How can I detect anomalies/outliers in my online streaming data on a real-time basis?
There are two well known algorithms called Isolation Forest and One-Class SVM for outlier detection. You will find implementations of these in Sckikit learn.

Doing a search for "Anomaly Detection" on github, there seem to be entries to the NAB competition available publicly eg. nareshkumar66675/Numenta. This one has a Jupyter notebook which mainly uses Scikit learn and some custom, but simple, feature engineering. They may serve your purpose. Although the author of this one has not included Licensing information, it seems simple enough to re-implement.

However, as I understand it, the NAB datasets are more "time series" detection, i.e. a signal is an anomaly if it is very different from previous / recent values. It does not have any notion of patterns in the data, as sine pulses after step pulses, and does not include learning larger patterns as the dataset grows in size.

I'm not aware of algorithms solving your specific problem, though they might well exist in the literature. The key issue in your problem is that you cannot predict if a long sequence is an anomaly until you've seen enough data. It may suffer from combinatorial explosion.

The sines and pulses of your problem can be replaced with 0s and 1s, so your problem is one of detecting patterns in strings. Genomics is concerned with patterns in DNA, so that  body of work may have what you need. (Note that is very different from Genetic algorithms)

There is an older set of algorithms called variously, Market Basket Analysis, the Apriori algorithm or Association Set Mining which has the flavor of increasing set size, but not anomaly detection. See this video explaining it. Apriori creates sets of items commonly bought together. When you have small amounts of data, you can reliably create only small patterns. As the amount of data increases you can create larger patterns.