trading-dna-analyzer

Extract shadow trading rules from trade journals and backtest them across markets.

Updated Apr 14, 2026
One-click install
npx skills add https://github.com/loanntc/Paave --skill trading-dna-analyzer
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: trading-dna-analyzer
Source: https://github.com/loanntc/Paave/tree/main/skills/shadow-account
Command: npx skills add https://github.com/loanntc/Paave --skill trading-dna-analyzer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you understand what patterns in your own trades actually drive profitability, then quantifies how much of your results could be explained by those patterns versus noise.

Core Features & Use Cases

  • Shadow strategy extraction: Extracts 3–5 plain-language “rule cards” that describe your profitable roundtrips, then asks you to confirm whether they match you.
  • Multi-market backtesting: Backtests your derived shadow strategy across China A-shares, Hong Kong, US, and crypto, producing per-market and combined performance.
  • Delta attribution + report: Generates an 8-section HTML/PDF report with signed “difference PnL” breakdown (noise, early/late exits, overtrading, and missed signals) plus a Top 5 counterfactual list with dates and rationale.

Quick Start

Upload your trade journal, then ask: “提炼我的策略并回测影子账户,生成PDF报告。”

Frequently Asked Questions about trading-dna-analyzer

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?

Shadow strategy extraction identifies patterns in your profitable roundtrips and converts them into 3-5 plain-language rule cards. This process helps you understand what actually drives your profitability versus market noise.

Can I backtest my trading strategy across multiple markets like crypto and US stocks?

Yes, you can backtest your derived shadow strategy across multiple markets including China A-shares, Hong Kong, US, and crypto. The analyzer performs per-market backtests to produce both individual and combined performance evaluations.

What is delta attribution in trade analysis and how does it work?

Delta attribution in trade analysis quantifies your performance gaps by generating a signed difference PnL breakdown. It isolates specific impacts like noise, early or late exits, overtrading, and missed signals to show exactly what harmed or helped your results.

How do I generate a PDF report for my trade journal analysis?

You can generate a PDF report by uploading your trade journal and requesting the analysis. The system renders an 8-section HTML-to-PDF report containing your performance breakdown and counterfactual list, automatically falling back to HTML if PDF rendering fails.

Does trade journal backtesting work for identifying missed trading signals?

Yes, trade journal backtesting identifies missed signals through a Top 5 counterfactual list. It evaluates your extracted shadow rules against historical data to highlight specific dates and rationale for profitable trades you missed.