Next, let's take an example to demonstrate how to combine low pass and high pass filters to generate a band pass filter. The basic idea is to apply the characteristics of a high pass filter (such as ROC, momentum, or first derivative) to the output of a low pass filter (such as moving average). Firstly, the most typical high pass filters are ROC (Rate of Change) and momentum indicators because they measure price changes, filtering out the absolute levels of prices and emphasizing their variations. When I transform the input of ROC or momentum indicators from raw price data to some kind of moving average (such as SMA, EMA, etc.), I first apply a low pass filter because the purpose of the moving average is to smooth out price data and filter out short-term noise or fluctuations. Then, when I apply ROC or momentum indicators to this smoothed data, I am applying a high pass filter again. By combining the characteristics of low pass and high pass filters, we obtain a band pass filter that allows signals within a specific frequency range to pass through while suppressing low-frequency and high-frequency signals. However, since ROC and momentum indicators themselves emphasize price changes, even if their input is moving average data, their output still mainly focuses on price changes, which also makes them have the characteristics of high pass filters. Therefore, this combined indicator can be considered to have the characteristics of both a band pass filter and a high pass filter.