I have dataset of traders' transaction data: trade id, date, stock id, sector of stock id, buy-or-sell, volume $
The goal is to identify anomalies in transactions data of traders. For example to find suspicious transactions.
As I dont have information how real outliers look like so it is unsupervised algorithms. As usual unsupervised algorithms I tried k-mean, z-score, lot, one class svm and different things with moving average etc.
Ive got results, but I was able to use only volume and date for every trader.
Is there any algorithm that can help me to include sector data/stock data/buy-sell data to do my model better? Can I include this information in above unsupervised algorithms or Im missing something?
How would you tackle this task, are there any other solutions maybe?
Thanks in advance.