shadow-account

Extract trading rules from user journals and backtest across markets.

Updated May 15, 2026

Search Tags:#backtesting #report-generation #pnl-attribution #trade-journal #multimarket #shadow-account

Ready to run

npx skills add https://github.com/philipcoller-777/Vibe-Trading-TV2 --skill shadow-account-philipcoller-777

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: shadow-account
Source: https://github.com/philipcoller-777/Vibe-Trading-TV2/tree/main/agent/src/skills/shadow-account
Command: npx skills add https://github.com/philipcoller-777/Vibe-Trading-TV2 --skill shadow-account-philipcoller-777

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Shadow Account helps users derive profitable patterns from trade journals, backtest across multiple markets, and produce an emotion-free narrative report.

Core Features & Use Cases

Rule extraction: generate 3-5 human-friendly rules from the uploaded trade journal.
Cross-market backtesting: run simulations across A-share, HK, US, and crypto markets with attribution.
Report generation: create a structured 8-section PDF report with PnL attribution.

Quick Start

Provide your uploaded trade journal and run the shadow-account workflow to obtain a shadow_id and the 3-5 rules.

Frequently Asked Questions about shadow-account

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

FAQPage Schema

How do I extract actionable trading rules from my trade journal?▼

Backtesting across multiple markets allows you to run cross-market simulations on A-share, HK, US, and crypto markets. The system provides attribution analysis to validate the extracted rules across different asset classes.

Can I generate a PnL attribution report from my trade history automatically?▼

Shadow-account requires an uploaded trade journal to function. You provide your historical trade data, and the system returns a shadow_id alongside the 3-5 extracted rules to start backtesting.

Does trade journal backtesting work for crypto and US markets?▼

Trade journal backtesting works for crypto and US markets, alongside A-share and HK markets. It processes cross-market trade data to produce attribution analysis and narrative reports for these supported assets.

What is the best way to turn trade journals into profit rules?▼

The best way to turn trade journals into profit rules is using automated rule extraction and cross-market backtesting. This translates historical trade data into lightweight, actionable patterns with PnL attribution.