I spent 3 weeks on Transformer models for crypto quant trading. Switched to LightGBM. Got better results in one day. Here's what I actually learned.

When building a quant system, model selection is where most people waste the most time. I did too.

I went through rules-based systems, random forests, LightGBM, Transformer, TFT, and back to LightGBM. Not going in circles — each stage had real lessons.

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What happened with Transformer

Three weeks training a Transformer to predict crypto price direction. Tuning hyperparameters, changing architectures, adjusting learning rates. Validation accuracy stuck around 54% — barely better than random.

The bigger problem: I had no idea what it was learning. No feature importance. No explanation for why any parameter was set the way it was. When something went wrong, I had no starting point for debugging.

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LightGBM in one day

Switched. Validation accuracy hit 67.5% the next day.

But the accuracy wasn't the main point. LightGBM gave me something a Transformer can't: feature importance.

After training, I could see exactly which features were driving predictions and which were noise.

The three most important features in my system:

• 4-hour momentum
• Long liquidation ratio
• Cosine-encoded hour of day

That last one surprised me. The model learned when to trade — not just what direction. Certain hours produced more reliable signals. I didn't design this in. The model found it in the data.

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Why interpretability isn't optional

When something breaks in live trading — and it will — can you find the cause?

Transformer: no idea. Retrain and hope. LightGBM: check feature importance, identify the problematic feature, fix it directly.

A live system will break. How fast you can find the root cause determines whether you fix it before the losses compound.

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The full model iteration path

Rules-based → Rules + statistical indicators → Random Forest → LightGBM → Transformer → TFT → back to LightGBM

The conclusion isn't that LightGBM is the best model. It's that for a high-leverage live trading environment, debuggability matters more than theoretical accuracy ceiling.

Start simple. Every layer of complexity you add should be something you need and can explain — not something that looks impressive.

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Live now. Starting equity $902. Real numbers posted daily.

X: @dayou_tech