Systematic trading isn’t about predicting the next candle. It’s about defining rules, testing them honestly, and only then deploying capital. The hard part is rarely the idea. It’s the validation: • In-sample vs out-of-sample splits • Transaction costs, slippage and funding rates that actually match live conditions • Survivorship and look-ahead bias removed • Performance across different market regimes, not just the one that looks good in a backtest AI can generate hypotheses faster than ever, but it doesn’t magically fix a poorly constructed test. The edge still comes from process. Programmes that reward reproducible work, disclosed assumptions and even negative results over pretty equity curves are useful. The rest is just content. If you’re applying or already building in this space, what’s the single most common backtesting failure mode you still see?