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shadow-account

Extract trading rules from trade journals for multi-market backtesting and PnL attribution.

Updated Jun 30, 2026
Search Tags:#analysis#strategy#finance#backtesting#attribution#trading
One-click install
npx skills add https://github.com/20YN04/vibe-trading-macos --skill shadow-account-20yn04
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shadow-account
Source: https://github.com/20YN04/vibe-trading-macos/tree/main/agent/src/skills/shadow-account
Command: npx skills add https://github.com/20YN04/vibe-trading-macos --skill shadow-account-20yn04

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires weasyprint.

What problem does it solve?

This Skill solves the problem of emotional trading and lack of self-awareness by distilling your actual historical trade data into objective, rule-based strategies.

Core Features & Use Cases

  • Strategy Extraction: Analyzes your profitable trade history to identify 3-5 core, repeatable trading rules.
  • Multi-Market Backtesting: Validates your extracted strategy across A-shares, HK, US, and crypto markets to measure performance.
  • Performance Attribution: Provides a detailed breakdown of PnL, identifying the impact of emotional noise, early/late exits, and missed signals.
  • Use Case: If you are unsure why your performance fluctuates, use this to see how your "Shadow" (your objective self) would have performed compared to your actual emotional trades.

Quick Start

Use the shadow-account skill to analyze my uploaded trade journal and generate a strategy report.

Frequently Asked Questions about shadow-account

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

FAQPage Schema
How do I extract objective trading rules from my historical trade journal?▼

To extract trading rules from a trade journal, you input structured historical trade data to identify 3-5 core, repeatable strategies based on your profitable history. This process distills actual trade data into objective, rule-based strategies.

Can I backtest my extracted trading strategy across multiple financial markets?▼

Yes, you can backtest extracted trading strategies across A-shares, HK, US, and crypto markets. Multi-market backtesting validates your strategy's performance to measure how it behaves under different global market conditions.

What is performance attribution in retail trading behavior analysis?▼

Performance attribution in trading analysis provides a detailed breakdown of PnL. It identifies the specific impact of emotional noise, early or late exits, and missed signals by comparing objective backtested results against your actual trades.

Do I need structured trade data to generate comparative PnL reports?▼

Yes, generating comparative PnL reports requires structured trade data input. The quantitative analysis relies on your historical trade journal records to accurately extract rules and produce actionable strategy insights.

What is the best way to identify emotional trading patterns from past trades?▼

The best way to identify emotional trading patterns is by comparing your actual trade journal against an objective "Shadow" strategy. This performance attribution highlights how emotional noise, early exits, and missed signals impacted your overall PnL.

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