shadow-account

Name: shadow-account
Availability: InStock
Author: 0xzknw

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

Updated Jun 30, 2026

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

Ready to run

npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill shadow-account-0xzknw

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: shadow-account
Source: https://github.com/0xZKnw/vibe-trading-tap/tree/main/agent/src/skills/shadow-account
Command: npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill shadow-account-0xzknw

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill addresses the lack of objective self-awareness in trading by distilling raw transaction journals into actionable, rule-based strategies and identifying the impact of emotional noise on portfolio performance.

Core Features & Use Cases

Strategy Extraction: Automatically distills 3-5 clear, human-readable trading rules from your profitable trade history.
Multi-Market Backtesting: Simulates your extracted strategy across A-shares, HK, US, and crypto markets to measure performance metrics like Sharpe ratio and drawdown.
Attribution Analysis: Provides a detailed breakdown of how emotional trades, early exits, and overtrading impact your bottom line compared to your shadow strategy.

Quick Start

Load the shadow-account skill to analyze my uploaded trade journal and generate a shadow 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 need to input structured transaction data so the system can distill 3-5 clear, human-readable rules from your profitable trade history and quantify emotional noise impact.

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

Yes, you can backtest trading strategies across A-shares, HK, US, and crypto markets using extracted journal rules to measure performance metrics like Sharpe ratio and drawdown.

How does attribution analysis measure the impact of emotional trades on portfolio PnL?▼

Attribution analysis measures the impact of emotional trades by comparing your actual transaction journal against a systematic shadow strategy, providing a detailed breakdown of how early exits and overtrading affect your bottom line.

Do I need a structured trade journal format to perform shadow backtesting?▼

Yes, shadow backtesting requires structured trade journal inputs to successfully extract objective rules, perform multi-market simulations, and generate comparative PnL reports with actionable strategy insights.

What is the best way to quantify emotional noise versus systematic strategy execution?▼

The best way to quantify emotional noise versus systematic execution is to run a multi-market backtesting simulation on your historical trade data and generate a comparative PnL report highlighting performance deviations.