal layer task for a specific asset, including data retrieval, strategy generation, backtesting, and final selection.

Quick Start

Execute a signal run for the asset 000300.SH from 2024-01-01 to 2024-12-31 with the command python -m strategy_lab.cli signal new-run 000300.SH --start-date 2024-01-01 --end-date 2024-12-31.

Frequently Asked Questions about signal-run

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

FAQPage Schema
How do I automate stock strategy backtesting and signal generation end-to-end?▼

Automating stock strategy backtesting requires a multi-agent framework to handle data acquisition, strategy generation, and final selection. This Skill orchestrates the entire Signal Layer task lifecycle to eliminate repetitive manual investment research processes.

How do I start a new signal run for a specific asset date range?▼

To start a new signal run, execute the command line interface with your target asset and date range. This initializes a standardized directory structure and a run_state.json file to track the automated execution state for your specified period.

Do I need DeepAgents and LangChain to run automated investment strategy research?▼

Yes, you need DeepAgents and LangChain dependencies to execute automated investment strategy research. These frameworks provide the complex multi-agent task automation capabilities required to manage data retrieval, market profile generation, and backtesting.

What is the best way to manage market data acquisition for strategy backtesting?▼

The best way to manage market data acquisition for strategy backtesting is using an automated signal run framework. It handles data retrieval and storage preparation natively, ensuring your market profiles and datasets are structured for informed decision-making.

Can I review and select the best trading strategy automatically after backtesting?▼

Yes, you can review and select the best trading strategy automatically after backtesting. The automated workflow facilitates the review and selection phase, evaluating generated strategies to finalize the most effective investment approach for your asset.

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