How to Properly Split Data for Strategy Development

Most trading strategies don’t fail because of bad logic.
They fail because of bad data splitting.

A strategy can look flawless in backtests. Smooth equity curve. High profit factor. Minimal drawdown.
But once deployed live? It collapses.

なぜ？

Because it was trained on everything.

In this video, I break down the correct way to structure your data when building strategies — so you actually understand:

• 何 In-Sample really represents

• How In-Sample Validation differs from Out-of-Sample

• Why True Out-of-Sample is the most powerful robustness test

• How to avoid hidden bias inside genetic optimization

• How to configure data splits based on volatility regimes

• Why building on less data can create stronger strategies

You’ll also learn:

• How much data to reserve for Out-of-Sample

• Whether to evaluate OOS during generation — or after

• How to set realistic validation conditions

• Why most traders unknowingly overfit

If you want strategies that survive unseen market conditions — not just look good in backtests — this is essential.

The goal is simple:
Build on limited data. Validate on what the strategy has never seen.

Watch the full video and learn how to structure your data the professional way.