Does this sound at all familiar?
There once was a trader, with a small amount of trading experience, who was doing very nicely indeed. So good, in fact, that his account was up almost 200% for the year.
The world looked good. He had cracked this trading game. And perhaps all the promises the broker’s site had made when he opened the account were being fulfilled.
Of course it was not the broker or anyone else that was to be credited for the success of his trading but his skill and ability to recognise an opportunity, take appropriate action and win.
Rather suddenly, things changed. It was not the trader’s fault. He was still doing what worked.
No. A policy maker in a central bank about whom he knew little made an unexpected announcement that shocked markets.
There was no market crash, but a 3-day spike in volatility. This can sometimes happen in a normal correction even when the trend remains in place.
There was nothing very unusual about this correction. Indeed it might have happened anyway and it was soon largely forgotten by the media and market commentators as they moved quickly on to the next worthy news item.
But not so for the trader. A very high proportion of his trades lost during the spike in volatility although he continued to follow his successful methodology as well as he had always done.
After 3 days, the trader’s account had lost 70% of its value. Why had the market done this to him?
How could he have known there would be an unexpected policy announcement? He could not. It was a shock to the market.
And even if this had not happened the general consensus was that a correction such as occurred would be likely to happen every so often in any case. He could not have been expected to know when this would happen.
In any case, the market soon regained its stability. While the news was a shock it did not really change the underlying drivers of the market’s trend.
And after a few days the trader realised something. He had been right about the market’s direction after all. If other traders had just kept their cool and waited out the disruption all would have been fine.
But there was nothing he could have done about the market and how other traders had behaved. So his losses were not his fault.
Even so he is down 70%. But things are returning to normal and he has a system that works. He can make it back and then more. All he has to do is continue to do what he had been.
Is the trader correct?
Is the trader correct in his assessment of the situation?
He clearly has a fair amount of ability and a good methodology for choosing trades given his high levels of profitability before the correction.
However, he should stop trading and step away from the market immediately. There is a serious flaw in his trading plan. He doesn’t understand risk.
The problem is that this trader is trying to make a killing in the market. He is planning to triple his account to regain the 70% loss.
This sort of feat is certainly possible. After all he did it already.
And it is by no means so outrageous when compared to the expectations of some traders. It’s not unusual to find a trader who thinks they can turn a fund of perhaps $500 or $1,000 into a million in maybe a year.
They may not put is like that but if you are thinking of 80% or 90% winning trades then that is what you are implying.
But making this kind of return is highly unlikely, while the chance of ruin is almost certain if you start with this expectation – even if you are a very smart trader.
The real problem faced by the trader in the story above was not that the market did something unusual or that a policy maker acted unexpectedly or that other traders over-reacted.
These sorts of things happen all the time and so it should be expected that something like this will happen.
So a trading plan should be based on the expectation that this can happen – indeed that this will happen – at some stage.
The actual problem was that the trader was taking on too much risk. He was taking positions that were too big, or perhaps opening too many correlated positions at the one time.
The inevitable result was that one bad period, when markets acted in an unforeseen way, wiped out such a huge portion of his trading fund.
The trader did not understand the importance of position sizing for controlling risk.
Intelligence and Risk Control
Otherwise competent traders and intelligent people often make this mistake. It appears that normal intelligence is of little help to traders when understanding risk control, even though it is critical for trading success.
To test this idea, a researcher called Ralph Vince conducted an experiment using 40 PhDs from a range of backgrounds. The aim was to see if there was a correlation between their natural intelligence and the ability to recognise risk and the strategies required to control it.
People with a background in statistics or trading were excluded on the basis that their experience might have trained them to recognise the issues.
The participants were each given a computer game with $10,000 and 100 trials or opportunities to play the game. The game was loaded so that the participants would win 60% of the time.
If they lost all their money they would be out of the game.
Each trial was independent i.e. whether a player won or lost on one particular trial had no bearing on the outcome of the next round.
When they won, they won the amount of money they risked in that trial. When they lost, they lost the amount of money they risked for that trial.
It’s not difficult to see that this game approximates what is faced by Binary Options traders in its structure and the fact that a high proportion of traders have little prior experience in trading or statistics.
Apart from the fact that most traders don’t have PhDs there are two further differences: traders come to Binary Options with a 50% chance of winning, not 60%, and the fact that the payout ratio is always less than 100% means you do not get back when you win what you put at risk.
So, your chances of profitability are not as good as those faced by the players in the game in the experiment.
The rules of the experiment meant that each trial (or trade) had a risk to reward ratio of 1. So, if you played this game you would win what you risked 60% of the time and lose what you risked 40% of the time.
Therefore, you would expect that the players would win overall. That means that the game has a positive expected outcome and the odds are much better than you might find when trading.
However, only two of the PhD’s made money over the 100 trials in the experiment. The other 38 lost money. In other words, 95% of the PhD’s lost money playing a game that had been engineered to let players win.
The explanation lay in the risk strategies that were employed by the players. It is notable that these are rather similar to those that would typically be employed by new traders.
How Could they be so Dumb?: The Gambler’s Fallacy
Interviews with players conducted after the game experiment was over showed that a typical player started the game risking €1,000 on each trade.
However, losing three trials in a row in a 60% winning game is a distinct probability and certainly within normal statistical expectations. We’ll assume for simplicity that this happens at the start.
When this happened the participant is down to $7,000. He thinks, “I’ve had three losses in a row, so I’m really due to win now.”
This is the gambler’s fallacy at work. The player thinks that there’s a high probability of a winner after several losses. This is also part of the thinking that leads some to consider the Martingale strategy to be a winning strategy in Binary Options.
In fact, the chances of winning on any given trade in this game were always 60%, regardless of the past results. This is what the computer was programmed to ensure.
However, based on the gambler’s fallacy, the typical trader in this situation would increase his risk. Let’s say he decides to risk $3,000 on the fourth trade because he is so sure he will win.
The probability of four consecutive losses are slim with an expectation that this will happen of only 2.56% in this game. However, this probability means that a run of 4 losses is actually likely to occur at least once in this 100 trial game.
So it is quite possible that the fourth trade results in another loss. Now the player only has $4,000 left in the account and must make 150% just to reach breakeven.
This participant’s chances of making money in the game have grown very slim and if he keeps playing this way he might easily be broke within a few more turns.
Another typical strategy employed by players was to start out risking $2,500 on each turn. In this case, three losses in a row would take the account down to only one more trade of $2,500.
Now there is a 40% chance the next trade will lose and wipe the player out i.e. the probability of losing on the next trade remains 40% and if the stake is $2,500 this is all there is in the account and what the player stands to lose.
Additionally, such a player must now make 300% just to get back to breakeven in the game. Which is the more probable: a 300% gain or the 40% chance of a loss? Is this player likely to experience a profitable end to this game, or bankruptcy?
The problem is clear. Even though the odds of success were in their favour the players could not win because nearly all of them risked too much of their equity on each round during the game.
In fact, there are many ways that the trader could have gone broke. The researchers concluded that the excessive risk taking occurred for psychological reasons: greed, failure to understand the odds, and, in some strange cases, a desire to fail.
Indeed the fact that the participant knew that the game was loaded in their favour may have increased their tendency to take on excessive risk. They were over confident.
The losses occurred because players placed too much at risk on each trial. Had the players understood risk control, they would have done much better in the game.
Risk Control and Trading
It is not too difficult to draw parallels between the above game and trading.
Even if you have a good methodology for identifying trading opportunities – getting 60% winners would be good – if you fail to trade in a way that controls your risk then you will greatly reduce the probabilities of success over the longer term.
You may do very well for a while, like the trader at the start of this article, but the odds are not in your favour in the long run. And that is what actually matters.
Even the best systems will experience periods of drawdowns and your risk management strategy must control these drawdowns.
The important conclusion is that, while it is important, simply concentrating on the proportion of trades won is not enough for success.
Your trading system’s expectancy tells you the probability of winning versus losing on each trade. The well known trader Ed Seykota said that once you know the expectancy of your system, the most important question a trader could ever ask is “How much should I invest?”
If you have an idea of the expectancy of a system you must have a strategy to answer the question “How much should I put at risk?”
However, while this is clearly significant in terms of determining the success of your trading, this is also probably the least discussed and least understood part of a trading system.
As a result, most individual traders never come to understand the importance of being able to answer this question.
What about You?
Remember the trader from the beginning of the article? He was up 200% and then down 70% a few days later.
It’s safe to infer that he was risking far too much on each trade. Is it likely he knew how to control risk?
What about you? Are you risking too much on your trades? Do you know how much risk you should be taking on any trade?
Do you know how to control your risk? Do you take the appropriate decisions?
These are key questions. So important that are discussed before any reference to analysing markets or finding trades in the free eBook Introductory Guide to Trading Binary Options.
Be sure you know how to control risk and that you have clear rules included as part of your trading plan. The alternative is that you risk wiping out your account.
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