Binary options, in their most common form, are very different to real options. They are a bet that the price of an asset will rise or fall within a given time frame. If you win the bet, the broker pays your stake multiplied with a win payout factor in the 75%..95% range. If you lose, you pay the stake minus a possible loss payout. You’re trading not against the market, but against the broker. The broker needs you to lose, otherwise they would not make any profit. Even if they really pay out your wins, and even if they do not manipulate the price curve, they can still control your profit with their payout factors. So it seems that even if you had a winning system, the broker would just reduce the payout for making sure that you lose in the long run.

However this conclusion is a fallacy. It can in fact be of advantage for the broker to offer a payout that allows you to win, as long as most other traders still lose. A broker has not the freedom of arbitrarily reducing the payout. He’s competing with other brokers. But why would you want to trade binary options anyway, when you also can trade serious instruments instead? If you wanted a binary outcome, you can also achieve this by opening a Put or Call Spread with real options – and this with a serious broker, much higher payout factors (even > 100% in some cases) and the possibility to sell the options prematurely.

But aside from tax advantages in some countries, there is one single compelling reason that might make a binary options trading experiment worthwhile. Profit and trading cost of a binary option are independent of the time frame. So you can trade on very short time frames, which would be difficult, if not impossible with real options or other financial instruments. You can find a discussion of this problem in the Scalping article.

Binary scalping math

The required minimum win rate for binary trading can be calculated from the broker’s win and loss payout:

[pmath size=16]W~=~ {1-Pl}/{1+Pw-Pl}[/pmath]

W = required win rate for break even
Pl = Loss payout
Pw = Win payout

With 85% win payout and no loss payout, you need a win rate of

[pmath size=16]W ~=~ 1/{1.85} ~=~ 54%[/pmath]

54% win rate seem to be manageable on short time frames. The transaction costs of a non-binary, conventional broker would require a much higher win rate, as in the following graph from the Scalping article:

You had to win almost 80% of five-minutes trades – impossible for a trading system under normal conditions unless you enforce that win rate with some tricks, which however won’t help getting in the profit zone.

So, smaller trading costs on low time frames are the obvious benefit of trading binary options. With all the side benefits of low time frames, such as more data for backtests, and shorter drawdown periods in live trading. But how can we take advantage of that? There are three problems to solve.

Three steps to potential binary profit

1. Find a strategy with a win rate that is better than the W determined with the above payout formula. But be aware that prices on small time frames are strongly feed dependent. Normally you won’t know your binary broker’s price source (if he has any at all). For being on the safe side, test with different historical price data from different serious brokers (f.i. Oanda or FXCM) and stay some percent points above the minimum W.
    
2. Find a way to trade automated. Binary brokers often do not want you to do that. Consequently most do not offer a platform or API for automated trading. But they all have a web interface. So you need either a software tool (such as Zorro) that can send key strokes and mouse clicks to a website, or some other means to get your trades to the broker.
     
3. Find a broker that’s halfway honest. At least one that allows you to really collect your gains. All binary brokers make it easy to deposit, but some follow the philosophy: “If you gave it to me, it’s mine.” Make test withdrawals before you deposit large amounts. Keep the account balance small. Check the broker’s opportunity to manipulate the price curve: the more customers they have and the more bets they handle, the more difficult is it to manipulate without attracting unwanted attention. Retrieve as many information as possible about your broker: Where are they really located? For some reason, fraudsters seem to be concentrated in Tel Aviv. Check what customers say about that broker – but be aware: positive statements on trader forums are often planted by the broker himself.

All those issues make trading binary options sort of “messy”. However it’s the messy methods that sometimes offer the best opportunities. Ed Thorp made his first millions not with ‘serious trading’, but with a Blackjack strategy and with a method to estimate the value of warrants, both also considered messy and hard to calculate at that time.

Step 1: The system

A price curve is no random walk. At least not all of the time. Long time frames are often dominated by trend, short time frames by mean reversion. When transaction costs do not matter, it’s not very hard to find a system with > 54% win rate on 5-minutes bars. Here’s a simple example that exploits the mean reversion tendency of short time frames (script for Zorro):

var objective()
{
	return ((var)(NumWinLong+NumWinShort))/(NumLossLong+NumLossShort);
}


function run()
{
	BarPeriod = 5;
	LookBack = 100;
	NumWFOCycles = 20;
	NumCores = -1;
	
	set(BINARY);
	WinPayout = 85;
	LossPayout = 0;

	set(PARAMETERS);
	int TimePeriod = optimize(20,10,100);
	var Threshold = 0.01*(HH(TimePeriod)-LL(TimePeriod));

	if(NumOpenLong+NumOpenShort == 0) 
	{
		LifeTime = 1;
		if(HH(TimePeriod) - priceClose() < Threshold)
			enterShort();
		else if(priceClose() - LL(TimePeriod) < Threshold)
			enterLong();
	}
}

In the C code above we defined an individual objective() function that optimizes the system for binary trading. It measures the system performance as the number of winning trades divided by the number of losing trades. Otherwise the optimizer would hunt for the most robust profit factor, which makes no sense for binary trading.

The setup establishes a 5 minutes bar period, which is the time frame of our bets. We use 20 WFO cycles and let the optimizer use all CPU cores but one. This way the training run takes about 5-10 minutes for 5 years data. The BINARY flag activates binary trades, and we’re simulating a broker with 85% win payout and no loss payout.

We have a mean reverting system that trades whenever the current price is closer than a threshold – here, 1% of recent volatility – to its previous High or Low. The time period for determining the High and Low is the only system parameter that we optimize. You could improve the system in many ways, for instance by optimizing also the threshold, by modifying the objective() function so that it prefers systems with more trades, and by applying a filter that prevents trading in non mean-reverting market regimes. Since we bet on the price in 5 minutes, we’ve set the LifeTime of a trade to one bar. Here’s the equity curve from a 5 years walk forward test with EUR/USD:

The system has about 56% win rate and a notable, although not spectacular positive return. Which is not achieved by the crude mean reversion mechanism, but mostly by amplifying the small entry-exit price differences through binary trading, even though the payout is only 85%. You won’t get a similar result with conventional trades. The same system not trading binary options, but leveraged forex positions produces a very different equity curve (for testing, comment out the BINARY flag and the Payout settings in the code):

With the same trades we have now only 40% win rate and an overall loss, since all the trade profit is eaten up by spread and commission. 

Step 2: Automatizing

How do you let your script automatically enter a bet at the right moment? This is a technical issue unrelated to trading, but it comes up whenever you have a broker with a web based platform and no proper connection for automatizing. Here’s a code snippet for detecting the positions of [Buy] and [Sell] buttons on a website, and automated clicking them:

void main()
{
	int BuyX,BuyY,SellX,SellY; // button coordinates

// open the browser	
	printf("\nOpening the broker's website...");
	exec("http://financial-hacker.com/shark.htm",0,1);

// get the position of the Buy button
	printf("\nRight click on [Buy]!");
	while(wait(50)) {
		int button = mouse(&BuyX,&BuyY,window(""));
		if(button & 2) break;
	}
// wait until right mouse key released	
	while(wait(50)) {
		int x,y,button = mouse(&x,&y,0);
		if(!(button & 2)) break;	
	}

// get the position of the Sell button	
	printf("\nRight click on [Sell]!");
	while(wait(50)) {
		int button = mouse(&SellX,&SellY,window(""));
		if(button & 2) break;
	}
// wait until right mouse key released	
	while(wait(50)) {
		int x,y,button = mouse(&x,&y,0);
		if(!(button & 2)) break;	
	}

// send mouse clicks to Buy and Sell	
	printf("\nI will now click on [Buy]!");
	keys("[click %d,%d]",BuyX,BuyY);
	printf("\nI will now click on [Sell]!");
	keys("[click %d,%d]",SellX,SellY);
	printf("\nDone!");
}

Start the script, and wait until the broker’s website pops up in your browser. Then follow the instructions in Zorro’s message window. Manoever the mouse onto the “Buy” button and hit the right mouse key. Then do the same with the “Sell” button. The script will store the button positions and then use the keys function to send test clicks to both positions of the active window. For testing purposes I’ve imitated a typical binary broker’s trading platform.

You now only need to glue together your trading script with the button clicking script, and adapt the latter to the website of your broker. This is left as an exercise to the reader. And better use improved versions – the scripts here are kept simple for demonstration purposes. As long as the script trades, make sure that the browser window stays in the foreground, or else it can not click on the buttons. For the position size, either enter a fixed size for all positions, or let your script click into the size field and send key strokes to set individual sizes.

Step 3: The broker

Of course I don’t want to recommend a particular binary options broker. In the end, they’re all crooks – but some are crookier than others. Finding a suited broker is, also, left as an exercise to the reader. Binary broker comparison websites are often – surprise, surprise – installed and paid by binary brokers. US citizens are normally not allowed to trade binary options with brokers that are not regulated in the US. Some brokers will accept your deposit nevertheless, but use that as pretext to refuse payout. If you’re a citizen of Israel, you might not be accepted by many binary brokers since they’re not allowed to fraud compatriots. 

Conclusion

It’s often the “messy” and despised trade instruments that can still provide opportunities when they are correctly understood. I’ve uploaded the two scripts to the 2016 repository. You’ll need Zorro 1.52 or above for running them. When you now make huge profits with binary options, don’t forget where the money comes from: Not from the broker, but from his less fortunate customers that maybe just haven’t read the right blog.

Addendum: From all articles on this blog, this one attracted by far the most spam comments. From them it appears that a new lucrative business has established in the orbit of binary brokers: recovery fraud. As soon as you’ve lost your money, you’ll receive offers by “hackers” or “law firms” to recover it, for a fee of course. Where did they get your email from? Naturally from the very broker that bagged your money…

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!