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vibe-trading

Automate finance research and backtesting with AI agent swarms.

Updated May 15, 2026
Search Tags:#research#ai#finance#swarm#backtesting#trading#mult-agent
One-click install
npx skills add https://github.com/philipcoller-777/Vibe-Trading-TV2 --skill vibe-trading-philipcoller-777
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: vibe-trading
Source: https://github.com/philipcoller-777/Vibe-Trading-TV2/tree/main/agent
Command: npx skills add https://github.com/philipcoller-777/Vibe-Trading-TV2 --skill vibe-trading-philipcoller-777

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates end-to-end finance research and backtesting with AI-powered agent swarms, accelerating strategy discovery, validation, and reporting.

Core Features & Use Cases

  • AI-powered backtesting across multiple engines and data sources.
  • 74 finance skills and 29 swarm teams for strategy exploration and collaboration.
  • Shadow Account loop to extract implicit trading rules from a journal and evaluate them against backtests.

Quick Start

Install vibe-trading-ai and run vibe-trading to interactively load skills or start a backtest immediately.

Frequently Asked Questions about vibe-trading

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

FAQPage Schema
How do I automate finance research and backtesting with an AI agent swarm?▼

You can automate finance research and backtesting by deploying an AI-powered agent swarm to handle strategy discovery, validation, and reporting across HK/US equities and crypto data sources.

What is a Shadow Account loop for extracting implicit trading rules?▼

A Shadow Account loop extracts implicit trading rules from a trading journal and evaluates them against backtests, allowing you to validate undocumented strategies using AI.

Can I use AI backtesting tools for both US equities and cryptocurrency?▼

Yes, this AI backtesting toolkit supports HK/US equities and crypto assets, applying multi-agent swarm teams to evaluate trading strategies across these financial data sources.

What is the best way to discover new trading strategies using multi-agent AI?▼

Using 29 swarm teams and 74 finance skills, you can explore and collaborate on trading strategy discovery, accelerating the validation of new approaches through automated backtesting engines.

How do I start a backtest immediately after installing the toolkit?▼

After installation, you can run the main command to interactively load specific finance skills or start a comprehensive backtesting session immediately without complex configuration.

Does the finance research toolkit require external dependencies or components?▼

No, the toolkit operates with no external dependencies or components, running its executable runtime instructions natively while supporting optional assets, scripts, and MCP integration.

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