We believe the algorithms of Unhedged are all pretty awesome on their own. During live trading and in back-tests they’ve had solid periods where they outperformed the market benchmarks. However, each algorithm has a certain hypothesis and looks at the financial world in a particular way, therefore they can be a bit early or late to act.

By combining these algorithms we can create pre-set allocations which generate different risk-return profiles. Unhedged has two pre-set allocations: Equal Weight and Equal Risk.

Equal Weight  Allocation

Backtested Performance of the Equal Weight Allocation

Hypothesis

The hypothesis of Equal Weight is that if we combine algorithms whose return streams are more or less uncorrelated we get an end result that is less volatile than a single algorithm.

Inner Workings

Because there will be times when one algorithm does better than the other, we need to rebalance. This allocation should be rebalanced every month to make your result look like the graph above. We are working on an improvement where we will auto rebalance within the fund but that will likely be launched mid 2022.

The Details

Every month, not on the same day, we look at the ratios of the algorithms and see if they have drifted more than a set percentage from the target allocation. If they have drifted we sell and buy assets so the allocation reverts back to Equal Weight. Because the Unhedged Fund is growing very fast we assign new funds in such a way that the Equal Weight balance is maintained, so in reality rebalancing only occurs when the market is very volatile.

Equal Risk Allocation

Backtested Performance of the Equal Risk Allocation

Hypothesis

The hypothesis of Equal Risk is that if we combine algorithms whose return profiles are more or less uncorrelated and we weigh them according to the risk it adds to the Equal Weight pre-set, then we generate returns that are less volatile than a single algorithm and less volatile than Equal Weight.

Inner Workings

This preset ideally works with a monthly rebalance but it employs a so-called Meta Algorithm that calculates the ideal mix on a daily or weekly basis. So this is an algorithm to determine the weighting of the other algorithms! This Meta Algorithm looks at the risk, volatility and correlation of each algorithm and finds the weight that makes each algorithm equal in its risk attribution via a complex machine learning algorithm. This allocation should be rebalanced every month to make your result look like the graph above. We are working on an improvement where we auto rebalance within the fund but that will probably be launched in mid 2022.

The Details

Every month we take the last 12 years of data and slice it into 200 days of trading data. All those so-called windows are analysed for their optimal combination. Then the model repeats this for 60 days and 30 days. A fancy calculation determines the weighting of the optimal weights and spits out the ratio that the algorithm predicts has the lowest volatility in the coming month and applies that ratio as the Equal Risk allocation. By looking at different periods we can optimize for look-a-like periods. This algorithm is not iterative so it doesn’t confirm whether the choice was a good choice but we are building new allocation algorithms that use computationally-heavy genetic or self learning algorithms. Really exciting for nerds like our quant team!

To learn more about each individual algorithm check out our algorithms explainer page.

The Information on this page is based on backtesting and is, therefore, to be considered as HYPOTHETICAL. Any financial products described on this page will be issued by third parties, as disclosed in the relevant disclosure document. All information is general information only and does not take into account your personal circumstances, financial situation or needs. Before making a financial decision, you should read the relevant disclosure document and consider whether the product is right for you and whether you should obtain advice from a professional financial adviser. Any information contained on this page may have been automatically generated by an algorithm based on raw data inputs, has not been independently verified and is subject to change.