Margin increase ahead of French presidential election

The second round of the French presidential election will take place on 7 May 2017.

After Brexit and Trump, we cannot risk the polls being wrong on a 3rd election in a row (market has priced in a 7 % move should LePen win, which would take EURUSD to near parity if not through it on a liquidity spike).

In order to protect our customers from volatility spikes on market open, please be informed that our margin requirements are changing on 5 May 2017 at 06:00 UK time as described below (a brief server restart is needed).  The new margin requirements will remain in force until further notice (presumably until 8 May 2017, depending on the market reaction to the election’s results).

New margin requirements will affect both EXISTING and NEW positions, so please make sure you fund your account to post enough available margin the days leading up to the voting/announcement of results, and failing that, reduce your exposures.

Instrument Current Margin New Margin

(French Election)

FOREX
AUDCAD 0.50% = (1:200 leverage) 2.00% = (1:50 leverage)
AUDCHF 1.00% = (1:100 leverage) 2.50% = (1:40 leverage)
AUDJPY 1.00% = (1:100 leverage) 2.50% = (1:40 leverage)
AUDNZD 1.00% = (1:100 leverage) 2.00% = (1:50 leverage)
AUDUSD 0.50% = (1:200 leverage) 2.00% = (1:50 leverage)
CADCHF 1.00% = (1:100 leverage) 2.50% = (1:40 leverage)
CADJPY 1.00% = (1:100 leverage) 2.50% = (1:40 leverage)
CHFJPY 1.00% = (1:100 leverage) 2.50% = (1:40 leverage)
EURAUD 1.00% = (1:100 leverage) 5.00% = (1:20 leverage)
EURCAD 0.50% = (1:200 leverage) 5.00% = (1:20 leverage)
EURCHF 1.00% = (1:100 leverage) 5.00% = (1:20 leverage)
EURGBP 0.50% = (1:200 leverage) 5.00% = (1:20 leverage)
EURJPY 0.50% = (1:200 leverage) 5.00% = (1:20 leverage)
EURNOK 1.00% = (1:100 leverage) 5.00% = (1:20 leverage)
EURNZD 1.00% = (1:100 leverage) 5.00% = (1:20 leverage)
EURUSD 0.50% = (1:200 leverage) 5.00% = (1:20 leverage)
EURTRY 3.00% = (1:33 leverage) 5.00% = (1:20 leverage)
GBPAUD 1.00% = (1:100 leverage) 2.00% = (1:50 leverage)
GBPCAD 0.50% = (1:200 leverage) 2.00% = (1:50 leverage)
GBPCHF 1.00% = (1:100 leverage) 2.50% = (1:40 leverage)
GBPJPY 1.00% = (1:100 leverage) 2.50% = (1:40 leverage)
GBPNOK 1.00% = (1:100 leverage) 2.00% = (1:50 leverage)
GBPNZD 1.00% = (1:100 leverage) 2.00% = (1:50 leverage)
GBPUSD 0.50% = (1:200 leverage) 2.00% = (1:50 leverage)
GBPTRY 3.00% = (1:33 leverage) 3.00% = (1:33 leverage)
NZDCAD 1.00% = (1:100 leverage) 2.00% = (1:50 leverage)
NZDCHF 1.00% = (1:100 leverage) 2.50% = (1:40 leverage)
NZDJPY 1.50% = (1:66 leverage) 2.50% = (1:40 leverage)
NZDUSD 1.00% = (1:100 leverage) 2.00% = (1:50 leverage)
USDCAD 0.50% = (1:200 leverage) 2.00% = (1:50 leverage)
USDCHF 1.00% = (1:100 leverage) 2.50% = (1:40 leverage)
USDHKD 5.00% = (1:20 leverage) 5.00% = (1:20 leverage)
USDMXN 3.00% = (1:33 leverage) 3.00% = (1:33 leverage)
USDJPY 0.50% = (1:200 leverage) 2.50% = (1:40 leverage)
USDTRY 3.00% = (1:33 leverage) 3.00% = (1:33 leverage)
USDNOK 1.00% = (1:100 leverage) 2.00% = (1:50 leverage)
USDSEK 1.00% = (1:100 leverage) 2.00% = (1:50 leverage)
USDSGD 5.00% = (1:20 leverage) 5.00% = (1:20 leverage)
COMMODITIES
XAUUSD 0.50% = (1:200 leverage) 5.00% = (1:20 leverage)
XAGUSD 2.00% = (1:50 leverage) 2.00% = (1:50 leverage)
XTIUSD 3.00% = (1:33 leverage) 3.00% = (1:33 leverage)
XNGUSD 3.00% = (1:33 leverage) 3.00% = (1:33 leverage)
XPDUSD 4.00% = (1:25 leverage) 4.00% = (1:25 leverage)
XPTUSD 4.00% = (1:25 leverage) 4.00% = (1:25 leverage)
INDICES
STOXX50E 2.00% = (1:50 leverage) 5.00% = (1:20 leverage)
GDAXI 2.00% = (1:50 leverage) 5.00% = (1:20 leverage)
SPX500 1.00% = (1:100 leverage) 5.00% = (1:20 leverage)
WS30 2.00% = (1:50 leverage) 5.00% = (1:20 leverage)
NDX 1.00% = (1:100 leverage) 5.00% = (1:20 leverage)
J225 2.00% = (1:50 leverage) 5.00% = (1:20 leverage)
AUS200 2.00% = (1:50 leverage) 5.00% = (1:20 leverage)
FCHI40 2.00% = (1:50 leverage) 5.00% = (1:20 leverage)
UK100 2.00% = (1:50 leverage) 5.00% = (1:20 leverage)

*Red color = amended margin requirements.

For the ease of reference, Margin in % =  (1/max. leverage)* 100. In other words:

0.50 % = 1:200 leverage

1.00 % = 1:100 leverage

2.00 % = 1:50 leverage

5.00 % = 1:20 leverage, etc.

Have questions ? At info@darwinex.com we’ll be happy to assist you!

 

Darwinex amended trading hours (1 May 2017)

Please note the amended Darwinex trading hours for the upcoming bank holiday on 1 May 2017 (all times are in UK time).

 Instrument Trading Hours
 FX 22:05 Sun – 22:00 Mon
  DARWINS 22:05 Sun – 22:00 Mon
 COMMODITIES  
Gold 23:01 Sun – 21:59 Mon
Silver 23:00 Sun – 22:00 Mon
Platinum 23:00 Sun – 22:00 Mon
Palladium 23:00 Sun – 22:00 Mon
US Crude 23:00 Sun – 22:00 Mon
Natural Gas 23:00 Sun – 22:00 Mon
 INDICES  
Australia 200 23:00 Sun – 22:00 Mon
 Europe 50* Closed
France 40* Closed
Germany 30* Closed
Spain 35* Closed
Japan 225 23:00 Sun – 22:00 Mon
 UK 100* Closed
 US SPX 500 23:00 Sun – 22:00 Mon
 US Tech 100 23:00 Sun – 22:00 Mon
Wall Street 30 23:00 Sun – 22:00 Mon
*Amended Darwinex trading hours.

As always, at info@darwinex.com we’ll be happy to assist you!

Hidden Markov Models

Hidden Markov Models & Regime Change: DARWINs vs. S&P500

In this post, we will employ a statistical time series approach using Hidden Markov Models (HMM), to firstly obtain visual evidence of regime change in the S&P500. We will then compare the index’ performance to a DARWIN Portfolio, between June 2014 and March 2017.

Research Objective:

Demonstrate the stability of risk adjusted returns from a DARWIN Portfolio, in both favourable and adverse market conditions, relative to that of the S&P500.

Detecting significant, unforeseen changes in underlying market conditions (termed “market regimes“) is one of the greatest challenges faced by algorithmic traders today. It is therefore critical that traders account for shifts in these market regimes during trading strategy development.

Why use Hidden Markov Models?

Hidden Markov Models for Detecting Market Regime Change (Source: Wikipedia)

Hidden Markov Models for Detecting Market Regime Change (Source: Wikipedia)

Hidden Markov Models infer “hidden states” in data by using observations (in our case, returns) correlated to these states (in our case, bullish, bearish, or unknown).

They are hence a suitable technique for detecting regime change, enabling algorithmic traders to optimize entries/exits and risk management accordingly.

We will make use of the depmixS4 package in R to analyse regime change in the S&P500 Index, and subsequently compare its performance during those states to a DARWIN Portfolio over the same time period.

 

Did you know?

Other popular applications of Hidden Markov Models can be found in the fields of handwriting, gesture and speech recognition, as well as bioinformatics.

Hidden Markov Model - State Space Model (Source: StackExchange)

Hidden Markov Model – State Space Model (Source: StackExchange)

With any state-space modelling effort in quantitative finance, there are usually three main types of problems to address:

  1. Prediction – forecasting future states of the market
  2. Filtering – estimating the present state of the market
  3. Smoothing – estimating the past states of the market

We will be using the Filtering approach.

Additionally, we will assume that since DARWIN Portfolio or S&P500 returns are continuous, the probability of seeing a particular return R in time t, with market regime M being in state m, where the model used has parameter-set P, is described by a multivariate normal distribution with mean μ and standard deviation σ [1].

Mathematically, this can be expressed as:

\(p(R_t | M_t = m, P) = N(R_t | μ_m, σ_m)\)

As noted earlier, we will utilize the Dependent Mixture Models package in R (depmixS4) for the purposes of:

  1. Fitting a Hidden Markov Model to S&P500 returns data.
  2. Determining posterior probabilities of being in one of three market states (bullish, bearish or unknown), at any given time.

We will then use the plotly R graphing library to plot both the S&P500 returns, and the market states the index was estimated to have been in over time.

Once these steps are complete, we’ll compare the risk adjusted performance of the S&P500 to that of a DARWIN Portfolio over the same time period.

You may replicate the following R source code to conduct this analysis on the S&P500.

Step 1: Load required R libraries

library(quantmod)
library(plotly)
library(depmixS4)

Step 2: Get S&P500 data from June 2014 to March 2017

getSymbols("^GSPC", from="2014-06-01", to="2017-03-31")

Step 3: Calculate differenced logarithmic returns using S&P500 EOD Close prices.

sp500_temp = diff(log(Cl(GSPC)))
sp500_returns = as.numeric(sp500_temp)

Step 4: Plot returns from (3) above on plot_ly scatter plot.

plot_ly(x = index(GSPC), y = sp500_returns, type="scatter", mode="lines") %>%

layout(xaxis = list(title="Date/Time (June 2014 to March 2017)"), yaxis = list(title="S&P500 Differenced Logarithmic Returns"))

S&P500 Differenced Logarithmic Returns
(June 2014 to March 2017)

S&P500 Differenced Logarithmic Returns (June 2014 to March 2017)

S&P500 Differenced Logarithmic Returns (June 2014 to March 2017)

Step 5: Fit Hidden Markov Model to S&P500 returns, with three “states”

hidden_markov_model <- depmix(sp500_returns ~ 1, family = gaussian(), nstates = 3, data = data.frame(sp500_returns=sp500_returns))

model_fit <- fit(hidden_markov_model)

Step 6: Calculate posterior probabilities for each of the market states

posterior_probabilities <- posterior(model_fit)

Step 7: Overlay calculated probabilities on S&P500 cumulative returns

sp500_gret = 1 + sp500_returns
sp500_gret <- sp500_gret[-1]
sp500_cret = cumprod(sp500_gret)

plot_ly(name="Unknown", x = index(GSPC), y = posterior_probabilities$S1, type="scatter", mode="lines", line=list(color="grey")) %>%

add_trace(name="Bullish", y = posterior_probabilities$S2, line=list(color="blue")) %>%

add_trace(name="Bearish", y = posterior_probabilities$S3, line=list(color="red")) %>%

add_trace(name="S&P500", y = c(rep(NA,1), sp500_cret-1), line=list(color="black"))

S&P500 Market Regime Probabilities
(June 2014 to March 2017)

S&P500 Hidden Markov Model States (June 2014 to March 2017)

S&P500 Hidden Markov Model States (June 2014 to March 2017)

Interpretation: In any one “market regime”, the corresponding line/curve will “cluster” towards the top of the y-axis (i.e. near a probability of 100%).

For example, during a brief bullish run starting on 01 June 2014, the blue line/curve clustered near y-axis value 1.0. This correlates as you can see, with movement in the S&P500 (black line/curve). The same applies to bearish and “unknown” market states.

An interesting insight one can draw from this graphic, is how the Hidden Markov Model successfully reveals high volatility in the market between June 2014 and March 2015 (constantly changing states between bullish, bearish and unknown).

With all our analysis work complete, we can now compare risk adjusted returns’ performance of the S&P500 and a DARWIN Portfolio over the same time period (June 2014 to March 2017).

DARWIN Portfolio vs. S&P500 (June 2014 - March 2017)

DARWIN Portfolio vs. S&P500 (June 2014 – March 2017)

Conclusion

A DARWIN Portfolio, carefully constructed using Darwinex’ proprietary Analytical Toolkit, consistently outperformed the S&P500 regardless of shifting market regimes as unearthed by the Hidden Markov Model employed.

It also visibly demonstrated more stable risk adjusted returns than the S&P500, confirmed separately by conducting the necessary calculations on the underlying data.

References:

[1] Murphy, K.P. (2012) Machine Learning – A Probabilistic Perspective, MIT Press.
https://www.cs.ubc.ca/~murphyk/MLbook/

Influences:

The honourable Mr. Michael Halls-Moore. QuantStart.com
http://www.quantstart.com/

Additional Resource: Learn more about DARWIN Portfolio Risk (VIDEO)
* please activate CC mode to view subtitles.

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

Darwinex - The Open Trader Exchange

Darwinex – The Open Trader Exchange

Volatility expected due to French presidential elections

Please note that high volatility is expected on EUR pairs, XAUUSD and European indices on market open next Sunday (23 April 2017) due to the French presidential election. Also, liquidity providers are expected to significantly limit their liquidity, which can give rise to abnormal widening of spreads, potential increased amounts of slippage on executed orders and, eventually, gaps in the charts due to the low liquidity available.If you plan to keep open trades over the weekend, please make sure you post margin / equity in your account ahead as volatility is expected to increase heading into the election. Please note that we reserve the right to change our margin requirements as we move closer to the election / depending on the outcome.

Should we decide to introduce any changes to our trading conditions, we’ll endeavour to contact you via email, MT4 or by changes made to our blog / website ASAP.

D-Leverage & Risk Manager

Further, starting on Friday at 18 UK time, our Risk Manager will be more restrictive when replicating DARWINS’ trades to protect investors from increased volatility ahead of the weekend and on market open.

As always, please do not hesitate to contact us at info@darwinex.com for support.

Darwinex Trading Hours for ANZAC Day

Please note the amended Darwinex trading hours for the upcoming ANZAC Day on 25 April 2017 (all times are in UK time).

 Instrument Trading Hours
 FX 22:05 Mon – 22:00 Tue
  DARWINS 22:05 Mon – 22:00 Tue
 COMMODITIES  
Gold 23:01 Mon – 21:59 Tue
Silver 23:00 Mon – 22:00 Tue
Platinum 23:00 Mon – 22:00 Tue
Palladium 23:00 Mon – 22:00 Tue
US Crude 23:00 Mon – 22:00 Tue
Natural Gas 23:00 Mon – 22:00 Tue
 INDICES  
Australia 200* 08:10 – 22:00
 Europe 50 23:00 Mon – 22:00 Tue
France 40 23:00 Mon – 22:00 Tue
Germany 30 07:00 – 21:00
Spain 35 07:00 – 19:00
Japan 225 23:00 Mon – 22:00 Tue
 UK 100 23:00 Mon – 22:00 Tue
 US SPX 500 23:00 Mon – 22:00 Tue
 US Tech 100 23:00 Mon – 22:00 Tue
Wall Street 30 23:00 Mon – 22:00 Tue
*Amended Darwinex trading hours.

As always, at info@darwinex.com we’ll be happy to assist you!

DARWIN Portfolio Returns (June 2014 - March 2017)

DARWIN Filters: A Practical Alternative to Markowitz Portfolio Theory

In 1952 [1], the great Harry Markowitz published a paper on portfolio selection that essentially set the stage for modern portfolio theory in a mathematical context.

Harry Markowitz

Harry Markowitz – Nobel Prize Winning Economist

For those not familiar with this Nobel Prize winning economist [2], he devised a methodology whereby investors could mathematically evaluate the proportion of total available capital to allocate, to each constituent asset in a portfolio of assets.

His method was based on just the means and variances of asset returns.

For different choices of capital allocation per asset in a portfolio, different combinations of mean (μ) and variance (σ²) would materialize, collectively referred to as the attainable set.

As investors always want the highest possible return for the lowest possible risk, Markowitz termed all those combinations of μ and σ² where either:

1) σ² was the minimum possible value for a given μ, or

2) μ was the maximum possible value for a given σ²,

.. as the efficient set, or “efficient frontier” as it’s more popularly known.

Harry Markowitz - Efficient Frontier ModelHow did it benefit investors?

Markowitz Portfolio Theory (MPT) stated that investors should select a portfolio from the efficient set, depending on their risk appetite.

However,

The variances of asset returns in a portfolio do not fully explain the risk taken by an investor, and MPT is therefore not entirely applicable in practice.

For instance, MPT does not reveal the Value-at-Risk (VaR), extreme variations in an asset’s risk profile during times of high volatility, nor the Capacity of a given portfolio.

Darwinex’ Solution to Markowitz Portfolio Selection

Years of proprietary R&D at Darwinex, reliably addresses some of the inherent problems in traditional mean-variance portfolio construction & optimization.

All DARWIN (Dynamic Asset & Risk Weighted INvestment) assets listed on The Darwin Exchange are measured in terms of 12 Core Investment Attributes that go far beyond mean and variance.

These are:

  1. Experience
  2. Market Correlation
  3. Risk Stability (in terms of VaR)
  4. Risk Adjustment (in terms of intervention to stabilize VaR)
  5. Open Strategy
  6. Close Strategy
  7. Positive Return Consistency
  8. Negative Return Consistency
  9. Duration Consistency
  10. Loss Aversion
  11. Performance
  12. Capacity

With these robust behavioral analytics, DARWIN investors are able to iteratively filter assets in order to maximize expected returns and minimize standard deviation (risk), with zero mathematical optimization necessary to achieve desired allocations.

In fact, even an equally-weighted portfolio arrived at using DARWIN Filters presents a more statistically robust set of portfolio allocations, than mean-variance optimization where the possibility of overfitting to asset returns is a hidden risk.

DARWIN Filters

Creating custom combinations of the 12 investment attributes allows investors to analyse the behavioral machinery of assets they wish to include in their portfolios.

As Value-at-Risk (VaR), Excursion Analysis (+/- return consistency) and Capacity among others, become integral components of an investor’s selection criteria, the risks presented by traditional MPT (as discussed earlier), are effectively mitigated.

Perhaps the best way to demonstrate the effectiveness of this approach to portfolio construction, is through an example.

EXAMPLE: Real portfolio constructed using DARWIN Filters

A portfolio of 15 highly uncorrelated DARWIN assets (with an impressive Sharpe Ratio) was built using just DARWIN filters and zero mathematical optimization.

For inspiration, here are the actual realised returns of this portfolio between June 2014 and March 2017, both gross and net of performance fees:

DARWIN Portfolio Returns (June 2014 - March 2017)

DARWIN Portfolio Returns (June 2014 – March 2017)

And here is this DARWIN Portfolio’s performance against the S&P500 over the same time period:

DARWIN Portfolio vs. S&P500 (June 2014 - March 2017)

DARWIN Portfolio vs. S&P500 (June 2014 – March 2017)

Steps used in portfolio construction:

1) DARWIN Filters were first created using a combination of the 12 available Investment Attributes (as listed earlier), to define the investment criteria.

Create DARWIN Investment Attribute Filters

Create DARWIN Investment Attribute Filters

This filtered the initial full list of over 1,000 listed DARWIN assets, down to 15.

 

2) Monthly Returns listed publicly on each of the 15 DARWINs’ pages, were then used to construct a Variance-Covariance Matrix.

DARWIN Asset Returns

DARWIN Asset Returns

 

DARWIN Variance-Covariance Matrix

DARWIN Variance-Covariance Matrix

 

3) Assigning equal weights of 6.67% to all 15 assets, Expected Portfolio Returns and Standard Deviation were then duly calculated.

This led to a DARWIN portfolio with the following features:

DARWIN Portfolio Backtest Statistics

DARWIN Portfolio Backtest Statistics

 

 

 

 

4) For sake of exercise, here is a comparison of what MPT optimized allocations would be for the same portfolio:

Equal vs. MPT Optimized Portfolio Weights

Equal vs. MPT Optimized Portfolio Weights

 

 

 

 

 

 

 

 

 

 

 

 

MPT Optimized Portfolio Backtest Statistics

MPT Optimized Portfolio Backtest Statistics

 

 

 

 

An MPT optimized DARWIN portfolio would indeed have lead to a higher Sharpe Ratio (5.92 vs. 4.17), higher Expected Return (38.80% vs 28.61%), and a slightly higher Standard Deviation (5.71% vs. 5.66%)..

.. but at the risk of allocating a majority of available capital to only 4 out of 15 assets in the portfolio.

In this scenario – and as per Markowitz Portfolio Theory – a conservative investor would likely have opted for the equally-weighted portfolio, while a more aggressive investor may have opted for the MPT-optimized portfolio.

In both cases however, DARWIN Filters enabled both profiles of investor to consider the important attributes of Value-at-Risk (VaR), Capacity, Risk Stability and Consistency..

.. as opposed to traditional MPT mean-variance analysis where these would have been overlooked.


References

[1] Markowitz, H. (1952) Portfolio Selection. The Journal of Finance, Vol. 7, No. 1, 77-91. March. 1952.
www.jstor.org.proxy.lib.chalmers.se/stable/10.2307/2975974?origin=api (2012-10-30)

[2] The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 1990.
http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1990/markowitz-facts.html

Watch this video to learn more about a DARWIN’s Investable Attributes:
* please activate CC mode to view subtitles.

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

Darwinex - The Open Trader Exchange

Darwinex – The Open Trader Exchange

novedades darwinex

Diversification rebates for investors

Since the launch of Darwinex Reloaded, the investors in our platform are receiving a daily bonus in the form of return of execution commission: the new diversification rebates.

So why have we decided to introduce this new incentive for investors? Many will think that we have gone mad, others that it is a dream come true… we hope that with this post you will convince yourselves, as we have, of the advantages that this bonus will have for our trader movement. Below we will present the principal reasons  behind the creation of the diversification rebates for the investors.

Reason 1:

The trader´s compensation system actually disincentivises the investor to diversify their portfolio. We remember that the performance fees are individually charged above the obtained yield in each DARWIN on an individual basis. This causes situations such as:

If a user has a portfolio with two DARWINS and in a period one DARWIN gains 2,000 € and the other loses 2,000€, the result would have been a net zero. Nevertheless, at the end of the period the investor would pay 400€ in performance fees (= 20% x 2,000€) to the DARWIN that gained 2,000€, despite his portfolio not generating any aggregated gains.

However, from the investor’s point of view, there must be diversification incentives, because this is the only way to protect themselves from unexpected events in the markets (like, for example the “flash-crash” in cable on the 7th October last year, or the huge movement of the Kiwi dollar on 24th August 2016).

Given that the risk manager cannot protect himself from these sudden movements, the diversification of the DARWINS is the only way to moderate the losses in their accounts during unexpected events, making the diversification rebates a form of incentive for the investors to diversify .

Reason 2:

The old structure of execution costs was, in a certain way, “unfair” for investors that wanted to diversify their accounts. It was difficult to appreciate, but it was the case that adding DARWINS to the account, the commissions remained but the potential benefit (= account risk) would be reduced.

As an example, we are going to compare two investors that do not have diversification rebates.

Investor 1 :  Invests  10,000€ in DARWIN NTI

Investor 2 :  Invests    5,000€ in DARWIN NTI and 5,000€ en DARWIN PLF

We have chosen these DARWINS because they rotate their capital more or less the same every month (same commissions). We assume that both generate 0.5% of commissions each month.

The second investor pays 50€ in commissions every month , but his potential returns are less. If we consider that NTI and PLF don´t have correlations between them, the risk of each investment is 7.07% instead of the 10% that investor one has. The second investor , would have had to receive ideally a compensation of 14.65€ for his ratio between commissions and potential return to be  the same as the first investor.

Calculation of the rebates:

Our net margin above the investor commission is approximately 40%, so that any potential rebate paid must be less than 40% ( we are not mad). So, well how can they be shared out?

We have found a manner that is transparent, easy and, in time, has a correlation to the diversification that exists in the account.

If we assume the the correlation between the Darwins in the portfolio is zero, the diversification factor is:

Diversification factor = √(∑invi)/∑invi

Being invi the amount invested in each DARWIN

Making the supposition that all the DARWINS generate the same commission , and therefore, that they are dependant on the volume invested in them, we can use the last diversification .

 

Thus , with a maximum diversification factor of 40%, the rebate would be equal to

Rebate = 0.4*(∑ci-√(∑ci)

Being C¡ the daily  commission charged in each DARWIN portfolio.  

 

Results

To mention the efficiency of the diversification rebates we have done a backtest of the rebates that would have been paid to the investors in the past, and we have compared the performance fees charged at source

Rebates / performance fees=74.01%

 

That is to say, the rebates, without compensating all the performance fees, they do solve a vast majority of the problems that are generated with diversifying the accounts.

Please note this is an average value, the % of each user will vary depending on the skill of the investor when they select the DARWINS in their portfolio.

We hope the video below is useful, please feel free to reach out to info@darwinex.com should you have questions in this connection!

And the DarwinIA winners are…

The March edition of our DarwinIA trading challenge came to its end. Below you can find the 48 winners of our € 4,000,000 notional allocation for a 6 month period (yes, you read well: Darwinex Reloaded has doubled the prizes due to the DARWINS’ risk being lower!).

Place DARWIN March notional allocation
1st KBP € 300,000.00
2nd YVG € 250,000.00
3rd JCW € 210,000.00
4th PHD € 170,000.00
5th DNA € 150,000.00
6th VLD € 140,000.00
7th PIJ € 130,000.00
8th RWL € 120,000.00
9th UEI € 110,000.00
10th XGU € 110,000.00
11th KGL € 110,000.00
12th FGC € 100,000.00
13th GPZ € 100,000.00
14th GTD € 100,000.00
15th LZL € 90,000.00
16th VQG € 90,000.00
17th RDD € 90,000.00
18th WIZ € 80,000.00
19th TAK € 80,000.00
20th XIN € 80,000.00
21st FTA € 70,000.00
22nd UYZ € 70,000.00
23rd LOM € 70,000.00
24th SLR € 70,000.00
25th CLA € 60,000.00
26th WIT € 60,000.00
27th PZN € 60,000.00
28th XXR € 60,000.00
29th VTJ € 60,000.00
30th XXC € 60,000.00
31st RFQ € 50,000.00
32nd IZT € 50,000.00
33rd VTG € 50,000.00
34th NTI € 50,000.00
35th CKV € 50,000.00
36th LCA € 50,000.00
37th STP € 50,000.00
38th DAG € 40,000.00
39th SVI € 40,000.00
40th NMM € 40,000.00
41st DZL € 40,000.00
42nd PRC € 40,000.00
43rd DHJ € 40,000.00
44th AXS € 40,000.00
45th CSI € 30,000.00
46th XYT € 30,000.00
47th CEZ € 30,000.00
48th WPB € 30,000.00

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The transition from 20% to 10% of the monthly VaR

As we have already explained in our previous articles, one of the main changes in Darwinex reloaded is the reduction of the monthly VaR of the DARWINS to 10%.

At the time of introducing the new DARWINS with the the new risk profile, we have had to face up to various operational challenges: What do we do with the actual DARWINS of 20% VaR? What happens with the investors that stay open in the “old” DARWINS?

In this article we explain the principal unknowns that have been presented to us, and the solutions that we have adopted to introduce DARWINS with 10% monthly VaR.

What historical data de we exhibit in the DARWIN

Imagine that DARWIN AAA which from 2015 until 2017 was quoted with a 20% VaR. In March of 2017 it is recalculated and converts to the new 10% format. The doubt is: What history do we exhibit in the graph of DARWIN AAA from now on?

After a lot of thought, we have opted to recalculate ALL the DARWIN  history at 10% (i.e in the future the data of the DARWIN is going to exhibit like it has always been quoted since it’s inception at a VaR of 10%)

  1. Taking into account that the calculation of the attributes has changed, the old graphs of the 20% VAR calculations based on the old attributes  would not offer an accurate image of the 10% DARWIN’s function and they would confuse future investors
  2. To launch the tools to backtest the DARWINS, the data has to be uniform, in terms of the calculation of the VaR and the D-Score. To do backtests using the old 20% VaR data would not give out consistent results.

As an example , we are using the graph of BLI with 10% VaR , and the new risk manager.

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What is going to happen with the investors that remain open in the old 20% DARWINS?

The investors in the 20% VaR DARWINS will carry on figuring in the investor’s portfolios that have remained open and these will be closed but there will not be any further investment in those DARWINS. On the other hand, from the launch date of Darwinex reloaded, the DARWINS in the portfolio will behave like the new 10% DARWINS, but with a leverage of 1:2 (in fact they continue to operate as with a 20% VaR).

We hope that the following example will be helpful. Let’s imagine that you have the DARWIN BLI with 20% VaR (old DARWIN), in your portfolio since 2015. At the end of March 2017, the DARWIN is calculated with a base of the new attributes, and is converted to the 10% VaR (new DARWIN ). What will you see in the portfolio?

In your portfolio you will see the old DARWIN BLI accompanied by a red dot to remind you of the investment in the old version of the DARWIN.

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From  the inception date of the new DARWIN, your capital will be invested in the new 10% DARWIN but with a leverage of 1:2, so that the volatility of your investment will carry on being a 20% VaR. Important: if you want to invest in the new version of the 10% BLI, you would have to close your inversion in the old BLI and invest it in the new BLI. Unfortunately, for calculations of the high watermark they cannot coexist in the same investment portfolio in the old form of the DARWIN and the new at the same time. This way, we are to maintain the current high watermarks for all DARWINS and investors.

For those who use our mobile app to invest, they would have to close the inversion in the old  BLI from the portfolio section of the app , because the search engine will show by default only the new version of the DARWIN, and it will not permit investment in it, if the old investment has not been closed.

At the time that the inversions in the old DARWIN have closed, we recommend closing the investment in the moment that the DARWIN doesn’t have any open positions  to therefore avoid associated commissions in the closure of open positions.

For your information, those of you who want to maintain a 20% VaR, in the new Darwinex reloaded, an option exists to use the 1:2 leverage like an investor to replicate the new 20% VaR DARWINS.

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What will happen with the DarwinIA prizes?

Those DARWINS that accumulated prizes in previous editions of DarwinIA will keep the prizes invested in their DARWINS and their watermarks will remain the same. What has arisen here is the necessity to adapt the investment to the new level of risk (the profitability changes on reducing the VaR to 10%,so what would be fair would be to double the investment so as to maintain the conditions of the prize).

To adapt the investment in the DARWINS that had obtained prizes in the past, it will be taking into account the open equity in DarwinIA at the launch date of Darwinex Reloaded and will duplicate the invested quantity.

We are going to use an example to explain it. Let’s imagine that DARWIN AAA had won a prize in DarwinIA of 100,000 EUR in February. We arrive at the launch day of Darwinex reloaded in March and the DARWIN had lost 10%: there would be 90,000 EUR left invested from DarwinIA. Given that the new DARWIN AAA operates with half of the VaR from before the launch of the reloaded version, what would be fair would be to double the investment in the DARWIN that operates at 10%. Given that 90,000 EUR invested at 20% remain  the solution that we have adopted is to duplicate the investment at 10% VaR, resulting in our example in an investment of 180,000 EUR (2 x 90,000 EUR)

We are sure that thousands of questions will arise with the launch of the new web, but we hope that this article will help to resolve the main doubts

As always, in info@darwinex.com we are always at your service for whatever doubt you may have!

Why re-load Darwinex?

Hi there – and thanks for bearing with us for such a long time!

As you’ve noticed, the Darwin Exchange re-loaded overnight, so this post lays out what’s changed – but more importantly, WHY.

What changes?

A lot. The entire Darwinex team, in one way or another, shifted to “Re-loaded” from the 2016 summer break, and this is a large functionality release involving thousands of hours.

You’ll no doubt explore for yourself, but here’s a summary:

  1. Same ethos – new message: gone is “The Broker that invests in its traders”, long live “The Open Trader Exchange”. The new public site: explains why YOU!!! are an asset worth listing, and brings the journey that began with TradeSlide 5 years ago full circle
  2. New Algorithms: every algorithm has been re-programmed from the ground-up,
  3. New DARWINsevery DARWIN has been re-listed, with 10% VaR target risk and the new, generally less “intrusive”  risk manager,
  4. Private site: offers DARWIN providers & managers a faster, more intuitive and rewarding journey,
  5. Diversification rebates: whereby Darwinex subsidises healthy behaviour (diversification) and investor performance with a substantial chunk of Darwinex’s commission revenue

We think the wait was worth it – and sincerely hope you’ll agree! We’ll cover new functionality & algorithms in blog and webinar form in the coming weeks and months!

Is re-loaded stable?

We’ve tested for quite some time now (thank you, alpha testers!), but things won’t be perfect from day one.

With your help, we’ll get there faster: share your feedback in the community forum – every suggestion reaches the product & technology team via an internal Slack Channel. Hint: they work harder and faster if you share encouraging feedback from time to time…

But, enough intro. Why scrap the entire legacy interface?

Why re-load?

Darwinex, explained

From day one, defining Darwinex was a Challenge. Is it a broker? A social trading platform? A P2P asset manager? What “is” Darwinex?

Don’t get this wrong – from day one, we’ve been a movement where traders come first, and walked that talk for 5 years now. It started off as a trader diagnostic toolkit, with grades to tell skilled traders from lucky punters. Then we secured FCA Asset Manager permission2 years ahead of anyone else, and eventually became our own broker honing in on the best way to protect trader IP.

Today, in addition to receiving rave reviews as a broker (thank you, reviewers!), Darwinex is a venue that protects trader intellectual property, legally covering for traders to earn 20% success feefrom investors protected by a real time risk management engine.

Furthermore, because we believe in the movement, we actively risk our balance-sheet on as opposed to against customers. In our “first investor in traders” capacity, we’ve paid EUR 312.000 for trader IP to date (THANK YOU DARWIN providers!), and the current monthly Darwinia success fee run-rate is EUR 30.000.

It’s going well. Your word-of-mouth grows your movement 10+% per month logarithmic growth, without meaningful advertising spend… and yet: everyone struggled to describe above combination of features.

We labelled ourselves “the broker that backs its traders” – with friends providing honest feedback & literally thousands of suggestions on this note (thanks again 50, TradeSignalMachine, Klondike, ForexDuet, Krechendo and many others, sorry for only mentioning 5).

And then, eventually, it sank in. Darwinex has been all along, it’s just we didn’t realise …

The new Darwinex tag-line

The DARWIN Exchange is a 2-sided marketplace where:

  • Independent traders (DARWIN providers) legally market intellectual property
  • DARWIN managers (and DARWIN traders too!) allocate capital to said IP, at arm’s’ length, via a neutral venue…

On a first come, first served, level playing field.

Unlike proprietary trading operations, our (=YOUR) Exchange, is OPEN to anyone with thousands of dollars. This creates positive externalities. One of them is traders hailing from traditional assets learning to manage DARWINs to conquer a brand new asset. Imagine that. Traders managing DARWINs (=intellectual property) that other independent traders willingly and legally contribute. A crowd-sourced, crowd-funded movement collectively pooling information and capital for private, but also public benefit.

That’s what makes the movement different. Others want markets for themselves. The trader movement returns markets to society, for private, but also social profit.

So there you go. We hope the “Open Trader Exchange” tag-line helps YOU spread the movement. Yes, YOU, not just us – we’re all in this together: the sooner “retail trader” <> “brainless punter“, the sooner you’ll make a living from trading, because really, Darwinex is but the technology team at the service of the movement.

Note that it’s all a process – if you have a better one, please share!

Implications

Note the tag-line is about far more than PR. Because an Exchange is:

  1. Neutral: gone are judgement calls (Experience, Risk Management, etc.), in are fundamental trader DNA metrics (Ex = old Experience, Pf = legacy Performance, etc.).
    • 0-10 Grades remain, but YOU choose which ones to weight, how. Is R+ (Positive Return Consistency) meaningless to you? Ignore it! Lots of equity fund-managers consider Price Earnings Ratios useless… but respect the NYSE’s duty to track and publish them.
    • Levels (amoeba, Pro, etc.) are GONE from the interface. Note: we’re not giving up on them, just re-locating to a dedicated education effort, on which more when the time is ripe
  2. Transparent: more and more indicators will join the core set of metrics. The movement is publicly committed to beating the market, so we’ll publish in the coming months more information on how DARWIN providers & managers fare,
  3. Committed: Note that it’s an open exchange – so we will continue to back traders & expand proprietary investing activities in the coming months. Data suggests it’s possible to make money from the DARWIN asset and we’ll share how (techniques & tools), as we learn.

Hopefully that provides the gist of what’s coming – we’ll flesh this out in more blog posts / webinars / videos in the coming weeks/months, but please keep questions & challenge coming.

Algorithms

This is worth several posts in its own right. For now, a brief summary:

  1. Evolution: pretty much every algorithm has been re-factored from the ground up,
  2. Granularity: 6 grades morph into 12 elements, including all the old 0-10 sub-grades plus a new element called Market Correlation. We’re working on better (more intuitive, more to the point) videos to replace the old ones.
  3. Performance: computational performance has  dramatically improved. You’ll notice more frequent grade updates over time (although initially we’ll stay at daily until all code has been validated). This was a first step towards the next wave of functionality… on which more in due course,
  4. Stable & stored: we’ve started populating the world’s first public trader behaviour database. Guess what that could be used for…

Leave it at that for now: let’s say on the algorithm a lot of work has gone into building the foundations for our next product. Stay tuned.

Private site

We know. Remember when you first signed up to Darwinex? WTF? Where do I start? How do I open an account?

The legacy interface was designed for traders, and the more functionalities we added, the messier it got. It was complex before “investors” arrived. Launching the investor terminal completed a royal usability mess. Two separate interfaces with re-directs, lots of functionality, it all looked sort of pretty but it took commitment to do even the simplest things…

We know 🙁 Everyone back at movement Tech headquarters is hooked on an internal DARWIN portfolio Challenge – and because we privately use Darwinex as much or more than anyone else, WE were as frustrated as anyone. SO we took a step back to reflect… and the decision was made: scrap the whole thing, incorporate everyone’s feedback, and re-load for better:

  1. Responsiveness: a brand-new front-end framework, implemented with performance in mind. Gone are  endlessly loading scripts. Get there before forgetting what you’re after,
  2. Navigation: The left hand bar. Explore the catalogue. Zoom in on your first DARWIN. Discover what DARWINs others trade. Favourite DARWINs. Buy DARWINs from anywhere in the interface.
  3. Modularity: DARWIN providers & managers want different user journeys. From now on, everyone builds their personal Darwinex by simply activating different modules on THEIR left hand bar,
  4. Filtering: a much improved bar, top of the interface. Don’t search, find. DARWINs. Users. Strategies. Your call, not the search bar’s 🙂
  5. Control: portfolio performance, always at a glance, bottom of your screen. Click it, activate full screen view. Trade. Go back to hunting mode.
  6. Scalability: this was but a foundation for future evolution. The new framework will seamlessly include more and more powerful tools without unnecessarily adding complexity,

… and last, but not least, trade-ability. 

Want to guess the biggest insight from 1 year odd-year since”investor” platform launch?

DARWINs are NOT just for passive investors. Some users are experiencing DARWIN trading strategies – doing quite well out of it. Others are more “passive investors. It’s evolution itself: movement members  independently explore alternative investment strategies, and we’ll continue to observe and provide the most demanded functionality enhancements.

What comes next? We won’t give it away that just yet, but this hopefully gives you an idea…

Diversification rebates

Traders first: good traders are worth more Assets under Management.

Re-loaded introduces a new portfolio commission tier, whereby the more Darwins in a portfolio, the more trading commissions Darwinex rebates back to managers / investors.

Why? AuM are growing 10+% a month, organically, but nothing spreads faster than a better investment. Diversification rebates help DARWIN providers earn more success fees by leaving more profit with investors. On average across today’s portfolios, diversification rebates add 2-4% performance to yearly P&L. 

Our internal bet is that 1) diversification rebates plus 2) the improved risk manager plus 3) the steadily improving DARWIN pool will dramatically improve organic investor performance. Stay tuned.

What’s next?

That’s it for now!

Give us a bit of time as it’s all still a huge work in progress, but rest assured that there’s TONS of additional things in the oven.

For now, we need YOUR help. Please, please point our attention to any bugs you discover in the coming weeks  – this way we’ll quickly re-load to the next level.

Meanwhile, here’s a huge THANK YOU to all of you who constantly raise the level at the community forum, and a toast to even MORE evolution!