Verified:GitHub

vibe-trading

Name: vibe-trading
Availability: InStock
Author: loanntc

Convert trading journals and research hypotheses into measurable performance via multi-market backtests and Alpha Zoo benchmarking.

Updated Apr 14, 2026

Search Tags:#backtesting #factor analysis #multi-agent research #trade journal #options pricing #shadow account #alpha zoo

Ready to run

npx skills add https://github.com/loanntc/Paave --skill vibe-trading-loanntc

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: vibe-trading
Source: https://github.com/loanntc/Paave/tree/main/skills/vibe-trading
Command: npx skills add https://github.com/loanntc/Paave --skill vibe-trading-loanntc

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires vibe-trading-ai.

What problem does it solve?

Turning investment ideas into evidence-backed strategies is slow because it requires assembling data, running multiple backtests, analyzing factors/alphas, and validating against your own trading behavior.

Core Features & Use Cases

Multi-engine backtesting across equities, futures, forex, and crypto, with benchmark comparison panels to quickly assess robustness.
Factor analysis & Alpha Zoo benchmarking using 452 pre-built alphas (qlib158/alpha101/gtja191/academic) with one-line CLI benchmarking and exportable manifests.
Shadow Account loop that analyzes a CSV trade journal, distills actionable if-then rules, backtests them across multiple markets, and renders an HTML/PDF report to show where the rules would have helped.

Quick Start

Ask the skill to analyze your broker CSV trade journal, extract a shadow strategy with minimum support of 3, run the shadow backtest for the last year, and render the shadow report.

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 from a CSV trade journal?▼

To backtest trading strategies from a CSV trade journal, you can use the Shadow Account loop to analyze your trades, distill actionable if-then rules with a minimum support of 3, and run multi-market backtests to measure performance. The system then renders an HTML or PDF report showing where your rules would have helped.

What is factor analysis and Alpha Zoo benchmarking in quantitative research?▼

Factor analysis and Alpha Zoo benchmarking evaluate trading hypotheses against 452 pre-built alphas using qlib158, alpha101, gtja191, and academic models. This process converts investment ideas into measurable performance metrics through one-line CLI benchmarking and exportable manifests.

Can I run multi-market backtests across equities, crypto, and options?▼

Yes, you can run multi-engine backtests across A-share, HK/US equities, futures, forex, crypto, and options. The system provides benchmark comparison panels to quickly assess strategy robustness across these different markets.

Do I need API keys to use TUSHARE_TOKEN for A-shares and OPENAI_API_KEY for swarm teams?▼

You need Python >=3.11 to run the MCP command entrypoint, while TUSHARE_TOKEN for A-shares and OPENAI_API_KEY for swarm teams are optional API keys. These optional keys enhance multi-agent research and A-share data access capabilities.

Does multi-agent research work with options pricing and behavioral diagnostics?▼

Yes, multi-agent research works with options pricing and behavioral diagnostics by applying swarm teams to strategy generation and robustness checks. This requires the optional OPENAI_API_KEY to fully enable the multi-agent research workflows.

Why does my shadow account backtest fail to generate an HTML/PDF report?▼

Your shadow account backtest might fail to generate an HTML/PDF report if the extracted trading rules do not meet the minimum support of 3. Ensure your broker CSV trade journal contains enough data for the system to distill actionable if-then rules before rendering.