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In empirical finance, the increase or decrease in the number of stock buy/sell orders is aroused by the information asymmetry, which eventually affects the change of the stock price. To monitor the change in the stock order flow, we propose a multilayer change-point detection algorithm which makes use of the multi-resolution property of wavelet transformation. We first detect the change-points in the lower level resolutions of wavelet transforms and then map them back to the points in the original time series. Different weights are assigned to the different levels for computing the confidence of the mapped points to be the change-points in the original time series. The change-points obtained by our method are more reliable than the change-points detected only from the original time series. The experiments on both artificial Poisson sequences and real-world stock order flows from Shanghai Stock Exchange (SSE) show the effectiveness of our detection method.