Change from fixed VaR to variable VaR in the DARWIN assets

Photo by ManelTor / CC BY


On January 11th 2020 we will be making changes in the way in which the Darwinex risk manager works.

Here we will explain:

  1. The reason for the change
  2. The operation of the new manager
  3. The effects achieved
  4. The implementation of the change
  5. Appendix: Examples of popular DARWINs

1. What the reason for the change is

The risk manager is in charge of ensuring that all DARWIN assets trade with a known risk, regardless of the risk taken on by the underlying strategy on which the logic for market entry and exit decisions is based.

This means that the DARWIN replicates the asset and the timing of the trader’s operation, but investor volume is defined by Darwinex through the risk management logic. The manager’s involvement thus makes the respective returns of the underlying strategy and of the DARWIN different.

As of today, all DARWINs have the same target risk – 10% monthly VaR. Setting it at a given value and not allowing it to be variable has a series of benefits.

  • It makes it possible to compare DARWIN returns in a very intuitive way.
  • The investor knows the risk of any DARWIN portfolio composition at any given time.
  • It counters the bias of many traders who, for emotional reasons, are unable to take on significant investment amounts and, to protect themselves, lower their strategy risk, even though there is no evidence that their predictive capacity has worsened.
  • It removes the statistical illusion of trading strategies whose performance is mainly based on the gradual use of leverage, such as more aggressive martingales, which are so widespread in copytrading.
  • It makes it easier to estimate the maximum capacity that a DARWIN can absorb, that is, the maximum investment that it can manage without impairing its investors’ returns too much.
  • It makes it possible to apply management fees adjusted to the potential returns of the DARWIN in question.

However, in addition to these features, the risk manager should be as least “intrusive” in a trader’s strategy as possible, so that the trader does not feel that the risk manager is harming their investors. Otherwise, the best managers will be unwilling to provide their systems to our market.

In the past, we received many complaints along these lines. Many of them were baseless, or only came when the trader was harmed, but we were not thanked when it was the other way round (that’s the human condition).

Obviously, we must continue to improve the risk manager to make it less “intrusive”, but it’s hard to establish what that means.

After compiling feedback, we have reached the conclusion that many traders do not always trade with the same risk. There may be long periods (even 1 year long, but usually 1-6 months) in which the trader, because they believe that their system will not perform well due to market conditions, because they are making adjustments, or for any other reason, lowers their risk.

If these periods in which traders lower their risk last more than 45 days (the benchmark period used to calculate the underlying strategy VaR), at the end they significantly lower the benchmark VaR calculated by the platform. Then the DARWIN multiplies the trader’s leverage by a higher factor than usual, and consequently there is a sudden loss of proportionality between the underlying strategy and the DARWIN.

If the trader was right and their strategy does not perform well during that period, the DARWIN will lose significantly more than the underlying strategy with respect to its usual performance (if the trader was not right, the opposite would be the case, obviously).

Most traders are UNABLE to predict the market. Consequently, lowering their risk usually generates a random performance from the DARWIN. The problem is that it is precisely the best traders who ARE ABLE to adjust their risk to market conditions.

We have reached the conclusion that the risk manager should cease to have a fixed VaR to adjust better to the best traders when, for whatever reason, they believe that it is better to lower their strategy risk for reasonably short periods (less than 6 months).

Having said this, risk should fall within a range – otherwise, we would cease to comply with the rest of reasons why we preferred to make it fixed.

2. The operation of the new manager

With this change, DARWIN risk becomes variable, between 5% and 10%.

To determine the target VaR, historical VaR data is taken into account, starting with the most recent data with a look-back window of maximum 6 months, until the ratio between maximum and minimum VaR is 2:1.

The current VaR then gets divided by the maximum VaR calculated before. Last, this ratio gets multiplied by 10%. The resulting VaR will move between 5% and 10%.

Should there be no 2:1 ratio in the last 6 months, the maximum in the last 6 months is taken into account.

2.1 Examples

Current VaR: 8%
Maximum VaR: 12% one month ago
Minimum VaR: 6% five months ago
Target VaR: (8%/12%)*10%=6.67%

Current VaR: 9%
Maximum VaR: 14% 2 months ago
Minimum VaR: 8% in the last 6 months
Target VaR: (9%/14%)*10%=6.43%

In summary, the Risk Manager tolerates changes in Var up to 2 times (rises or falls) with the aim to better adapt to the trader’s risk management.

2.2 Additional change

To protect investors from abnormally high leverage, there is an additional control measure that does not allow investors’ D-Leverage to exceed in any case the 20-15-15 threshold for 15 min-30 min-1 hour positions.

This restriction mainly affects short-term DARWINs, or DARWINs that use short-term leverage peaks.

This change also makes that the DARWIN and the underlying strategy look more “similar” in shape.

3. The effects achieved

DARWIN performance at times of drawdown is significantly improved, without penalising too much total returns when the DARWIN is recovered. That is, an improvement in the return/drawdown ratio is achieved, and above all, we achieve greater similarity in DARWIN performance with respect to the medium-term risk decisions of the underlying strategies.

To better understand the impact of the change in the risk manager on DARWINs, we will give a very illustrative example. This is LZB, backtested using the current and the new manager. In both cases, a backtest is used because we have no real data using the new manager, and so the comparison is better using the same trading data (backtest).



Blue: Darwin backtest with the current risk manager
Red: Darwin backtest with the new risk manager


LZB underlying

LZB underlying strategy curve


The example shows that the current risk manager is responsible for the strategy drawdown being hugely amplified in the DARWIN, and later the strategy recovery is not apparent to the same extent in the DARWIN. This is an obvious case in which the current Darwinex manager damaged the strategy.

By contrast, the new manager, by offering a lower risk (between 5% and 10%) is able to perfectly replicate the proportionality with the underlying strategy.

4. Implementation

The change will be automatically implemented. Neither traders nor investors need do anything.

Investible attributes will continue to function, at least for the time being, as though the VaR were still fixed and 10%. This has implications for certain notes, but they may take a long time to change, and we have opted to make the risk manager our priority.

Past graphs and charts won’t be recalculated.

5. Appendix: More examples of popular DARWINs


LVS, last 2 years






SYO since migration






ULI last 3 years



WSS last year






NTI last 3 years



ZVQ since migration

For discussion and feedback on this change, we invite you to visit this community post.

The new Risk Manager 2.0 (Part 3)

This is the third post of our series of articles regarding the new Risk Manager. If you still have not read them, we recommend that you read the first post about the reduction of VaR from 20% to 10% and the second post in which we spoke about the changes in the calculation of the VaR.

The change that we are going to explain in this third article is the fruit of the feedback that we have received from a few traders in our community. During the last few years, we have received various reasonable complaints from traders that have criticised that a DARWIN has experienced losses when the underlying strategy, having a reasonably low and stable  VaR , would generate positive returns.

In our continual drive to improve our system, we decided to analyze all of our strategies to see what the reasons were behind the phenomenon that our users had reported.

This analysis helped us to find an error in the approach at the time of defining the maximum permitted leverage for the underlying strategies, from which our risk manager closed the investor’s exposure in order to guarantee the 20% monthly VaR risk of the investors ( n.b: from the launch of Darwinex reloaded , the VaR objective for the investor will become 10%).

Just like we have explained in previous articles, the investor leverage (i.e of the DARWIN) is calculated from the following function:

Investor leverage = strategy leverage *VaR (10%) / VaR (strategy) % risk adjusted for excessive leverage

The risk adjustment always reduces the percentage  leverage in such a way that the DARWIN yields and those of the strategy are left to behave in a linear way.

Thus it is very important to be very rigorous in the calculation. Now we will proceed to the detailed explanation of the changes that we are going to introduce in the launch of the new risk manager.

  1.   Why do we need the second component of the formula (the red component)

To explain the structure of the above formula, we are going to illustrate in an example. Let´s suppose that a strategy operates by always opening the same number of positions every month, 20 for example, with the same leverage, for example 10:1, and whose durations are always an hour. The VaR of this strategy we are going to assume is 8%.

What would occur if suddenly this strategy decided to leave  it’s positions open for a longer time or even indefinitely. Obviously, if the investors maintained this leverage during the month, the investor’s risk would be higher than 8% monthly (with 300 pips of expected movement in a month investors could lose 30% of the capital, a lot higher than the 8% calculated). It is because of this, the time elapsed, that the risk manager would have to act to close a part of the investor’s exposure.

This example (and there are many more examples), serves to explain why the maximum defined leverage for a position must depend on the duration of the position, and therefore, the risk manager must be able to act along the entire life of the position, and NOT only at the opening. Every time that a position is opened, there are temporary windows of risk performance, and for each of them, there has to be a definition of the maximum tolerated leverage, which in addition, always must  decrease as time passes.

  1.   Position history

The maximum leverage of a position is going to depend on the type of underlying strategy, so that we must base ourselves on that history to fix it. Mathematically, the most sensible way is that the selected history to define the VaR of the DARWIN coincides with that selected to determine the VaR of the strategy, so that the adjustment must be connected with the  VaR calculated before  the strategy that you want to control is opened.

  1.   Room for improvement in our closing algorithm

The fact that we actually were fulfilling the risk objective in the DARWINS, does not signify that we are optimally closing all of the positions in all of the DARWINS. This point was one that we didn’t notice when we initially designed our model.

Initially, we assumed that the fact that we were getting an objective VaR in the 20% DARWINS was because the adjustments that we were using at the end were correct. Time has shown us that, unfortunately, this assertion isn’t certain , and because of that, we have given it a “facelift” to optimize our criteria of closing positions.

If we deviate from our historical sample of “position duration” vs “leverage” of a strategy, and group them in unlimited ranges for those instances where their performance has been predetermined by the risk manager, we can obtain for each duration range the medium and dispersion of it’s leverage. We have attached a graphical explanation.



There is a maximum permitted leverage for the risk manager in terms of the “number of dispersal times” that has been taken into account in ALL the resultant  DARWINS, the monthly profitability distribution works in that only 5% lost more than 20% (in the future we will work with a VaR of 10%).

In the previous example, in a timeframe of 4 to 8 hours, the maximum permitted value is 17.07 so that  at 4 hours the risk manager would close whatever position whose leverage was higher than that at that time (red dots in the picture).

Essentially , thus we have fixed the closing criteria (as we have mentioned before, this solution is only one of thousands of possible options).

What can we improve?

Our initial solution is valid for our universe of aggregated DARWINS, but not for each one of the individual DARWINs. Over time we have seen that you cannot fix a value dependant on the dispersion of whichever type of operation, and the same for every one of the performance bars in the risk manager

This procedure systematically works against those operations that have a lot of positions in a month and all types of durations, closing positions excessively when in reality, the actual risk had not gone above the VaR of the strategy.

In the following example, we can see the problem:


The position highlighted in grey, with a 17 minute duration and D-Leverage of 10, in accordance with the old analysis, would have suffered an adjustment of 40.6% in the risk for the investors in the DARWIN created from this strategy.

The question is: had we not closed this position, would the VaR of the strategy have increased? The reply is most probably yes, but with a very reduced %. In the snapshot of the month tested,  this strategy had a lot of positions (327 in total), that resulted in a VaR of 10.72%. That is to say that the contribution of this to the VaR, at most was 1/327 of the total (we can make this approximation because the other positions have a much longer duration).

Definitively, in the case that this position had been left open to the investors, the increase in VaR would not have been too significant, and, therefore, we can conclude that it wasn’t necessary to apply any adjustment.

Instead, it was closed because in the range of 15 to 30 minutes, the leverage was outside of the range of the maximum tolerated dispersion and was much higher than it’s historical median. The result in this case is that the trader closed the said position positively (+0.44%) , despite the profit of his DARWIN being seen to be reduced by 40.2%. It could be said therefore that in certain cases our risk manager was over-protecting the investor.

  1.   New risk adjustment algorithm

In the moment of opening a position, the new risk manager analyses the maximum thresholds for each time interval that, if surpassed, would suppose a potential rise in risk to a VaR of 10% in an underlying strategy (to say, a 10% +1% of tolerance = 11% of VaR in the DARWIN).

The  tolerance threshold of 10% that we have concluded upon is that which we see to be apt for whichever type of operation, and how they reflect the studies completed by our team of quants. Without wishing to enter into the details of the VaR calculation, so as not to be excessively complex, the most important conclusion is that this new adjustment works in an effective manner, limiting the risk of the DARWINS to 10%

  1.   Results

What effects has this adjustment had over the DARWINS? The two most notable conclusions of this new adjustment are :

  1. On one hand, those strategies that were being excessively closed by the risk manager by overtrading, happened to behave “better”, in the sense that the DARWIN and the strategy is going to appear more similar).
  2. On the other hand, those strategies that operate with only a few trades every month, and with a short duration, will have smaller acceptable standard deviation in the dispersion of it’s tolerated leverage above it’s historical median (the less number of positions, each one has more weight in the VaR).

We hope that this series of articles has been useful to understand the changes in the Risk Manager that are in the offing. As always, at we will be very happy to help you with whatever doubts you could have.