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vibe-trading

Orchestrate AI-powered backtesting and multi-agent research for finance professionals.

Updated Aug 23, 2026
Search Tags:#analytics#ai#data#finance#swarm#backtesting
One-click install
npx skills add https://github.com/prinzeval/Vibe-Trading --skill vibe-trading-prinzeval
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: vibe-trading
Source: https://github.com/prinzeval/Vibe-Trading/tree/main/VALENDATA/agent
Command: npx skills add https://github.com/prinzeval/Vibe-Trading --skill vibe-trading-prinzeval

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Streamline complex finance research workflows by providing an AI-powered, all-in-one toolkit for backtesting, multi-agent collaboration, and rule extraction from trading journals.

Core Features & Use Cases

  • Backtesting across seven engines, 71 specialized skills, and multiple data sources to evaluate strategies and surface actionable insights.
  • Shadow Account loop to extract and test implicit trading rules from journal data, enabling data-driven refinement and performance visualization.
  • 29 pre-built swarm teams for end-to-end research, debate, risk review, and PM decision support across asset classes (A股/港股/美股/crypto).

Quick Start

Install vibe-trading-ai and start the interactive vibe-trading CLI to explore backtests and swarm workflows.

Frequently Asked Questions about vibe-trading

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

FAQPage Schema
How do I backtest trading strategies across multiple asset classes like A-shares and crypto?▼

Backtesting trading strategies across A-shares, HK/US equities, and crypto relies on seven backtesting engines and five data sources to evaluate performance and surface actionable insights.

What is a multi-agent swarm workflow for finance research?▼

Multi-agent swarm workflows for finance research orchestrate 29 pre-built AI teams to execute end-to-end research, debate strategies, review risk, and support portfolio management decisions.

How do I extract and evaluate implicit trading rules from my trading journal?▼

Extracting and evaluating implicit trading rules from trading journals uses the Shadow Account loop to enable data-driven refinement and performance visualization of extracted strategies.

Do I need Python 3.11 to use AI-powered finance research and backtesting tools?▼

AI-powered finance research and backtesting tools require Python 3.11 or higher, the vibe-trading-ai package, and environment settings for data providers and LLMs to function correctly.

Can I use a multi-agent swarm for portfolio management risk review?▼

Multi-agent swarms support portfolio management risk review through 29 pre-built teams designed for end-to-end research, strategy debate, and decision support across multiple asset classes.

What is the best way to automate finance research with a multi-agent AI toolkit?▼

Automating finance research with a multi-agent AI toolkit involves orchestrating 71 specialized skills and 29 swarm teams to evaluate strategies and extract rules from trading journals.

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