In this study a continuous wavelet transform is performed on Bitcoin’s historical returns. Despite the asset’s novelty and high volatility, evidence from the wavelet power spectra shows clear dominance of specific investment horizons during periods of high volatility. Thanks to wavelet analysis it is also possible to observe the presence of fractal dynamics in the asset’s behavior. Wavelet analysis is a method to decompose a time series into several layers of time scales, making it possible to analyze how the local variance, or wavelet power, changes both in the frequency and time domain. Although relatively new to finance and economic, wavelet analysis represents a powerful tool that can be used to study how economic phenomena operates at simultaneous time horizons, as well as aggregated processes that are the result of several agents or variables with different term objectives.