1. Which trading accounts get analyzed?

    Darwinex’s core diagnostic toolkit is offered free of charge to all MT4 account holders - both linked and Darwinex real and demo trading accounts.

    Diagnostics for demo trading accounts are less extensive.

    Linked or Darwinex trading strategies with more than 50% of trades below 10 minutes or more than 5% of trades in assets not offered by Darwinex Broker aren't diagnosed.

  2. Linked Trading Account

    MT4 accounts held with other brokers can be linked to Darwinex for diagnostic purposes.

    In order to report MT4 trades automatically to us, please provide us with your investor password (read-only, we can't trade with it!). Our system will log into your account on your behalf to retrieve both history and ongoing trades.

    You may read our user guide or link your account here.

    Linked trading accounts can't create a Darwin unless you request their migration to Darwinex and thus convert them into a Darwinex trading account.

  3. How frequently get account metrics and charts updated?

    Basic metrics like return and drawdown get updated upon closing trades and at end-of-day (20:59 GMT).

    Charts and Investable Attribute scores get updated daily accounting for position at end-of-day (20:59 GMT) including open trades. The actual analysis is done during the night and becomes visible in the morning.

  4. Return (Metric)

    Measures the return of a strategy over a chosen reference timeframe.

    We track return as percentage changes to account equity i.e. return take into account profits / losses incurred. Cash-deposits / withdrawals to/from the account don't affect profitability, as we filter out their effect in our calculations.

    Returns are updated both on trade close and at end-of-day (20:59 GMT) accounting also for unrealized P&L.

  5. VaR (Metric)

    VaR is short-hand for Value at Risk.

    VaR measures the worst monthly loss by a strategy, at 95% confidence (technically, VaR is the 95% percentile of the distribution of % returns on equity, projected over a 1-month horizon).

    A strategy trading with stable risk will experience losses larger than VaR 1 month every 20 months (1-95% = 5% or 1/20).

    VaR is a risk measure widely applied in finance. In a trading context, many mistake VaR for historical drawdown even though both are conceptually different.

    VaR tracks risk, describing what could happen. Drawdown is backward looking: it captures what did happen. Contrary to misguided conventional wisdom, it is entirely possible for trading strategies with extremely high risk (VaR!) to muster low drawdown, for a while.

    Unsure about what we mean? Read on!

    Why VaR is NOT Drawdown?

    Play this (thought!) experiment: introduce a single bullet in an empty 6 load revolver barrel. Randomly rotate drum. Aim revolver at your head. Pull the trigger.

    ◦ You might survive 50 runs of Russian Roulette… this can´t be ruled out statistically,

    ◦ This doesn't change a fact: on average, you're dead after the worst of 6 runs

    The 95% percentile is overkill for this experiment, 83.3% percentile (1 - one sixth) captures the range of all possible outcomes :-(

    Surviving 50 rounds of Russian roulette isn't low risk. Mistaking Drawdown for risk amounts to thinking that what did happen is the same as what could happen. Historians care about Drawdown, but smart investors focus on risk.

    VaR and Drawdown would converge, given enough repetitions of a controlled experiment in which conditions stay constant. Unfortunately, markets don´t stay constant and can´t be controlled. Don't learn this lesson the hard way!

    Want to learn more? This 40-minute-long webinar explains VaR more in depth and why Drawdown can't be used to assess risk.

  6. Drawdown (Metric))

    Drawdown tracks the worst peak-to-trough fall for a strategy in a given reference period. Drawdowns are normalized to percentage of Equity in the strategy's account - which takes into account unrealized profits / losses.

    Drawdown is measured from the beginning of a fall until a new maximum is reached (this is because it is impossible to define a valley without two mountains!). Once a new peak is reached, the percentage drop is measured from the previous peak to the absolute minimum.

    Drawdown is NOT a measure of risk, but contributes information that helps approximate how much risk was deployed in a timeframe of reference - a gradual drawdown of 50% over a year is very different to a sudden 50% weekly loss!

    This webinar by our CEO explains why Drawdown can't be used to assess risk.

  7. D-Leverage (Metric)

    D-Leverage normalises economic leverage for all your positions to multiples of nominal of EUR / USD leverage.

    What’s the point?  Example 1 - Different couples have different risk

    GBPJPY is more volatile than USDCAD, which makes a 5:1 GBPJPY trade riskier than a 5:1 USDCAD: D-Leverage for the GBPJPY trade is 5.5-6, because GBPJPY is 10-20% more volatile than the EURUSD!

    What’s the point? Application 2 multiple trades/assets in a position

    Take a position made up of these two trades:

    -          $ 70,000 upwards in USDCAD (7:1)

    -          $ 70,000 down on GBPJPY (7:1)

    Nominal leverage, as it´s normally computed, would be 14:1.

    How about economic risk, in EURUSD multiples? It could be anywhere from 0 (if perfectly negative correlation between USDCAD and GBPJPY prevailed during the position´s duration) and 20:1 if USDCAD and GBPJPY were perfectly positively correlated - at a time of high market volatility!

    D-Leverage tracks economic risk. D-Leverage = 9, implies some positive correlation between USDCAD and GBPJPY: risk would be equivalent to a $ 90,000 (9 * 10,000 equity) EURUSD trade.

    D-Leverage works for any number of concurrent trades in a position, because it accounts for recent market volatility and correlation!

    Want to see D-Leverage in action? Visit your Trading Journal!

  8. Trading Journal (Chart)

    Plots, with interactive zoom, ALL your strategy in a timeline with 5 overlaid charts:

    ◦ Return: evolution of equity over any reference period

    ◦ Risk: D-Leverage for EVERY position in the track-record

    ◦ Open trades: Number of simultaneously open trades

    ◦ Score: Evolution of your D-score over time

    ◦ Trade Distribution

    The table on the right hand side of the chart provides additional information on the position at which the mouse is pointing (this is highlighted by vertical tracking lines), and the trades involved in each position are shown in the timeline at the bottom of the chart.

  9. Return (Chart)

    Plots % changes in mark to market value of equity at end of day (20:59 GMT), for the timeframe selected on the visor at the bottom of the chart. Monthly/yearly periods in the table below the chart are interactive and will automatically set the chart to that period.

    What’s the point?

    The chart plots equity liquidation values - i.e. accounts for ALL P&L, including both:

    ◦ Realized P&L (balance, accounting for cash-flows and closed trades) AND

    ◦ Equity at risk, on trades open at end of day

    The chart thus tracks changes to the hypothetical net asset value for any investor replicating the strategy, regardless of whether trades are closed or remain open.

    What’s NOT the point?

    This is NOT plotting account balance.

    Darwinia’s return calculation is different from many alternative services - in accounting for unrealized P&L it accounts for ALL available information!

  10. D-Period (Metric)

    Experience is tracked in D-Periods. 1 D-Period is approximately equivalent to one month (22 market days) of full time trading.

    BUT: Experience doesn´t just credit time. It accounts for market exposure, as follows:

    ◦ Independent trading decisions - e.g. multiple simultaneous trades in a single asset (or highly positively correlated assets) accrue the same experience as a single trade in that asset

    ◦ Scattering of length of individual positions

    ◦ Scattering of D-Leverage across individual positions 

    Global D-Score is penalizedfor all strategies with Experience below 12 D-Periods - the heavier, the further away from 12 D-Periods. Similarly, trades further away in history than 12 D-Periods are ignored for the scoring, to ensure that analysis is focused on the most recent (and therefore, relevant) data.

  11. What are the IA grades for, the DARWIN, or its underlying strategy?

    Investable Attributes grade the DARWIN – not the underlying strategy – as their purpose it to assist investors in their purchasing decisions.

  12. Experience (Investable Attribute)

    Is a filter for statistical significance - it's impossible to rule out luck from a track-record with a single trade! The more data you feed the system, the more reliable the assessment!

    Lack of experience seriously handicaps a strategy´s ranking: D-score for strategies below an Experience grade of 10 are penalized - the further from 10, the heavier. The good news is that Experience is the only Investable Attribute that only grows - it CAN´T fall.

    A 10 is granted for Experience equivalent to 1 full year of non-stop, daily trading of uncorrelated assets with homogeneous leverage and duration. Or, a 10 grade in Experience = 12 D-Periods, if you will.

  13. Risk Management (Investable Attribute)

    We assess a strategy’s risk management at two levels:

    ◦ Position level: does the trader attempt to leverage positions with D-Leverage consistent with the strategy´s global risk appetite (as measured by VaR)?

    ◦ Strategy level:  Does the trader’s Risk Appetite (VaR!) stay stable over time?

    Strategies yielding stable risk through consistently leveraged positions are investable - they prove risk management skill conclusively. Strategies delivering stable VaR through inconsistent position leverage decisions don´t prove skill as conclusively.

  14. Timing (Investable Attribute)

    Assesses the quality of timing decisions, both at trade open and close.

    A proprietary trader implicitly claims a systematic ability to outsmart the market by both entering and exiting trades at the most favorable timing.

    Darwinia checks how strategies stack up against this claim - we create alternative strategies opening/closing the same trades earlier/later than the strategy, and see whether the strategy builds up a lead against all other alternatives.

    Strategies that cumulatively outperform all alternatively timed strategies, provide evidence (but not proof) of non-random returns!

  15. Consistency (Investable Attribute)

    We reward systematic trading patterns (irrespective of outcome) and penalize evidence of loss aversion.

    Other things being equal, consistent strategies are more investable than non-consistent ones because their performance: 

    ◦ is statistically more significant, given any number of trades

    ◦ scales with more capital, provided it does not suffer loss aversion

    ◦ is more predictable, with risk both easier to track and to manage

    Consistency is tracked by identifying common patterns to:

    ◦ Duration consistency

    ◦ Return consistency

    The consistency grade corresponds to the better of the two assessment.

    The assessment penalizes any evidence of loss aversion, since this could compromise consistency under stress.

  16. Performance (Investable Attribute)

    Tracks strategies´ risk adjusted performance.

    The spirit is similar to Sharpe/Sortino ratios, but the implementation overcomes shortcomings that arguably render Sharpe/Sortino unfit to assess high-risk, high rotation trading strategies.

    Strategies are benchmarked against 10.000 monkeys randomly trading exactly the same risks. The resulting ranking accurately reflects non-random performance.

    The Performance Investable Attribute grades the output DARWIN’s performance (i.e. the performance on 20% VaR after algorithms manage D-Leverage independently from the underlying strategy).

  17. Scalability (Investable Attribute)

    Tracks performance loss as strategy's assets under management grow.

    Market liquidity is finite - every marginal euro under management moves the trading price against the strategy as assets under management grow.

    The scalability assessment factors in: trade frequency and length, liquidity and volatility of underlying assets, and average trade-level leverage as a proportion of target risk appetite as measured by VaR (Target D-Leverage / VaR).

    For every strategy, Darwinia identifies the assets under management threshold beyond which incremental volumes harm investors long-term profitability.

    The more assets under management a strategy supports, the more investable it is, and the better it scores on this investable attribute.

  18. D-Period Duration (Chart)

    This chart can be found by clicking on the Experience score on the strategy page.

    D-Period Duration (Chart)

    You can´t rigorously rate Darwins without accounting for statistical confidence: you shouldn't trust an assessment for a 1 trade track-record as much as you'd trust conclusions for another with thousands of trading decisions.

    What’s in the chart?

    Displays a Darwin’s cumulative experience in D-Periods, plotting natural days required to clock 1 D-Period on the vertical axis.

    1 D-Period is the basic measure of Experience at Darwinex - and amounts to about one month (22 market days) of full-time trading.

    The graph is very useful to assess how active a Darwin is, and how long it requires to clock a full month in the market. It´s impossible to clock it in less than 15 days, and there´s no higher bound.

    What's the point?

    There are two core purposes to it.

    The first is comparing experience apples-to-apples across strategies. A scalping strategy with 5 D-Periods proves the same experience as a swing strategy with 5 D-Periods - despite radically different trading styles.

    The second is to assess trading intensity: Darwins with stable trading intensity -horizontal graph without spikes- are likely more disciplined and predictable - and thus investable - than others where spikes likely hint at changes to the strategy.

    To keep things up-to-date, data older than 12 D-Periods does not count.

  19. Risk Stability (Chart)

    This chart can be accessed by clicking on the Risk Management score on the strategy page.

    Rational investors back risks that fit their appetite. If they ignore the risk involved, they don´t know if it fits their appetite, so they don´t invest. All of which makes stable (=known!) risk investable.

    NB: stable risk is a necessary but not sufficient condition for investability. Rational investors will question what holds risk stable, and won't invest without evidence for position level leverage consistent with the strategy level risk appetite.

    What’s in it?

    Plots evolution of risk over time, as approximated by the evolution of 95% monthly VaR.

    To highlight changes in risk, an additional area around the plot is shadowed:

    ◦ Above, by the maximum Risk / VaR in the last month and

    ◦ Below, by the minimum

    What’s the point?

    Investable strategies trade with stable risk: if risk fluctuates, it does so slowly and gradually, with small volatility around maximum and minimum risk in a given reference period (=narrow shadowed area!).

    Darwinia punishes recent fluctuations more than distant ones.

    What’s NOT the point

    What matters is stability: this is NOT about risk level. 

    Stability matters because investors with 4% VaR risk appetite can replicate strategies with 30% VaR by adjusting position level leverage, provided VaR stays stable at 30%!

    That’s why the vertical axis has logarithmic scale: it penalizes relative VaR changes - investors struggle more in replicating a strategy whose VaR scales from 4% to 8% (risk doubles!) than they do with a change from 24% to 28% (risk “just” grows by 16%).

  20. Darwin Risk Adjustment (Chart)

    Consistent Leverage is the component of Darwinia risk management that assesses position level risktaking (the other being strategy level is Risk Stability).

    Proving stable, strategy level risk, AND consistent position level leverage offers investors re-assurance - whoever achieves stable risk by leveraging every position towards a strategy wide risk appetite proves competent risk management.

    The causal link is not reversible: yielding stable risk from inconsistent position leverage does NOT prove risk management skill conclusively: it could be that risk stayed stable for a while by chance - which is not what investors are after.

    What’s in it?

    The graph plots every position (winners in green, losers in red) in a reference period, with

    ◦ D-Leverage on the vertical axis

    ◦ Duration on the horizontal axis

    Three additional horizontal lines plot maximum, minimum and latest optimal D-Leverage (the D-Leverage yielding strategy stable risk if deployed consistently across positions).

    What’s the point?

    Leverage consistency is about the spread of positions vs. the ideal target D-Leverage line, which delivers stable risk for the period:

    ◦ Investable: Darwinia rewards patterns of dots on or near the target D-Leverage line that prove skill at managing position level risk competently.

    ◦ Poor: widely-spread scatter plots evidence lack of skill that scares investors away

    ◦ Risk trends: dots consistently above/below the ideal line increase/reduce VaR from the previous level - as long as trading patterns for the strategy aren´t changing (itself a worrying sign that will be penalized by Darwinia on the Discipline grade).

  21. Consistency per Trade (Chart)

    A strategy winning with discipline across trade duration and profitability (risk!) proves consistency to profits. Investors confide in such strategies more than in strategies without patterns in trade length and profitability.

    What's in it?:

    Plots, for a 3 months reference period all trades in a grid with:

    Horizontal lines: trade profitability growing left to right (in pips)

    Duration:  growing bottom to top

    Two buttons activate additional functionality:

    Zoom: focuses on winning/losing trades for greater granularity

    Excursions: in addition to trade closures, it highlights maximum favourable/adverse trade excursions to highlight strategy discipline in S/L and T/P

    What's the point?

    Investable strategies visually prove discipline with trade concentrations in specific parts of the grid delimited by vertical and horizontal lines:

    Horizontal patterns: trades respect discipline in duration

    Vertical patterns: S/L and T/P levels systematically enforced - there´s discipline in profitability

    The Consistency investable attribute rewards entropy: the more focused trade closures (and favourable/adverse excursions) are in a few quadrants (the less, the better), the better the strategy scores for discipline, as long as it doesn't evidence loss aversion.

  22. Max. Win vs. Max. Loss per Trade (Chart)

    Diagnoses strategies for loss aversion, because loss averse traders are NOT investable.

    What’s in it?

    Tracks, for every trade in the strategy, in % of account equity:

    ◦ Green bar: maximum favorable excursion

    ◦ Red bar: maximum adverse excursion

    ◦ In a line in each bar: performance locked at position close

    The x-axis (horizontal) plots position sequence from left (oldest) to right (most recent).

    What’s the point?

    Look for:

    ◦ Green/Red Asymmetry: especially for extreme outliers - if red is taller than green, you're probably looking at loss aversion

    ◦ Trends: Recent growth in red with shrinking green hints at increasing loss aversion

    Another interesting application is telling manual from algorithmic traders. Algorithmic traders don't usually suffer from loss aversion, and display more symmetrical patterns than manual ones if Take Profits & Stop-losses are well calibrated.

    What’s NOT the point?

    This chart is different from Consistency per Trade chart. We´re plotting positions here (trades plotted there) and % equity changes, accounting for leverage (which is not plotted there).

  23. Monkey Test Ranking (Chart)

    How does a good trader prove that luck contributes nothing to their trading the right asset, at the right time, in the right direction?

    Easy! All it takes is to beat strategies that trade the same assets, at the same time, with the same risk, just the way a monkey would: randomly!

    What's in it?

    A return leaderboard benchmarking the strategy and 10.000 virtual monkeys trading with the same market exposure, position length and D-Leverage.

    What's the point?

    You guessed it: the more monkeys you beat, the better.

    The goal is to lead the monkey challenge consistently over time!

    Monkey Test Ranking Chart

  24. Open/Close Strategy (Chart)

    It´s impossible to rule out luck entirely. Any data sample contains random statistical illusions - extremely good or bad data points.

    But: our analysis is designed to tell those trading for systematic profit from lucky punters. It rules out luck at higher confidence than traditional analysis. How? By looking beyond return (the what? answer) and looking for how? Returns were obtained - and using all available information.

    Take trade timing: a trader who can really predict the market ought to open and close trades at the best possible time! If you really had talent akin to a crystal ball, wouldn´t you time trade open/close other than at local minima/maxima?

    What's in it?

    The chart tracks trade timing, showing, on a trade by trade sequence

    ◦ on trade open: does the chosen trade open point beat opening that same trade slightly earlier or later?

    ◦ on trade close: idem, for close decisions

    The chart ranks the strategy´s return, alongside hypothetical cumulative returns by 10 strategies opening/closing the same trades, slightly earlier/later, on an x% (± 10%, 20%, 30%, 40%, 50%) systematic basis.

    For each trade open/close, the chart ranks 11 outcomes (in black the original one, and the 10 hypothetical alternatives) on a cumulative basis (on the y axis), taking into account all trades accrued up to each point in the sequence (plotted on the x axis).

    What´s the point?

    A strategy that yields Performance, optimally timing trade open / close, is likely better than a strategy with comparable performance through inferior timing.

    Optimized strategies muster upward trends - they build up their lead over inferior timing choices, trade by trade.

    Strategies trending towards the Chart middle are worrisome - their trade open/close timing is no better than random, which means their performance could be statistical illusion (luck!).

    We weigh recent timing more than distant one: the Chart can be filtered to plot the last 12, 6, 3 and last D-Period, up to a maximum of 12 D-Periods. This way you can see both recent and long term developments in timing quality.

  25. Divergence sensitivity

    Every additional euro under management hurts a strategy’s profitability: by using up finite market liquidity, it moves the market price (increasingly) against it!

    This chart is part of the Scalability Investable Attribute: a strategy delivering profits on 1 BN is more investable than one whose profitability fades with the second million AuM!

    What's in it?

    Compares a strategy´s historical performance with hypothetical one: what would have happened if market spread at the time of trade had been:

    ◦ 0.2 pips

    ◦ 0.5 pips

    ◦ 1 pip wider?

    What's the point?

    All other things equal, a strategy is more sensitive to spread:

    ◦ The more frequently it trades: a scalping strategy with small profits on many trades is more sensitive to spread than a swing strategy achieving the same profitability, on less trades - by making more per trade.

    ◦ The more homogeneously leveraged: given a risk appetite (VaR!), the more spread leverage is across trades, the more sensitive a strategy is to spread. A strategy with leverage scattered from 1:1 to 9:1 is more sensitive than that with constant 5:1 leverage, because liquidity isn't evenly matched across ticks of market depth

    What's NOT the point?

    High sensitivity to spread is not necessarily bad, because

    ◦ we track the outcome of spread increases, without

    ◦ questioning the underlying root-cause to that spread increase!

    Spreads widen because of missing liquidity at the point of placing trades. Strategies trading at times of high-liquidity structurally overcome spread sensitivity, i.e. we're factoring all relevant factors in tracking investability!

  26. Open/Close Trade Volatility (Chart)

    Every additional euro under management hurts a strategy’s profitability: by using up finite market liquidity, it moves the market price (increasingly) against it!

    This chart is part of the ¨Scalability” investable attribute: a strategy delivering profits on 1 BN is more investable than one whose profitability fades with the second million AuM!

    What’s in it?

    To plot this chart, we divide historical time intervals into 5 market volatility buckets:

    ◦ 95%+ - Very High Volatility

    ◦ 75-95%

    ◦ 25-75%

    ◦ 5-25%

    ◦ Below 5% - Very Low Volatility

    For each of these volatility buckets, the Left Hand Side chart tracks % of all trades taken (open & close) in each bucket by:

    ◦ This strategy

    ◦ Benchmark: average of all Darwinia strategies

    The right hand side plots % of all trades taken by the strategy in each of the five volatility buckets.

    What’s the point?

    Strategies opening / closing trades at more volatile times are likely to be less scalable - since they trade at times where there´s less market liquidity.

  27. Liquidity per Trade (Chart)

    Every additional euro under management hurts a strategy´s profitability: by using up finite market liquidity, it moves the market price (increasingly) against it!

    This chart is part of the ¨Scalability” investable attribute: a strategy delivering profits on 1 BN is more investable than one whose profitability fades with the second million AuM!

    What’s in it?

    The chart plots, for every trade placed by the strategy in its chosen timeframe, prevailing liquidity (as represented by market depth) at the time of trade. Liquidity is broken up in 5 different buckets, defined by their ability to accommodate a 5 MM nominal "benchmark" trade:

    ◦ Very high

    ◦ High

    ◦ Average

    ◦ Low

    ◦ Very low

    The market impact of the benchmark trade is logarithmic of base 2: the same trade will impact the spread twice as much in High liquidity as it will in Very High liquidity conditions, and so on.

    What’s the point?

    Investor returns scale better/worse with strategies trading high/low liquidity assets or timeframes, which makes it very relevant to identify what liquidity is available to absorb investor volumes.

  28. Investor's Volume per Trade (Chart)

    Every additional euro under management hurts a strategy’s profitability: by using up finite market liquidity, it moves the market price (increasingly) against it!

    This chart is part of the ¨Scalability” investable attribute: a strategy delivering profits on 1 BN is more investable than one whose profitability fades with the second million AuM!

    What’s in it?

    Tracks investor volume per trade, assuming 1 MM investor assets under management, with a risk appetite of 8% monthly VaR.

    Answers this investor question: were I to track this strategy with 1 MM of my own money and a monthly VaR target of 8%: what trade nominal would I have triggered replicating every trade of this strategy?:

    ◦ Columns: plot trade by trade nominal investor volume in chronological sequence

    ◦ Line: tracks cumulative volume up to any trade

    What’s the point?

    All other things equal, a strategy scales better the lower the trade level investor volume, as a proportion of investor assets under management. This penalizes strategies trading with high trade level leverage.

    This is because market liquidity at any point is finite - therefore the bigger the absolute size of the investor trade tracking the strategy, the bigger the chance that it will move the market against it.

  29. Position vs Trade

    When you've placed a one and only trade, the behaviour of your equity is described perfectly by the behaviour of your traded pair - they coincide!

    What happens when you simultaneously place several trades in multiple assets? In doing so, you create synthetic assets - positions - whose economic attributes differ from standard assets.

    Rather, the properties of your position are driven by:

    ◦ the relative weight of each of the component assets in the trade

    ◦ every asset’s volatility

    ◦ prevailing correlation between all ingredient assets

    We assess traders for skill at building synthetic assets (positions) whose value grows with controlled risk, as measured by D-Leverage. We don't analyze individual trades in isolation - this could lead to the wrong conclusions!

  30. D-Score (Metric)

    Global 0-100 score (100 = perfect) based on the six 0-10 investable attribute grades.

  31. Backtests

    You can upload your Mt4 backtests in .HTML & .csv format so our algos can analyse your backtest’s VaR, D-Score, Darwinia grades etc.

    How does it work?

    Having your backtest analysed and diagnosed is pretty simple. All you’d need to do is log in to Darwinex and select “Backtest” in the “Trading Accounts” section.

    Darwinex backtests

    Once there, you’ll be asked to choose a base currency for your backtest, as well as a strategy name, etc. Also, as you can see below, you’ll need to pick the format of the backtest you are about to upload (MT4 backtest in HTML, MT4 statement in HMTL or backtest in .csv format).

    Darwinex backtests

    Once done, click the “Upload” button and our algos will calculate your account’s basic statistics (return, DD, etc.) as well as more complex variables like VaR, D-Score, Investable Attributes, etc. within a few minutes.

    Please note that backtests’ results will not be publicly visible, they will be available only to you at the bottom of your “Backtest” section (see “Uploaded Backtests” in the screenshot above).

    Backtests vs. demo accounts

    The advantages of uploading a backtest vs. a demo account are numerous. First, you don’t need to spend days (or even weeks or months!) to build a track record and test how it would have performed.

    Secondly and more importantly, our algorithms do analyse the D-Score & investable attributes for backtests, whereas demo accounts are analysed for basic figures such as return and DD only. This makes backtests way more powerful to optimise your strategy than demo accounts!

    The one thing backtests & demo accounts have in common is that neither of them are eligible for DarwinIA or for investors.

Who are we?

We're traders and investors, who know the internet will change financial services for good. We bring you the regulatory, technological and financial solvency your savings deserve.


Forex and CFDs are leveraged products. They may not be a suitable investment for you as they carry a high degree of risk to your capital and you can lose more than your initial deposits. Please make sure you understand all the risks involved. The Darwinex® trademark and the domain are owned by Tradeslide Trading Tech Limited, a company duly authorised and regulated by the Financial Conduct Authority (FCA) in the United Kingdom with FRN 586466. Our Company number is 08061368 and our registered office is Acre House, 11-15 William Road, London NW1 3ER, UK.