Does this skill only analyze mismatches or does it fix them?

It is designed to be actionable. The skill must provide concrete code or configuration fixes for any discovered discrepancies rather than just reporting them.

Why are relaxed tolerances discouraged?

Relaxed tolerances are only used as temporary diagnostic tools to locate root causes. They are not considered evidence of parity; only strict validation serves as a pass/fail gate.

What is the 'source of truth' for this skill?

The research notebook (.ipynb) is the primary authority for indicator math and logic. If Pine differs from research, Pine is corrected first, followed by the Python implementation.

How does it handle timezone differences?

The skill infers the UTC offset directly from the data (comparing timestamps) rather than requiring manual user input, ensuring timezone parity during validation.

What defines a successful validation?

Success is achieved when there is a 100% trade match rate (entry/exit timing and side). While PnL differences are tolerated, trade execution must be identical.

Pine Trade Validator

Name: Pine Trade Validator
Author: AlphaDogsResearch

byAlphaDogsResearch

0•

Data Science & ML

Aligns Python trading strategies with TradingView Pine Script exports and research logic to ensure 100% backtest parity.

This skill provides a rigorous workflow for synchronizing Python-based trading engines with original research notebooks and TradingView Pine Script behaviors. It automates the detection and resolution of discrepancies in indicator math, signal timing, and risk management between disparate environments. By enforcing a strict hierarchy of truth—prioritizing research notebooks then Pine scripts—it systematically identifies and fixes mismatches to achieve perfect trade-matching parity, ensuring that execution logic in the Coinbase-Python engine mirrors the validated research model.

Key Features

01Classification of discrepancies including timing, side, and fill-logic mismatches

02Strict pass/fail reporting based on 100% trade-matching benchmarks

03Automated parity validation between Pine CSV exports and Python backtest outputs

04Line-by-line mapping of indicator math and signal logic from research to code

050 GitHub stars

06Automated generation of code and configuration fixes for strategy alignment

Use Cases

01Verifying that a Python strategy implementation matches a proven TradingView script

02Debugging discrepancies between research notebook math and production strategy code

03Standardizing execution timing and timezone offsets across backtesting platforms

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add alphadogsresearch/coinbase-python validate-with-pine-trades

For use in Claude.ai and ChatGPT

Download Skill