DevOps risk is the term that is used to describe the risk potential when algorithmic trading strategies are deployed to live markets. This involves building and deploying correct trading strategies and configuring the configuration, the signal parameters, the trading parameters, and starting, stopping, and monitoring them. Most modern trading firms trade markets electronically almost 23 hours a day, and they have a large number of staff whose only job is to keep an eye on the automated algorithmic trading strategies that are deployed to live markets to ensure they are behaving as expected and no erroneous behavior goes uninvestigated. They are known as the Trading Desk, or TradeOps or DevOps.

These people have a decent understanding of software development, trading rules, and exchange for provided risk monitoring interfaces. Often, when software implementation bugs end up going to live markets, they are the final line of defense, and it is their job to monitor the systems, detect issues, safely pause or stop the algorithms, and contact and resolve the issues that have emerged. This is the most common understanding of where operation risk can show up. Another source of operation risk is in algorithmic trading strategies that are not 100% black box. Black box trading strategies are trading strategies that do not require any human feedback or interaction. These are started at a certain time and then stopped at a certain time, and the algorithms themselves make all the decisions.

Gray box trading strategies are trading strategies that are not 100% autonomous. These strategies still have a lot of automated decision-making built into them, but they also have external controls that allow the traders or TradeOps engineers to monitor the strategies, as well as adjust parameters and trading strategy behavior, and even send manual orders. Now, during these manual human interventions, there is another source of risk, which is basically the risk of humans making mistakes in the commands/adjustments that are sent to these strategies. Sending incorrect parameters can cause the algorithm to behave incorrectly and cause losses.

There are also cases of sending bad commands, which can cause an unexpected and unintentional large impact on the market, causing trading losses and market disruptions that add regulatory fines. One of the common errors is the fat finger error, where prices, sizes, and buy/sell instructions are sent incorrectly due to a fat finger. Some examples can be found at https://www.bloomberg.com/news/articles/2019-01-24/oops-a-brief-history-of-some-of-the-market-s-worst-fat-fingers.