Lessons from building a Multi-Horizon Crypto Prediction System with Transformers

Short summary

An engineer shares lessons from building a hybrid Transformer + BiLSTM system for predicting Bitcoin price direction across multiple time horizons. After fixing critical bugs — temporal data leakage, gradient flow issues, and fake 96% accuracy from correlated samples — the system achieved 54.9% average directional accuracy on real Binance data. The post emphasizes transparent, timestamped verification over cherry-picked backtests.

• •Hybrid Transformer+BiLSTM architecture for multi-horizon crypto prediction built from scratch in PyTorch
• •Three major bugs (temporal dependence, future leakage, gradient flow) taught more than any course
• •Honest 54.9% accuracy with public audit panel beats fake backtest claims

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