What is exposure

Exposure tells us what percentage of the capital assigned to a particular strategy or portfolio is involved during a given period of time.

Why exposure is important

Exposure is one of the key parameters when evaluating a strategy and building a portfolio.

It answers three key questions:

1. Which strategy is better?

E.g. Strategy 1 provides a 10% annual return with an average exposure of 100%

Strategy 2 also provides a 10% annual return but with an average exposure of 20%

Which strategy would you choose if the other key metrics of these strategies were similar?

The answer is obvious, strategy 2 has the same return but uses capital only 20% of the time, so the remaining capital can be used e.g. by other strategies.

2. How to choose strategies for a portfolio?

Each of the 3 strategies has an average exposure of 20%. Can they be used together by giving each the same capital? Or do they will use all the capital x3 exactly in the same short period, causing additional risks and costs? The ability to assess this information can be crucial in selecting strategies for a portfolio.

3. How much capital to allocate to individual strategies within the portfolio?

In Algocloud, we have the ability to conveniently manage what percentage of capital we allocate to individual strategies. However, how to make this decision responsibly? Do I need to consider using margin and to what extent?

Some key answers, such as maximum drawdown, are provided by Strategy Quant in the portfolio creation process. However, we do not have information on optimal exposure here.

The question becomes more complex the more strategies we have. Fortunately, we have developed a specific supporting solution for you, which I will write about below.

But let's start with visualizing what exposure is.

Exposure example in Stockpicker strategy

Look at the following example of 6 transactions in the same period.

This is how these transactions look in the Algocloud/SQX history:

And this is what their visualization of exposure on the timeline looks like:

The transaction periods partially overlapped. Each transaction had a similar level of exposure at around $10k (=open price x size), which resulted in a total maximum exposure in this period of around $50k and an average of around $30k (corresponding to 50% and 30% of the capital allocated to the strategy).

Does this provide a preliminary idea of the level of capital that should be available if you wanted to trade with this strategy during this period of time?

Now let's look at an example of the total exposure for this strategy over the entire test period (30 years) for the Stock Monthly Mover strategy:

We see that the maximum allocated capital is 100k, but it is not needed for most of the time. The average capital utilization in this strategy is only 12.85%. This is good news, provided that you can manage it at the portfolio level.

Exposure and Instrument

In Stockpicker strategies, exposure can vary significantly depending on the index we are trading on, due to the smaller or larger number of stocks included in the index.

Below is an example of the average exposure for the RSI Range Rider strategy with the same time settings and MM$ but on three different indices:

Nasdaq100 - Avg. Exposure 38.6%

S&P 500 - Avg. Exposure 67,92%

Russell1000 - Avg. Exposure 73,5%

As you can see, where we have more stocks in the index, a Stockpicker will have more options to choose from and will trade more. Therefore, when creating a portfolio, we need to pay attention not only to the strategy but also to the instrument/index on which it will be trading.

Exposure shown in Algocloud/SQX

In backtests on Algocloud and SQX, we also have information about exposure.

It is generally correct and helpful for single-asset type strategies, but for Stockpicker type strategies, it is not an average measure.

For example, in the R2 Turbo* strategy, the exposure shown is almost 63%.

During our in-depth analysis, the average exposure was only 24%. Additionally, for 4/5 of the time, the exposure was less than 50%.

We should consider utilizing this available capital, for example by combining this strategy with others.

Unfortunately, in SQX, information about the true exposure is also not available at the portfolio level, which makes it difficult to choose a strategy for the portfolio.

The good news is that we have developed a solution that is available for free as a BONUS, which you can read about here.

It is a dedicated Power BI analytics that, based on a simple import of transaction data from Algocloud/SQX, provides instant information on all exposure parameters at both the individual strategy and portfolio level.

Portfolio-level exposure

Accurately estimating exposure when creating a portfolio of strategies is even more important than for individual strategies. Portfolio-level exposure sums up the exposures of individual strategies over time. More important than averages are the maximum values and percentiles, which represent the ranges in which, for example, 80-95% of all periods fall. This also allows you to assess how often and in what periods the portfolio required the use of margin and in what amounts.

Take advantage of the Bonus and get our exposure evaluation analytics for free, which you can use at both the strategy and portfolio level.

We will dedicate a separate article to examples of evaluating exposure at the portfolio level.

*Test conducted on S&P500 stocks for the years 1994-05.2024 with MM% and a maximum capital allocated to the strategy of $100k.