A piece of advice which is found quite commonly on the internet – particularly involving articles dealing with manual trading – is that one of the most important characteristics for profitable trading is to trade using a favorable reward to risk ratio, meaning that each trade’s target profit should be higher than its target risk. Certainly it make sense when you first hear about it since you’re aiming to make more money than the money you’re prepared to lose but once strategies unravel and you look at the long term characteristics there may be some problems derived from this idea. Does a high reward to risk ratio does make more sense that a low one? What are the real advantages of using a high reward to risk ratio? Within the following few paragraphs I will talk about the difference between the two and which one might be the best choice.
What are the implications of trading a high reward to risk strategy? First of all if your strategy aims to make more per trade than what it loses the first large implication is that you’ll have to win less trades in order to be profitable because each profit you make covers up a higher magnitude than your losses. Second, the fact that you’re aiming for a higher profit also puts random odds against you meaning that you are more likely to generate a losing trade on each entry. This means that whenever you have a high reward to risk ratio you will inevitably have a lower winning percentage than for a symmetric strategy. In essence a high reward to risk strategy makes less, bigger profits than a symmetric strategy (equal risk and reward).
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The opposite case – an unfavorable risk to reward ratio – causes your strategy to face the opposite effect. You’ll have to trade more to make up for losses but you will also have profits more often as the random odds of facing a profitable outcome increase as the profit becomes smaller than the potential loss. In essence what we have here are two sides of the same coin and by looking merely at this you won’t be able to tell which one of the two is better than the other. Is it better to have more frequent winners and large losses or small frequent losses and large winners? If the future was known and we could have absolute certainty about the characteristics of both strategies then the answer would be that they are exactly the same. Both types of strategies can in theory have positive expectancy results and both can generate profitable long term outcomes.
However the main difference – and the most important one – comes when you consider the possible under and over estimations of these two ways of trading under limited statistical evaluations (such as the ones we do). If you evaluate two trading strategies for a period of 10 years and one strategy has 80% losing trades and 20% winning trades and the other has 80% winning trades and 20% losing trades and they both reach the same profit outcome, which one is better? The important thing to consider here is that the first strategy is probably underestimating the profitable side of the equation while the other one is underestimating the losing side. If the high reward to risk strategy happened to have a 10% reversion in the net outcomes of trades then the most likely thing is that we would end with a more profitable strategy while in the case of the strategy with poor risk to reward we would most likely end with a losing strategy.
You need to think about simulations – especially those in forex trading – as a limited picture of what really is a much more complex outcome which in reality has more distortions which are not seen through the lens of a limited backtests on one broker’s data set. When we use Monte Carlo simulations it becomes clear that a strategy that has been elucidated with a favorable risk to reward ratio has a higher chance of actually being better in real life than its counterpart with a bad risk to reward since the strategy with the bad behavior would have dramatically worse results if the number of losing trades increased just slightly. We could say that we have better certainty about the “losing side” of the strategy with the good risk to reward ratio and therefore underestimations of risk are unlikely when compared to the other strategy.
Nonetheless it is also worth mentioning that the “good risk to reward” approach should not be taken to extremes. If you have a system that generates a profit during a 10 year simulation but all the profit came from 1 trade then it is very likely that the strategy will fail going forward as that single trade could just turn into a loser and ruin the whole strategy. From my experience in trading the best thing is to trade strategies with reward to risk ratios from 1 to 5 with the best zone for trading being probably around 1.5-2.5. The problem with larger reward to risk ratios comes from the psychological problems that come when using strategies with winning ratios below 40%, any strategy that wins such a small number of trades will have a very high Ulcer index and will be very hard to trade since the amount of reward given to the trader is minimal and large draw down periods have to be surpassed if one is to profit from them.
Overall we could say then that there are good reasons to choose high reward to risk ratio strategies over strategies with unfavorables ratios as our certainty about the “risky side” of our systems becomes better and therefore we are much less likely to underestimate draw down levels. Certainly a strategy with a very low reward to risk ratio can be very dangerous as it can give the illusion of risk-absence when in reality risk has merely been postponed to a later date (imagine a strategy with a 5 pip TP and a 500 pip SL). Also good risk to reward ratio strategies can be easier to trade in the long run as we are already psychologically prepared to face long losing periods (before large profits) while users of poor risk to reward ratio strategies are shocked when they face huge draw downs mainly because they had extreme underestimations of risk and they expected profit, not losses.
In the end the important thing about favorable risk to reward ratio strategies is that they prepare you for the facing of losing periods, there is more statistical certainty regarding historical results and there is certainly a smaller probability of having an under-estimation of draw down figures. Remember that the most important thing in trading simulations is to have as much certainty as possible around past risk, NOT around profit. If you would like to learn more about my work in automated trading and how you too can learn to design strategies with reliable simulations and Monte carlo simulations please consider joining Asirikuy.com, a website filled with educational videos, trading systems, development and a sound, honest and transparent approach towards automated trading in general . I hope you enjoyed this article ! :o)
