shadow-account

Name: shadow-account
Availability: InStock
Author: OpenSucker

Analyzes trading journals and generates HTML/PDF reports with performance metrics.

15|2|Updated May 1, 2026

Search Tags:#quantitative analysis #strategy #profitability #performance report #backtest #trade journal

Ready to run

npx skills add https://github.com/OpenSucker/OpenSucker --skill shadow-account-opensucker

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: shadow-account
Source: https://github.com/OpenSucker/OpenSucker/tree/main/skills/vibe_skills/shadow-account
Command: npx skills add https://github.com/OpenSucker/OpenSucker --skill shadow-account-opensucker

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires weasyprint, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps traders extract profitable rules from trade journals, perform multi-market backtests, and generate comprehensive performance reports, revealing hidden insights into their trading behavior.

Core Features & Use Cases

Rule Extraction and Validation: Summarizes key trading patterns from user-uploaded journals into simple, human-readable rules.
Multi-market Backtesting: Runs backtests across A股,港股,美股及加密货币市场 to evaluate strategy performance.
Report Generation: Produces detailed HTML and PDF reports including performance metrics, attribution, and critical insights into trading effectiveness.
Use Case: A user uploads their交易交割单, runs rule extraction to identify their common patterns, then backtests these rules across multiple markets to assess profitability.

Quick Start

Upload your trade journal, then ask the AI to extract your trading rules and see how they perform through backtesting and report summaries.

Frequently Asked Questions about shadow-account

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

FAQPage Schema

How do I extract profitable trading strategies from my trade journal?▼

To extract trading strategies from a trade journal, upload your trade records to the AI. It automatically analyzes the data to identify common patterns and summarizes them into simple, human-readable rules.

Can I backtest trading rules across multiple markets like A-shares and crypto?▼

Yes, you can backtest trading rules across multiple markets including A-shares, Hong Kong stocks, US stocks, and cryptocurrencies. The tool runs multi-market backtests to evaluate strategy profitability.

What is the best way to generate a performance report for my trading backtest?▼

The best way to generate a performance report for trading backtests is using automated report rendering. It produces detailed HTML and PDF reports including performance metrics, attribution, and critical insights.

Do I need weasyprint to create PDF performance reports from my trade data?▼

Yes, you need weasyprint installed to render PDF performance reports. This dependency enables the tool to generate detailed PDF documents from your backtested trade data and statistical analysis.

How does natural language extraction work for trading journal analysis?▼

Natural language extraction for trading journal analysis works by automatically processing your uploaded trade records to derive readable rules. It identifies hidden insights into your trading behavior without manual coding.