Risk management for an investment portfolio is a much more robust process than just selling the under performing stocks.
While reviewing investments that have had done poorly is a good idea, it needs to have some structure around the review.
This is especially difficult in algo trading, which happens quickly and when doing a back test is very hard to monitor.
Running a multi year backtest takes a few minutes, but if you were really running the strategy you would have all those days/months/years to stew over the positions your algo is suggesting/trading, when it is under performing the urge to act and sell stocks that have had recent poor performance can be very strong. With a simple strategy of selling all losers, there can be issues:
1. If the market as a whole has a significant decrease in a short time, you might end up liquidating several stocks right as the price has hit a low.
2. You may be trading on short term noise, is the stock that was sold volatile (higher standard deviation) than the rest of the portfolio, if so then the mark of 10% may be arbitrary and inappropriate for that particular stock.
It would be better to look at why those stocks were selected in the first place and what caused the issue. Maybe go back and look at which stocks were sold and see if there is an obvious reason why? Then you can tweak your algo to exclude them in the first place. Did it drop because the company had a major issue? Did it drop just because it is generally more volatile? Was it part of a larger market move or isolated to that particular security? Are there other factors other than just returns that would better signal a time to sell, maybe fundamental factors or credit quality of the firm?
As far as general risk management, max drawdown is important but should be calculated on a portfolio level, not individually. If you have a long/short strategy, selling a stock that loses money may not be taking into account the unusually good performance of the other side of the portfolio. If your portfoio has large drawdowns, then you need to look at your selection criteria and the market conditions in which your algo does poorly. Maybe you built an algo that out performs in a low-volatility market, but if volatility increases it does poorly or you have a great defensive algo that does well in market downturns, but poorly in a bull market. It is incredibly difficult to make an algo that performs well in all conditions, the mythic money printing algo.
The nuance of market conditions and the specifics of the stocks that are being traded can easily be lost in the blizzard of pipeline selection and high speed backtesting, but it is critical in real life.
There are a lot of metrics, other than just returns, that will help you identify and manage risk.
A great first book on risk management is:
The Essentials of Risk Management, Second Edition 2nd Edition
by Michel Crouhy (Author), Dan Galai (Author), Robert Mark (Author)