In this post, we’re going to introduce a key risk management variable: R, the reward to risk ratio. Understanding it will help you trade profitably and effectively. Before we start, let’s recap a few key concepts:
Your budget size is how much money you’ve got to invest with.
The entry price is the price at which you buy into an asset.
Risk size, measured in percentages, is how much money you’re willing to give up in each losing trade.
Your position size is how much of an asset you’re buying.
Your target price is the price point you want to leave a position at.
A stop loss is a type of order that automatically sells off your assets after losing trades.
With the above in mind, let’s move on to R and its applications.
The formula for R is very simple: R = reward/risk. A reward is the percent difference between your entry price and your target price. Risk is the percent difference between entry price and your exit point or stop loss point.
So let’s say you go into a trade where your stop loss is 10% below entry, and your target price is 40% above entry.
In this case, R = 40%/10% = 4R.
A high R means that your potential reward is high while your risk is low.
An R below 1 means that you’re risking more money than you’re likely to gain — which is almost always a bad sign.
Using R to Calculate Minimum Win Rate
Win rates are a useful way to know how many of the trades we’re making are productive — but combined with R, they’re even more useful. The reason is that we can use R to calculate the minimum win rate we need to get in order to be profitable.
The formula is simple:
Minimum win rate = 100 / (1+R)
So let’s say that your average trade has a risk of 10% and a target reward of 25%. This gives you an R of 25/10, or 2.5. Given this, what’s the minimum win rate you need to have in order to break even?
It’s 100 / 1+R, which is 100 / 1+2.5, which is 28.57%.
In other words, if your average risk is 10%, and your average reward is 25%, you’d roughly break even winning 28.57 trades out of 100.
If you look at the above formula, you might infer that there’s an inverse correlation between R and Win Rate.
If your R is high, meaning you tend to win a lot while risking little, your Win Rate can be small and still allow you to break even or make a profit.
If your R is low, meaning you win less while risking more, you need to have a high Win Rate to break even or make a profit.
For example, an R of 1 requires a win rate of 50%. An R of 2 requires a win rate of 33%. An R of 3 requires a win rate of 25%.
And so on and so forth.
Applying R dynamically
In addition to being useful for static calculations, R can also be applied to dynamic ones. For example, let’s say that your potential reward decreases over time — for example because you missed your entry price. Now your R has shrunk, giving you a new, higher minimum win rate.
Calculating this dynamically can be hard for a person, but if you use code or a widget instead, you’ll get meaningful results much more quickly.
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