Risk & Reward Tells You the Truth about Trading (but Not as You Think)

9 min read
Davin Wu

Davin Wu

Market Analyst and Educator

Dear traders,

How well do you know your risk and reward? Heck, what do you NOT know about it? What if all that you knew were impractical myths, “theoretical nothings” that every trading coach, analyst, strategist sells to you but never did themselves employ?

This short article explores how subjective something as simple as risk and reward can be, it’s elusive nature, and aims to debunk the traditional perspective, practices and myths when it comes to the topic of risk to reward.

A facts-only, in your face revelation of the financial markets and it’s inner workings, coupled with the distinction between the characteristic factors of risk and reward hones critical thought-processes that happens in the sharp minds of professional traders daily.

Risk and Reward

How do we properly determine risk and reward in a trading perspective? I don’t think anyone can ever provide a definitive answer – it’s akin to asking how many layers do you need to explore the Willowbank Wildlife Reserve in Christchurch during this time of year. Right now as Google’s thermometer reads a balmy 6 degrees Celsius as I am writing this at 3 in the morning, you’ll need about three layers to make it an enjoyable experience without being too bothered with the cold. But just last week, you would have needed 10.

Trading, like the weather, is an imprecise, highly volatile proposition. Consequently, the conundrum of risk and reward contently reshapes with the circumstances of the moment.

Traditional View

The traditional, static view on risk to reward is to set the ratio to at least 2:1 – risking half the quantity of pips you are trying to make. Ergo, if your profit target was $10, then your stop would be $5.

Theoretically, this sounds genius! We only need to be correct 4 out of 10 times to make money. The whole trading thing suddenly seems like child’s play – heck, we don’t even NEED a winning system! Literally free cash for every trader, regardless of whatever instruments you’re trading.

Having said that, I’ve never met a single real life trader who actually puts this principle into practice. Sure, I’ve received plenty of such advice on this topic from analysts, strategists, trading coaches, and a whole bunch of many others who hasn’t ever wagered so much as their breakfast money on a trade, but never in my life have I witnessed this 2:1 ratio trash employed by anyone who actually makes a living from the markets.


Primary reason being that people who never trade don’t realize that there is no such thing as reward in the markets. There is only risk.

Markets aren’t factories that manufacture profits to your order. Far from it. In fact, it really does the contrary – basically every perceivable thing possible to wreck your goals.

Imagine with me a trade where you’re risking 100 pips with a take profit of 200. Initially, the trade goes your way and the floating P&L quickly rises until your trade reaches +199. Stoically disciplined in your 2:1 theory, you wait patiently for price to hit TP, that you could bank yet another good trade. But guess what?

Price makes an engulfing bar out of the blue and aggressively reverses. You watch in horror as the otherwise largely profitable trade crashes at break-neck speed and plunges through your stop. I know. Barbaric. Very much like the flash crash on the 3rd of January this year. Rude start to the trading year indeed.

On paper you might have lost a hundred pips, but in actual fact you lost -299 (100 pips on your stop and that -199 you failed to bank). Welcome to world of trading where the simplistic “theoretical” 2:1 risk to reward ratio is indeed, far more elusive than you would have ever thought.


The fact is that we are unable to forecast profits in the market. The only factor within our control is risk. That is why we always trade two facets. That is why we bank shorter first targets first, and why we meticulously control risk by moving our stop loss levels according to our strategy, whether be it fractal stop trailing for SWAT® or ATR/CAMM hybrid price trailing for CAMMACD®. It might not be glamorous, but it’s the only proven way we know in tackling the risk reward conundrum here at ECS.

The Correct Way of Applying R:R

There is good news though. There is a correct way of applying reward to risk ratio (r:r ratio). I asked Chris Svorcik how he thinks r:r ratio should be used and here is what he had to say!

Rule 1: do not aim for a specific reward to risk ratio beforehand when entering the trade setup. Always use a target that is based on what the charts are indicating and do not exit the trade setup based on some random r:r ratio.

How do we do it?

  • For ecs.CAMMACD this means using the average true range for intra-day trading.
  • For ecs.SWAT this means using the Fibonacci levels and tools for swing trading (the Fib levels are also fine tuned with the market volatility because larger price movements will create larger Fibs and hence larger targets).

Rule 2: it is ok or filter out a trade setup if your entry is really too close to a strong support or resistance (S&R) zone. Although we do not advocate for taking profit based on the reward to risk ratio, it is a useful practice not to take setups where the stop loss is much bigger then the potential reward at the key S&R. Our recommendation is not to go below 0.5:1 reward to risk with your target.

Rule 3: although we do want to base our target on a theoretical r:r, it is a good practice to calculate what kind of reward to risk ratios are you managing to achieve with your closed trades. Use it as a method to analyse your approach and to understand whether you are on the right track.

Scenario A: for instance, a high win rate like 95% could be totally useless if you only win a few pips at a time but risk much more. Let’s say you win 2 pips in 19 trades but the 20th trade causes a loss of -200 pips, then you have just lost 172 pips (assuming equal risk on all setups). What is that 95% win rate worth? Not much.

Scenario B: on the other hand, are you better off if you have a super high reward to risk ratio but you only manage to win 1 in 20 setups? No, because the series of losses could be so large that you lose confidence in your own system and usually traders then quit. Or if you take too much risk per setup, then your drawdown could be too large to recover even if you hit that massive win.

You want to make sure that you have a healthy r:r ratio in relationship to your average win rate.

The golden formula for calculating long-term profitability is this:
(win rate * average win) – (loss rate * average loss) = average trade expectancy

For instance:

If you have a 60% win rate on average with an average win that is 1.5 times as large as your loss, then your trade expectancy is:

(60% * 1.5) – (40% * 1) = (0.9) – (0.4) = 0.5

So if you trade with 1% risk per setup, you can expect to get 0.5% return on average per setup. Of course, all trading goes with ups and downs and an equity curve is never a straight line… but this is what you can roughly expect on average, in the long run, with such stats.

In any case, here is how you can also improve your trading:

  • If you see that your r:r ratio is low, then use that information to improve your trading by not cutting wins too soon and letting winners run.
  • If you see that your approach is avoiding losses too soon, then use that information to find proper moments for moving the stop loss.

Let’s summarize it:

  • Risk Management is the Holy Grail.
  • Win ratio on its own is meaningless.
  • Net pips on its own is meaningless.
  • R:R ratio on its own is meaningless.
  • Nobody can make a judgment about your trading performance just based on one of the factors mentioned above. It’s about the combination!

So, what does matter?

  1. Your net profitability
  2. Your drawdown
  3. Your win rate in combination with your r:r ratio

All in all, keep your losses small, and let your winners run!

Many trading educators talk about doing this but we are one of the few who actually show you how.

Trade Safe!
Chris Svorcik and Davin Wu

Leave a Reply


This site uses Akismet to reduce spam. Learn how your comment data is processed.

Notify of