This　research　proposes　a　novel　approach　for　forecasting　cryptocurrency　prices,　specifically　Bitcoin　which　dominates　the　market.　Accurately　predicting　cryptocurrency　values　is　challenging　due　to　their　highly　volatile　nature.　The　proposed　hybrid　model　uses　ResNet　Convolutional　Neural　Network　to　encode　Bitcoin　price　time　series　data　into　discriminative　representations.　These　representations　capture　long-range　dependencies　using　XGBoost　regression.　Additionally,　wavelet　denoising　is　applied　to　filter　noise　from　the　price　data.　The　combined　ResNet-XGBoost-Wavelet　model　achieves　satisfactory　results　for　Bitcoin　price　forecasting　and　has　practical　applications　for　developing　quantitative　trading　strategies.　While　incorporating　sentiment　analysis　and　additional　influencing　factors　could　further　improve　predictions,　this　work　presents　a　competitive　approach　for　minimizing　investment　risks　and　maximizing　profits　in　the　complex　domain　of　cryptocurrency　markets.　©　2023　Copyright　held　by　the　owner/author(s).　Publication　rights　licensed　to　ACM.