Managing the Risk of Algorithmic Strategies

So far, we have built a good understanding of how algorithmic trading works and how we can build trading signals from market data. We also looked into some basic trading strategies, as well as more sophisticated trading strategies, so it may seem like we are in a good place to start trading, right? Not quite. Another very important requirement to be successful at algorithmic trading is understanding risk management and using good risk management practices.

Bad risk management practices can turn any good algorithmic trading strategy into a non-profitable one. On the other hand, good risk management practices can turn a seemingly inferior trading strategy into an actually profitable one. In this chapter, we will examine the different kinds of risk in algorithmic trading, look at how to quantitatively measure and compare these risks, and...

Differentiating between the types of risk and risk factors

Risks in algorithmic trading strategies can basically be of two things: risks that cause money loss and risks that cause illegal/forbidden behavior in markets that cause regulatory actions. Let's take a look at the risks involved before we look at what factors lead to increasing/decreasing these risks in the business of algorithmic trading.

Risk of trading losses

This is the most obvious and intuitive one—we want to trade to make money, but we always run through the risk of losing money against other market participants. Trading is a zero-sum game: some participants will make money, while some will lose money. The amount that's lost by the losing participants...

Differentiating the measures of risk

Let's explore different measures of risk. We will use the trading performance from the volatility adjusted mean reversion strategy we saw in Chapter 5, Sophisticated Algorithmic Strategies, as an example of a trading strategy in which we wish to understand the risks behind and quantify and calibrate them.

In Chapter 5, Sophisticated Algorithmic Trading Strategies, we built the Mean Reversion, Volatility Adjusted Mean Reversion, Trend Following, and Volatility Adjusted Trend Following strategies. During the analysis of their performance, we wrote the results into the corresponding CSV files. These can also be found in this book's GitHub repository, https://github.com/PacktPublishing/Learn-Algorithmic-Trading---Fundamentals-of-Algorithmic-Trading, or by running the volatility adjusted mean reversion strategy (volatility_mean_reversion...

Making a risk management algorithm

By now, we're aware of the different types of risks and factors, including the risks in a trading strategy and the most common risk metrics for algorithmic trading strategies. Now, let's have a look at incorporating these risk measures into our volatility adjusted mean reversion trading strategy to make it safer before deploying it into live markets. We will set the risk limits to 150% of the maximum achieved historically. We are doing this because it is possible that there is a day in the future that is very different from what we've seen historically. Let's get started:

1. Let's define our risk limits, which we are not allowed to breach. As we discussed previously, it will be set to 150% of the historically observed maximums:

# Risk limits
RISK_LIMIT_WEEKLY_STOP_LOSS = -12000 * 1.5
RISK_LIMIT_MONTHLY_STOP_LOSS = -14000...

Summary 

In this chapter, you learned about the different types of risks and risk factors. Then, we went through the sources of risk and learned about quantifying the risks. Moving ahead, we also learned about how to measure and manage the risks (market risk, operational risk, and software implementation bugs) of algorithmic strategies. We incorporated a full production-ready risk management system into our previously built trading strategy, thus making them safe for deployment to live trading markets. Finally, we discussed and built a practical risk scaling system that starts with very low-risk exposure and dynamically manages the risk exposure over time as the strategy performance evolves.

In the next chapter, we will look at how the algorithm's trading interacts with the different factors in the trading arena. You will learn how to build a trading bot from scratch...