Strategies come in two flavors

You can use mainly two methods to develop trading systems: model-based and data-mining. A model-based system starts with a model of a market inefficiency – based on trader psychology, economy, market microstructure, or any other price affecting force. The inefficiency produces a price curve anomaly or pattern that deviates from the random walk and can – when predictive – be used for a trade algorithm. Examples of model based trading methods are trend following, mean reversion, price cycles, price clusters, statistical arbitrage, and seasonality.

The problem: A model is not the reality. It is a simplified image of it. It can not be proven and can often not even be falsified. Its validity can only be determined by its effects on the price curve. The usefulness of this method thus depends on the significance and long-term stability of its price curve anomalies. For judging this you need good test algorithms. 

The pure data mining method works the other way around. It just looks for price curve patterns and attempts to fit an algorithm to them. By which market forces the patterns are caused is of no interest; only assumption is that patterns of the past will repeat in the future. This allows the generation of trade systems, often, but not always with machine learning software. The most popular methods in this approach are trial-and-error TA, candle patterns, regression, autocorrelation, k-means clustering, neural networks, support vector machines, and decision trees.

The advantage of data mining is that you do not need to care about market hypotheses. The disadvantage: those methods usually find a vast amount of random patterns and thus generate a vast amount of worthless strategies. Since mere data mining is a blind approach, distinguishing real patterns – caused by real market inefficiencies – from random patterns is a challenging task. Even sophisticated reality checks can normally not eliminate all data mining bias. Not many successful trading systems generated by data mining methods are known today.

Are you cleverer than the market?

Obviously, no trading system would work when market inefficiencies do not exist. And it would not work either when they exist, but can not be exploited since better equipped players are doing that already. In this first part of the mini-series I look into the possibility of trading better than the majority of market participants, a prerequisite of a successful strategy. 

The three hypotheses of market efficiency that you’ll hear from time to time are as follows: 

• Hypothesis A: The markets are efficient. Prices follow real events, such as the publication of company results, and reflect the real value of the asset. All traders are ‘informed’, decide rationally and act immediately. Price curves are mostly random-walk curves with no information for predicting future prices. Technical trading systems can not work, or if they do, it’s just luck.
   
• Hypothesis B: The markets are not efficient, but their inefficiencies are of no value for private traders. Only large trading firms and hedge funds can exploit them successfully, since they have lots of capital, very fast computers, very experienced analysts and very clever quants – much more intelligent than you. Beware of entering their terrain, or else you’ll become their prey.
   
• Hypothesis C: Enough market inefficiencies are free for you to exploit. Large trading firms and hedge funds are too slow and cumbersome to tackle them effectively. Their capital and their fast computers give them no real advantage in the game that you’re going to play. Neither do their clueless analysts, overpaid traders, and overestimated quants.

Not many today do still believe in hypothesis A. It can be easily shown that most price curves do not follow a random walk (a fellow blogger recently posted a great article about Hacking the Random Walk Hypothesis). And the markets are anything but rational or effective. Counter-examples are plenty. Jack Schwager, in his book ‘Market Sense and Nonsense’, listed cases of blatant market dumbness and grotesque analyst failures. More often than not, asset prices are far, far away from their true value. Although all this is anecdotical evidence, a pattern is visible. The markets react fast and firm when rumors or news give them a clear direction. But when the information is a little more subtle and requires a minimum of interpretation, they react slow or not at all. Here’s the story of a typical example.

The Swiss Franc case

In September 2011 the Swiss National Bank established a price cap to the Swiss Franc. Purpose was protecting the tourism and export industries against an overvalued currency. The limit was set to an EUR/CHF price of 1.20, and the SNB vowed to defend it against all enemies.

A price cap is a rare and striking market inefficiency. It can immediately be translated into a highly profitable, almost risk-free trading system (how this works is explained below). So you would normally expect a strong market reaction after the EUR/CHF price move to 1.20. But the reaction was a long time in the coming.

No doubt, Switzerland is an obscure European country and for the major US trading firms probably known for cheese, if at all. They did either not notice the price cap, or they had just forgotten to equip their European offices with modern communication gear. So it took the mounted messenger from Europe three months riding over hill and dale, sailing over the Atlantic Ocean, maybe fighting off brigands, pirates, and indians on his way, to reach New York City and shout: “The Swiss have a price cap!”.

But what the heck can you do with a price cap? By January 2012, large market participants had finally come up with an idea. Not something as subtle as a trading system. Instead, they began to buy mad amounts of Francs for bringing pressure on the EUR/CHF price: 

The obvious idea was that when there is a price boundary, there must be some profit in breaking it. A lot of effort, patience and money was put in that game. From May 2012 on the EUR/CHF price was nailed shut to its 1.20 limit. But alas, the price cap collapse did not happen. You do not mess with the SNB. During 2012 the Swiss erected a wall of 200 billion dollars for defending the price cap. The attackers never had a chance. The first gave up in September 2012, and by the end of January 2013 all had retreated with their tails between their legs (and probably painful losses):

Now the way was free for algorithmic systems. During the 2012 CHF battle they were forced to inactivity, since private traders and hackers lack the capital to participate in a market manipulation game. In January 2013 the first hackers started to exploit the market inefficiency with a specific method, a Grid Trader. This turned out a money-printing machine.

The money press algorithm

A grid trader is a very simple system. It places pending long and short trades at a fixed grid above and below the current price, with profit targets of the same grid distance. So trades are opened and closed whenever the price crosses a grid line in any direction. Such a system has a hypothetical 100% win rate, since trades close either in profit or not at all. But grid traders normally use a virtual hedging mechanism that closes an open position instead of opening a new one in opposite direction. This improves the total profit by reducing trade costs and margin. But it allows trades to be closed with a loss. So the real win rate of a grid trader is in the 60% area.

This is the Zorro script of such a grid trader:

// helper function to check if the grid line has no trade
bool isFree(var Price,var Grid,bool IsShort)
{
  bool result = true;
  for(open_trades) {
    if(TradeIsShort == IsShort
      && between(TradeEntryLimit,Price-Grid/2,Price+Grid/2))
        result = false;
  }
  return result;
}

// EUR/CHF grid trader main function
int run() 
{
  BarPeriod = 60;
  Hedge = 5; // activate virtual hedging

  var Grid = 20*PIP; // set grid distance to 20 pips
  var Close = priceClose();
 
// place pending trades at 5 grid lines above and below the Close
  int i;
  for(i = Close/Grid - 5; i < Close/Grid + 5; i++)
  {
    var Price = i*Grid;
// place short trades with profit target below the current price
    if(Price < Close && isFree(Price,Grid,true))
      enterShort(1,Price,0,Grid); 
// place long trades with profit target above the current price
    else if(Price > Close && isFree(Price,Grid,false))
      enterLong(1,Price,0,Grid);
  }
}

A grid trader is a typical model-based system. It assumes that some market force keeps the price inside a channel. This is the case here: The cap prevents the EUR/CHF from falling below 1.20, but it also prevents it from rising too high, since the SNB must eventually buy back all the Francs they have sold for defending the cap. The mathematical model of this would be a random walk with a 1.20 boundary and some drift term that pulls the price down. Such a constraint is a prerequisite for a grid trader; without it grid trading would be just high-risk gambling and is consequently listed in the irrational trade methods collection.

This is the P&L-curve (blue) of the above script applied to the EUR/CHF in 2013:

We can see that large price fluctuations, as in January and May, cause large drawdowns (the red underwater peaks in the chart). But since the fluctuations have a limit, we can estimate the maximum loss and just keep enough capital on the account. This way the above script produces an annual return of 130% and a Sharpe Ratio of 1.7 – with virtually no risk ( as long as the price cap stays in place).

The news of such a trading strategy slowly spread in 2013. More and more private traders and financial hackers, and also more and more large market participants jumped on the bandwagon. Three years after installation of the price cap, thousands of such systems sat on the EUR/CHF price curve like leeches and sucked off money. The result was a continuously falling price volatility:

Lower volatility means lower profits to a grid trader. More capital must be invested and the grid must be tightened for compensating. But there is a natural limit. You can not have a grid size smaller than the trading costs. By autumn 2014 the volatility was close to zero. And this was accompanied by an ominous price downwards drift, as if some large market participant (possibly the SNB itself) would continously sell EUR and buy CHF in anticipation of some future event. That would have been high time for private traders to retreat from the game. Of course, thickheads like me didn’t. It is well known what then happened to the Swiss Franc:

In the morning of 15 January 2015, the SNB gave a press conference and announced the cancellation of the price cap. The EUR/CHF fell in minutes like a stone from the 1.20 limit to below parity. Obviously a fast and extreme market reaction – very different to the introduction of the price cap 4 years before. The price drop killed many accounts and even a few brokers. By the way, the ‘real value’ of the EUR/CHF, based on the relative buying power of the two currencies, was in the 1.50 area all the time. 

What can we learn from this and from similar examples?

Conclusions

• The financial markets react immediately and often hysterically on news with a clear price upwards/downwards direction.
• The markets react slow or not at all on more subtle information. It can take years until they become aware of new inefficiencies or trading methods.
• The markets prefer brute-force methods. Complex strategies are normally only used by a small part of market participants.
• Simple systems based on very obvious inefficiencies can be extremely profitable, but have a limited lifetime. 

The next parts of the Build Better Strategies series will deal with model-based systems, with known market inefficiencies and with a methodical approach of exploiting them. 

⇒ Build Better Strategies – Part 2

When you start a trading strategy, you’re almost always under water for some time. This is a normal consequence of equity curve volatility. It is the very reason why you need initial capital at all for trading (aside from covering margins and transaction costs). Here you can see the typical bumpy start of a trading system:

You can estimate from the live equity curve that this system was rather profitable (it was a grid trader exploiting the CHF price cap). It started in July 2013 and had earned about 750 pips in January 2014, 7 months later. Max drawdown was ~400 pips from September until November. So the raw return of that system was about 750/400 ~= 180%. Normally an excellent value for a trade system. But you can also see from the curve that you were down 200 pips about six weeks into trading, and thus had lost almost half of your minimum initial capital. And if you had started the system in September, you had even stayed under water for more than 3 months! This is a psychologically difficult situation. Many traders panic, pull out, and this way lose money even with highly profitable systems. Algo trading unaffected by emotions? Not true.

Not so out of sample

The basic problem: you can never fully trust your test results. No matter how out-of-sample you test it, a strategy still suffers from a certain amount of Data-Snooping Bias. The standard method of measuring bias – White’s Reality Check – works well for simple mechanically generated systems, as in the Trend Experiment. But all human decisions about algorithms, asset selection, filters, training targets, stop/takeprofit mechanisms, WFO windows, money management and so on add new bias, since they are normally affected by testing. The out-of-sample data is then not so out-of-sample anymore. While the bias by training or optimization can be measured and even eliminated with walk forward methods, the bias introduced by the mere development process is unknown. The strategy might still be profitable, or not anymore, or not at all. You can only find out by comparing live results permanently with test results.

You could do that with no risk by trading on a demo account. But if the system is really profitable, demo time is sacrificed profit and thus expensive. Often very expensive, as you must demo trade a long time for some result significancy, and many strategies have a limited lifetime anyway. So you normally demo trade a system only a few weeks for making sure that the script is bug-free, then you go live with real money.

Pull-out conditions

The simplest method of comparing live results is based on the maximum drawdown in the test. This is the pull-out inequality:

[pmath size=18]E ~<~ C + G t/y – D[/pmath]

E = Current account equity
C = Initial account capital
G = Test profit
t = Live trading period
y = Test period
D = Test maximum drawdown

This formula means simply that you should pull out when the live trading drawdown exceeds the maximum drawdown from the test. Traders often check their live results this way, but there are many problems involved with this method:

• The maximum backtest drawdown is more or less random.
• Drawdowns grow with the test period, thus longer test periods produce worse maximum drawdowns and later pull-out signals.
• The drawdown time is not considered.
• The method does not work when profits are reinvested by some money management algorithm.
• The method does not consider the unlikeliness that the maximum drawdown happens already at live trading start.

For those reasons, the above pullout inequality is often modified for taking the drawdown length and growth into account. The maximum drawdown is then assumed to grow with the square root of time, leading to this modified formula:

[pmath size=18]E ~<~ C + G t/y – D sqrt{{t+l}/y}[/pmath]

E = Current account equity
C = Initial account capital
G = Test profit
t = Live trading period
y = Test period
D = Maximum drawdown depth
l = Maximum drawdown length

This was in fact the algorithm that I often suggested to clients for supervising their live results. It puts the drawdown in relation to the test period and also considers the drawdown length, as the probability of being inside the worst drawdown right at live trading start is l/y. Still, the method does not work with a profit reinvesting system. And it is dependent on the rather random test drawdown. You could address the latter issue by taking the drawdown from a Montecarlo shuffled equity curve, but this produces new problems since trading results have often serial correlation.

After this lenghty introduction for motivation, here’s the proposed algorithm that overcomes the mentioned issues.

Keeping cold blood

For finding out if we really must immediately stop a strategy, we calculate the deviation of the current live trading situation from the strategy behavior in the test. For this we do not use the maximum drawdown, but the backtest equity or balance curve:

1. Determine a time window of length l (in days) that you want to check. It’s normally the length of the current drawdown; if your system is not in a drawdown, you’re probably in cold blood anyway. Determine the drawdown depth D,  i.e. the net loss during that time.
2. Place a time window of same size l at the start of the test balance curve.
3. Determine the balance difference G from end to start of the window. Increase a counter N when G <= D. 
4. Move the window forward by 1 day.
5. Repeat steps 3 and 4 until the window arrived at the end of the balance curve. Count the steps with a counter M.

Any window movement takes a sample out of the curve. We have N samples that are similar or worse, and M-N samples that are better than the current trading situation. The probability to not encounter such a drawdown in T out of M samples is a simple combinatorial equation:

[pmath size=18]1-P ~=~ {(M-N)!(M-T)! }/ {M!(M-N-T)!}[/pmath]

N = Number of  G <= D occurrences
M = Total samples = y-l+1
l = Window length in days
y = Test time in days
T = Samples taken = t-l+1
t = Live trading time in days

P is the cold blood index – the similarity of the live situation with the backtest. As long as P stays above 0.1 or 0.2, probably all is still fine. But if P is very low or zero, either the backtest was strongly biased or the market has significantly changed. The system can still be profitable, just less profitable as in the test. But when the current loss D is large in comparison to the gains so far, we should stop.

Often we want to calculate P soon after the begin of live trading. The window size l is then identical to our trading time t, hence T == 1. This simplifies the equation to: 

[pmath size=18]P ~=~ N/M[/pmath]

In such a situation I’d give up and pull out of a painful drawdown as soon as P drops below 5%.

The slight disadvantage of this method is that you must perform a backtest with the same capital allocation, and store its balance or equity curve in a file for later evaluation during live trading. However this should only take a few lines of code in a strategy script. 

Here’s a small example script for Zorro that calculates P (in percent) from a stored balance curve when a trading time t and drawdown of length l and depth D is given:

int TradeDays = 40;    // t, Days since live start
int DrawDownDays = 30; // l, Days since you're in drawdown
var DrawDown = 100;    // D, Current drawdown depth in $

string BalanceFile = "Log\\BalanceDaily.dbl"; // stored double array

var logsum(int n)
{
  if(n <= 1) return 0;
  return log(n)+logsum(n-1);
}

void main()
{
  int CurveLength = file_length(BalanceFile)/sizeof(var);
  var *Balances = file_content(BalanceFile);

  int M = CurveLength - DrawDownDays + 1;
  int T = TradeDays - DrawDownDays + 1;
 
  if(T < 1 || M <= T) {
    printf("Not enough samples!");
    return;
  }
 
  var GMin=0., N=0.;
  int i=0;
  for(; i < M-1; i++)
  {
    var G = Balances[i+DrawDownDays] - Balances[i];
    if(G <= -DrawDown) N += 1.;
    if(G < GMin) GMin = G;
  } 

  var P;
  if(TradeDays > DrawDownDays)
    P = 1. - exp(logsum(M-N)+logsum(M-T)-logsum(M)-logsum(M-N-T));
  else
    P = N/M;

  printf("\nTest period: %i days",CurveLength);
  printf("\nWorst test drawdown: %.f",-GMin);
  printf("\nM: %i N: %i T: %i",M,(int)N,T);
  printf("\nCold Blood Index: %.1f%%",100*P);
}

Since my computer is unfortunately not good enough for calculating the factorials of some thousand samples, I’ve summed up the logarithms instead – therefore the strange logsum function in the script.

Conclusion

• Finding out early whether a live trading drawdown is ‘normal’ or not can be essential for your wallet.
• The backtest drawdown is a late and inaccurate criteria.
• The Cold Blood Index calculates the precise probability of such a drawdown based on the backtest balance curve.

I’ve added the script above to the 2015 scripts collection. I also have suggested to the Zorro developers to implement this method for automatically analyzing drawdowns while live trading, and issue warnings when P gets dangerously low. This can also be done separately for components in a portfolio system. This feature will probably appear in a future Zorro version. 

There is no real answer, as the inventors of those methods usually forgot to mention – aside from some vague financial verbiage – how and why they are supposed to work, and which market pattern or inefficiency they are supposed to exploit. Often the methods are merely recipes to be followed meticulously, like the spells in ancient conjuring books. Superstition and esotericism in financial trading are approved by seemingly serious organizations such as the Market Technicians Association, and even trained in their certification programs! Here’s a certainly non-complete list of ways of trading that I still fail to understand.

1. Staring at Price Curves. Opening and closing positions manually without a trading system can work. When you have some information about the particular asset that the other traders don’t have. Or when you got a stroke of luck. Otherwise you can rightfully expect to lose your money at the rate of the transaction costs. In reality it’s even worse – private traders lose on average 13 pips per trade, according to FXCM statistics. The human mind can do a lot of things, but it can not identify a market inefficiency by looking at a price curve. Studies have shown that ‘expert traders’ can not even distinguish real price curves from meaningless random numbers – something even a simple computer algorithm has no problem with. And as to luck, it indeed endows about 35% of private traders every year with some profit or at least no loss, but it has one problem: it can end anytime.

   

2. Technical Analysis. Do traditional ‘technical indicators’, such as MACD, Stochastic, Ichimoku etc., really work? A study by David Aronson, described in his “Evidence-Based TA” book, suggests otherwise. None of the studied basic indicators was any better than throwing a coin. However, the study only applied the indicators to the S&P500 index. The trend experiment suggests that this index is the least predictable of the tested asset types, at least for short-term trend following. Also, Aronson only tested the basic indicators, but not complex combinations of them. So it’s still unknown whether traditional indicators do sometimes work, or not at all. And as long as this is the case, it’s not clear to me why they are so widely and naively used.

 

3. Elliott Waves. Ralph Nelson Elliott claimed in his 1938 published book that the prices of financial assets always move up and down in a fractal pattern of five waves. He gave many examples. And indeed you can see all sorts of waves when you stare at curves long enough. Elliott explained his waves with “mass psychology”, but was not interested in going into details. Although cycles in price curves are real, they have no reason to appear in series of five, or in any series whatsoever. Lacking any rational background, one should think that there was at least some statistical evidence of Elliott Waves – but no. No serious research has ever found any sign of them in real price curves, nor of the countless wave variants invented by Elliott’s many imitators.

 

4. Gann Magic. In the early 20th century, the trader William Delbert Gann was desperate, as he could not support his family with his trading.

   

   But suddenly he discovered the way to success: Planting anecdotes that promoted himself as a genius trader, and selling esoteric trade systems and books. It seems that Gann was the ancestor of all scammers in the trading scene. He did not die rich, though, as in his late years he apparently began to believe in his own methods. He lost at the stock market almost all wealth he accumulated by marketing his methods. I know of no tests that found any value in his magic squares, lines, cycles, pyramids, or angles. But even today, many traders still believe in them, to the great joy of their brokers.

 

5. Astrology. It’s widely accepted as a normal trade method and discussed in many trading books. Even Perry Kaufman, in his standard reference of trading systems, published code to calculate the Jupiter-Saturn cycle! Indeed, trading would be a breeze if you could calculate tomorrow’s prices just from the positions of sun, moon, and planets. But sorry, Perry: Celestial bodies still stubbornly refuse to predict earthly events. No test ever confirmed a correlation of the full moon and the EUR/USD price. Neither was Saturn ever observed dragging down the S&P500 index. And contrary to popular belief, even the sun is not responsible for the change of seasons (it’s the tilt of the earth axis). As long as we got no interstellar stock exchange, trading still happens on earth.

 

6. Rice Candle Patterns. With names like “Three Stars in the South” or “Concealing Baby Swallow”, they bring at least some poetry into trading. Candle patterns had been developed in the 18th century by Japanese traders for predicting the local rice markets. And indeed they might have had some value back then. But even today many traders are still squinting at price charts, hoping for a lucky trade when a candle formation matches a bullish pattern in their “Get Rich with Candle Patterns” book. The TA-Lib fortunately contains indicators of all those patterns, so I could run quick tests of them with several assets, similar to the trend indicator test. You can imagine the result. – By the way, new patterns invented by trade book authors – “Lizard’s Day”, “Gilligan’s Island” etc. – didn’t fare any better.

 

7. Fibonacci Numbers. This simple number series – 1, 2, 3, 5, 8, 13, etc. – can be found in some patterns of geometric growth. But there’s no reason to expect it in the price levels or time periods of financial assets. And indeed, it isn’t there. As far as I know, no one has ever discovered any price series property related to Fibonacci Numbers, or to Golden Ratios, Golden Squares, or Golden Whatevers derived from them. Nevertheless traders seem to like the word “Fibonacci”, maybe because it lets them imagine that they apply serious math. When a system uses Fibonacci Numbers for trade signals, you can safely assume that it would also work, and most likely better, with any other numbers.

 

8. Harmonic Trading Patterns. By connecting pivot points on the price curve, you can produce funny polygonal figures such as diamonds, butterflies, crabs, or bats. Their shapes predict profitable trade entry points. Or do they? I don’t know – I admit I have not yet tested a system based on trade predictions by polygonal figures. I’m only sure that harmonic trading is profitable for the tool and seminar vendors who promote it.

 

9. Your Trading Style. In trading books you’ll often read advices like ‘place the stop loss at a distance that suits your trading style‘. This makes you wonder what Your Trading Style might be. Do you trade fast, slow, risky, greedy, or in the style of the Kamikaze? And should this style affect the distance of the stop loss? I think not. Since any parameter in a trade system has an optimal or most robust value, any other value will therefore produce a less than optimal result. If it suits your style to win rather than lose, better select your trade methods and parameters not by style, but by performance.

 

10. Your Trading Plan. You trade for increasing your fortune in three years from $5000 to $500,000. So you’ve created a detailed plan by which percentage it has to grow every week. Fortunately, it’s just 3%. Unfortunately, the markets could not care less about Your Trading Plan. And if you’re following some system, your wins and losses will often have serial correlation. So when you continue trading after a loss until you reached your weekly goal, usually only your loss will grow. And the initial $5000 will become zero dollars – not in 3 years, but much faster.

 

11. The Holy Grail. On any trader forum you’ll find some lengthy thread about a system with miraculous profits. The thread starter has discovered the ultimate trade method. He feeds the thread periodically with reports of his impressive trade results – such as, doubling his account every month – and vague hints about his miracle algorithm and its complex math. His devotees eagerly absorb any droplet of information in their attempts to replicate the trade method – but alas, some essential ingredient is always missing. Miracles have the nasty habit of disappearing at closer look. The thread will eventually dry out when either the miraculist got broke or when the last follower has realized that he was following a Fata Morgana. If you found a miracle method where this did not happen, please let me know!

 

12. Robots. They are offered by anonymous vendors on countless websites. And supported by likewise anonymous users that all claim on trader forums that they have earned millions with that particular robot. Theoretically, a correctly developed and tested robot could indeed work, if algorithmic trading works at all. But apparently none is offered for sale. The simplest way to sell robots is offering a free trial period: When trades are entered at random, about 55% of users will lose during that period and 45% will win. Those will then order the robot – at least that’s the vendor’s hope.More sophisticated robot vendors provide faked trade histories or even faked live trading equity curves, like this one:

    

    Can you see from the above curve when this robot started selling? Hint: It’s close to the end. How to program a scam robot with an impressive faked equity curve on a verified MyFXBook account is explained in this book (of course for educational purposes only!). But if you ever encounter a real person who really made money by buying such a robot, please notify me…

 

13. Book systems. There are countless books describing countless trade systems – and certainly not all of them are garbage. But amazingly, many of the described systems survive not even a simple backtest. Their authors often suspect this already. That’s why some – for instance, best selling trade book writer Thomas Carr – warn strongly against testing their systems because “backtests are useless anyway”. This is correct – insofar as a positive backtest does not prove that the system works. But a negative backtest means that the money you paid for the book will be the least of your worries when you really trade one of the praised systems. Almost 90% of all systems from books, forums or websites that I’ve tested were clear losers – and this could often be determined in 5 minutes.

 

14. Trade Copy Services. The best-known is Zulutrade®, but many competitors have meanwhile entered this lucrative business. The principle is always the same: Let others place your trades. Follow successful traders and copy their trading. A brilliant idea. If those successful traders really existed. And indeed, you seem to have plenty of choice on the service’s website. Select some of the Top Traders with 500% profit and impressive equity curve, place an investment, and wait for the money to roll in. After a while you will inevitably find that it rolls in the other direction. One after the other of your selected Top Traders will encounter a nasty drawdown, just after you started following them. And their equity curves now all look like the robot curve above. Damn bad luck! – Or is anything else behind it?
    Behind it is no scam, but simple statistics. Thousands of traders compete for followers on copy services. In the Top Traders list you’ll only find those who trade risky but had a stroke of luck so far. Because luck does not last, the Top list is changing permanently. A trader’s survival time in that list is a few weeks, maybe months – then his fortune takes a dip, and takes all his followers with it. The trader won’t mind. He just opens a new account under a new name. He has earned considerably more follower commission than he lost with his trading. Tip: Look not into the Top Traders, but into the Top Followers list (if the service dares to publish one). That might tell you something about the chance of keeping your money when copying trades.
    

 

15. Martingale or d’Alembert Methods. They are used by many trading robots, signal providers, and beginners in roulette: you bet on red or black and double your stake after every loss. Alternatively, you open two new positions for every lost trade. The theory is that a loss increases your win chance the next time. Unfortunately, it won’t. On the contrary, market inefficiencies can be autocorrelated, so after losing a trade there’s a good chance that you’ll also lose the next one. Although martingale systems at first seem to make steady profits – even when trades are entered at random – they will encounter a long loss streak sooner or later and wipe out the account.
    How long will it take until this happens? Assume that you invest 1% of your capital per trade. After a loss you double your investment: 2%, 4%, 8%, 16%, 32%… and the sixth loss will empty your account and cause a margin call. With 50% win rate and uncorrelated returns, the probability of 6 consecutive losses is 0.5 ⁶ = 0.015625. The probability of this event not happening in n trades is (1-0.015625) ⁿ, so the number of trades until the margin call probability exceeds 50% is log(0.5)/log(1-0.015625) = 44. With one trade per day, your account will last about 2 months. A higher win rate, like 90%, won’t help – the average loss is then normally also 9 times higher, so the account lifetime is still the same 2 months. Investing only 0.1% instead – $10 per $10,000 capital – would extend the average account survival time to about 17 months.

 

16. Deadly Accuracy. A 99% win rate is easy – just use a 5 pips profit target and a 500 pips stop loss distance. You’ll then probably win the next 99 trades, regardless of your strategy, and will be worshipped as a god on your trader forum. Unfortunately the 100th trade will hit the stop and eat up all profit. Even worse, that fatal trade can happen anytime, even right at the beginning. You can easily identify scammers that use a high win ratio for selling their systems: their published profit curve looks like a straight upwards slope, with any trade winning about the same small amount. They usually disappear when hit by the 100th trade.
    

 

17. Grid trading. Such a system is a special case of using a high win ratio, up to 100%. It opens many trades at a fixed price grid, and takes profit when the price crosses the next grid line. This method can indeed generate a stream of profits for a while when the prices move up and down, but don’t move too far away from their initial position. Problem is that they eventually do… How much capital would you need for a grid trader to survive such a price move?
    Assume that you have n open trades and the price moves by x pips, x being much larger than the grid size. The move will then increase the balance by x pips and reduce the open trade value by about n/2 * x pips, causing an overall equity drawdown of (n/2-1) * x pips. On a Forex account with 10 mini lots trade size, 1 $ pip cost, and an average of 20 open trades, a 500 pips (5 cents) price move produces a 9*10*500*1$ = 45,000 $ drawdown. Such moves can occur several times per year. More capital and smaller trade sizes can delay, but not avoid that event.
    It’s easy to tweak grid trading systems for surviving any backtest, so they are often found in trade robots and on trade copy services. There are few exceptions where grid trading can make sense – for instance when runaway prices are hedged with some method, or when the price movement is limited by external factors such as a price cap.

Looking at all those methods it seems not really surprising that most traders lose money. But maybe I’m mistaken – if you have some solid evidence that one of the above methods does in fact work, please let me know!