Allele-specific copy number alteration (ASCNA) analysis is for identifying genetic abnormalities in cancer by comparing the copy number in normal cells with tumor cells. In practice, tumor cells are often heterogenous with different subclones having different genetic abnormalities. We propose a hidden Markov model (HMM)-based two-step algorithm that infers the genotype of major subclone, proportion and genotype of subclones as well as the percentage of tumor cells (tumor purity) and the number of sets of chromosomes in tumor cells (ploidy). In this framework, the hidden state is the conglomeration of clonal genotypes and subclonal status. The first step of the algorithm is a standard hidden Markov model with this conglomerated state space, while the second step estimates subclone characteristics on a regional basis using standard mixture modeling. We apply the proposed methodology to a renal cell carcinoma dataset from The Cancer Genome Atlas (TCGA). In addition, we conducted simulation studies that showed the good performance of the approach.