DWC DARWIN- Mean Reversion, Stable Volatility

Advantages of Trading $DWC vs. Other Assets

This post describes some of the advantages of trading $DWC over other listed assets on the DARWIN Exchange.

Click here to watch the Webinar Recording.

The underlying fundamentals of DARWIN $DWC give rise to historically consistent behaviours in the asset that benefit both active and passive traders.

Compared to any other DARWIN on the Exchange (where only the DARWIN Provider’s decisions affect the asset’s movements), it considers a large number of community participants’ behaviours in its decision-making.

This affords its investment attributes and returns profile, a higher level of statistical significance than other DARWINS, and presents advantages for both active and passive trading.

We go on to describe these benefits in terms of:

  1. Return
  2. Risk and,
  3. Suitability as a Trading Instrument.

1) Return: Range-Bound Returns / Cycles

$DWC’s returns visibly demonstrate a fair degree of mean reversion, fluctuating in a stable range compared to other assets on the Exchange.

This range-bound behaviour presents itself on all time-frames in the $DWC, enabling short, medium and long-term traders to trade the asset.

For a quantitative perspective on this, you may find the following blog post useful, where we have conducted mean reversion tests using the $DWC dataset on six different timeframes:

[Darwinex Blog] Mean Reversion Tests on DARWIN $DWC


Active traders in particular can benefit from this tendency by e.g. being able to trade the $DWC standalone if they wish, designing strategies around the asset as it approaches its historical peaks (resistances) and troughs (supports) on any time-frame of their choice.

DWC Range-Bound Returns Cycles

DWC Range-Bound Returns Cycles (1-Year)


As its primary function is trading the opposite of asymmetric community exposure – behaviour which in itself is mean reverting by definition – $DWC demonstrating cyclical returns in this manner makes sense, further strengthening the case for active range-trading strategies leveraging this tendency.

This benefit is not limited to just active traders, as passive traders too can leverage this behaviour to time $DWC portfolio allocations for better value.


For more on this, you may find this blog post and the following webinar recordings helpful:

  1. [Webinar Recording] $DWC – Real Time Sentiment Index & Security
  2. [Webinar Recording] Effects of Market Volatility on Trader Performance
  3. [Darwinex Blog] Hedging DARWIN Portfolio Risk with $DWC


2) Risk: Stable Volatility Profile

$DWC trades a large number of currency pairs simultaneously.  The combined portfolio of currency pairs results in individual assets effectively cancelling out each other’s excess volatility, leading to stable movements overall in the $DWC.

DWC - Assets & Timeframes Summary

DWC – Assets & Timeframes Summary

Stable volatility coupled with mean reverting returns cycles, therefore makes $DWC a strong candidate for range-trading.

3) Suitability as a Trading Instrument

Active traders in the Darwinex Community frequently engage in trading price ranges and short-term retracements on currency pairs.

Retracement Trading Example

Range/Retracement Trading – Negatively Impacted by Deep Market Movements


Deep market movements in currency pairs can adversely affect such strategies, making range and retracement trading opportunities incredibly hard to exploit in live trading – especially for beginner traders.

With its stable volatility and mean reverting properties, $DWC presents traders (particularly those with mechanical trading strategies) with stable range and retracement trading opportunities, making it a suitable alternative to volatile currency pairs.


DWC - Normalized (With Range Boundaries)

DWC – Normalized (With Range Boundaries)

Webinar Recording: Advantages of Trading $DWC vs. Other Assets

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Darwinex - The Open Trader Exchange

Darwinex – The Open Trader Exchange

Loss Aversion (Behavioural Finance)

Hedging DARWIN Portfolio Risk with $DWC

In this blog post, we’ll discuss how DARWIN Investors can diversify away some of the excess risk posed to their portfolios by Loss Aversion, a common and well-researched phenomenon in behavioural finance.

In particular, we’ll discuss why it makes sense to include DARWIN $DWC in a portfolio that’s partially or entirely composed of loss averse DARWINS.

But first,

  1. A quick recap on what Loss Aversion is,
  2. Why even the most perfectly diversified portfolio of DARWINs can be susceptible to unforeseen shocks due to loss averse behaviour,
  3. What DARWIN Investors can do to hedge this risk.

Loss Aversion (Illustration)

Loss Aversion (Illustration)

Loss Aversion:

Simply put, traders are said to be loss averse when they hold on to losing trades for extended periods of time, but take quick profits on winning trades.

It yields two main outcomes:

  1. Returns growth looks fairly steady during periods of profitability, small profits smoothing the curve.
  2. Major drawdowns however, are disproportionately larger – sometimes leading to prior profits being wiped out by the closure of large losing trades that were being held on to for a long period of time.


Granted, a diversified portfolio of reasonably uncorrelated DARWINs has its advantages in terms of minimizing overall portfolio risk.

However, if it contains DARWINs with poor scores for the Loss Aversion attribute (La), it may still be susceptible to shocks during:

  1. Periods of market turbulence,
  2. Deep unforeseen movements,
  3. Unusually volatile news releases,
  4. Black swan events, etc.

.. where diversification benefit temporarily breaks down, owing in part to losing trade closures that distort the portfolio’s original risk profile.

What can DARWIN Investors do to protect themselves?

In one of our recent posts – $DWC – A Real Time Sentiment Index & Security – we highlighted the fact that DARWIN $DWC replicates the opposite of the Darwinex trader collective’s behaviour.

GBP Flash Crash (October, 2016)

GBP Flash Crash (October, 2016)

It typically rises during times when loss averse traders experience undiversifiable risk.

For example, $DWC profited from the GBP Flash Crash.

Undiversifiable risk also frequently presents itself when loss aversion eventually leads traders towards margin calls, causing sudden, unexpected volatility in the overlying DARWINs.


In such situations, portfolios that contain DARWIN $DWC can benefit from DWC hedging away a significant proportion (depending on position management of course) of undiversifiable risk experienced by investors.

When does it make sense to include $DWC in a portfolio?

  1. Investors can include $DWC in their portfolios to hedge against DARWINs with a Loss Aversion (La) score < 4.0 and Capacity (Cp) score > 5.0.
  2. Capacity (Cp) > 5.0 describes DARWINs that primarily trade long term movements. If such DARWINs also have a Loss Aversion (La) score < 4.0, the investor’s portfolio is likely exposed to undiversifiable risk at some point in the future.

    Low Loss Aversion Score

    Low Loss Aversion Score

  3. Check the Correlation of low La DARWINS against the $DWC – If they are negatively correlated, it becomes more likely that $DWC will offset excess risk should the loss averse DARWIN encounter  undiversifiable risk.

    Check Correlation of Loss Averse DARWIN Against DWC

    Check Correlation of Loss Averse DARWIN Against DWC

What composition of assets could well diversified DARWIN portfolios (hedged against loss aversion) contain?

Example Portfolio #1:

  1. 50% Short Term DARWINs (Capacity < 5.0)
  2. 25% Long Term DARWINs (Capacity >= 5.0)
  3. 25% allocation to $DWC as a hedge against loss aversion / undiversifiable risk.

Example Portfolio #2:

  1. 40% Short Term DARWINs (Capacity < 5.0)
  2. 30% Long Term DARWINs (Capacity >= 5.0)
  3. 30% allocation to $DWC as a hedge against loss aversion / undiversifiable risk.

Note: Investors should of course exercise their own discretion in selecting portfolio allocations. The examples above illustrate what such allocations could look like, accounting for multiple timing horizons whilst hedged to a reasonable degree against loss aversion.

Webinar Recording: Effects of Market Volatility on Trader Performance

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Darwinex - The Open Trader Exchange

Darwinex – The Open Trader Exchange

$DWC 1-Minute Differenced Series

Mean Reversion Tests on DARWIN $DWC

In a previous post – Quantitative Modeling for Algorithmic Traders – we discussed the importance of Expectation, Variance, Standard Deviation, Covariance and Correlation.

In this post we’ll discuss how those concepts can be applied to DARWIN assets.

As a practical example, we will employ a series of statistical tests to assess if DARWIN $DWC is a Mean Reverting time series or otherwise.


These will include:

1) Hurst Exponent
2) Augmented Dickey-Fuller Test (ADF)
3) Half-life of Mean Reversion


In case you missed it, the mean reverting nature of DARWIN $DWC was discussed in our most recent post here.

Tests will be conducted on 1-Minute returns from $DWC, results and interpretation being published along the way. As always, please share your comments, feedback and suggestions in the comments at the end.

Note: Different statistical tests don’t always lead to similar outcomes, therefore it’s considered good practice to use at least two when evaluating mean reversion or any other statistical properties.

Before proceeding further, it’s important that we understand what Autocorrelation and Stationarity are.

Autocorrelation (Serial Correlation)

Autocorrelation (Serial Correlation)


Also referred to as Serial Correlation.

It is a measure of the similarity or relationship between a time series and a delayed or “lagged” version of the same time series, over successive periods in time.




Stationary Time Series

Stationary Time Series


A time series is considered stationary if its core statistical attributes remain constant over time.

These include mean, variance, standard deviation, autocorrelation, etc.

Stationary series demonstrate high predictability.



If a time series (e.g. DARWIN) can be mathematically transformed to approximately stationary, future Quotes of the time series (or trade entry direction / entries) can be reverse engineered from future points in its forecasted stationary series.

More on this in future blog posts.

Prior Assumptions:

Prior to conducting these tests on $DWC data, we are expecting to see a reasonable degree of mean reversion for the following reasons:

  1. There is visual confirmation (see below) that mean reverting tendency may exist.
  2. As $DWC behaves in relation to real time trader sentiment, it is reasonable to assume that it could exhibit cyclical behaviour.
$DWC 1-Minute Data Plot

$DWC 1-Minute Data Plot

$DWC 1-Minute Differenced Series

$DWC 1-Minute Differenced Series

Mean Reversion Test #1: Hurst Exponent

Mean Reversion in a time series can be assessed in terms of its rate of diffusion from inception.


For a time series X to be considered mean reverting:

Rate of Diffusion (X) < Rate of Diffusion of a Geometric Random Walk (GBM)


This rate of diffusion can be measured as the variance of the logarithm of the time series, at a random time interval T:

\(Var(T) = \left \langle \left | log(t + T) – log(t) \right |^{2} \right \rangle\)


If a time series is a GBM, then Var(T) ~ T, as T gets larger:

\(\left \langle \left | log(t + T) – log(t) \right |^{2} \right \rangle\) ~ T


If a time series is either trending or mean reverting, then:

\(\left \langle \left | log(t + T) – log(t) \right |^{2} \right \rangle\) ~ \(T^{2H}\)

.. where H is the Hurst Exponent, a measure of the extent to which the time series trends or mean reverts.


Hurst Exponent Interpretation:

If H > 0.5, the time series is TRENDING
If H < 0.5, the time series is Mean Reverting
If H = 0.5, the time series is a Geometric Random Walk


The DWC’s Hurst Exponent can be easily calculated in R, using the “pracma” library.

Note: For all code examples in this blog post, we have pre-loaded M1 data as “DWC.M1” to save time.

library(pracma)# Print M1 data Hurst Exponent
> hurstexp(log(DWC.M1$quote))
Simple R/S Hurst estimation: 0.8962816
Corrected R over S Hurst exponent: 0.9945418
Empirical Hurst exponent: 1.001317
Corrected empirical Hurst exponent: 0.9938308
Theoretical Hurst exponent: 0.520278

This first test shows that though this sample of DWC data is not demonstrating mean reverting behaviour (Theoretical Hurst Exponent > 0.5), it is not trending significantly either -> i.e. it is almost behaving like a GBM as per this test’s results (H = 0.520278), reducing the probability of DWC being a non-stationary random walk process.

Mean Reversion Test #2: Augmented Dickey-Fuller Test

If the $DWC time series is not a random walk (non-stationary series), then any Quote in the series will have a proportional relationship with the Quote immediately before it.

If $DWC is mean reverting, then any move higher above its mean would likely be followed by a move lower and vice versa.

The ADF Test checks for the presence of unit roots in a time series that’s autoregressive in nature, and for the tendency of a time series to mean revert.

Consider the following autoregressive model of order p:

\(\Delta x_{t} = \alpha + \beta t + \gamma x_{t-1} + \delta _{1}\Delta x_{t-1} + … + \delta _{p-1}\Delta x_{t-p+1} + \epsilon _{t}\)

The ADF test will statistically evaluate if γ = 0 (the null hypothesis) can be rejected at a given confidence interval.

If the null hypothesis can be rejected, it implies that the time series is not a random walk (non-stationary / no linear relationship between data points), and that there is a linear relationship between the current DWC Quote and the one immediately before it (stationary).


The ADF Test can be carried out in R quite easily, using the “urca” library.

ADF Test (1-minute DWC data):

> library(urca)
> summary(ur.df(DWC.M1$quote, type="drift", lags=1))
# Augmented Dickey-Fuller Test Unit Root Test #
Test regression driftCall:
lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)
Min 1Q Median 3Q Max
-1.66860 -0.01990 0.00008 0.02011 1.16945
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0347880 0.0114228 3.045 0.00232 **
z.lag.1 -0.0003287 0.0001075 -3.057 0.00224 **
z.diff.lag -0.0365180 0.0045255 -8.069 7.22e-16 ***
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.04493 on 48479 degrees of freedom
Multiple R-squared: 0.001542, Adjusted R-squared: 0.001501
F-statistic: 37.45 on 2 and 48479 DF, p-value: < 2.2e-16
Value of test-statistic is: -3.0566 4.849Critical values for test statistics:
1pct 5pct 10pct
tau2 -3.43 -2.86 -2.57
phi1 6.43 4.59 3.78


Interpretation of ADF Test Results

Referring back to the autoregressive model earlier:

\(\Delta x_{t} = \alpha + \beta t + \gamma x_{t-1} + \delta _{1}\Delta x_{t-1} + … + \delta _{p-1}\Delta x_{t-p+1} + \epsilon _{t}\)

z.lag.1 = The value of the test-statistic γ (gamma) in the above equation.

tau2 = Critical values corresponding to the null hypothesis (γ = 0)

In order to reject the null hypothesis (γ = 0 – i.e. to reject that DWC is a non-stationary random walk), the value of the test statistic must be smaller than the critical values in tau2 (1%, 5% and 10% confidence intervals).

As z.lag.1 is -3.0566 (smaller than the critical values for the 5% and 10% confidence intervals), the null hypothesis can be rejected at the 90% and 95% confidence intervals, i.e. the probability of DWC being stationary (or not a random walk) is very high.

The tests above were also conducted on 30-minute, 1-hour, 2-hour, 4-hour and Daily precision $DWC data.

  1. Daily precision lead to the null hypothesis for the presence of a unit root being rejected at the 90% confidence interval. This test will be repeated periodically as more data is accrued over time.
  2. 30-minute, 1-hour, 2-hour and 4-hour tests all lead to the null hypothesis for the presence of a unit root being rejected at the 95% confidence interval.

Mean Reversion Test #3: Half-life of Mean Reversion

An alternative to the autoregressive linear model described above, is to consider how long any particular time series takes “to mean revert”.

By definition, a change in the next periodic value of a mean-reverting time series is proportional to the difference between the historical mean of the series and the current value.

Such time series are referred to as Ornstein-Uhlenbeck processes.

The differential of the earlier model leads us to the expected value of x(t):

\(E(x_{t}) = x_{0}e^{\gamma t} – \frac{\mu }{1 – e^{\gamma t}}\)

If DWC is a mean reverting series, and has a negative \(\gamma\), then the equation above tells us that DWC prices decay exponentially, with a half-life of \(\frac {-log(2)}{\gamma}\).

This means we now have two tasks ahead of us:

  1. Find \(\gamma\) and check if it is negative.
  2. Calculate the half-life and assess whether it is a practical length of time for traders to consider a mean reverting strategy on DWC.

Once again, we can easily conduct both steps in R.

Step 1: Calculate \(\gamma\) and check sign.

> <- as.ts(DWC.M1$quote)
> <- lag(, -1)
> <- diff(
> <- cbind(,,
> <-[-1,]

> M1.regression <- lm( ~,

> gamma <- summary(M1.regression)$coefficients[2]
> print(gamma)
[1] -0.0003588994

\(\gamma\) is negative (-0.0003588994), so this $DWC 1-minute data sample can be considered mean reverting.


Step 2: Calculate half-life and assess practicality of mean reversion strategy.

> <- -log(2) / gamma> print(paste("Half-life: ",, " minutes, or ",, " Hours", sep=""))
[1] "Half-life: 1931.31306610404 minutes, or 32.1885511017341 Hours"

The half-life calculated for this $DWC 1-minute data sample is 32 hours.


Another important feature of the calculated half-life, is that it can be used as the period of a moving average employed in a mean reverting trading strategy[1].

If we plot a Simple Moving Average of period 1931 (in minutes, not hours), we get:


$DWC 1-Minute Data with SMA(1931)

$DWC 1-Minute Data with SMA(1931)


  1. We conducted three statistical tests to ascertain the degree of mean reversion in $DWC 1-minute data, namely Hurst Exponent, Augmented Dickey-Fuller (ADF) and Half-Life of Mean Reversion.
  2. Hurst Exponent did not indicate mean reverting behaviour in the $DWC, but a rather close estimate for possible GBM behaviour.
  3. The Augmented Dickey-Fuller test results indicated stationary behaviour at the 95% confidence interval.
  4. The Half-life of Mean Reversion test indicated $DWC possesses mean reverting properties.
  5. We used the half-life calculated above as the period for a moving average, which when plotted on the chart revealed mean reverting Quote behaviour.

What are your thoughts after reading this research? ..please share in the comments section below!



[1] Chan, Ernest, 2013. Algorithmic Trading: Winning Strategies and Their Rationale, John Wiley and Sons.

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Real-Time Trader Sentiment

$DWC – A Real Time Sentiment Index & Security

DWC - Normalized (With Range Boundaries)

DWC – Normalized (With Range Boundaries)

(We recommend you to watch this webinar,hosted by our beloved Integracore2, which is the perfect complement for the article you are about to read)

Fundamental and Technical trading indicators have long been used as a proxy for market sentiment.

But by definition, these indicators have always lagged the movements they’ve been used to forecast.

With the advent of “Big Data”, social data too has joined the ranks, e.g. Twitter, Facebook, LinkedIn, with various attempts being made to harness any potentially predictive patterns through “opinion mining“.


However, Real-Time Sentiment Analysis continues to be an elusive, ever-evolving challenge.

The Challenge: Measuring in real-time, how market participants are presently orientated.

The Solution: DARWIN DWC? ..continue reading for the answer.

If you missed our first post introducing DARWIN $DWC, you may read it here.

DWC – An Index & Tradable Security bundled into one.

DWC is Darwinex Labs’ first attempt at openly addressing the problem of lagged sentiment.

By leveraging community exposure in a manner that protects individual trader IP, DWC shows how the Darwinex trader collective is inclined in real-time.

As a Real-Time Sentiment Indicator, DWC also exhibits the following features:

  1. 24/5 LIQUIDITY during market hours.
    DWC - Instantly Liquid During Market Hours

    DWC – Instantly Liquid During Market Hours


  2. Tradable 24/5 -> the ability to buy into sentiment directly (i.e. buy DWC) eliminates the need for considering purchases of index-linked Exchange Traded Funds (ETFs) or Notes (ETNs) as is the case with say the VIX Implied Volatility Index.
    DWC - Real Time Community Sentiment

    DWC – Real Time Community Sentiment


  3. Cyclical / Mean Reverting -> trading a reasonably predictable, well-defined, mean-reverting price range is high on every trader/investor’s wishlist.
    DWC - Price Quotes

    DWC – Price Quotes


    DWC - Normalized (With Range Boundaries)

    DWC – Normalized (With Range Boundaries)


  4. HIGH CAPACITY – With a current maximum investment capacity of 1.2 to 1.5 Billion USD, there is plenty of retail volume this asset can take to market with little to no market impact -> community IP remains protected at all times, trader/investor interests remain aligned, no conflict of interest.
  5. DWC replicates the opposite of the trader collective’s behaviour – A significant majority of traders routinely demonstrate Loss Aversion (keep losing trades open in confidence that odds are favourable, but close winning trades too soon). The pitfalls of loss aversion make themselves particularly well known during deep market movements. For more details on this, please refer to our first post on DWC.
  6. As DWC replicates the opposite of trader behaviour, it rises during such times.

DWC can therefore be used by Darwinex traders and investors as:

1) A “hedge” during turbulent, deep market movements.

2) A reasonably stable, range-bound asset to trade on its own otherwise.



  1. This post highlighted that the DWC moves with trader sentiment in real-time, realizing progress in “human cognition” led asset management.
  2. Traders stand to benefit from the mean-reverting nature of the DWC offering range-trading and hedging opportunities.
  3. DWC is a sentiment indicator and directly tradable asset.


What happens next?

In future posts, we will commence publishing several streams of quantitative research (e.g. ARIMA/GARCH modeling, deep learning experiments, etc) that we’ve been conducting on the DWC dataset.

Stay tuned!

Webinar Recording: $DWC – Real Time Sentiment Index & Security

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Introducing DarwinexLabs – Prop Investing DARWINs

Introducing DarwinexLabs

We’re sending our Quant team on a new mission – and what better way to start than a new name?

Introducing DarwinexLabs!

What mission?

DarwinexLabs’ next mission is to openly beat the market leveraging the DARWIN data-set.

Why, and why now?

Re-loaded introduces a new visual interface, but the bigger changes are under the hood. All diagnostic and risk-management algos have been re-factored from the ground up to work better, faster. Informed DARWIN managers now enjoy good enough trader choice and tools to beat the market – or at least that’s our strong belief.

Because nothing beats walking one’s talk, we’ll openly take up the DARWIN prop investing challenge.

On that note, what does “openly” mean??

Read on for an explanation, including the strategic soul-searching that led to DarwinexLabs!

Open Trader Exchange – re-visited

To date, the Open Trader Exchange was open to anyonebut us. Proprietary investing in a limited capacity asset seemed a conflict of interest best avoided.

DarwinexLabs marks a shift in the thinking, for 3 reasons.

The first is that Darwinex is traders first. What pleases a trader more than investors queuing to back his strategy?

Sure, proprietary investments by Darwinex in a hypothetical capacity constrained marketplace could lead to conflict of interest. Until then, traders welcome every investor.

DarwinexLabs’ first mission is to saturate capacity… once saturation is accomplished, we’ll send it on another (capacity optimization?).

The second is efficiency. Optimizing investor performance accelerates the model.

More winning investors viralize AuM acquisition – our core trader value proposition. Investors making sub-optimal use of the available trader base and toolkit slow everyone down.

If winning more is possible, why not publicly commit to it?

The third is strategic. Any party reaping more dividends from the trader movement than Darwinex could compromise the Open model.


Open Trader Exchange – securing long term independence

Because, the better a strategy, the higher its ratio of trading profits to trading commissions.

A large, knowledgeable investor might earn more from DARWIN investor profits than Darwinex books in brokerage revenues (which is little, after rolling out diversification rebates).

If one believes – as we firmly do – that the movement’s individual and collective property is edge to beat the market… we must invest for profit.

Yes, Darwinex has an edge in that we charge everyone for execution – so we’ll have to manage a conflict of interest, but we’ll cross that bridge when we get there.

So, in a nutshell.

DarwinexLabs: the first step to close the Exchange and become a Hedge Fund?


DarwinexLabs means we’re:

  1. Sending our best assets (people & capital) to earn our best traders’ trust (THANKS), leveraging 5 years of technological development.
  2. Publicly committing to the DARWIN asset, by being first to invest meaningful capital in the DARWIN community.

And we’re doing so staying within the vision for an Open Trader Exchange.

DarwinexLabs – investor backing DARWINs

The “DarwinexLabs” investor will back DARWINs:

  1. For profit – unlike Darwinia ,  DarwinexLabs is about maximising returns. Rise of the fittest. Period.
  2. As a bog-standard user – it will enjoy no preference in access to DARWINs or execution prices,
  3. Transparently – DARWIN providers will see DarwinexLabs investments in the exact same way they see for other investors,
  4. Openly – DarwinexLabs will publish our learning curve – when we lose as well as when we win

DarwinexLabs’ only edge will be beta testing.

DarwinexLabs will be first to use any functionalities we come up with… with an Open commitment to release for public use any tools DarwinexLabs leverages profitably (of which there’s a ton cooking 🙂

Put it another way: we can’t expect people to back our asset unless we back it ourselves. So here we go.

Open Investment – How about community data?

This is another potential avenue for DarwinexLabs – a more complex, but potentially promising option.

The trader movement gathers momentum. The first investable traders has attracted more traders – and this creates community data. We thought all along that community behaviour is an edge facing the market… and Hedge Fund offers to market the community data validate the thinking.

However, before leveraging the data, a number of questions must be answered:

  1. Could this information be used for profit?
  2. If it canshould it be used?
  3. Whose information is it, anyways?
  4. Who could access it?
  5. Who could invest in it – if there are capacity constraints?
  6. Should any strategies leveraging community data command a success fee?
  7. If success fees accrue – whose bank account should receive them – given the community is an abstract entity?

These are non-trivial questions, and we appreciate getting the answer right is key for the movement – as a matter of fact, we’d like you to look at these & write up your own answers before reading on.

If, after reading ours, you disagree, we’d love to hear your take.

DarwinexLabs – trader leveraging Community Dataset

They’re tricky questions. If Darwinex is all about protecting trader intellectual property – shouldn’t community data be off-limits?

After quite some soul searching, “traders first” was the key guiding principle, and we hope you agree that our answers are indeed “traders first”:

  1. The trader movement could use trade-flow information, it would be naif to believe otherwise for institutional counterparties highly value it,
  2. The movement not only could but should use it. Winning strategies formulated from collective behaviour stand on a firmer statistical foundation, are more likely to attract investment in the DARWIN asset class (expanded to include collective data), which is good for traders long term,
  3. Whilst it’s Darwinex that kick-started the trader community, the community data is the community’s. Darwinex leveraging it for proprietary benefit would be “traders second”.
  4. Publicly granting access to the information would compromise trader IP – once published, there’s no way to collect success fees from investors. Which is why:
    • Short term, DarwinexLabs will be the only party with access
    • Medium term, we’re open to technological options letting data scientists leverage the data, without accessing it (more on this in the coming months)
  5. Strategies will be open for investment. Should they become capacity constrained, “traders first” will apply
  6. Waiving success fees on said strategies would disadvantage individual DARWIN providers – which is why DarwinexLabs will collect 20% success fees on its DARWINs…
  7. Because the data are the community’s, not DarwinexLabs’, success fees will be paid out to the DARWIN provider community.

All of which introduces an additional question: how should success fees be split?

We still need to figure out the best option – ideally with community feedback. For now our inclination is merit based. Rather than split e.g. EUR 100.000 success fee amongst 30.000 users = 3 EUR / user, it will definitely be top heavy.

Can collective & individual IP conflict?

If you’ve read this far… you’re probably wondering: where to draw the line between trader & trader community?

It’s obviously a tough one – so DarwinexLabs will ALWAYS stay well clear of any potential conflict of interest. The last we’d do would be compromise trader trust – our most sacred asset.

For that reason, and in line with “traders first”, we’re handing the decision over to a community member. The person in charge of DarwinexLabs will be Ali – Darwin provider Integracore2

We can’t think of a better option than Ali’s know-how, commitment to the vision and community contributions to get DarwinexLabs off to the best start. Further, we hope you’ll take our appointing a member of the trader ranks as proof to our commitment to integrity.

As ever, any suggestions on how to transparently put traders first will be most welcome!

Introducing DWC, the hedging DARWIN

Introducing DWC, the hedging DARWIN

DWC, the hedging DARWIN is DarwinexLabs first creation. It’s already listed at the Exchange.

This post lays out:

  1. The rationale for a hedging DARWIN?
  2. Lessons learnt in the development process
  3. How DWC revenues will be shared with the community

As you’ll gather, actively leveraging community data is a major strategic milestone for Darwinex. We’d be extremely grateful for your thoughts on the decisions made!

Why create a hedging DARWIN?

Because there are periods when markets become abnormally “tough” and sweep most traders.

DWC leverages community behaviour indicators to protect investors in those periods. It offers 2 use cases for DARWIN managers and investors:

    1. Hedging “deep” and “tough” tail market moves where DARWIN portfolio diversification breaks down – similarly uncorrelated DARWINs end up correlated
    2. Market sentiment based on real time exposure by the Darwinex trader collective – without compromising individual trader IP

NB: standalone investment is NOT a use-case. DWC is NOT designed for passive returns.

Back since launching the brokerage offering (May 2014), we’ve observed what appear to be recurring “market cycles” that last anywhere between 4-6 months.

These cycles typically involve:

  1. A 3-4 month “benign” cycle where DARWIN portfolios perform well
  2. A 1-2 month “sweep” cycle where portfolios struggle – without obvious options to diversify the sweeps away

All of which makes a hedging strategy a valuable addition to standard DARWIN portfolios…

What explains market sweeps?

Analysis of community behaviour suggests that the Darwinex trader collective suffers loss aversion – as highlighted by La (Loss Aversion attribute) grades below 5. It also highlights market conditions that “sweep” (literally) low La rated strategies trading longer than 1 day timeframes.

Loss aversion is a well documented cognitive bias. This gives us comfort in extrapolating the behaviour of our community to the global community. i.e. we’re onto something systematic – and therefore “predictable”. Loss aversion as a standalone reality might be a major driving factor behind the BOOM in the forex industry in recent years, and is certainly an explanation of CFDs in their “cash for dealers” impopular description.

(As a matter of fact, it probably explains why retail traders are offered top of book spreads well below the spreads available to institutional participants!)

How does the DWC hedging DARWIN work?

DWC trades whenever normally “symmetric” community sentiment (everyone randomly on either side of a currency) turns overly “asymmetric” in one or several pairs. In line with its hedging nature, DWC then trades the opposite way as a liquidity taker – e.g. DWC trades are routed to the market just like every other ordinary DARWIN – Darwinex does NOT deal against customers. DWC doesn’t enjoy a particularly high success rate, BUT it does make significantly more on winning trades than it loses on losing ones.

Designing DWC boiled down to efficiency: replicate (the inverse of) community positioning with the least transaction costs – remember, we’re not dealing, but paying the market to hedge community exposure. Whilst we won’t give away the key secret sauce , both backtest and live testing suggest we’ve reached a good compromise – possibly because DWC only works for FX pairs, where all the community “votes” on a few select currencies.

DWC has traded live funds since shortly before the GBP flash-crash (which it anticipated and profited from :-). Both backtest and live behaviour have met ingoing requirements, so with the announcement of DarwinexLabs, it’s time for Exchange listing!

 What have we learnt?

DWC has been a rewarding research project, with the following core takeaways:

  1. DWC’s La (Loss Aversion) grade is 9.2 – which makes DWC yin to the community’s loss averse yang
  2. DWC sports a Cp (Capacity) grade of 10 – we believe it could well reach 500+ million AuM, e.g. there’ll be plenty of supply
  3. Luckily (or perhaps not?) it nailed both of the latest extreme market sweeps (SNB CHF move and GBP Flash-Crash)
  4. It’s a valuable leading indicator – watch out for DWC holding few trades as this could anticipate a swing in the market
  5. It’s a procyclical strategy – which explains its poor timing (Os & Cs grades)

Whilst we won’t disclose DWC´s live trades, its equity curve is valuable assistance in spotting “benign” vs. “tough” market conditions. Because it struggles when the community profits, it could be used as an indicator when:

  1. Approaching the end of it’s 3-4 down cycle could be a harbinger of “toughening” to come (winter is coming 🙂
  2. Correlating negatively with any DARWIN (starting by one’s own) might be worth some consideration!

 Who owns DWC?

That’s the million USD question!

For “Regular” DARWINs Darwinex collects a 20% success fee which is then then paid out to its provider. Given DWC’s provider is DarwinexLabs (i.e. Darwinex’s Quant team), should Darwinex pocket 20% success fee? Surely this is a conflict of interest given DWC builds on community data? Should DarwinexLabs waive a success fee?

As you can see, this is but one of several interesting questions.

The first decision made is for DWC to collect 20% success fee – just like any other DARWIN. Waiving the success fee would amount to “dumping” on regular providers.

The second, and perhaps more controversial one, is for DarwinexLabs NOT to retain any share in success fees collected for its DARWINs (including DWC and follow-up strategies). These will be collected, then shared with the DARWIN provider community, on merit.

We are internally debating on the best allocation key, and will explain the criteria in upcoming blog posts. What we do know is that we will share fees on a meritocratic basis (e.g. the better the trader, the higher the share).

Reaching both conclusions has required a fair amount of soul searching – so much so it’s quite relevant to share the main sticking points. As a matter of fact, it’s imperative, for we might revise our decision as we gather more information.

 What happens going forward?

Community unequivocally belongs to traders. Period.

Having said that

  1. Darwinex actively invests in growing and supporting the community,
  2. DarwinexLabs requires highly qualified (=expensive) and motivated personnel. One thing is to access the community data, quite another is to derive and optimize investable strategies from it. Broker-dealers generate outsize returns b-booking off the back of said information. Whilst Darwinex will never conflict with its users, arguably it merits some claim on any profits generated.
  3. The community benefits from the strategies as it is – anyone is free to leverage the strategies for an 80% (100% minus the 20% success fee) share in the upside.

We have decided NOT to make community datasets publicly available, as this could compromise trader IP: any community member could leverage community data in competition with DarwinexLabs.

For as long as we can’t remedy this, we’ll waive any share in the success fee… and we want your take on this, for this is a key strategic decision for Darwinex going forward!

Do you want to know more about this unique DARWIN? Your wish is our command! Here’s an insightful webinar about DWC hosted by Juan Colón where he explains the rationale, for Darwinex and DARWIN investors, of backing the DWC.


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