## Blog / Scale in / out of positions or not and Why? Part #1.

### Scale in / out of positions or not and Why? Part #1.

Some of the questions that comes up related to scaling in / out of positions:

Can we make definite answer for all trading situations for this question?

What is needed to make the decision generally about this very important topic in trading?

Can we have some general guidelines?

Can we have general solution for all trading setups?

Can we estimate the benefits of a good solution?

We saw discussions about this topic on trader forums and found the situation, that traders generally not aware of the most important aspects of this, and some never does scaling, some trader does it in one way, some in another way, and again others call that counter intuitive…

To try to answer the above questions we created some examples.

Those examples will refer to the levels, presented on the following chart:

LRef is the reference level, where we are now, when we are about to make the decision about scaling.

L1, L2, L3 Our Hypothetical first, second and third profit targets.

To make life and our solution simple we created only three levels to differentiate profit objectives.

Can we say something about scaling without knowing, what are the probabilities, that we reach a specific profit target? No, unfortunately math is no wizard without food, without those numbers.

So assume that we are diligent, and collected statistics about the probabilities, that the asset in question will reach the L1, L2, L3 levels.

These probabilities are P1 to reach L1 level, P2 to reach L2 level and P3 to reach L3 level.

Having our predefined profit targets and the probability of reaching those profit targets we created a very simple mathematical model to give us the solutions.

We just need to enter these numbers into our model and it gives us the solution.

Obviously we have less and less probability to reach higher and higher levels.

The difference between the L1, L2, L3 levels and our reference level LRef can be anything, like

percentage changes or dollar changes, the final answer will be the same independently from the units we apply.

For demonstration and for further examinations we entered two sets of numbers, and got the output for those inputs from the model:

With these numbers we got that on a long term basis, optimally we need to play this setting in a way such that:

Having a 100% position opened at LRef level, we need to sell our first position, which is about 85 – 100% of the total position at L1 levels, and need to sell the remaining 0 – 15% position at L2 level.

The worst play would be to sell all positions at L2 level.

The difference between the worst scaling out and the best scaling out for the long term results is about 100%

In this case we used two positions to scale out, in a way such that **Pos1 > Pos2**

The second sets of input data for our model application is in the following table:

We modified the L3 level number, and the probabilities to reach those levels.

With these numbers we got that on a long-term basis, optimally we need to play this setting in a way such that:

Having a 100% position opened at LRef level, we need to sell our first position, which is about 0 – 25% of the total position at L1 levels, and need to sell the remaining 75 – 100% position at L2 level.

The worst play would be to sell all positions at L3 level.

The difference between the worst scaling out and the best scaling out for the long term results is about 30%

In this case we used two positions to scale out, in a way such that **Pos1 < Pos2**

Based on this and other testing we can make some conclusions:

Scaling in and scaling out of positions is a complex methodology, and the optimal play of it depends on the situation, which could be described by the levels, relative to our reference levels, and the probabilities, reaching those predefined levels.

As those probabilities shifting between our predefined levels, the scaling will also need to be shifted.

When we scale out of existing positions using two, three…positions, sometimes the Pos1Size > Pos2Size > Pos3Size will give optimal solution (Where Pos2Size, Pos3Size might be zero) other times the Pos1Size < Pos2Size < Pos3Size.

So sometimes we need to close the smallest position first, other times we might need to sell the biggest position first to reach optimal profit levels.

It is because of these complexities, that one individual, intuitive trader can hardly adapt to apply the optimal solution in different kind of environments, different kind of setups, where probabilities and ranges are different.

So there is no general solution, the optimal solutions will be dependent on the predefined levels and the probabilities, that we can reach those levels. Each and every setup is different even the same setup in different time-frame might require different scaling strategy if we really want to optimize it.

We have to get those probabilities from research, depending on the time-frame we are about to play.

Those L1, L2, L3 levels could be used generally on any time frame, but for example if applied on daily historical data, those can represent the R1, R2, R3 resistance levels.

What can we say without knowing those predefined levels and the corresponding probabilities?

We always need to think forward, and need to think not only the potential risk levels, that we are taking but equally importantly the possible / potential profit levels that we are about to shoot for and the possible probabilities that we reach those levels and select our scaling strategy accordingly.

As the above example also demonstrate it, the maximum total profit not necessarily comes with the maximum Win / Loss ratio. So once we have an optimal solution for a setup, we need to stick with it.

When we consider scaling into positions:

Assuming that the high probability profit target are really far from our position opening level, we can scale into positions, but it is also very important where are those levels, where we open the second, third.. position, as the probability that even the lastly opened position will be a winner is decreasing. So if we scale in too late, we might not play optimally, and if we scale in too early, that might not be optimal play either.

To translate this into some practical situations, and simplify it for those who like it that way:

During range markets, when we have smaller profit potentials than scaling into positions might not be ideal, but when we have trending days, when relatively high probability targets might be at high-enough levels (At a good distance from our entry levels.) for nice profits it is better strategy to increase our total position as the prices reach higher levels (in case of an uptrend) and enter multiple positions.

As we could see the difference between the total results of the best position scaling, and the worst position scaling (or no scaling) could be 100% and in some cases even much bigger than that. It is dependent on the situation. But the first step to get close to optimal solution is to collect data about the setups we are about to play and have statistics about probabilities reaching our predefined profit target levels.

In our next entries we try to highight these ideas with some more practical examples.