Risk management is an area of trading that is often overlooked by many new traders, this is especially true when it comes to position sizing. We receive many emails from traders complaining about their trading performance, which allows us to find the root cause of many problems. One of the main causes is the lack of risk management.

By finding the right position size for a trade, you can maximise your profit in good situations, while minimising your potential losses in risky situations.

Broadly speaking, there are two main approaches to position sizing that most traders use: static and dynamic position sizing. We don’t encourage the use of static position sizing, as I will explain.

Static Position Sizes

There are a number of different ways that traders use static position sizes and each of them is an inappropriate method.

The first method is when a trader uses a standard position size for every opportunity. This means the size of the trade is only linked to the size of the account, rather than the opportunity itself.

For example, if a trader opens one lot for every $10,000 in their account. This means if they have a $10,000 account they will open one lot and if they have a $30,000 account they will open three lots.

Let’s go through an example:

Imagine you have a trade with a relatively high expected chance of success. The trade requires a 10 pip stop loss and when opening one lot, this works out at 1% of the account at risk. That may sound quite reasonable, right?

However, what if the next trade has a relatively low expected chance of success (lower than the previous trade but high enough to justify entering the trade). This time the trade requires a 30 pip stop loss. That means based on opening the trade with one lot, the amount at risk for this trade is 3%.

Is this reasonable? Is it justified?

In the first trade, you have a greater chance of success (therefore, a lesser chance of losing money) and you are risking 1% of the account. In the second trade, you have a lesser chance of success (therefore, a greater chance of losing money) and you are risking 3% of the account.

Surely it should be the other way around. But when using static position sizes, this sort of situation is going to happen very often.

Another common way of using a static position size is when a trader risks the same percentage on every trade opportunity. For example, rather than it being one lot per every $10,000 in the account (as in the previous example) it is now a standard 2% at risk per trade.

In this case, the same points apply as in the previous example. There will be situations where more is being put at risk in riskier situations than the less risky, more dependable situations.

Since the probability of succeeding in each trade will vary, it would be logical to base our position size on the opportunity itself. This brings us onto the second type of position sizing which is known as dynamic position sizing. This is our preferred method.

Dynamic Position Sizing

When we use dynamic position sizing, the amount we put at risk is dictated by a combination of our account size and the opportunity itself.

In this situation, if two trades have the same expected chance of succeeding but have different stop loss sizes, the percentage of the account at risk will still be equal.

It also means that we allow a larger percentage at risk for opportunities that have a greater chance of succeeding and a lower percentage at risk for the relatively riskier situations.

This will make a huge difference to your performance over the long term since you are giving more weight on the high probability trades.

What percentage should we have at risk?

The methods and tools you use to analyse an asset will allow you to identify different factors that you see as being worthy of opening a trade. Each of these factors will have different characteristics and different levels of certainty.

With our approach to trading (the Duomo Method), we have a strong emphasis on technical analysis and, in particular, we are basing a trade on a combination (what we call a ‘double confirmation) of ‘significant levels’. I will explain an example using our method, but you can substitute any of these points for the factors you look for when assessing a trading situation.

When we identify a significant level, there are broadly two characteristics we can judge it based on:

1. Dependability: How certain are we that the price will react to the significant level in the way we expect? This is a combination of how dependable the level itself is and how dependable our identification of that level has been. This, therefore, translates into our expected chance of success. For example, we may conclude that a level has a 55% chance of success (55 times out of 100 the price will react in the way we expect).

2. Strength of the move: How far do we expect the price to move if we get the expected reaction at that level?

We have a number of tools in our arsenal and we can rank them. Obviously, not everything is so black-and-white but it can give us a general idea.

Let’s say that we have five tools, which we will refer to as A, B, C, D & E. We can then rank them from 5 down to 1, with 5 being the strongest. Since we are looking for double confirmations it means we will have a combination of at least two of these tools.

Let’s say that we have a potential opportunity with tool ‘A’ worth 5 and tool ‘C’ worth 3. That gives us a strength rank of 8.

Now, let’s say we have another opportunity with tool ‘B’ at 4 and tool ‘C’ at 3. This opportunity has a rank of 7.

This means the second opportunity is less favourable and will require a smaller position size than the first opportunity.

Due to there being many other variables, there won’t be a perfect table to rank the tools since they won’t always be consistent in every situation. Therefore, the table should be used only as a guide to keep in mind as part of an overall decision-making process.

So let’s take this a step further.

Imagine we have a gauge that represents how much certainty we have with a trade. This is a very basic way of viewing it, as there are many other things to consider as well, but it’s a good starting point.

As we start the trading session the gauge is at zero, we would need to work through our system in order to move it upwards.

Imagine we identify a market that is in a high activity area on the daily chart. We may be able to move that gauge upwards slightly. How far we move it up will depend on a number of factors that we mentioned earlier.

While we are at this stage, we may also include our fundamental analysis and assess our overall sentiment. Do we expect to take a bullish trade with bullish fundamentals? If so, we may want to filter out any short positions.

Now we spot a potential long opportunity on our working time frame. We take into account 1) the setup itself, 2) the structure, and 3) the path of least resistance. We decide to move the gauge up some more.

At the moment we are looking at a position size of 0.8%, however, we should now start to look at ‘negative filters’. These are factors that show us elements of uncertainty and may cause us to reduce our position size. Let’s say our negative filters are showing us increased uncertainty with our trade, we would now want to reduce the certainty gauge proportionately.

This leaves us with a position size of 0.5% for the trade entry.

This was a very basic example, just to allow you to understand the process we go through. In reality, there are more factors in a trading situation that may increase or decrease the overall uncertainty. However, by taking a more systematic and logical approach like this, it means your position sizes should be appropriate for the opportunity, relatively speaking.

You may also find that this has a positive impact on your mindset since you will be less prone to volatile swings in your account. You must stay disciplined and not let your emotions take control of your position sizes, you have to avoid increasing a position size to make up for losses.

This can be the difference between being average and being a phenomenal trader. And, as we know, being average is not good enough to be profitable in the markets.