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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
Roberto Coccaro
Hello,
i would add also change data and symbols, if the strategy is robust enough it would do well in market regime changes
Yuri Lopukhov
good idea from general Data Science, but most trading metrics on periods with different lengths will not be comparable, starting with Sharpe ratio (returns and volatility scale differently with time).
Also, walk-forward validation usually implies more than one split, otherwise it is just an Out-of-Sample Testing.
Most of these technics are not possible to implement in a single QuantConnect project code. They basically require either manual implementation of all steps (very tedious and error-prone) or standalone scripts that will either use QuantConnect via API or run LEAN instance in Docker to set project parameters, run multiple backtests and collect data.
In the end of the day I am curious if there are any actual profitable strategies that can jump all these hoops? ;)
Ney Torres
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