Background and Objective: While there are many different methods for peak detection, no automatic methods for marking peak boundaries to calculate area under the curve (AUC) and signal-to-noise ratio (SNR) estimation exist. An algorithm for the automation of liquid chromatography tandem mass spectrometry (LC–MS/MS) mass chromatogram quantification was developed and validated. Methods: Continuous wavelet transformation and other digital signal processing methods were used in a multi-step procedure to calculate concentrations of 6 different analytes. To evaluate the performance of the algorithm, the results of the manual quantification of 446 hair samples with 6 different steroid hormones by two experts were compared to the algorithm results. Results: The proposed approach of automating LC–MS/MS mass chromatogram quantification is reliable and valid. The algorithm returns less non-detectables than human raters. Based on signal to noise ratio, human non-detectables could be correctly classified with a diagnostic performance of AUC = 0.95. Conclusions: The algorithm presented here allows fast, automated, reliable, and valid computational peak detection and quantification in LC–MS/MS. We provide an open source reference implementation of the proposed algorithm on the Open Science Framewok (https://osf.io/rfqkx).