good algorithm for outliers detection I have 2 independent data sets (1. 300 rows and 2.3000 rows) with 6 months trades observations for 50 traders. In both datasets I have:
trader id,
stock title,
buy/sell volume,
date of trade,
sector of stock

My goal is to detect possible outliers (suspicious trades) in this two datasets. 


What algorithm you would recommend for this 2 tasks and why?
What can we use for 1 task when we have average only 5-6 trades per trader?
I'd go out on a limb to say this is a difficult problem without incorporating other data. Models just try to explain future action with past information. Traders act differently in different scenarios. 

Without data on their analysis or financial data on the stocks they are analyzing, a model on the previous transaction history of traders would not be effective. Unless, if the "traders" you describe are more like etf fund managers that operate on a predefined set of rules.