Hi everyone,

I've created a comprehensive guide for robust backtesting in QuantConnect that consolidates best practices for validating strategies before live trading.

**GitHub Repository:** https://github.com/Neyt/quantconnect-backtest-improvement

## What's Included:

✅ **Walk-Forward Validation** - Code examples for splitting data and avoiding overfitting✅ **Transaction Costs** - How to implement realistic fees and slippage models✅ **Out-of-Sample Testing** - Best practices for reserving test data✅ **Parameter Sensitivity Analysis** - Using GetParameter() for robustness testing✅ **Implementation Checklist** - Track your validation progress✅ **Complete Code Examples** - Ready-to-use Initialize() templates

## Use with AI Assistants

The guide is designed to be used with ChatGPT, Claude, or other AI coding assistants. Simply copy the relevant section and paste it with your algorithm code to have the AI implement the validation technique.

## Why This Matters

Backtests can lie due to:- Overfitting (curve fitting to noise)- Look-ahead bias (data leakage)- Market friction (ignoring real trading costs)

This guide helps you validate strategies properly using industry best practices and QuantConnect's official documentation.

Feel free to fork, contribute, or suggest improvements. Hope this helps the community avoid costly mistakes in live trading!

Best,Ney