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Abstract Background Accurately detecting the Sleep Period Time (SPT) window in the daily life is essential for understanding habitual sleep and health. Although actigraphy devices (accelerometers) placement varies across studies, most SPT-detection algorithms are developed for wrist data. Open-source algorithms support reproducibility and transparency in estimating the SPT. Aims To optimise and evaluate two open-source algorithms, HDCZA and HorAngle, for estimating the SPT window using hip-worn accelerometer data. Methods A total of 109 children and 194 adults wore wrist and hip accelerometers for six nights and completed sleep diaries. An established algorithm combining wrist and diary data served as the reference. HDCZA and HorAngle parameters were optimised using Bayesian optimisation on 60% of the sample and evaluated in the remaining 40%. Results Mean differences for sleep onset and wake-up were –3 and 4 minutes for HDCZA (limits of agreement [LoA]: −221,215 and −185,194; root-mean square error [RMSE]=111 and 97) and 0 and −4 minutes for HorAngle (LoA: −199,199 and −223,214; RMSE=111 and 112). For SPT duration, mean differences were 7 minutes (LoA: −252,266; RMSE=132) for HDCZA and −4 minutes (LoA: −254,246; RMSE=128). No significant differences in SPT duration were found (P=0.774; P=0.237). Both algorithms showed moderate agreement with the reference in ranking sleep duration (κ ≈ 0.56−0.58). Differences were unrelated to age or sex but linked to non-wear time. Conclusions Both open-source algorithms demonstrated value for estimating the SPT window from hip data. While HDCZA requires no additional sensor-specific parameters, HorAngle depends on accurate axis identification. Statement of Significance Accurately estimating the sleep period time (SPT) window from hip-worn accelerometers is essential for studies assessing sleep in free-living conditions. However, most available algorithms were developed for wrist-worn data. This study optimised and validated two open-source algorithms, HDCZA and HorAngle, for hip-worn accelerometer data in children and adults. Both algorithms performed comparably to a wrist-based reference using sleep diaries, showing consistent agreement across age and sex. These methods enable researchers to estimate habitual sleep without additional sensors or diaries, improving reproducibility and scalability in observational research. The algorithms are openly implemented in the GGIR R package, offering accessible and standardised tools for analysing hip-based accelerometer data.