In this blog post, we are going to explain the most recent improvements in the last investment attribute (IA) to join Darwinex’ ranks: Mr. Mc, AKA. Market Correlation.
However, before we get our hands dirty, let me briefly explain what correlation is for those of you who are not familiar with this concept.
Correlation is a statistic that measures the degree to which two variables move in relation to each other.
What are the 2 variables we use in Darwinex to calculate the Mc grade?
You are 100 % right! The relationship between your DARWIN’s return curve and the return curves in the underlying assets in which you trade.
Let me explain this further with an illustrative example.
Imagine that your DARWIN has yielded nice returns over the last year. As a result, 100% of Investors would think that you are a trading superstar and money would be pouring in into your DARWIN, right? Well, not that fast.
What if Darwinex’ algos discover that you have always been long EURUSD in 2017?
In this case, the return of your DARWIN will be 100% correlated with the EURUSD and you should not be awarded with any trading medal since you have basically made 1 trading decision in 2017: go long EURUSD.
In this extreme example, your Mc grade would be 0 which, in turn, would deteriorate your D-Score very badly, making it impossible to get a D-Score over 50, irrespective of the rest of the 11 investment attributes. Therefore, no AuM, no DarwinIA, no fame, no superstar status and an empty pocket 🙁
Darwinex Improves The Mc Investment Attribute
However, after having given this a lot of thought, we have reached the conclusion that this is not the most accurate way to measure the Mc attribute.
Well, to be totally honest with you, we already knew that this calculation was just an approximation. Nevertheless, we decided to implement it anyway for 2 primary reasons:
- It would add much more value to our proprietary diagnostic toolkit
- It would penalize “one-trick ponies” strategies which would likely prevent investors from investing in a “random” strategy -100% dependent on an exogenous factor, EURUSD evolution-
Why was our calculation only an approximation instead of 100% accurate?
This is due to the fact that, in the old Mc version, Darwinex didn’t take into consideration either the DARWIN’s leverage, which is now considered to be a trading decision in and of itself, nor the nº of D-Periods of experience accrued with such correlation
- DARWIN leverage
Going back to our example, remember that your DARWIN has always been long EURUSD, imagine that the leverage applied by our risk manager in your DARWIN, in order to offer an asset with a monthly target risk set at 10% VaR, had varied over time based on leverage changes in your underlying trading strategy. Modifications due to your technical or fundamental analysis/market conditions/predictions… on the EURUSD.
The DARWIN could have been using 5:1 leverage in some trades, 2:1 in others and then up to 8:1, etc. This way, both your DARWIN’s return curve and the EURUSD curve could look very different.
It is a fact that you have always been long EURUSD but your DARWIN could have been using very low leverage when the EURUSD went down, and more leverage when it went your way.
You’d thus be making a much better return % than the underlying asset in which you were trading.
- Experience accrued: nºof D-Periods
The experience factor is a new variable that we have decided to introduce in the final calculation of Mc.
It is not the same to be highly correlated with the underlying asset during 1 week than in the course of 1 year, and we believe that the impact on the final grade can not be the same.
The tolerance level of the Mc algorithm will be inversely proportional to the number of D-Periods of experience during which said correlation is maintained.
Following our example, if the algorithm detected a significant correlation with the EURUSD, but this has occurred for a short period of time -1 D-Period-, the deterioration in the note of Mc would be lower than if you had 5.
The greater the nº of D-Periods keeping such correlation, the lower the degree of tolerance of the Mc attribute and the greater the penalty imposed to the D-Score.
So, after having thrown your strategy to the wolves, it turns out that you could still be trading superstar!
In summary, we have tweaked the Mc algo so we calculate its grade based on positions in the same asset– considering both leverage a trading decision in and of itself and the Experience factor.
Please note that the “leverage factor” change will improve accuracy of the Mc score in “medium-long term” strategies while not affecting scalpers or day traders and the “experience factor” will improve the Mc in almost all DARWINs.