shadow-account

Extract profitable trading patterns from trade journals into strategy rules and attribution reports.

Updated Jul 29, 2026

Search Tags:#analysis #strategy #finance #backtesting #attribution #trading

Ready to run

npx skills add https://github.com/santoosaraujo/vibe-trading-claude --skill shadow-account-santoosaraujo

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill addresses the gap between emotional, inconsistent trading and a disciplined, rule-based strategy by identifying the specific patterns that actually generate profit for the user.

Core Features & Use Cases

Strategy Extraction: Analyzes uploaded trade journals to distill 3-5 natural language rules that define the user's profitable trading habits.
Multi-Market Backtesting: Validates these extracted rules across A-shares, HK, US, and crypto markets to measure performance and attribution.
Performance Attribution: Provides a detailed breakdown of PnL, identifying the cost of emotional noise, early exits, and missed signals.

Quick Start

Ask the shadow-account skill to extract your trading strategy and run a backtest based on your uploaded trade journal.

Frequently Asked Questions about shadow-account

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

FAQPage Schema

How do I extract a trading strategy from my trade journal data?▼

Yes, you can backtest extracted strategies across A-shares, HK, US, and crypto markets. Multi-market backtesting validates your rules to measure performance and calculate delta-PnL attribution across these asset classes.

What is performance attribution in trading and how does it identify emotional noise?▼

Performance attribution provides a detailed breakdown of PnL, identifying the cost of emotional noise, early exits, and missed signals. It calculates Sharpe ratios and drawdown to measure the exact impact of trading habits on profitability.

Does this strategy extraction approach work with crypto and stock market backtesting?▼

Trade journal processing requires uploading your historical trade records for statistical analysis. The skill applies journal data processing to calculate Sharpe ratios, drawdown, and delta-PnL attribution for strategy extraction.

How do I run a multi-market backtest using extracted trading rules?▼

Strategy extraction identifies profitable trading patterns from your journal data to generate actionable rules. This bridges the gap between emotional trading and disciplined, rule-based strategies by targeting specific profit-generating habits.

What metrics are used for trade journal analysis and performance attribution?▼

Trade journal analysis requires historical trade records including entry and exit data to process journal information. The skill applies statistical analysis to calculate Sharpe ratios, drawdown, and delta-PnL attribution for performance measurement.