In addition to the theory:

Difference equations can also use coefficients, for example, the difference equation can looks like this:

Close = a * Close[n] + b * Std[n](Close[n])

The coefficients used here are therefore a and b.

How could you use these coefficients?

Suppose you use the LONG system again and when you, for example, let the coefficient b vary when closing the LONG position, you give more “space” to the price in practice to be able to move before the position is closed.

DefParam CumulateOrders = False

If Close > Close[n] + Std[n](Close[n]) then

Buy 1 contract at Market

EndIf

If Close < Close[n] – 2 * Std[n](Close[n]) then

Sell at Market

EndIf

Hi @Khaled

We may have talked about the standard deviation before, but this difference equation is “new”…

My basic setup for any market is as follows:

Long system (Bull market) + Short system (Bear market)

Optimizing the period for the Long [n1] and Short [n2] sides.

In my optimizations, [n] is robust and fairly constant for the different markets.

The only coefficient I then use in practice is (b), to adjust the standard deviation to the volatility of the different markets. In a Long/Short system, I use the coefficient (b) only to close the position.  You can (b) vary between 1 and 5, where it usually comes to 1, 2 or 3.

When optimizing the coefficient (b) I use steps of 1 to keep the whole thing robust.

I have mentioned all coefficients as a supplement to the theory of a difference equation, but I only use (b).

Of course, the story is not finished yet, because the other parts of the system (MM, TF, etc.) are just as important…

Hi @Khaled

Nice that you think it’s a good idea.

You can also visualize the equation in this way: you compare the price with a shift of itself (Close = Close[n]) and on this you project the volatility (Close[n] + Std[n](Close[n]).

It is a “break-out” system (Close > …)

First there was the idea and that turned out to fall into the category of “difference equations”. The system itself falls under the category of LTD systems.

LTD = Linear Time Invariant Discreet (you just must look it up on the internet, is super interesting).

I have never used STE myself, but I will definitely try it.

To solve a “complete” difference equation, including the coefficients, is (still) a step too far for me, I also only know the code of PRT (and BASIC) but no other programming language.

It would of course be super interesting if you / someone could do this.

I also have a full-time job and two children 😉 so my time is also limited to learn a whole new programming language.

I also think it is not necessary at all, there are already enough possibilities available to make something beautiful out of it …

I am working on a filter based on the STD and with MM connected to the system.

That’s right, the Profit/Loss ratio is very good with this system and the Risk/Reward ratio.

To make the system even better, it must be totally consistent.

The most important factor in the system is the standard deviation and this important factor should also play a role in buying and selling the contracts.

Now it simply says: “Buy 1 contract at Market” and the most important factor does not appear here. The system and the MM are not consistent with each other, it is not one whole.

What I am showing now is not for practice but to show that when a system is consistent it performs much better overall.

When you change the code from:

“Buy 1 contract at Market” in “Buy Std[n](Close[n]) at Market”

Now you made the system consistent by using both the standard deviation in the condition and in the Money Management.

This makes all the ratios better and the best thing is, which is incredible, that the period [n] for a lot of markets comes out at about the same values.

If Close > Close[n] + Std[n](Close[n]) then

Buy Std[n](Close[n]) at Market

EndIf

Again, this is not for trading in practice (or you have to have very deep pockets) but as an example of how to make a system consistent.