Engineer builds Bitcoin AI predictor from scratch, hits 59.8% accuracy after fixing key bugs

A developer began building a multi-horizon Bitcoin price prediction system in January 2026, using a custom hybrid Transformer and BiLSTM neural network architecture built entirely in PyTorch. The system generates predictions across ten time horizons ranging from five seconds to one day, with all results verified against real Binance prices in a publicly auditable pipeline. During development, the engineer uncovered a critical data-leakage bug that inflated accuracy to a misleading 96% on five-minute predictions, which dropped to a realistic 52% after applying temporal subsampling to ensure independent trade records. A second bug was also fixed to prevent future price information from leaking into the training signal via a delayed reward buffer. After training on 365 days of Binance data, the final model achieved an average directional accuracy of 54.9% across short horizons and 59.8% on one-hour predictions.