How to find anomalies in (almost) constant stream of data?
Try looking into the Robust Random Cut Forest Algorithm (RRCF). There's a python library implementation that supports streaming data where you create rolling windows (called shingles): https://github.com/kLabUM/rrcf
RRCF builds a binary decision tree by randomly picking a number between the min and max of your variable and splitting the data there. If a point is alone after the split it becomes a leaf in the tree. The more separate from the rest of the data a point is (ie the more of an outlier/anomaly it is), the more likely it is to become a leaf higher in the tree. This works well for higher-dimensional data since it will just split all variables randomly.
Here's a video that explains RRCF better than I could in a written post: https://youtu.be/yx1vf3uapX8?t=355