Avoiding overfitting in unsupervised ML
You could partition your dataset between train and test sets. Then arrive at the pattern parameters from the train set. After that apply your learnt model on the test set to evaluate its performance.
If you figure out that your model is performing poorly then you know you have overfit.
Also since this is a trading strategy and possibly involves timeseries, you want to ensure there is no data leakage and the test set is of a time frame later than that of the test set.