analyzing-backtests

Analyze TradingView backtest metrics for edge quality and overfitting risks.

1|Updated Feb 9, 2026

Search Tags:#analysis #metrics #trading #risk #edge #backtest

Ready to run

npx skills add https://github.com/piriya33/antigravity-skills --skill analyzing-backtests-piriya33

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: analyzing-backtests
Source: https://github.com/piriya33/antigravity-skills/tree/main/trading-stack/analyzing-backtests
Command: npx skills add https://github.com/piriya33/antigravity-skills --skill analyzing-backtests-piriya33

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyzes backtest results to reveal edge quality, risk, and reliability of strategy performance.

Core Features & Use Cases

Extracts key metrics (Net Profit, Total Trades, Win Rate, Profit Factor, Max Drawdown) from backtests
Performs sanity checks and quality assessments
Generates a summarized verdict with actionable recommendations and red flags

Quick Start

Run the backtest data through the analyzer to generate a structured report.

Frequently Asked Questions about analyzing-backtests

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

FAQPage Schema

How do I analyze TradingView backtest results for overfitting risks?▼

Analyzing TradingView backtest results for overfitting risks involves extracting key metrics like Net Profit, Win Rate, and Max Drawdown, then performing sanity checks to evaluate strategy edge quality and reliability. This Skill processes backtest data to generate a structured summary with red flags and actionable recommendations.

What metrics are needed to evaluate trading strategy edge quality?▼

Evaluating trading strategy edge quality requires key metrics including Net Profit, Total Trades, Win Rate, Profit Factor, Max Drawdown, and Avg Win/Loss. These metrics are extracted from backtest results to perform sanity checks and assess the overall performance and risk profile of the trading strategy.

How do I compare backtest versions after Pine Script iterations?▼

Comparing backtest versions after Pine Script iterations requires applying a standardized analyzer to extract consistent metrics like Profit Factor and Max Drawdown from each version. This process highlights performance changes, validates improvements, and identifies any newly introduced red flags across iterations.

Can I use this backtest analyzer to validate changes to my trading strategy?▼

Yes, you can use this backtest analyzer to validate trading strategy changes. It processes backtest outputs to perform quality assessments, generating a summarized verdict with actionable recommendations and red flags to confirm whether the modifications improved edge quality and reliability.

What is the best way to identify red flags in trading backtests?▼

Identifying red flags in trading backtests is best done by running the results through an automated analyzer that extracts performance metrics and applies sanity checks. This process reveals overfitting risks and outputs a structured summary highlighting specific red flags and actionable recommendations.

Does this backtest analyzer work without requiring external dependencies?▼

Yes, the backtest analyzer works independently without requiring external dependencies or components. You simply run the backtest data through the analyzer to generate a structured report containing extracted metrics, sanity checks, and a summarized verdict with actionable recommendations.