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ebrahim-sani
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vibe-trading

Orchestrate automated backtesting and multi-agent swarm research across multiple data sources.

Updated Apr 10, 2026
Search Tags:#ai#multi-agent#finance#swarm#data-analysis#backtesting#toolkit
One-click install
npx skills add https://github.com/ebrahim-sani/trading-automation --skill vibe-trading-ebrahim-sani
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: vibe-trading
Source: https://github.com/ebrahim-sani/trading-automation/tree/main/vibe-trading/agent
Command: npx skills add https://github.com/ebrahim-sani/trading-automation --skill vibe-trading-ebrahim-sani

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires vibe-trading-ai.

What problem does it solve?

Streamlines complex finance research workflows by orchestrating backtests, factor analysis, options pricing, and multi-agent swarm collaborations locally.

Core Features & Use Cases

  • Backtesting across 6 engines and 5 data sources for HK/US equities, crypto, and macro data.
  • 68 specialized skills and 29 swarm teams to accelerate research workflows.
  • Local execution via MCP/LLM integration with a Python-based toolchain.

Quick Start

Install vibe-trading-ai, then start exploring skills and backtest ideas by running vibe-trading locally.

Frequently Asked Questions about vibe-trading

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

FAQPage Schema
How do I run automated backtesting across multiple data sources for equities and crypto?▼

Automated backtesting across multiple data sources is executed by orchestrating 6 backtest engines and 5 data sources for HK/US equities, crypto, and macro data locally. You configure a Python-based workflow to trigger the backtest MCP tools.

How does multi-agent swarm teamwork work for finance research?▼

Multi-agent swarm teamwork operates by coordinating 29 specialized swarm teams and 68 skills to execute collaborative finance research workflows. It uses local MCP and LLM integration to route research tasks across the swarm.

Do I need Python to use the vibe-trading toolkit for factor analysis?▼

Yes, Python is required to use the toolkit for factor analysis. The workflow operates via a local Python-based toolchain and environment configuration that exposes the factor_analysis MCP tool.

Can I use local execution for options pricing and backtesting without sending data externally?▼

Yes, local execution is supported for options pricing and backtesting workflows without external data transmission. The orchestration runs locally via MCP and LLM integration using a Python-based toolchain.

What is the best way to explore available skills before running a backtest?▼

The best way to explore available skills before running a backtest is by using the list_skills MCP tool. This tool exposes the 68 specialized skills available within the framework to help configure your research workflow.

How do I load a specific skill for my finance research workflow?▼

To load a specific skill for your finance research workflow, you use the load_skill MCP tool provided by the local Python toolchain. This initializes the selected skill for subsequent backtesting or swarm operations.

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