## Darwinian Dividends

The DARWIN Exchange will soon pay Darwinian Dividends (April 2018) in addition to success fees on the current DarwinIA notional capital allocation.

Exchange members are aligned to make the most return on capital using everyone’s data. This facilitates a collective Darwinian Dividend

If you’re wondering:
• Where do the dividends come from?
• How is the amount determined?

We hope you’re as excited about this development as we are! It’s a merit-driven icing on the proverbial “DarwinIA cake” if you will.

## Collective data -> Darwinian Dividends

The dividend source is Community DARWINS, e.g. $DWC, DarwinexLabs‘ first, tradable community sentiment indicator, and recently followed by its sibling,$DWF.

Both let investors trade real-time community loss aversion. In particular, their “performance fees” sections suggest users trade it for both pleasure and profit.

### Feeling left out?

A Community DARWIN is like every other DARWIN in that:
• It competes for investor capital with other signals.
• Investors replicate trades while not knowing trade specifics in order to protect trader intellectual property (IP)
• Investors pay the standard 20% performance fee
It differs from all other DARWINs, in benefiting from private information that belongs:
• Not to the Exchange
• Not to any one Exchange member
• But to every Exchange member
For this reason, Darwinex will pay out all the performance fees you see on a Community DARWIN’s performance fees profile section, as Darwinian Dividends.

## Community data -> just $DWC &$DWF?

In the long run, Darwinex aims to share all raw community signals, not just towards existing Community DARWINs.

### Before that, however..

It needs signal wrapping/encryption technology.

Barring that, publishing the raw data would gift free-riding signal users more than 80% profit share, as it would be possible for them to trade on the signal outside Darwinex, at the expense of the Darwinex community

Alas, encrypting everyone’s real-time signal to secure 20% success fee is a tough technical cookie.

### Until we crack it, DarwinexLabs will act as the community data’s caretaker.

In this regard, $DWC and$DWF are the result of transforming raw community loss aversion data into tradable assets.

Rest assured that DarwinexLabs is hard at work investigating further siblings… but for now, you know why DarwinIA “just” pays dividends on $DWC and$DWF.

On to the distribution key.

## Distribution key -> meritocracy

Darwinex hopes you agree that:
• Member community data is valuable
• 20% performance fee is the fair price for it
• Dividends belong to Darwinex members
But, what is the best way to structure dividend payouts resulting from community signals?
• Skewed payment: according to some meritocratic criteria?

### Our take: meritocracy!

Darwinex will pay dividends (=cash prizes) to the best providers, because it’s a win-win for traders, investors and Darwinex:
• The better the traders supplying individual DARWINs..
• The bigger the incentive for new traders and investors to join.
• The more statistically significant the collective signal available to all..
• The bigger:

– the 80% kept by demand (=investors) for individual and collective signals.

– the 20% earned by signal suppliers (=DARWIN Providers)

## Payout format

Dividend payouts will be skewed to maximise incentive from every pennie of performance fee paid by data users.
As such, dividends will be paid:
• To the top 10 DarwinIA participants in a given month should $DWC had accumulated 10K in performance fees paid, if not it will be paid in multiples of 1000 starting from the winner downwards • In T+1 monthly cycles, meaning that the winners in month X will share the performance fees accumulated up until the end of such month For illustration purposes, assume 3 success-fee scenarios for total success fees collected by$DWC and/or $DWF in a given month: • Performance fees paid 5500 : Top 5 DarwinIA winners will receive 1000 each, with 500 rolled over to the following months payout • Performance fees paid 12.500 : Top 10 DarwinIA winners will receive 1000 each, with 2500 rolled over to the following months payout • Performance fees paid 1.500 : The DarwinIA will receive 1000 , with 500 rolled over to the following months payout The initial payout in April 2018 will: • Be paid to the top 10 participants in DarwinIA rankings in March 2018 if performance fees reaches 10000 • Be based on cumulative performance fees earned by DWC through end March 2018 • Per subsequent payouts, be capped at EUR 10k, with excess performance fees generated rolled over to the following months payout ## What’s your take? What are your thoughts? Do you: • Agree that the community signal be leveraged? • Agree with the distribution system? • Want to contribute to encrypt it? – we’ll ### We look forward to your thoughts! ### Want to know more about$DWC?

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 community sentiment indicator:

Almost forgot to say! Please, do not hesitate to share this content with your fellow traders & investors by using the icons you see on the left hand side of this post 😉

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 (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:

## 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

Stable volatility coupled with mean reverting returns cycles, therefore makes $DWC a strong candidate for range-trading. ## 3) Suitability as a Trading Instrument Active traders in the Darwinex Community frequently engage in trading price ranges and short-term retracements on currency pairs. Range/Retracement Trading – Negatively Impacted by Deep Market Movements Deep market movements in currency pairs can adversely affect such strategies, making range and retracement trading opportunities incredibly hard to exploit in live trading – especially for beginner traders. With its stable volatility and mean reverting properties,$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)

### Webinar Recording: Advantages of Trading $DWC vs. Other Assets Do you have what it takes? – Join the Darwinex Trader Movement! Darwinex – The Open Trader Exchange ## Hedging DARWIN Portfolio Risk with$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 $DWC in a portfolio that’s partially or entirely composed of loss averse DARWINS. ## But first, 1. A quick recap on what Loss Aversion is, 2. Why even the most perfectly diversified portfolio of DARWINs can be susceptible to unforeseen shocks due to loss averse behaviour, 3. What DARWIN Investors can do to hedge this risk. Loss Aversion (Illustration) ## Loss Aversion: Simply put, traders are said to be loss averse when they hold on to losing trades for extended periods of time, but take quick profits on winning trades. ### It yields two main outcomes: 1. Returns growth looks fairly steady during periods of profitability, small profits smoothing the curve. 2. Major drawdowns however, are disproportionately larger – sometimes leading to prior profits being wiped out by the closure of large losing trades that were being held on to for a long period of time. Granted, a diversified portfolio of reasonably uncorrelated DARWINs has its advantages in terms of minimizing overall portfolio risk. However, if it contains DARWINs with poor scores for the Loss Aversion attribute (La), it may still be susceptible to shocks during: 1. Periods of market turbulence, 2. Deep unforeseen movements, 3. Unusually volatile news releases, 4. Black swan events, etc. .. where diversification benefit temporarily breaks down, owing in part to losing trade closures that distort the portfolio’s original risk profile. ## What can DARWIN Investors do to protect themselves? In one of our recent posts –$DWC – A Real Time Sentiment Index & Security – we highlighted the fact that DARWIN $DWC replicates the opposite of the Darwinex trader collective’s behaviour. GBP Flash Crash (October, 2016) It typically rises during times when loss averse traders experience undiversifiable risk. For example,$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?

### Example Portfolio #2:

1. 40% Short Term DARWINs (Capacity < 5.0)
2. 30% Long Term DARWINs (Capacity >= 5.0)
3. 30% allocation to $DWC as a hedge against loss aversion / undiversifiable risk. Note: Investors should of course exercise their own discretion in selecting portfolio allocations. The examples above illustrate what such allocations could look like, accounting for multiple timing horizons whilst hedged to a reasonable degree against loss aversion. Webinar Recording: Effects of Market Volatility on Trader Performance Do you have what it takes? – Join the Darwinex Trader Movement! Darwinex – The Open Trader Exchange ## Mean Reversion Tests on DARWIN$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.

Tests will be conducted on 1-Minute returns from $DWC, results and interpretation being published along the way. As always, please share your comments, feedback and suggestions in the comments at the end. Note: Different statistical tests don’t always lead to similar outcomes, therefore it’s considered good practice to use at least two when evaluating mean reversion or any other statistical properties. Before proceeding further, it’s important that we understand what Autocorrelation and Stationarity are. Autocorrelation (Serial Correlation) ## Autocorrelation: Also referred to as Serial Correlation. It is a measure of the similarity or relationship between a time series and a delayed or “lagged” version of the same time series, over successive periods in time. Stationary Time Series ## Stationarity: A time series is considered stationary if its core statistical attributes remain constant over time. These include mean, variance, standard deviation, autocorrelation, etc. Stationary series demonstrate high predictability. If a time series (e.g. DARWIN) can be mathematically transformed to approximately stationary, future Quotes of the time series (or trade entry direction / entries) can be reverse engineered from future points in its forecasted stationary series. More on this in future blog posts. ## Prior Assumptions: Prior to conducting these tests on$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 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.

> 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)Residuals: Min 1Q Median 3Q Max -1.66860 -0.01990 0.00008 0.02011 1.16945Coefficients: 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 ‘ ’ 1Residual 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-16Value 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 $$\frac {-log(2)}{\gamma}$$.

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.

> M1.data <- as.ts(DWC.M1$quote) > M1.data.lag <- lag(M1.data, -1) > M1.data.delta <- diff(M1.data)> M1.data.frame <- cbind(M1.data, M1.data.lag, M1.data.delta) > M1.data.frame <- M1.data.frame[-1,] > M1.regression <- lm(M1.data.delta ~ M1.data.lag, data=as.data.frame(M1.data.frame)) > gamma <- summary(M1.regression)$coefficients[2] > print(gamma) [1] -0.0003588994

### 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:

$DWC 1-Minute Data with SMA(1931) # Summary: 1. We conducted three statistical tests to ascertain the degree of mean reversion in$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.

### References:

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

Additional Resource: Measuring Investments’ Risk: Value at Risk (VIDEO)
* please activate CC mode to view subtitles.

Do you have what it takes? – Join the Darwinex Trader Movement!

## 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

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

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 – 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.

## Conclusions:

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

Do you have what it takes? – Join the Darwinex Trader Movement!

## 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.

Why?

## 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?

No.

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!