Market risk

Finally, we have market risk, which is what is commonly thought of when we think of risk in algorithmic trading. This is the risk of trading against and losing money to more informed participants. Every market participant, at some point or the other, on some trade or the other, will lose money to a more informed participant. We discussed what makes an informed participant superior to a non-informed one in the previous section. Obviously, the only way to avoid market risk is to get access to more information, improve the trading edge, improve sophistication, and improve technology advantages. But since market risk is a truth of all algorithmic trading strategies, a very important aspect is to understand the behavior of the algorithmic trading strategy before deploying it to live markets.

This involves understanding what to expect normal behavior to look like and, more importantly, understanding when a certain strategy makes and loses money and quantifying loss metrics to set up expectations. Then, risk limits are set up at multiple places in an algorithmic trading pipeline in the trading strategy, then in a central risk monitoring system, then in the order gateway, sometimes at the clearing firm, and finally sometimes even at the exchange level. Each extra layer of risk check can slow down a market participant's ability to react to fast-moving markets, but it is essential to have these to prevent runaway trading algorithms from causing a lot of damage.

Once the trading strategy has violated maximum trading risk limits assigned to it, it will be shut down at one or more places where the risk validation is set up. Market risk is very important to understand, implement, and configure correctly because incorrect risk estimates can kill a profitable trading strategy by increasing the frequency and magnitude of losing trades, losing positions, losing days, and even losing weeks or months. This is because the trading strategy could have lost its profitable edge and if you leave it running for too long without adapting it to changing markets, it can erode all the profits the strategy may have generated in the past. Sometimes, market conditions are very different than what is expected and strategies can go through periods of larger than normal losses, in which cases it is important to have risk limits set up to detect outsized losses and adjust trading parameters or stop trading.

We will look at what risk measures are common in algorithmic trading, how to quantify and research them from historical data, and how to configure and calibrate algorithmic strategies before deploying them to live markets. For now, the summary is that market risk is a normal part of algorithmic trading, but failing to understand and prepare for it can destroy a lot of good trading strategies.

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